Gemma Cadby - UWA Research Repository€¦ · The genetic epidemiology of melanoma susceptibility...

The genetic epidemiology of melanoma

susceptibility and prognosis and investigations of

causal pathway modelling in complex disease

aetiology.

Gemma Cadby

Bachelor of Science (Honours)

This thesis is presented for the degree of

Doctor of Philosophy

of The University of Western Australia

School of Population Health.

May, 2011

i

Declaration

This thesis is the author’s own composition. All sources have been acknowledged

and the author’s contribution is clearly identified in this thesis. This thesis con-

tains published work and/or work prepared for publication, some of which has

been co-authored. The permission of all co-authors has been obtained to include

the work in this thesis.

For work which has been prepared for publication, the work performed by the

author of this thesis has been described in detail.

This thesis was completed during the course of enrolment in this degree at the

University of Western Australia and has not previously been accepted for a degree

at this or another institution.

iii

Abstract

Genetic epidemiological studies are increasingly being used to investigate the role

of genetic and environmental factors in common human disease aetiology, with the

ultimate aims of control and prevention. This thesis investigated the association

of candidate genes with melanoma susceptibility and prognosis in the Western

Australian population. In addition, methods research into novel statistical meth-

ods to model causal pathways in epidemiological studies was undertaken.

Australia has the highest incidence of cutaneous malignant melanoma in the world.

Several melanoma-susceptibility genes have been identified, however a greater un-

derstanding of these genes and their interactions with environmental factors would

lead to better interventions and control of the disease. The genetic determinants

of melanoma progression, and therefore prognosis, are largely unknown. Knowl-

edge of the genes which affect melanoma progression and prognosis would aid

in identifying individuals at risk of poorer prognosis, and may also elucidate the

mechanisms underlying melanoma progression. The collection of population-based

clinical, phenotypic and genetic data is integral in facilitating investigation into

the genetic and environmental factors affecting melanoma susceptibility and prog-

nosis.

This thesis involved the establishment of the Western Australian Melanoma Health

Study (WAMHS). The WAMHS is a population-based case-collection and linked

biospecimen resource established to investigate the genetic epidemiology of melanoma.

All eligible Western Australian adult cases of melanoma, diagnosed between Jan-

uary 2006 and September 2009 and notified to the Western Australian Cancer Reg-

iv

istry (WACR), were invited to participate in the study. Clinical and questionnaire-

based phenotypic data and blood samples for extraction of DNA, RNA and serum

were collected from consenting cases. Clinical data consisted of all pathological

data recorded by the WACR and the questionnaire covered major risk factors

for melanoma, such as sun exposure history and pigmentation. The final sample

consisted of 1,643 cases, of which 1,455 completed one or more components of

the study and 1,157 completed all components. The WAMHS comprises the only

population-based study of melanoma cases in Western Australia, and is one of the

largest single studies in the world.

A sub-sample of the WAMHS consisting of 800 European-ancestry participants

was used to investigate the genetic epidemiology of melanoma susceptibility and

melanoma prognosis. The genetic epidemiology of melanoma susceptibility was

investigated through association analyses of 42 single nucleotide polymorphisms

(SNPs) in genes previously reported to be associated with melanoma-risk, com-

paring the WAMHS sample to two general population samples. The genetic epi-

demiology of melanoma prognosis was investigated by association analyses of these

same SNPs with Breslow thickness, which is the best independent predictor for

disease prognosis. I hypothesised that some of the genes which increase melanoma-

risk may also be those which affect poorer disease prognosis, as reflected by in-

creased Breslow thickness.

As a result of this study, 14 SNPs in nine candidate genes were found to be

associated with melanoma-risk. The associations between six of these SNPs

(MC1R rs258322, TYR rs1393350, IRF4 rs12203592, MTAP rs7023329, MTAP

rs1011970 and BRAF rs6944385) remained significant after adjusting for multiple

v

testing. Four SNPs (IRF4 rs12203592, OCA2 rs1800401, TP53 rs1042522 and

BRAF rs1733826) were found to be associated with Breslow thickness. However,

none of these associations remained significant after adjustment for multiple test-

ing. The IRF4 rs12203592 SNP was associated with increased melanoma-risk and

thicker Breslow thickness, and therefore poorer prognosis. In addition, epidemio-

logical analyses of melanoma prognosis identified two phenotypic variables – age

at diagnosis and presence of naevi – which were associated with Breslow thickness.

In the second section of this thesis, I describe a statistical method known as

Mendelian randomisation, which is used to infer causal relationships between po-

tentially modifiable environmental exposures and some disease trait, using genetic

variants. More certain inferences regarding causal associations in epidemiological

studies are important as it may identify modifiable risk factors, i.e. information

that would allow individuals to modify their behaviours and exposures in order

to reduce the risk of some disease outcome.

As part of the methods work, I have developed an R library – MRsnphap – which

can be used to apply Mendelian randomisation to the genetic epidemiological set-

ting, by using SNPs and haplotypes as proxy variables for modifiable exposures.

While the use of SNPs as instruments is straightforward, the use of haplotypes

is not due to the uncertainty of haplotype phase. MRsnphap is the first soft-

ware for Mendelian randomisation analysis designed specifically for the genetic

epidemiological setting, which is able to incorporate the uncertainty surrounding

haplotypes into the analysis.

vi

The collection and analysis of population-based genotypic and phenotypic data

is integral in understanding the aetiology of disease. This is a critical step which

will enable the clinical utilisation of new knowledge and tools to improve clinical

practice and public health. The research presented in this thesis is both novel

and significant as it includes the first known study of the genetic determinants

of melanoma in a Western Australian population. This thesis also presents the

first comprehensive investigation into the role of candidate loci and melanoma

prognosis in a large, well-characterised sample of European-ancestry melanoma

cases. In addition, the availability of MRsnphap will help further elucidate the

role between modifiable exposures and disease, so that interventions can occur to

reduce the impact of disease in the community.

vii

Publications

Publications arising directly from this thesis

G. Cadby, S.V. Ward, (joint first authors), A. Lee, J.M. Cole, J. Heyworth,

M. Millward, T. Threlfall, F. Wood and L.J. Palmer. The Western Australian

Melanoma Health Study: Study design and participant characteristics. [IN PRESS:

CANCER EPIDEMIOLOGY]

G. Cadby, P.A. McCaskie and L.J. Palmer. (1923T). Mendelian Randomisation :

Modelling single SNPs and haplotypes in R. Presented at the 59th Annual Meeting

of The American Society of Human Genetics, October 24, 2009, Honolulu, Hawaii.

Available at http://www.ashg.org/2009meeting/abstracts/fulltext/. [ABSTRACT]

G. Cadby, S.V. Ward, J.M. Cole, M. Millward and L.J. Palmer. Breslow

thickness in the Western Australian Melanoma Health Study. Pigment Cell and

Melanoma Research, 22(6):903–904, 2009. [ABSTRACT]

S.V. Ward, G. Cadby, J.M. Cole, F. Wood, M. Millward and L.J. Palmer. The

Western Australian Melanoma Health Study. Genetic Epidemiology, 33:787, 2009.

[ABSTRACT]

L. Simpson, M. Cooper, G. Cadby, A.C. Fedson, K.L. Ward, J.D Lee, B. Szeg-

ner, C. Edwards, S. Mukherjee, D.R. Hillman, P. Eastwood and L.J. Palmer. Use

of the fat mass and obesity associated (FTO:RS9939609) single nucleotide poly-

viii

morphism to investigate the relationship between obesity and obstructive sleep

apnoea. Sleep and Biological Rhythms, 7:A18–19, 2009. [ABSTRACT]

M. Cooper, G. Cadby, J.D. Lee, A.C. Fedson, L. Simpson, K.L. Ward, D.R.

Hillman, S. Mukherjee and L.J. Palmer. Using Mendelian Randomisation to in-

vestigate the relationship between blood pressure and the severity of obstructive

sleep apnoea. Genetic Epidemiology, 33:778, 2009. [ABSTRACT]

G. Cadby, S.V. Ward, J.M. Cole, M. Millward and L.J. Palmer. Association

of candidate SNPs with melanoma susceptibility in Australian adults. Genetic

Epidemiology, 34(8):917-992, 2010. [ABSTRACT]

Publications not arising directly from this thesis

G. Cadby, K.W. Carter, S. Wiltshire and L.J. Palmer. Investigating aspects

of statistical power in meta–analysis of complex traits. Genetic Epidemiology,

33:791, 2009. [ABSTRACT]

S.V. Ward, G. Cadby, M. Millward, J.M. Cole, F. Wood and L.J. Palmer.

Scar outcome post melanoma excision. Pigment Cell and Melanoma Research,

22(6):903–904, 2009. [ABSTRACT]

ix

Contents

Declaration i

Abstract iii

Publications vii

Contents ix

List of Tables xviii

List of Figures xx

Glossary xxiii

Abbreviations xxvii

Acknowledgements xxix

Preface xxxi

1 Introduction 1

1.1 Introduction to Epidemiology . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Causality in epidemiology . . . . . . . . . . . . . . . . . . 2

1.2 Introduction to Genetic Epidemiology . . . . . . . . . . . . . . . . 3

1.3 Genetic Concepts and Definitions . . . . . . . . . . . . . . . . . . 5

x

1.3.1 Genes and alleles . . . . . . . . . . . . . . . . . . . . . . . 5

1.3.2 Genetic variation . . . . . . . . . . . . . . . . . . . . . . . 7

1.3.2.1 Single nucleotide polymorphisms . . . . . . . . . 8

1.3.2.2 Haplotypes . . . . . . . . . . . . . . . . . . . . . 10

1.3.2.3 Haplotype inference . . . . . . . . . . . . . . . . 12

1.3.3 Hardy-Weinberg equilibrium principle . . . . . . . . . . . . 13

1.3.4 Linkage disequilibrium . . . . . . . . . . . . . . . . . . . . 14

1.4 Gene Discovery in Human Disease . . . . . . . . . . . . . . . . . . 15

1.4.1 Simple and complex disease . . . . . . . . . . . . . . . . . 16

1.4.2 Linkage analysis . . . . . . . . . . . . . . . . . . . . . . . . 17

1.4.3 Genetic association studies . . . . . . . . . . . . . . . . . . 18

1.4.4 Multiple testing . . . . . . . . . . . . . . . . . . . . . . . . 19

1.5 Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.6 Outline of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2 Genetic Epidemiology of Malignant Melanoma: Susceptibility

and Prognosis in the WAMHS 23

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2.1 Skin cancer . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2.2 Melanoma biology . . . . . . . . . . . . . . . . . . . . . . 25

2.2.3 Melanoma incidence and mortality . . . . . . . . . . . . . 27

2.2.3.1 Melanoma in Australia . . . . . . . . . . . . . . . 27

2.2.3.2 Melanoma in Western Australia . . . . . . . . . . 34

2.2.4 The aetiology of malignant melanoma . . . . . . . . . . . . 36

2.2.4.1 Environmental and host melanoma risk factors . 36

2.2.4.1.1 Sun exposure . . . . . . . . . . . . . . . 36

xi

2.2.4.1.2 Skin type and pigmentation . . . . . . . 39

2.2.4.1.3 Family history . . . . . . . . . . . . . . 41

2.2.4.1.4 Naevi . . . . . . . . . . . . . . . . . . . 42

2.2.4.2 Melanoma genetic risk factors . . . . . . . . . . . 43

2.2.4.2.1 High penetrance melanoma-susceptibility

genes . . . . . . . . . . . . . . . . . . . . 43

2.2.4.2.2 Low penetrance melanoma-susceptibility

genes . . . . . . . . . . . . . . . . . . . . 45

2.2.5 The diagnosis and treatment of melanoma . . . . . . . . . 49

2.2.6 Breslow thickness . . . . . . . . . . . . . . . . . . . . . . . 50

2.2.6.1 Breslow thickness and prognosis . . . . . . . . . . 50

2.2.6.1.1 Breslow thickness and melanoma risk fac-

tors . . . . . . . . . . . . . . . . . . . . 52

2.2.7 Literature Review Summary . . . . . . . . . . . . . . . . . 54

2.3 The Western Australian Melanoma Health Study . . . . . . . . . 56

2.3.1 Author’s contribution . . . . . . . . . . . . . . . . . . . . . 56

2.3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 56

2.3.3 WAMHS population . . . . . . . . . . . . . . . . . . . . . 57

2.3.3.1 Recruitment . . . . . . . . . . . . . . . . . . . . . 59

2.3.3.2 WAMHS pilot study . . . . . . . . . . . . . . . . 62

2.3.3.3 WAMHS full-scale implementation I . . . . . . . 64

2.3.3.4 WAMHS full-scale implementation II . . . . . . . 66

2.3.3.5 Biospecimens . . . . . . . . . . . . . . . . . . . . 67

2.3.3.6 Study variables . . . . . . . . . . . . . . . . . . . 68

2.3.3.6.1 Phenotypic variables . . . . . . . . . . . 68

2.3.3.6.2 Demographic and clinical variables . . . 73

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2.3.3.7 Sample size and response rates . . . . . . . . . . 74

2.3.3.7.1 Consent, non-consent and non-response

rates . . . . . . . . . . . . . . . . . . . . 74

2.3.3.8 Characteristics of participants . . . . . . . . . . . 78

2.3.3.9 WAMHS sample summary . . . . . . . . . . . . . 83

2.3.4 WAMHS sub-sample used in this thesis . . . . . . . . . . . 85

2.3.4.1 Introduction . . . . . . . . . . . . . . . . . . . . 85

2.3.4.2 SNP selection . . . . . . . . . . . . . . . . . . . . 86

2.3.4.3 Laboratory methods . . . . . . . . . . . . . . . . 89

2.3.4.3.1 DNA preparation . . . . . . . . . . . . . 89

2.3.4.3.2 Genotyping . . . . . . . . . . . . . . . . 89

2.3.4.4 Methods for descriptive analyses . . . . . . . . . 90

2.3.4.4.1 Descriptive statistics of study variables . 90

2.3.4.4.2 Descriptive statistics of genotype data . 91

2.3.4.5 Results for descriptive analyses . . . . . . . . . . 91

2.3.4.5.1 Phenotypic data . . . . . . . . . . . . . 91

2.3.4.5.2 Genotypic data . . . . . . . . . . . . . . 100

2.3.4.6 Generalisation of sub-sample to population and

participating cases . . . . . . . . . . . . . . . . . 103

2.3.4.6.1 Introduction . . . . . . . . . . . . . . . . 103

2.3.4.6.2 Methods . . . . . . . . . . . . . . . . . . 103

2.3.4.6.3 Results . . . . . . . . . . . . . . . . . . 104

2.3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

2.4 Association of Candidate Loci with Melanoma Susceptibility . . . 111

2.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 111

2.4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

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2.4.2.1 Study populations . . . . . . . . . . . . . . . . . 111

2.4.2.2 Statistical methods . . . . . . . . . . . . . . . . . 113

2.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

2.4.3.1 Summary of results . . . . . . . . . . . . . . . . . 123

2.4.3.1.1 MTAP . . . . . . . . . . . . . . . . . . . 123

2.4.3.1.2 CDC91C1 . . . . . . . . . . . . . . . . . 124

2.4.3.1.3 TYR . . . . . . . . . . . . . . . . . . . . 124

2.4.3.1.4 KITLG . . . . . . . . . . . . . . . . . . 126

2.4.3.1.5 SLC24A4 . . . . . . . . . . . . . . . . . 126

2.4.3.1.6 HERC2 . . . . . . . . . . . . . . . . . . 127

2.4.3.1.7 IRF4 . . . . . . . . . . . . . . . . . . . . 127

2.4.3.1.8 MC1R . . . . . . . . . . . . . . . . . . . 128

2.4.3.1.9 BRAF . . . . . . . . . . . . . . . . . . . 129

2.4.3.2 Discussion . . . . . . . . . . . . . . . . . . . . . . 129

2.5 Association of Candidate Loci with Breslow thickness . . . . . . . 131

2.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 131

2.5.2 Statistical methods . . . . . . . . . . . . . . . . . . . . . . 132

2.5.2.1 Association analyses . . . . . . . . . . . . . . . . 132

2.5.2.1.1 Epidemiological analyses . . . . . . . . . 132

2.5.2.1.1.1 Univariate analyses . . . . . . . . 132

2.5.2.1.1.2 Multivariate analyses . . . . . . . 133

2.5.2.1.2 Genotypic analyses . . . . . . . . . . . . 133

2.5.2.1.2.1 Univariate analyses . . . . . . . . 133

2.5.2.1.2.2 Multivariate analyses . . . . . . . 134

2.5.2.2 Statistical power . . . . . . . . . . . . . . . . . . 135

2.5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

xiv

2.5.3.1 Epidemiological analyses . . . . . . . . . . . . . . 136

2.5.3.1.1 Univariate analyses . . . . . . . . . . . . 136

2.5.3.1.2 Multivariate analyses . . . . . . . . . . . 139

2.5.3.2 Genotypic analyses . . . . . . . . . . . . . . . . . 140

2.5.3.2.1 Univariate analyses . . . . . . . . . . . . 140

2.5.3.2.2 Multivariate analyses . . . . . . . . . . . 142

2.5.3.3 Statistical power . . . . . . . . . . . . . . . . . . 154

2.5.3.3.1 Genetic main effects . . . . . . . . . . . 154

2.5.3.3.2 Gene-environment interactions . . . . . 154

2.5.3.4 Summary of results . . . . . . . . . . . . . . . . . 156

2.5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

2.5.4.1 Introduction . . . . . . . . . . . . . . . . . . . . 162

2.5.4.2 Summary of population characteristics . . . . . . 162

2.5.4.2.1 Breslow thickness . . . . . . . . . . . . . 162

2.5.4.2.2 Sex . . . . . . . . . . . . . . . . . . . . . 167

2.5.4.2.3 Age at diagnosis . . . . . . . . . . . . . 168

2.5.4.2.4 Naevi . . . . . . . . . . . . . . . . . . . 168

2.5.4.3 Summary of genetic association analyses . . . . . 171

2.5.4.3.1 Review of genetic variants studied . . . 171

2.5.4.3.2 Details of associations . . . . . . . . . . 171

2.5.4.3.2.1 OCA2 . . . . . . . . . . . . . . . 172

2.5.4.3.2.2 TP53 . . . . . . . . . . . . . . . . 173

2.5.4.3.2.3 IRF4 . . . . . . . . . . . . . . . . 174

2.5.4.3.2.4 BRAF . . . . . . . . . . . . . . . 175

2.5.4.4 Causality . . . . . . . . . . . . . . . . . . . . . . 176

2.5.4.5 Potential limitations . . . . . . . . . . . . . . . . 176

xv

2.5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

2.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 180

3 Mendelian Randomisation: An Application of Instrumental

Variable Techniques 183

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

3.2 Vitamin D Levels and Breslow Thickness . . . . . . . . . . . . . . 184

3.3 Epidemiological Studies . . . . . . . . . . . . . . . . . . . . . . . 185

3.3.1 Experimental Studies . . . . . . . . . . . . . . . . . . . . . 186

3.3.2 Observational Studies . . . . . . . . . . . . . . . . . . . . . 187

3.3.2.1 Limitations of observational epidemiology . . . . 189

3.3.2.1.1 Confounding . . . . . . . . . . . . . . . 191

3.3.2.1.2 Reverse causation . . . . . . . . . . . . . 192

3.3.2.1.3 Selection bias . . . . . . . . . . . . . . . 192

3.3.2.1.4 Regression dilution bias . . . . . . . . . 193

3.4 Statistical Methods for Analysing Observational Epidemiological

Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

3.4.1 Ordinary least squares . . . . . . . . . . . . . . . . . . . . 194

3.4.1.1 Assumptions of ordinary least squares . . . . . . 195

3.4.2 Instrumental variable methods . . . . . . . . . . . . . . . . 196

3.4.2.1 Assumptions of instrumental variables . . . . . . 198

3.4.2.2 Instrumental variable estimator of β . . . . . . . 198

3.4.2.3 Variance-covariance matrix estimator of βIV . . . 199

3.4.3 Instrumental variable diagnostic tests . . . . . . . . . . . . 199

3.4.3.1 Strength of excluded instruments . . . . . . . . . 199

3.4.3.2 Overidentification test . . . . . . . . . . . . . . . 200

3.4.3.3 Endogeneity test . . . . . . . . . . . . . . . . . . 201

xvi

3.4.4 Instrumental variable methods for observational epidemio-

logical studies . . . . . . . . . . . . . . . . . . . . . . . . . 203

3.5 Mendelian Randomisation . . . . . . . . . . . . . . . . . . . . . . 203

3.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 203

3.5.2 Genetic variants as excluded instruments . . . . . . . . . . 205

3.5.2.1 SNPs as excluded instruments . . . . . . . . . . . 205

3.5.2.2 Haplotypes as excluded instruments . . . . . . . 205

3.5.3 Mendelian randomisation example . . . . . . . . . . . . . . 206

3.5.4 Benefits of Mendelian randomisation . . . . . . . . . . . . 210

3.5.4.1 Confounding . . . . . . . . . . . . . . . . . . . . 210

3.5.4.2 Reverse causation . . . . . . . . . . . . . . . . . . 210

3.5.4.3 Selection bias . . . . . . . . . . . . . . . . . . . . 211

3.5.4.4 Regression dilution bias . . . . . . . . . . . . . . 211

3.5.5 Limitations of Mendelian randomisation . . . . . . . . . . 211

3.5.5.1 Identification of suitable genetic variant for exposure 211

3.5.5.2 Reliable gene associations . . . . . . . . . . . . . 212

3.5.5.3 Population stratification . . . . . . . . . . . . . . 212

3.5.5.4 Pleiotropy . . . . . . . . . . . . . . . . . . . . . . 213

3.5.6 Current software for Mendelian randomisation analysis . . 213

3.5.7 Software implementation . . . . . . . . . . . . . . . . . . . 214

3.5.7.1 Using SNPs as instruments: mrsnp.quant . . . . 214

3.5.7.1.1 Example: mrsnp.quant . . . . . . . . . . 215

3.5.7.2 Using haplotypes as instruments: mrhap.quant . 216

3.5.7.2.1 Example: mrhap.quant . . . . . . . . . . 217

3.5.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 218

4 Summary and Suggestions for Further Research 219

xvii

4.1 The Western Australian Melanoma Health Study . . . . . . . . . 219

4.2 Mendelian Randomisation . . . . . . . . . . . . . . . . . . . . . . 221

4.2.1 Future development of MRsnphap . . . . . . . . . . . . . . 222

4.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224

Bibliography 227

A Letter to Doctor 271

B Doctor Information Sheet 273

C Letter to Patient 275

D Patient Information Brochure 277

E Patient Consent Form 281

F Mole-Counting Chart 285

G Questionnaire 287

H Questionnaire Brochure 329

I MRsnphap R Package User Manual 333

J Example output using mrsnp.quant 339

K Example output using mrhap.quant 341

xviii

List of Tables

1.3.2.2.1 Illustration of haplotypes derived from two bi-allelic loci or

SNPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2.6.1.1 Estimated 5-year survival rates by Breslow thickness (mm)

in Western Australia [147] . . . . . . . . . . . . . . . . . . . 51

2.2.6.1.2 Estimated 5-year survival rates by Breslow thickness (mm)

in New South Wales [148] . . . . . . . . . . . . . . . . . . . 52

2.3.3.1 Eligible ICD-9 codes for WAMHS eligibility . . . . . . . . . 58

2.3.3.7.1.1 Comparison between consenting, non-consenting and non-

responding subjects . . . . . . . . . . . . . . . . . . . . . . 76

2.3.3.8.1 Questionnaire, demographic and clinical characteristics of

WAMHS sample . . . . . . . . . . . . . . . . . . . . . . . . 83

2.3.4.2.1 Tagged SNPs from MC1R, BRAF and EGF genes . . . . . 87

2.3.4.2.2 Reported melanoma-risk assocations between genotyped SNPs

and melanoma-risk factors . . . . . . . . . . . . . . . . . . . 88

2.3.4.5.1.1 Questionnaire-based characteristics of subjects in the WAMHS

sub-sample . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

2.3.4.5.1.2 Demographic and clinical characteristics of subjects in the

WAMHS sub-sample . . . . . . . . . . . . . . . . . . . . . . 95

2.3.4.5.2.1 Genotype frequencies of SNPs in the WAMHS sub-sample . 102

xix

2.3.4.6.3.1 Comparisons between the WAMHS sub-sample, consenting

cases, and eligible population . . . . . . . . . . . . . . . . . 106

2.4.2.1.1 Participant characteristics of the BHS sample . . . . . . . . 113

2.4.3.1 Comparison between WAMHS and HapMap and BHS geno-

type frequencies . . . . . . . . . . . . . . . . . . . . . . . . 121

2.4.3.2 Phenotypic variables multivariately associated with melanoma

risk in the WAMHS . . . . . . . . . . . . . . . . . . . . . . 122

2.4.3.3 Summary of associations between significant SNPs and melanoma

risk in the WAMHS . . . . . . . . . . . . . . . . . . . . . . 122

2.5.3.1.1.1 Univariate analysis between phenotypic variables with Bres-

low thickness in the WAMHS . . . . . . . . . . . . . . . . . 138

2.5.3.1.2.1 Multivariate model of phenotypic variables associated with

Breslow thickness in the WAMHS . . . . . . . . . . . . . . 139

2.5.3.2.1.1 Univariate associations between Breslow thickness and SNPs

modelled codominantly in the WAMHS . . . . . . . . . . . 142

2.5.3.2.2.1 Multivariate associations between Breslow thickness and SNPs

in the WAMHS modelled codominantly . . . . . . . . . . . 146

2.5.3.2.2.2 Association between codominant SNPs and Breslow thick-

ness in the WAMHS . . . . . . . . . . . . . . . . . . . . . . 147

2.5.3.2.2.3 Association between additive SNPs and Breslow thickness in

the WAMHS . . . . . . . . . . . . . . . . . . . . . . . . . . 149

2.5.3.2.2.4 Association between rs1042522, modelled dominantly, and

Breslow thickness in the WAMHS . . . . . . . . . . . . . . 151

2.5.3.4.1 Summary of associations between significant SNPs and Bres-

low thickness in the WAMHS . . . . . . . . . . . . . . . . . 161

3.5.3.1 IV and OLS coefficients and confidence intervals . . . . . . 209

xx

List of Figures

1.3.2.1.1 Inheritance of a dominant effect in a family . . . . . . . . . 9

1.3.2.1.2 Inheritance of a recessive effect in a family . . . . . . . . . . 10

2.2.2.1 Cross-section of the skin highlighting the epidermal layer . . 26

2.2.2.2 Clark model of the five stages of melanoma development . . 27

2.2.3.1.1 Melanoma incidence rates for selected countries for males and

females . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.2.3.1.2 Melanoma incidence rates from 1982 to 2005 . . . . . . . . 30

2.2.3.1.3 Melanoma incidence rates by age-group for 2005 for males

and females . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.2.3.1.4 Melanoma mortality rates from 1968 to 2005 for males and

females . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.2.3.2.1 Melanoma incidence rates for all Australian states and terri-

tories from 2001 to 2005 . . . . . . . . . . . . . . . . . . . . 34

2.3.3.1.1 Recruitment process through the WACR . . . . . . . . . . . 60

2.3.3.6.1.1 Degrees of naevi from the WAMHS questionnaire . . . . . . 70

2.3.3.6.1.2 Number of naevi in two sections of the back from the WAMHS

questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . 70

2.3.3.6.1.3 Degree of freckling from the WAMHS questionnaire . . . . . 71

2.3.3.7.1 Consent and participation figures and rates for the WAMHS 75

2.3.4.5.1.1 Distribution of Breslow thickness . . . . . . . . . . . . . . . 96

xxi

2.3.4.5.1.2 Distribution of log-transformed Breslow thickness . . . . . . 97

2.5.3.1.2.1 Model diagnostic plots from the multivariate phenotypic model

for Breslow thickness in the WAMHS . . . . . . . . . . . . . 140

2.5.3.2.2.1 Model diagnostic plots for the association between OCA2

rs1800401 and Breslow thickness in the WAMHS . . . . . . 148

2.5.3.2.2.2 Model diagnostic plots for the association between BRAF

rs1733826 and Breslow thickness in the WAMHS . . . . . . 150

2.5.3.2.2.3 Model diagnostic plots for the association between IRF4 rs12203592

and Breslow thickness in the WAMHS . . . . . . . . . . . . 151

2.5.3.2.2.4 Interaction between rs1042522 modelled dominantly and the

presence or absence of naevi in the WAMHS . . . . . . . . . 152

2.5.3.2.2.5 Model diagnostic plots for the association between rs1052522

and Breslow thickness in the WAMHS . . . . . . . . . . . . 153

2.5.3.3.1.1 Estimated main effects statistical power under an additive

model for n=800, under varying MAF and SD . . . . . . . . 155

2.5.3.3.1.2 Estimated main effects statistical power under a dominant


2.5.3.3.1.3 Estimated main effects statistical power under a recessive


2.5.3.3.2.1 Estimated interaction effects statistical power under an ad-

ditive model for n=800, under varying MAF and SD for en-

vironmental exposure prevalence of 0.2 . . . . . . . . . . . . 158

2.5.3.3.2.2 Estimated interaction effects statistical power under an ad-

ditive model for n=800, under varying MAF and SD for en-

vironmental exposure prevalence of 0.8 . . . . . . . . . . . . 159

xxii

3.3.2.1.1.1 Confounding – the observed association between Breslow thick-

ness and vitamin D levels is due to confounding by a healthy

lifestyle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

3.4.2.1.1 DAG for the instrumental variables model. Z1, instrumental

variable; X1, exposure of interest; Y, outcome of interest;

and U, unmeasured confounders. . . . . . . . . . . . . . . . 199

3.5.3.1 DAG for the Mendelian Randomisation model of vitamin D

levels and Breslow thickness . . . . . . . . . . . . . . . . . . 208

xxiii

Glossary

Allele : alternative forms of DNA occurring at a single locus.

Association study : an investigation into the statistical association between an

allele and a phenotypic trait.

Bonferroni correction : a multiple testing correction method, where the false

positive rate is divided by the number of tests to form a new level of statistical

significance.

Candidate gene : a gene which can be reasonably posited to be involved in the

genesis of a phenotypic trait or disease on the grounds of biological plausibility.

Chromosome : a linear thread of DNA chains and proteins.

Complex disease : a disease which involves multiple genetic and environmental

factors, and does not exhibit a classic Mendelian pattern of inheritance.

Diplotype : the pair of haplotypes for a particular stretch of the chromosome.

Direct association : an observed association between a locus and a phenotypic

trait which is due to the causative nature of the locus.

Exposure : a factor or situation of potential aetiological contribution to a given

xxiv

trait or disease to which an individual is exposed.

Gene : a region of the genomic sequence which is inherited and is associated with

regulatory regions, transcribed regions, or other functional sequence regions.

Genome : the total genetic information carried by an individual.

Genotype : the combination of alleles present at one locus.

Haplotype : a series of alleles at linked loci along a single chromosome.

Hardy-Weinberg Equilibrium Principle : a principle which describes the dis-

tribution of genotypes at a locus in terms of allele frequencies.

Heterozygous : if the two alleles at a particular locus are different, the individual

is heterozygous at that loci.

Homozygous : if the two alleles at a particular locus are the same, the individual

is homozygous at that loci.

Indirect association : an observed association between a locus and a pheno-

typic trait as a result of linkage disequilibrium between the measured locus and

some unmeasured locus which has a direct effect on the trait.

xxv

Linkage : the situation where two syntenic loci are inherited together, such that

they are close on a chromosome so that recombination during meiosis is uncom-

mon.

Linkage disequilibrium : the increased frequency of haplotypes within a pop-

ulation due to co-inheritance of linked alleles.

Linkage study : a statistical study analysing the co-segregation of genetic loci

within families to infer their position in the genome.

Locus : a unique chromosomal location defining the position of an individual gene

or DNA sequence.

Mendelian randomisation : a method of using non-experimental studies to

examine the causal effect of a modifiable exposure on disease by using genotypes.

Penetrance : the proportion of individuals carrying an allele who will express

the phenotype associated with that allele.

Phase : the haplotypic configuration of linked loci.

Phenotype : a measurable characteristic of an individual, also referred to as a

trait.

xxvi

Recombination : the exchange of genetic information between two chromosomes

during meiosis.

Relative risk : a measure of disease-exposure relationship estimating the mag-

nitude of an association between exposure and disease.

Simple disease : a disease which exhibits a classic Mendelian pattern of inheri-

tance, where the inheritance of a mutation in a single gene will result in a certain

phenotype.

Single nucleotide polymorphism : a DNA variant that represents variation in

a single nucleotide.

xxvii

Abbreviations

AIC Akaike Information Criterion

AMS Atypical Mole Syndrome

BHS Busselton Health Study

BCC Basal Cell Carcinoma

BMI Body Mass Index

BRAF B-Raf proto-oncogene serine

CDK4 Cyclin-dependent kinase 4

CDKN2A Cyclin-dependent kinase inhibitor 2A

CEPH Centre d’Etude du Polymorphisme Humain

CEU Caucasian-European

CI Confidence Interval

DAG Directed Acyclic Graph

DNA Deoxyribonucleic Acid

EGF Epidermal Growth Factor

EM Expectation Maximisation

FDR False Discovery Rate

GWAS Genome Wide Association Study

HWE Hardy-Weinberg Equilibrium

IRF4 Interferon Regulatory Factor 4

IV Instrumental Variable

MAF Minor Allele Frequency

MC1R Melanocortin 1 Receptor

MTAP Methylthioadenosine Phosphorylase

OCA2 Oculocutaneous Albinism II

PLA2G6 Phospholipase A2, Group VI (cytosolic, calcium-independent)

xxviii

RCT Randomised Controlled Trial

RNA Ribonucleic Acid

RPH Royal Perth Hospital

QEII Queen Elizabeth II

SCC Squamous Cell Carcinoma

SE Standard Error

SLC2A4 Solute Carrier Family 2

SNP Single Nucleotide Polymorphism

TP53 Tumor Protein 53

TPCN2 Two Pore Segment Channel 2

TYR Tyrosinase (oculocutaneous albinism IA)

TYRP1 Tyrosinase-Related Protein 1

WADB Western Australian DNA Bank

WAGER Western Australian Genetic Epidemiology Resource

WAMHS Western Australian Melanoma Health Study

xxix

Acknowledgements

I must first thank my coordinating supervisor Professor Lyle Palmer, who took

me on as an Honours student in 2004, and then became my PhD supervisor in

2006. Thank you for your guidance, your enthusiasm, and your wisdom. I am so

fortunate to have a supervisor who has such belief in my abilities and who has

always encouraged me to be the best I can be.

To my co-supervisor, Dr Judith Cole, thank you for your assistance with estab-

lishing the study, and for reading my thesis so carefully and providing me with

invaluable feedback. I also need to thank Dr David Preen who stepped in and

became my coordinating supervisor in the final weeks of my PhD. Thank yous

must also go to Dr Steven Wiltshire and Dr Samuel Mueller who were part of my

supervisory team at various stages.

I could not have completed this PhD if it were not for the generosity of the study

participants who gave their time to be part of this study. Recruitment of study

participants would not have been possible if it were not for the kindness and assis-

tance of Dr Tim Threlfall and staff from the Western Australian Cancer Registry

who welcomed the study team into their workplace for three years.

I am very grateful to have spent the four years of my PhD at the Centre for Ge-

netic Epidemiology and Biostatistics. I have had the opportunity to work with

many wonderful people. Without the assistance of many of you, I would not have

completed this thesis.

xxx

Sarah, thank you for all the help you have given me during the past four years –

both in your role as study coordinator and for being so generous to read my many,

many thesis drafts. I would never have survived the past four years without you.

Thank you, thank you, thank you.

To my other friends from the Centre – Nicole, thank you for your friendship, and

for always being happy to answer my many, many questions – I promise that will

stop now. Jules, thank you for your encouragement and wisdom. Pam, thank you

for helping me when I first started my PhD and for allowing me to incorporate my

work into SimHap. Also, thank you to the boys in the Informatics team, without

whom I would not have been able to store or retrieve my data.

I wish to thank my parents, Sue and Alan, my brothers Dan and Matt, their

families, and Guy, for realising very early on that they should never ask me how

my thesis is going. Thank you for believing in me and for supporting me through

all my studies.

xxxi

Preface

Some of the work in this thesis has been prepared for publication.

Section 2.3 describes the establishment of the Western Australian Melanoma

Health Study, and this work has been accepted for publication. This is a jointly

co-authored paper by the author and Ms Sarah Ward. The author and Ms Ward

jointly wrote this paper, and the author of this thesis performed all analyses. The

other authors of this paper, Ms Amanda Lee, Dr Judith Cole, Dr Jane Heyworth,

Professor Michael Millward, Dr Fiona Wood, and Professor Lyle Palmer all pro-

vided feedback on the paper to prepare it for publication.

In addition, Section 2.3 also contains work contributed by others. Some of the

SNPs genotyped in this thesis were selected by the author, and others were se-

lected as part of the Western Australian Melanoma Health Study. Genotyping

was performed by the PathWest Molecular Genetics Service in Perth.

The Busselton Health Study data used in Section 2.4 was collected by the Bus-

selton Population Medical Research Foundation and was made available to the

author for use in this thesis.

All analyses in this thesis were performed and the results interpreted by the au-

thor.

The R library, MRsnphap, was developed by the author, using already established

xxxii

statistical methods. The MRhap function in this library includes haplotype esti-

mating functions from SimHap, which were developed by Dr Pamela McCaskie.

1CHAPTER 1

Introduction

It has been well established that our genes and environment contribute to the

pathogenesis of disease. The collection of population-based data is integral to fully

understand the role these factors play in both disease susceptibility and progno-

sis. This thesis describes the establishment of the Western Australian Melanoma

Health Study which is used to investigate the role of genetic, environmental and

host factors in melanoma susceptibility and prognosis. In addition, this thesis

describes a statistical technique which can be used to better understand causal

relationships between disease outcomes and modifiable exposures.

1.1 Introduction to Epidemiology

Epidemiology, literally meaning the ‘study of what is upon the people’, can be for-

mally defined as the ‘study of the distribution and determinants of health-related

states or events in specified populations, and the application of this study to con-

trol of health problems’ [1]. As early as the 5th century BC, the Greek physician

Hippocrates, often regarded as the world’s first epidemiologist, observed the ef-

fect of the environment on human health [2]. Since then, epidemiology has led to

a greater understanding of the distribution, causes and control of diseases. No-

table examples include Snow’s observations of the link between drinking water

and cholera in England in 1854 [3], the association between smoking and lung

cancer by Doll and Hill in 1950 [5], and the more recent discovery of the causal

link between certain human papillomavirus strains and cervical cancer [10].

Epidemiological studies fall into two major types of studies – intervention and

observational studies, and these are discussed further in Section 3.3 (Please see

2 Chapter 1. Introduction

page 185). One type of intervention study, Randomised Controlled Trials (RCT),

are often considered the ‘gold standard’ of epidemiological studies for aetiological

investigations. However, observational studies are often an effective precursor to

RCTs, and may even take the place of RCTs when it is not ethical or feasible

to conduct a RCT. The major difference between these studies is their ability to

determine causal associations, which is discussed in the next section.

1.1.1 Causality in epidemiology

Epidemiological associations between disease outcomes (for example, disease risk

or some quantitative measure of disease) and modifiable exposures are often ob-

served. However for these associations to make a public health impact by leading

to a reduction in disease, it is critical that these associations are causal. Causality

is an integral concept in epidemiology, and there are a number of aspects that

should be considered when determining if a significant association observed be-

tween an outcome and an exposure may be causal.

One commonly used set of conditions was described by Hill and includes consider-

ing the strength, consistency, temporality and plausibility of the association [11].

Hill stated that these conditions did not always need to be met; instead the

conditions should be considered when gathering evidence for causal associations.

However, for an association to be causal, it would seem necessary to have tem-

porality, that is, that the exposure occurred before the disease. Observational

epidemiological studies of disease outcomes are often unable to fully satisfy the

complete set of Hill’s conditions. For example, the strength of the observed asso-

ciation (e.g. odds ratio) is often small, and it is often not possible to determine a

temporal relationship between the exposure and the disease outcome. In view of

1.2. Introduction to Genetic Epidemiology 3

this, sophisticated statistical techniques, such as Mendelian randomisation, can

be used to provide evidence for a causal association; these are discussed further

in Chapter 3 (please see page 183).

1.2 Introduction to Genetic Epidemiology

There are many definitions of genetic epidemiology, including the definition by

Morton [12], who defined genetic epidemiology as ‘a science which deals with the

aetiology, distribution, and control of disease in groups of relatives and with in-

herited causes of disease in populations’. However, it is important to include the

role of environmental risk factors in the study of disease; therefore in this thesis,

genetic epidemiology is defined as the study of the genetic and environmental

causes of human disease, and their interactions.

During the 1980s, genetic epidemiology emerged as a new discipline incorporating

both genetics and traditional epidemiology. However, the importance of a dis-

cipline combining genetics and epidemiology, namely ‘epidemiological genetics’,

was first described as early as 1954 by Neel and Schull [14]. At this time, Neel

and Schull identified four main criteria to be used by epidemiologists to infer the

genetic causes of disease, namely:

1. the occurrence of the disease in definite numerical proportions among indi-

viduals related by descent,

2. failure of the disease to spread to non-related individuals,

3. onset of disease at a characteristic age without a known precipitating event,

and


4. greater concordance of the disease in identical than in fraternal twins.

Discovery of genetic and environmental factors associated with disease has the

ability to aid in the prevention of disease [15]. An individual who is at risk of a

disease due to their genetic makeup may be able to alter their lifestyle to ensure

the disease does not present, or does not have a large influence on their life. An

example of this is phenylketonuria which can be managed by a diet low in pheny-

lalanine, with little or no side effects [16]. Additionally, cardiovascular disease

may be prevented by diet and lifestyle modifications [17].

Knowledge of the genetic causes of disease can aid in disease diagnosis [18]. Iden-

tification of the genetic causes of disease enables high-risk individuals to be tested

before the onset of the disease, and may even help predict the probable age at

onset and probable severity of disease. Diseases such as Huntington’s disease and

cystic fibrosis are two diseases that can be diagnosed with a simple blood test

before the disease has become symptomatic [19,20].

Finally, knowledge of an individual’s genetic makeup can enable personalised

medicine; that is, matching therapeutic treatments to an individual’s genetic

makeup so that individuals can receive more effective treatments (often phar-

macological), which may result in a more successful disease treatment [21]. An

example of this is the drug abacavir which is used to treat individuals with human

immunodeficiency virus. Hypersensitivity reactions to abacavir in individuals are

dependent upon HLA-B*5701 genotypes. Therefore, it is recommended that prior

to commencing abacavir therapy, patients are screened for this mutation, so that

the drug can be given only to individuals who are not at high-risk of developing

1.3. Genetic Concepts and Definitions 5

hypersensitivity [22]. A recent study found this method significantly reduced the

prevalence of hypersensitivity, from 7.8% in the non-screened group to 3.4% in

the screened group [23].

1.3 Genetic Concepts and Definitions

This section presents an introduction to some fundamental genetic concepts and

terminology relevant to this thesis. Terms not defined here may be found in the

glossary (please see page xxiii).

1.3.1 Genes and alleles

A human individual possesses 23 pairs of chromosomes, one of each pair inherited

from their mother and the other from their father. Chromosomes are individual

structures consisting of deoxyribonucleic acid (DNA) chains and proteins. The

entire sequence of DNA across all chromosomes is referred to as the genome.

Contained by and stored along chromosomes are genes. Genes are the basic unit

of genetic information and consist of a sequence of DNA. Within each gene there

are four chemical building blocks called nucleotides. Each of the four nucleotides

differ in their nitrogen-containing base. These bases are adenine (A), cytosine (C),

thymine (T) and guanine (G).

It is the order of these bases which contains the genetic information responsible

for the developmental and metabolic processes of an organism. An allele is one of

the possible forms of DNA sequence that can exist at a particular position along

a gene, or genetic locus. Therefore, an individual will have two alleles at each


genetic locus, one allele inherited from each parent.

The two alleles at a genetic locus may come from a possible one, two or many dif-

ferent alleles. However, for simplicity, we will assume there are only two possible

alleles at each genetic locus. Therefore, an individual can have three possible ar-

rangements of these alleles on homologous chromosomes, referred to as genotypes.

By denoting A and a to be the possible alleles at a locus, an individual can have

either the AA, Aa or aa genotypes. If an individual has two copies of the same

allele (AA or aa), they are said to be homozygous. An individual with different al-

leles (Aa) is heterozygous. A phenotype is an observable biological or physiological

trait, most of which are influenced by a combination of an individual’s genotype

and their environment.

During gamete cell formation (i.e. ova and sperm), in which an individual inherits

one pair of each chromosome from their mother and one from their father, the

chromosomes exchange segments of DNA. This process is referred to as recombi-

nation. Recombination is a function of the distance between two loci, with loci

closer together experiencing less recombination than loci further apart. Loci that

are physically close along a chromosome, such that recombination between these

occurs less often than chance (i.e. 50%), are said to be linked. These loci will tend

to cosegregate within families, and this is referred to as linkage. Linkage studies

are a type of genetic study in families which exploit linkage, and this is discussed

further in Section 1.4.2 (please see page 17).


1.3.2 Genetic variation

A mutation in a gene, also known as a genetic variant, may cause a change in

the gene function. If this is a significant change, the gene may cause disease or

predispose an individual to disease.

The penetrance of a genetic variant refers to the proportion of individuals with

the genetic variant who will develop the disease. For example, if the penetrance

of a genetic variant is 0.1, then it is estimated that 10% of individuals with the

variant will also develop the disease; this would be referred to as low penetrance.

Penetrance values of approximately 1 indicate complete penetrance; this occurs

when all individuals with the variant develop the disease.

The relative risk of a genetic variant refers to the ratio of the disease rate in indi-

viduals with the variant to the disease rate in individuals without the variant. For

example, if we take a group of 100 individuals, and the probability of developing

disease with the risk variant is 20%, and the probability of developing disease

without the risk variant is 5%, then the relative risk will be 20/5 = 4. This means

that carriers of the disease variant are four times as likely to develop disease as

individuals without the disease variant.

There are many different types of mutations, including point mutations, insertions

and deletions. In this thesis, I deal only with a type of point mutations – single

nucleotide polymorphisms – and combinations of these as haplotypes. These are

described in the next sections.


1.3.2.1 Single nucleotide polymorphisms

A single nucleotide polymorphism, or SNP, is a small genetic change which occurs

within an individual’s DNA sequence, such that a single nucleotide differs between

individuals. Many SNPs are thought to have minimal effect on cell function, but

it is believed that others may cause disease, predispose an individual to disease,

or influence their response to drugs.

Suppose that the majority of individuals are homozygous for the A allele at a

locus. If a mutation occurs and another allele, such as a G, replaces one or both

of the A alleles, then this would be referred to as a SNP. In this case, the G allele

would be referred to as the minor or variant allele. The minor allele frequency

(MAF) of a SNP refers to the frequency of the less common or variant allele in a

given population. SNPs can be used as markers in genetic analysis to find asso-

ciations between genetic variation and the observed phenotypes of an individual,

and this is discussed further in Section 1.4.3 (please see page 18).

The effect of genes on phenotype expression can be either additive, co-dominant,

dominant or recessive. An additive effect is one where the expression of the phe-

notype increases or decreases linearly as the number of variant alleles increases,

while a co-dominant effect occurs when the expression of the phenotype increases

or decreases as the number of variant alleles increases, but not necessarily in a

linear pattern.

A dominant effect means that only one copy of the variant allele need be present

to change the expression of the phenotype. A recessive effect occurs when the


expression of the phenotype is altered only when two copies of the variant allele

are present.

Figure 1.3.2.1.1 shows the transmission of a dominant variant through a family.

A dominant effect requires only one copy of the variant allele, so the affected

father has one copy of the allele, and the unaffected mother carries no copies.

The four children represent the possible results from mating between affected and

unaffected individuals; that is, on average, 50% of the children will be unaffected

by disease, and 50% will be affected by disease.

Figure 1.3.2.1.1: Inheritance of a dominant effect in a family

Figure 1.3.2.1.2 shows the transmission of a recessive variant through a family.

Individuals who carry variant alleles, but do not have the disease are referred to


as carriers. A recessive effect requires two copies of the variant allele, so both

parents are carriers as they carry one disease allele and one non-disease allele.

The four children represent the possible results from mating between two carriers;

that is, on average, 25% of the children will be unaffected by disease, 50% will be

carriers, and 25% will be affected by disease.

Figure 1.3.2.1.2: Inheritance of a recessive effect in a family

1.3.2.2 Haplotypes

As a result of recombination during meiosis, each chromosome of a pair contains

a mixture of alleles from each parent. Syntenic alleles (i.e. on the same chro-

mosome) which have not taken part in recombination and have therefore been

inherited as a unit, are referred to as haplotypes. A pair of haplotypes is called a

diplotype.


The concept of a haplotype is best illustrated in an example. Consider two bi-

allelic loci with three possible genotypes at each locus; AA, Aa or aa at the first

locus and BB, Bb or bb at the second locus. To form a haplotype, one allele is

taken from each genotype and these are paired together. The remaining alleles in

the genotypes are also paired together to form a second haplotype.

Genotype BB Bb bb

AA AB AB AB Ab Ab Ab

Aa AB aB AB ab or Ab aB Ab ab

aa aB aB aB ab ab ab

Table 1.3.2.2.1: Illustration of haplotypes derived from two bi-allelic loci or SNPs

From Table 1.3.2.2.1, we can see that an individual who has the AA genotype

at one locus and the BB genotype at the second locus, will have the diplotype

AB AB. This means that this individual inherited one AB haplotype from each of

their parents. An individual who has the Aa genotype at one locus and the Bb

genotype at the second locus will have one of two possible diplotypes, AB ab or

Ab aB. Therefore we cannot be certain which alleles were inherited together from

the same parent. It may be that the individual inherited the AB haplotype from

one parent and the ab haplotype from their other parent or, alternatively, they

may have inherited the Ab haplotype from one parent and the aB haplotype from

their other parent.

The phase of a haplotype refers to the combination of alleles that an individual

inherits together from each parent.


1.3.2.3 Haplotype inference

It is not always possible to infer haplotype phase unambiguously. Recalling from

Figure 1.3.2.2.1, if an individual is homozygous at one or both loci, then their

haplotype pair, or phase, can be constructed with certainty. However, if the in-

dividual is heterozygous at both loci, then their haplotype pair is referred to as

phase-ambiguous. This is because the individual inherited either the AB ab or the

Ab aB diplotype.

Several methods can be employed to construct these haplotypes with certainty,

including collecting data from family members or the use of laboratory techniques

to isolate the haplotypes to a particular chromosome, however these procedures

are often costly and time consuming [24]. As a consequence, several statistical

procedures have been developed in order to estimate the phase of diplotypes from

available genotypic information.

The three main statistical methods of haplotype inference are Clark’s Algorithm

[25], a pseudo-Bayesian algorithm by Stephens et al. [26], and an Expectation

Maximisation (EM) algorithm by Excoffier and Slatkin [24]. Clark’s Algorithm is

prone to several problems, for example, the algorithm cannot begin if there are

no individuals who are homozygous at all loci. The pseudo-Bayesian algorithm

also exhibits problems, particularly with the calculation of distributions. Niu et

al. [27] have shown that the conditional distributions calculated do not correspond

to a proper joint distribution; also the distributions calculated may depend on the

order of the genotypes [28]. The EM algorithm appears to exhibit less problems,

and is utilised by the statistical software package, SimHap, which is introduced in


Section 3.5.7 (please see page 214) and described further in Section 3.5.7.2 (please

see page 216).

1.3.3 Hardy-Weinberg equilibrium principle

The Hardy-Weinberg Equilibrium (HWE) principle is a fundamental concept in

population genetics, describing the distribution of genotypes in a population. The

basic idea of this principle is that allele and genotype frequencies remain constant

from one generation to the next, if the following conditions are met [29]:

1. The population is large enough so that sampling variation is negligible,

2. mating within the population occurs at random,

3. there is no selective advantage for any genotype; all genotypes are equally

viable and fertile, and

4. there is an absence of factors (‘evolutionary forces’) such as mutation, mi-

gration and random genetic drift.

Consider a locus consisting of two alleles, A and a, and let the frequencies of these

alleles be represented by p and q, respectively, where q=1-p. The HWE princi-

ple states that the genotype frequencies of AA, Aa and aa will be in proportions

p2, 2pq and q2, respectively. As long as the above four conditions are met, then

these genotype frequencies will remain constant generation after generation. De-

viations from HWE can be tested using Pearson’s Chi-Square or an exact test;

if the observed genotype frequencies are significantly different from the expected

frequencies then we would conclude the population is not in HWE. Evidence for


deviations from HWE may point to the violation of one of the above four as-

sumptions, or undetected genotyping errors; both of which could bias statistical

analyses [30].

1.3.4 Linkage disequilibrium

Linkage disequilibrium is a measure of the association between two alleles in a

population. Linkage disequilibrium occurs when some combinations of alleles on

the same chromosome (i.e. a haplotype) occur more often than would be ex-

pected by chance. Linkage disequilibrium results from ancestral recombination

events. Both linkage and linkage disequilibrium measure cosegregation, however

linkage measures cosegregation within families, while linkage disequilibrium mea-

sures cosegregation within the population.

To explain linkage disequilibrium further, define PA, Pa, PB and Pb to be the fre-

quencies of the A, a, B and b alleles respectively, and PAB to be the frequency of

the AB haplotype. When there is no evidence for disequilibrium, that is, when al-

leles are in equilibrium, there will be independence between the allele frequencies

at the two loci. This means that PA|B = PA, or the probability of an individual

having the A allele given they have the B allele, is just equal to the probability

of having the A allele. Also, at equilibrium, PAB = PAPB, or the probability of

having the AB haplotype, is the product of the probabilities of having both the A

allele and the B allele.

When linkage disequilibrium is present, the probability of observing the AB hap-

lotype is PAB = PAPB + D, where D is the linkage disequilibrium coefficient and

1.4. Gene Discovery in Human Disease 15

measures the strength of disequilibrium. As D is highly dependent on allele fre-

quencies, D can be standardised to D′ (referred to as Lewontin’s D′) [31], by

D′ = DDmax

, where Dmax is the largest possible value that D can take and is equal

to min(PAPb, PaPB). D′ will take a value between 0 and 1, with a larger value

indicating stronger linkage disequilibrium. If D′ equals zero, then there is random

association between different alleles at different loci and we have equilibrium. A

D′ of 1 occurs when the two loci are perfectly correlated, and any value of D′

above 0.8 is generally considered evidence for strong disequilibrium.

Another measure of linkage disequilibrium is the correlation coefficient between

two loci, r [4] and is defined by DPA−Pa−P−BP−b

, where PA− is the probability of

the AB or Ab genotype, Pa− is the probability of the aB or ab genotype, P−B is

the probability of the AB aB genotype, and P−b is the probability of the Ab or

ab genotype. Similar to the standard correlation coefficient, r may take a value

between -1 and 1.

Evidence for linkage disequilibrium can be helpful in discovering disease genes,

and this is described further in the following section.

1.4 Gene Discovery in Human Disease

The recent sequencing of the human genome has facilitated a dramatic acceleration

in human disease gene discovery. This section describes some analytic methods

used to investigate and identify the genetic causes of human disease.


1.4.1 Simple and complex disease

Human diseases are often categorised into two types - simple disease and com-

plex disease. The terms ‘simple’ and ‘complex’ refer to the genetic nature of the

disease. Simple diseases are caused by a mutation in a single gene, and are often

referred to as monogenic or Mendelian diseases. The variants that cause simple

diseases are rare, and generally have high penetrance. These diseases are inher-

ited based on a known pattern of inheritance, whereby disease clearly segregates

within families. Examples of simple genetic diseases are cystic fibrosis which has

a recessive pattern of inheritance [32], and Huntington’s disease which has a dom-

inant inheritance pattern [33].

Complex diseases are caused by variation in many genes, and by environmental

factors and interactions of these; and it is likely that each individual genetic locus

contributes only modestly to the disease. Complex diseases are generally common

in a population relative to monogenic diseases and include cardiovascular disease,

asthma and many cancers, including melanoma. Complex diseases do not follow

a known pattern of inheritance, and while these diseases are more common in

relatives of those with the disease, they do not segregate within families with a

clear pattern of inheritance.

Due to the differences between simple and complex diseases, namely, that simple

diseases are rare and tend to segregate within families, while complex diseases

commonly occur in the general population, different gene discovery methods are

required. Two of these methods, linkage analysis and genetic association studies,

are described in the following sections – Section 1.4.2 and 1.4.3.


1.4.2 Linkage analysis

Traditionally, the identification of simple disease genes has begun with family-

based linkage analysis, as linkage analysis uses pedigrees and can be used to locate

areas of the genome that contain disease genes. Linkage analysis exploits the con-

cept of linkage, which was introduced in Section 1.3.4. That is, loci that are close

together on a chromosome will be inherited together more often than expected by

chance, as they will have experienced lower recombination events than loci further

apart. In linkage analysis, genetic markers that are evenly distributed through

the genome are genotyped and their segregation through a pedigree is studied.

If family members with disease often have the same genetic markers, that is, if

disease cosegregates with the markers, then it is possible to infer the position of

the disease gene.

Linkage analysis has been very successful in identifying disease-gene regions for

simple diseases involving a single locus, such as Huntington’s disease. Simple dis-

ease variants are often rare and highly penetrant, and therefore the disease allele

can be localised to a small chromosomal region, and linkage markers close to the

disease allele will cosegregate with disease [34]. In contrast, linkage analysis has

had only limited success at identifying the genes that cause complex diseases [35].

This lack of success is due to a number of factors, including the low heritability of

complex disease, low resolution to localise disease alleles to a particular chromo-

somal location and inadequately powered studies due to the small effect of each

disease locus [34]. It has been shown that for linkage analysis to have enough

statistical power to detect a relative risk of 2, at least 2,500 families need to be

studied [36]. A relative risk of 2 is considered to be a large relative risk conferred


by a disease locus in a complex disease [37], therefore any linkage analysis with less

than 2,500 families would be unlikely to detect genetic causes of complex disease.

1.4.3 Genetic association studies

Genetic association studies are used to discover both genetic and environmental

risk factors for disease. In contrast to linkage studies that are primarily used for

family-based studies, association studies can be used for both family-based and

population-based studies. However, association studies have often been used in

conjunction with linkage analysis; that is, when a disease locus is localised to a

chromosomal region, association studies can be used to investigate the region in

order to locate the specific disease-causing allele [38]; this is referred to as fine

mapping.

There are two major types of genetic association studies - candidate-gene stud-

ies and genome-wide association studies (GWAS). Both types of studies exploit

the concept of linkage disequilibrium between alleles which was described earlier

in Section 1.4.2. In a candidate-gene study, genes suspected of being involved in

causing disease are identified using selection criteria, such as biological plausibility,

animal models, or prior associations [39]. Tests of associations between variants in

these genes and the disease outcome are then performed. More recently, genetic

association studies are being performed over the entire genome, and are referred

to as GWAS. This approach is often preferred over the candidate-gene study in

complex disease genetics, as no assumptions are made about the identity of the

causal gene.


In both types of studies, associations detected between genetic variants and dis-

ease outcomes may be due to one of three reasons. The first of these is direct

association, that is, the variant tested is functional and directly causes the out-

come. These associations tend to be the easiest and most powerful to analyse [40].

However, the prior probability that a given associated variant is itself functional

is generally low. More likely is that indirect association occurred; that is, the

variant associated with the outcome is in linkage disequilibrium with the causal

variant. The third possibility is that the association was spurious, although this

possibility can be fully or partially eliminated by applying appropriate statistical

tools, such as adjusting for multiple testing.

1.4.4 Multiple testing

In statistical analyses, the probability of a type I error or ‘false positive’ is the prob-

ability of rejecting the null hypothesis when it is actually true. When performing

one statistical test, this probability, or α, is referred to as the significance level and

is commonly set at 0.05. This means that when performing one test, the probabil-

ity that the result will be a false positive is 0.05, and so for the association to be

deemed significant, the observed p-value needs to be below this value. When per-

forming n multiple comparisons or tests simultaneously, the probability of a false

positive increases to 1− (1− α)n. Multiple testing issues are common in genetic

association studies due to the testing of multiple genetic markers simultaneously.

For example, when testing associations between 42 genetic variants or SNPs and

some outcome, the probability of at least 1 false positive is 1−(1−0.05)42=0.8840.

Traditionally, the Bonferroni correction has been used to adjust for multiple com-

parisons. In this correction, the total error rate of all tests combined is set at α,


so that each test has an individual type I error rate of αn. When testing 42 SNPs,

this would lead to an adjusted significance level of 0.0542

= 0.00119. Therefore,

associations with a p-value < 0.00119 would be deemed significant. The Bonfer-

roni correction is considered by some to be too conservative when the number of

genetic variants tested is high [41].

Genetic analyses in this thesis use the False Discovery Rate (FDR) method [42]

instead of the Bonferroni correction to adjust for multiple testing. The FDR

approach controls the expected proportion of false positives, compared to the

Bonferroni correction that controls the chance of any false positives assuming a

null hypothesis at any genetic loci.

The FDR approach can be used to estimate an FDR threshold known as q. The

q value is calculated as q(r) =m∗p(r)r

, where m is the number of SNPs tested and

p(r) is the p-value of the rth SNP when the SNP p-values are ordered from lowest

to highest. In this thesis, this value of q was compared to α, and the test was

deemed significant when q < α, where α was defined at the 5% significance level.

The FDR approach was used to calculate the q threshold for each SNP at the

multivariate genotypic analysis stage.

1.5 Aims

This thesis investigates the genetic epidemiology of melanoma susceptibility and

prognosis in the Western Australian Melanoma Health Study (WAMHS). In ad-

dition, this thesis describes a technique known as Mendelian randomisation, and

an application of this technique for making causal inferences in epidemiological

1.6. Outline of thesis 21

studies.

The aims in this thesis are described below:

1. To assist in establishing the WAMHS,

2. to investigate associations between candidate loci and melanoma risk in the

WAMHS sample,

3. to investigate associations between known host, environmental and genetic

melanoma-risk factors with melanoma prognosis, as reflected by Breslow

thickness, in the WAMHS sample, and

4. to implement a statistical technique known as Mendelian randomisation in

R, and to undertake novel methods development to enable the use of both

SNP and haplotypes to model causality in epidemiological studies.

1.6 Outline of thesis

Chapter 2 of this thesis begins with a description of melanoma incidence and mor-

tality rates and follows with a comprehensive review of the genetic epidemiology

of melanoma susceptibility and prognosis. The WAMHS is described, and in the

following sections, these data are used to investigate the role of genetic variants

in melanoma susceptibility. WAMHS data are also used to test the hypothesis

that the genetic, environmental and host factors that increase melanoma risk are

also associated with Breslow thickness, which is the main prognostic feature of

melanoma. This chapter finishes with a summary of the key findings and their

implications in melanoma research.


In Chapter 3, a background to epidemiological studies, in particular observational

epidemiological studies, is presented. I then described a statistical method known

as Mendelian randomisation that can be used to detect causal associations between

disease outcomes and modifiable exposures in epidemiological association studies.

The implementation of this method in an R library, MRsnphap, is also described,

along with novel methods work to extend Mendelian randomisation to haplotypes.

Chapter 4 summarises the main results of this thesis and describes areas for further

research, including possible further development of MRsnphap.

23CHAPTER 2

Genetic Epidemiology of Malignant Melanoma:

Susceptibility and Prognosis in the WAMHS

This chapter begins with a description of the incidence and mortality of melanoma

in recent years, followed by a review of the genetic, host and environmental risk

factors for melanoma development and prognosis. Establishment of the WAMHS

is then described. Following this, I took known candidate variants for melanoma

risk and tested whether these genetic variants were associated with melanoma in

a sub-sample of the WAMHS. I also used the WAMHS data to investigate the role

of melanoma-risk factors in melanoma prognosis. The results of these analyses

are then presented and discussed.

2.1 Introduction

Melanoma is a significant public health issue in Australia and worldwide. Aus-

tralia has the highest incidence rate of melanoma in the world, with over 10,000

cases diagnosed each year [43]. Melanoma was first described as a disease by Rene

Laennec in 1804 [44], however the first recorded operation on metastatic melanoma

was performed some years earlier in 1787 by John Hunter [45]. As early as 1840,

the British surgeon Samuel Cooper recognised that ‘the only chance for benefit de-

pends upon early removal of disease’ [46]; to date, early excision of the melanoma

tumour is the standard treatment, and is the only potentially curative treatment

for melanoma [47].

Melanoma is a complex disease resulting from mutations in many genes, environ-

mental and host factors and their interactions. In 1956, John Lancaster observed

a geographical distribution of melanoma mortality which supported the role of

24Chapter 2. Genetic Epidemiology of Malignant Melanoma: Susceptibility and

Prognosis in the WAMHS

sun exposure in causing melanoma [48]. Sun exposure is still considered to be the

main environmental risk factor for melanoma, however its interactions with other

environmental, host and genetic factors and their role in melanoma development

and prognosis are less clearly understood. To date, several genes for melanoma

have been identified, however mutations in these genes are rare and only account

for a small proportion of melanoma diagnoses [49]. Population-based studies of

melanoma cases may help to elucidate the role of environmental, host and genetic

risk factors of melanoma development and prognosis.

2.2 Literature Review

2.2.1 Skin cancer

It is estimated that 2 in 3 Australians will be diagnosed with skin cancer be-

fore the age of 70 [50]. Cutaneous Malignant Melanoma (hereafter referred to as

melanoma), Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC)

are the three main types of skin cancers and accounted for approximately 82% of

all cancer diagnoses in Australia in 2002 [50]. Together, the non-melanoma skin

cancers, including mostly BCC and SCC, accounted for approximately 67% and

30% of Australian skin cancer diagnoses, respectively [51]. Melanoma is the rarest

form of these skin cancers and accounted for approximately 3% of Australian skin

cancer diagnoses [51].

While BCC and SCC are the most common types of skin cancer, mortality rates

from these cancers are much lower than those of melanoma. In 2005, approxi-

mately 3.1 per 100,000 males and 1 per 100,000 females died from non-melanoma

skin cancers [52]. In contrast, mortality rates from melanoma were approximately

2.2. Literature Review 25

three-fold those of the non-melanoma skin cancers, at 8.9 per 100,000 males

and 3.5 per 100,000 females [51]. In real terms, 411 Australians died from non-

melanoma skin cancers in 2005, while 1,262 Australians died from melanoma. As

such, it is important from a clinical and public health viewpoint that the mecha-

nisms underlying melanoma, in particular melanoma susceptibility and prognosis,

are more clearly understood.

2.2.2 Melanoma biology

Melanoma is a complex and aggressive form of cancer of the melanocytes. In the

skin, melanocytes are cells found in the deepest layer of the epidermis (See Figure

2.2.2.1). When exposed to sunlight, melanocytes produce and supply melanin to

protect the keratinocytes from mutation due to ultraviolet radiation. Melanin is

the pigment found in the skin, hair and eyes and once produced, is responsible for

tanning.

The Clark model [53,54] has been used to describe the progression of melanoma in

five stages, from normal melanocytes to development of metastatic melanoma. It is

estimated that up to 70% of melanomas may arise de novo from normal-appearing

skin, although this is difficult to determine [55–57]. Therefore, the development of

melanoma may begin in either stage 1 or stage 2 of the Clark’s model. In addition,

not all melanoma tumours experience radial or vertical growth [53]. As such, the

Clark model should be viewed as a general framework for melanoma growth, with

not all tumours progressing through all stages. The stages of Clark’s model are

shown in Figure 2.2.2.2.



Figure 2.2.2.1: Cross-section of the skin highlighting the epidermal layer

The five stages of the Clark model are described below:

1. Benign naevus – structurally normal melanocytes proliferate to form be-

nign naevus. These naevi may be flat or slightly raised lesions, are generally

a tan or dark brown colour, and rarely develop into melanoma.

2. Dysplastic naevus – aberrant growth begins in either a pre-existing be-

nign naevus, or in a new location. These lesions may be varying colours,

asymmetrical, or have irregular borders, and are generally 5mm or greater

in diameter.

3. Radial growth - cells acquire the ability to grow into the epidermis. If the

melanoma is removed before cells have spread outside of the epidermis, the


Figure 2.2.2.2: Clark model of the five stages of melanoma development, from

normal melanocytes to metastatic melanoma [54]

melanoma is referred to as in situ melanoma.

4. Vertical growth - lesions acquire the ability to grow into a deeper layer of

the skin, the dermis, and may also grow into the subcutis. These melanomas

are referred to as invasive melanomas, and are the focus of this thesis.

5. Metastatic melanoma - cancerous cells spread to other areas of the skin

and internal organs through the lymphatic system and via the blood stream,

where they can proliferate.

2.2.3 Melanoma incidence and mortality

2.2.3.1 Melanoma in Australia

Australia has the highest incidence of melanoma in the world [43] and this is ris-

ing [51]. Melanoma incidence rates in Australia are more than double those in



the United States of America, and about four times those in the United Kingdom

and Canada. Incidence rates for these countries and selected others are shown

in Figure 2.2.3.1.1. The age-standardised rates presented in Figure 2.2.3.1.1 are

based on the 2001 World Standard Population. To allow for comparisons between

Australian and Western Australian incidence and mortality rates, all rates here-

after are based on the 2001 Australian Standard Population.

Melanoma incidence rates in Australia and New Zealand are significantly higher

compared to other countries in the world. This may be due to a number of rea-

sons, including environmental, geographic, and cultural reasons. For example,

ultraviolet exposure is a known risk factor for melanoma [65], and ultraviolet ex-

posure levels are higher at lower latitudes. In addition, many Australians and

New Zealanders tend to have fair skin due to European ancestry, and therefore

their skin is naturally more suited to areas of low ultraviolet exposure. However,

even with skin poorly suited to the high levels of ultraviolet exposure in Australia

and New Zealand, many Australians and New Zealanders participate in outdoor

recreational activities throughout the year from childhood to adulthood, which

increases ultraviolet exposure and therefore melanoma incidence.

An estimated 10,684 new cases of melanoma were diagnosed in Australia in 2005.

Melanoma was the third most diagnosed cancer (excluding SCC and BCC) in

2005 in both Australian males and females, after prostate and colorectal can-

cer in males, and breast and colorectal cancer in females. Melanoma accounted

for approximately 10% of cancer diagnoses [51]. Males are approximately 1.5

times more likely to be diagnosed with melanoma compared to females, with 2005

age-standardised incidence rates of 60.9 per 100,000 males and 42.5 per 100,000


Figure 2.2.3.1.1: Melanoma incidence rates for selected countries for males and

females. Incidence rates are presented as age-standardised rates (per 100,000 per-

sons) based on the 2001 World Standard Population. Data obtained from Globocan

2002 [43].

females [51]. While the incidence of melanoma is significantly higher in males

compared to females in Australia, New Zealand, and the United States of Amer-

ica, female incidence tends to be higher than male incidence in European countries

(2.2.3.1.1). The reasons for this remain unclear.

Melanoma incidence has been increasing steadily since 1982 (see Figure 2.2.3.1.2),

with 2005 age-standardised rates approximately double those in 1982. Currently,

it is predicted that 1 in 26 Australians will develop melanoma before the age of

75 [51], however this risk was 1 in 35 in 1990 [58], and 1 in 49 in 1982 [59]. It



is predicted that incidence rates will continue to rise in Australia at least until

2011 [51]. It is evident from Figure 2.2.3.1.2 that in the early 1980s, incidence in

males and females was comparable, and it was only in 1985 that male incidence

increased in comparison to female incidence. As this disparity in male and female

melanoma incidence occurred only recently, it is not likely that the higher inci-

dence in males is driven by underlying biological differences between the sexes,

but may be due to an increase in ultraviolet exposure in males.

Figure 2.2.3.1.2: Melanoma incidence rates from 1982 to 2005 for males and females.

Incidence rates are presented as age-standardised rates (per 100,000 persons) based

on the 2001 Standard Australian Population. Data obtained from the Australian

Institute of Health and Welfare COGNOS data cube [60].

Melanoma is a cancer that affects both young and middle-aged individuals, unlike

most other cancers which tend to affect mainly older adults. In 2005, melanoma

was the most common cancer diagnosed in males aged between 16 and 51, and


females aged between 17 and 33 [51]. While melanoma is the most common can-

cer in these age groups, overall melanoma diagnoses are more common in older

individuals. Figure 2.2.3.1.3 shows melanoma incidence rates for each five-year

age group in 2005. Melanoma incidence increased linearly for females over all age

groups, while male rates increased at a similar rate to females until about age 50,

when male rates increased sharply.

Figure 2.2.3.1.3: Melanoma incidence rates by age-group in 2005 for males and fe-

males. Incidence rates are presented as age-standardised rates (per 100,000 persons)

based on the 2001 Standard Australian Population. Data obtained from Australian

Institute of Health and Welfare’s Cancer Incidence and Mortality series [59].

While melanoma is one of the most common cancer diagnoses, melanoma mortal-

ity rates are relatively low compared to other cancers, such as lung and colorectal

cancer. Mortality rates for males are over double those of females. In 2005, mor-

tality rates were 8.9 per 100,000 males and 3.5 per 100,000 females, accounting



for 3.9% and 2.4% of cancer deaths in males and females respectively [51].

The higher mortality rates in males may be due to the different characteristics of

male melanomas compared to female melanomas. For example, males are more

likely to have thicker melanomas excised, have melanomas excised from different

body sites, and have ulceration of the melanoma [271]. However, several studies

have found that even after adjustment for these factors, male sex remains an in-

dependent predictor for prognosis [270, 271], indicating that there may be other

sex-specific factors that affect mortality rates.

Figure 2.2.3.1.4 shows melanoma mortality rates from 1968 to 2005. Melanoma

mortality rates for females remained relatively stable from 1968 to 2005; however

male mortality rates have increased approximately two-fold since 1968. Melanoma

mortality rates are predicted to continue to rise slightly in males, while female

rates are predicted to remain stable [51]. The different trends in mortality be-

tween males and females is substantial, however few studies have been published

which attempt to explain this difference.

Although Australia has a much higher incidence rate than other countries, the

differences in melanoma mortality between Australia and other countries are not

as extreme. For example, the age-standardised incidence rate for melanoma in

the United Kingdom (UK) is approximately one–quarter of Australian melanoma

incidence, while the mortality rate of UK individuals with melanoma is less than

one–third of Australian mortality (8.9 in Australian males and 3.5 in Australian

females compared to 3.0 in UK males and 2.0 in UK females) [273]. This sug-


gests that while Australians are more likely to be diagnosed with melanoma, early

detection and improved treatment of melanoma results in proportionally less Aus-

tralians dying from the disease.

Figure 2.2.3.1.4: Melanoma mortality rates from 1968 to 2005 for males and females.

Mortality rates are presented as age-standardised rates (per 100,000 persons) based

on the 2001 Standard Australian Population. Data obtained from Australian Insti-

tute of Health and Welfare’s Cancer Incidence and Mortality series [59]

Australia-wide, five-year survival rates in both males and females increased over

the 22 year period from 1982 to 2004. Between 1982 and 1986, five-year survival

rates in males were 82.2%, increasing to 89.7% between 1998 and 2004. Similarly

for females, five-year survival rates increased from 90.5% between 1982 and 1986

to 94.1% between 1998 and 2004 [61]. These increases in survival may be partly

due to the earlier detection of melanoma (i.e. thinner melanomas with better



prognosis excised), and also advances in treatment.

2.2.3.2 Melanoma in Western Australia

Western Australia has the second-highest melanoma incidence in Australia, after

Queensland [51]. Average incidence rates of all Australian states and territories

from 2001 to 2005 can be seen in Figure 2.2.3.2.1.

Figure 2.2.3.2.1: Melanoma incidence rates for all Australian states and territories

from 2001 to 2005 for males and females. Incidence rates are presented as age-

standardised rates (per 100,000 persons) based on the 2001 Standard Australian

population. Data obtained from Australian Institute of Health and Welfare’s Can-

cer Report [51].

At the time of writing this thesis, 2007 melanoma incidence and mortality rates

in Western Australia were available; however due to an issue with data collec-

tion at the Western Australian Cancer Registry, the 2007 rates are believed to be


understated (further details are available in the 2007 Western Australian Cancer

Incidence and Mortality report [62]). Therefore, only melanoma rates for 2005

and 2006 will be discussed in this section.

In 2005, melanoma diagnoses accounted for 11% of cancer diagnoses in Western

Australian males and 9.8% in Western Australian females, which made melanoma

the 4th and 3rd ranked cancer in males and females respectively (after prostate,

lung and colorectal cancer in males, and breast and colorectal cancer in females)

[63]. In 2006, melanoma diagnoses accounted for slightly more of the cancer diag-

noses (11.1% for males and 10.6% for females), with melanoma the 2nd and 3rd

ranked cancer for males and females respectively (after prostate cancer in males,

and breast and colorectal cancer in females) [64].

Of the 10,684 melanomas diagnosed in Australia in 2005, approximately 9% were

in Western Australians [63]. This corresponded to incidence rates of 59.3 cases

per 100,000 males and 37.8 cases per 100,000 females [63]. Incidence rates in both

males and females increased in 2006 to 60.9 cases per 100,000 males and 42.5 cases

per 100,000 females [63].

In Western Australia, the 2005 mortality rates were 11.6 deaths per 100,000 males

and 3.2 deaths per 100,000 females [63]; mortality rates in females were compa-

rable to the 2005 Australian mortality rates, however male mortality rates were

significantly higher in Western Australian males. In 2006, Western Australian

mortality rates in males decreased to 9 deaths per 100,000, while mortality rates

in females increased to 4.3 deaths per 100,000 [64]. There is no obvious reason for



2005 mortality rates to be higher in Western Australian males compared to other

Australian males. This may have been a chance fluctuation in rates in 2005, as

in 2006 male mortality rates were consistent with the national average.

2.2.4 The aetiology of malignant melanoma

Melanoma is a cancer that results from complex interactions between genetic,

host and environmental factors. Epidemiological research in melanoma has led to

some understanding of the host factors which increase an individual’s melanoma

risk; these include pale complexion, red or blonde hair, skin that freckles easily

and tans poorly, many naevi (or moles), and a family history of melanoma [65].

The strongest risk factor for melanoma is a large number of naevi [66], while

the main established environmental risk factor for melanoma is sun exposure, in

particular exposure to ultraviolet radiation (UVA and UVB) [65]. Several genes

which may predispose an individual to melanoma have been identified. Like any

complex disease, however, it is thought that a combination of the genetic, host

and environmental factors contribute to the development of melanoma.

2.2.4.1 Environmental and host melanoma risk factors

2.2.4.1.1 Sun exposure

Sun exposure is the principle established environmental risk factor for melanoma

and this was recognised by the International Agency for Cancer Research in

1992 [67]. Sun exposure history can be measured by either migrant studies or

personal exposure studies; however sun exposure is notoriously difficult to mea-

sure. Personal exposure case-control studies that ask individuals to recall their


sun exposure are often subject to recall and other biases [68,69]. In these studies,

a proxy measure of sun exposure is often used (for example, sunburn history)

and individuals must rely on recollection, sometimes from sun exposures experi-

enced decades earlier. However, these observational studies are often performed,

as RCTs are not considered ethical, and longitudinal cohort studies can be costly

and time-consuming. Migrant studies, also known as ambient studies, have an

advantage over personal exposure studies, as migration from areas of low/high in-

solation to areas of high/low insolation tend to be easy to classify and record [70].

Cumulative sun exposure throughout a whole lifetime has been established as a

risk factor for melanoma; however it has been hypothesised that childhood sun ex-

posure may confer a higher melanoma risk than similar exposure in later life [70].

This has been investigated in both migrant studies [71–82] and personal exposure

studies [83].

In a meta-analysis by Gandini et al. [83] of 33 personal exposure studies, sunburn

was associated with an increased risk of melanoma, with an overall relative risk

of 2.03 (95% CI = 1.73, 2.37). When further stratified by age, childhood sunburn

(less than 15 years of age) conferred a relative risk of 2.24 (95% CI = 1.73, 2.89),

while adulthood sunburn (greater than 19 years of age) conferred a relative risk

of 1.92 (95% CI = 1.55, 2.37). However, the meta-analyses was not adjusted for

skin type or the ability to tan which are likely to affect both incidence of sunburn

and melanoma.

Migrant studies are often used to investigate the relationship between sun expo-



sure in childhood and melanoma risk. A comprehensive review by Whiteman et

al. [70] of migration studies and melanoma risk investigated place of birth and

age at migration. In 11 of the 12 place of birth studies, individuals born in ar-

eas of low insolation (e.g. United Kingdom) who later migrated to areas of high

insolation (e.g. Australia) had lower risks of developing melanoma than natives

of these high insolation areas [71–81]. Only one study showed the opposite effect,

with migrants to Hawaii having an increased risk of melanoma [82]. The results

from these 11 studies suggest that individuals who migrate to areas of high in-

solation from areas of low insolation have, in general, lower melanoma incidence

than native-born and raised individuals. This indicates that living in areas of low

insolation for the earlier years of life may protect against melanoma compared to

living in high insolation areas for a lifetime. These studies, however, do not take

age at migration into account which may also play a significant role.

Of the five comparable studies examining age at migration from low insolation to

high insolation areas [74,75,79,84,85], three studies found that a decreased risk of

mortality from melanoma was associated with older age at migration [74, 75, 84].

In particular, a study by Khlat et al. [74] comparing mortality rates of migrants

to native-born individuals, found that when migration to Australia occurred be-

tween the ages of 15 and 24, male melanoma mortality dropped to 0.41 (95% CI

= 0.32, 0.54), and migration after the age of 25 was associated with an even lower

risk of mortality, 0.32 (95% CI = 0.27, 0.39).

Similarly, in a study by Darcy et al. [84] melanoma incidence was lower in in-

dividuals who migrated at a later age. In particular, melanoma incidence was

reduced significantly at migration after 19 years of age, with a risk ratio of 0.25


(95% CI = 0.08, 0.83), when compared to migration before 19 years of age. These

studies suggest that melanoma incidence and mortality are both affected by age

at migration. More specifically, migration in childhood increases melanoma risk

to closer to that of a native-born individual and migration before the age of 15

increases melanoma mortality.

There is continuing debate over what type of sun exposure confers the highest

melanoma risk. It has been suggested that intermittent exposure, which can be

categorised as either recreational or vacation exposure [86], confers a higher risk

of melanoma than occupational or chronic sun exposure. In a 2008 meta-analysis

by Gandini et al. [83] of 33 case-control studies, intermittent sun exposure was

associated with melanoma with a relative risk of 1.61 (95% CI = 1.31, 1.91), while

occupational sun exposure was not significantly associated with melanoma with a

relative risk of 0.95 (95% CI = 0.87, 1.04). These results suggest that intermittent

sun exposure may increase the risk of melanoma, while occupational sun exposure

either reduces or has no effect on melanoma risk.

2.2.4.1.2 Skin type and pigmentation

Pigmentation, including skin, hair and eye colour, plays an important role in

melanoma development. In particular, fair-skinned individuals are more likely to

develop melanoma than darker-skinned individuals [87], with melanoma risk in-

versely correlated to degree of pigmentation [88]. In fact, dark-skinned individuals

are unlikely to develop melanoma, with the majority of all melanomas diagnosed

in Caucasians. In a recent report of melanoma risk in an Australian Indigenous

population, the melanoma rate in the Indigenous population was 3.4 cases per

100,000, compared to 48.2 cases per 100,000 in the Australian non-Indigenous



population [62]. Bliss et al., [89] reviewed seven studies investigating the associa-

tion between melanoma and skin colour. When compared to dark skin, light skin

colour was associated with an approximate two-fold increased risk of melanoma,

mostly unaltered by naevus count, hair colour and freckling.

Melanoma risk is also independently associated with other pigmentation features,

including light hair colour, blue eyes and the presence of freckles [90,91]. A meta-

analysis of eight case-control studies [89] found that when compared to dark brown

hair, light brown, blonde and red hair conferred melanoma relative risks of 1.49

(95% CI = 1.31, 1.70), 1.84 (95% CI = 1.54, 2.21) and 2.38 (95% CI = 1.90,

2.97), respectively. These associations were observed independently of freckling

and skin colour. This meta-analysis also showed that when compared to brown

eyes, blue eyes conferred relative risks of 1.55 (95% CI = 1.35, 1.75), however this

association was not significant after adjustment for freckling [89]. Additionally,

in this meta-analysis, the presence of freckling when compared to sparse or no

freckling, was associated with a melanoma relative risk of 2.25 (95% CI=2.00,

2.54) and this association remained unchanged when adjusted for the presence of

naevi, skin and hair colour.

However, other researchers have cast doubt on the assertion that it is fair skin

that increases an individual’s melanoma risk, but instead suggest the tendency

to burn rather than tan following sun exposure is a better predictor of melanoma

risk [90,92]. A Western Australian study found that when compared to having a

deep tan, individuals with a moderate, light or no tan, had increased melanoma

relative risks of 1.4 (95% CI = 1.1, 1.9), 2.3 (95% CI = 1.6, 3.3) and 3.5 (95% CI

= 1.8, 6.8), respectively [87]. This association appeared to be explained by skin


and eye colour, but not hair colour. Similarly, a case-control study of American

adults found that individuals who had a deep tan had a reduced risk of melanoma

with an odds ratio of 0.47 (95% CI = 0.34, 0.65) for males and 0.43 (95% CI =

0.28, 0.65) for females, when compared to those with no tan [92].

While the relationship between pigmentation and melanoma development is com-

plex, fair-skinned individuals with light hair, blue eyes, freckles and who are un-

able to develop a deep tan, generally appear to be at a greater risk of developing

melanoma.

2.2.4.1.3 Family history

Melanoma clearly aggregates within families. It has been estimated that 10% of

individuals with melanoma have a first- or second-degree relative with melanoma

[93]. Early studies estimated that family history of melanoma caused a two- to

eight-fold relative risk for melanoma development [91,94]. However, a more recent

meta-analysis of 14 studies estimated a family history of melanoma (having one

or more affected first-degree relatives) confers a relative risk of only 1.74 (95% CI

= 1.41, 2.14) [95].

This aggregation of melanoma within families could possibly be due to shared host

factors such as eye colour, hair colour and naevus counts and also shared envi-

ronmental exposures found within families. However, Ford et al. [96] pooled eight

studies and found that, independent of these host factors, first-degree relatives of

individuals with melanoma had an estimated relative risk of 2.24 (95% CI = 1.76,

2.86). It has also been proposed that relatives of individuals with melanoma tend



to have an earlier age at onset, have thinner melanomas and are more likely to

develop multiple melanomas [94,97].

2.2.4.1.4 Naevi

The presence of multiple naevi is the main risk factor for melanoma. Most epi-

demiological studies that have investigated naevi and melanoma risk have found

that the presence of naevi are an independent risk factor for melanoma, particu-

larly the presence of atypical naevi [98]. The link between melanoma and naevi

was discovered in melanoma families who often have the Atypical Mole Syndrome

(AMS) phenotype. AMS is characterised by the presence of many common and

atypical naevi, typically over 100, which tend to appear in abnormal areas of the

body [99]. The AMS phenotype has been shown to be associated with melanoma

with an odds ratio of 10.4 (95% CI = 5.0, 21.5) [100]. Additionally, the same study

found that individuals under 40 years of age with melanoma were more than twice

as likely to have the AMS phenotype (odds ratio of 16.1; 95% CI = 4.6, 57.5),

compared to individuals aged over 40 (odds ratio of 6.9; 95% CI = 2.9, 16.6). This

highlights the importance of age in relation to the AMS phenotype and melanoma.

Children in Australia with increased numbers of naevi were found to have more

sun exposure than those with fewer naevi [101]. As sun exposure is a known risk

factor for melanoma, it follows that naevi may also be associated with melanoma,

although this relationship may not be causal. A case-control study of Australian

adolescents with melanoma found that the presence of 100 or more naevi was

strongly associated with melanoma risk, with an odds ratio of 46.5 (95% CI =

11.4, 190.8), independent of sun exposure levels [102]. Therefore, it appears the

presence of naevi, particularly in individuals younger than 40, plays an important


role in the development of melanoma.

2.2.4.2 Melanoma genetic risk factors

Historically, genetic studies of families selected on the basis of multiple cases of

melanoma have been used to discover melanoma-susceptibility genetic variants

[103, 104]. These studies have been successful in identifying two high-penetrance

genes for melanoma - Cyclin-dependent kinase inhibitor 2A (CDKN2A) and Cyclin-

dependent kinase 4 (CDK4). More recently, population- and family-based GWAS

have successfully identified several melanoma-susceptibility loci [105–108]. In

addition, candidate gene studies and GWAS of unrelated individuals have suc-

cessfully identified low-penetrance common genes which modify melanoma risk

through known risk factors, including skin pigmentation and naevi count [105,109].

2.2.4.2.1 High penetrance melanoma-susceptibility genes

Two high-penetrance melanoma-susceptibility genes have so far been identified

– CDKN2A and CDK4. In 1994, germline mutations in CDKN2A (located on

chromosome 9p21) were discovered in families with a history of melanoma [103,

110]. CDKN2A encodes for two tumour suppressor proteins, p16(INK4a) and

p14(ARF), which have been shown to have a role in cell cycle regulation and senes-

cence. The p16 and p14 mutations have been found to be strongly associated with

familial melanoma, and it has been estimated that 20-55% of melanoma families

have these mutations [111]. While relatively common in melanoma-prone fami-

lies, CDKN2A mutations are rare in melanoma cases in the general population. A

large international population-based study by Berwick et al. [112] found CDKN2A

mutations in 3% of individuals diagnosed with more than one melanoma, and mu-



tations in 1.2% of individuals with only one diagnosed melanoma.

Estimation of the likelihood of developing melanoma among CDKN2A muta-

tion carriers varies depending on ascertainment. Bishop et al. [113] estimated

melanoma penetrance in individuals with CDKN2A mutations who had a fam-

ily history of melanoma to be 0.3 by age 50, and 0.67 by age 80. In contrast,

a population-based study by Begg et al. [114] which ignored family history of

melanoma, estimated penetrance to be 0.14 by age 50, and 0.28 by age 80. There-

fore, from these studies it appears that CDKN2A mutation carriers in the general

population have a much lower risk of developing melanoma compared to carriers in

melanoma families. However, these differences may also be due to ascertainment

bias, as individuals with a strong family history of melanoma may have been more

likely to participate in the study by Bishop et al. It is clear, however, that the

combination of the rarity of CDKN2A mutations and their low penetrance in the

general population suggests that CDKN2A does not account for many melanoma

diagnoses. In fact, in a population-based study of melanoma cases in Queensland,

Aitken et al. [115] estimated that only 0.2% of melanomas in Queensland were

due to mutations in CDKN2A.

Germline mutations in CDK4 - an oncogene located on chromosome 12q13 - were

identified in melanoma families by Zuo et al. [104] in 1996. Mutations in CDK4

have only been found in several families to date [104, 116], thus accounting for

only a small proportion of familial melanoma. CDK4 interacts with CDKN2A

and as such, clinical phenotypes including age at onset of melanoma and the

number of melanomas, are similar between melanomas cases in CDKN2A and

CDK4 mutation carrier families [117]. Among two CDK4 families, Goldstein et


al. [118] assessed the penetrance of CDK4 mutation carriers to be 0.63 (95% CI

= 0.42, 0.85).

2.2.4.2.2 Low penetrance melanoma-susceptibility genes

The first low-penetrance melanoma-susceptibility gene identified was the melanocortin

1 receptor (MC1R) gene, located on the 16q24.3 chromosome. Variants in this

gene have been associated with red hair, fair skin and freckling [119–121]. As

these are well-established melanoma risk factors, it is not surprising that variants

in MC1R are also associated with melanoma risk. Valverde et al. [122] found

MC1R mutation carriers had an increased relative risk of melanoma of 3.9 (95%

CI = 1.48, 10.35). In a subsequent study, Palmer et al. [121] found the three

major red hair colour variants each conferred an odds ratio of melanoma of 2.2

(95% CI = 1.6, 3.0), while having two of these variants conferred an odds ratio

of approximately double this at 4.1 (95% CI = 2.1, 7.9). When adjusted for hair

colour and skin type, the association between one variant allele and melanoma

risk became non-significant for individuals with fair skin, however this association

remained significant for individuals with medium skin type (odds ratio of 2.2;

95% CI = 1.2, 4.1) and olive or dark skin type (odds ratio of 10.8; 95% CI = 1.2,

4.1). Thus, it appears that MC1R plays an independent role in melanoma risk,

independent of the association with skin pigmentation.

A recent meta-analysis of MC1R variants and melanoma risk and pigmentation

phenotypes by Raimondi et al. [123] found seven of the nine most commonly

investigated putative candidate risk variants were significantly associated with

melanoma. Additionally, two of these variants were associated with red hair and

fair skin, while three variants were associated with red hair only. More recently,



in a GWAS undertaken by Bishop et al. [106], the rs258322 SNP near the MC1R

locus was strongly associated with melanoma (combined GWAS and replication P

= 1.97 x 10−6). This SNP was previously identified in a GWAS by Han et al. [124]

as a hair colour and skin pigmentation locus, although Han et al. suggested that

the signal from this SNP was explained by three already identified MC1R variants.

This indicates that the rs258322 SNP may not be an independent risk SNP for

pigmentation.

While MC1R variants have been proven to be low-penetrance melanoma loci

[121,122,125], MC1R variants are also associated with increased CDKN2A pene-

trance and lower age of melanoma diagnosis [126]. In an Australian study of 15

melanoma-families, Box et al. [126] found CDKN2A penetrance to be 0.5, with a

mean age of diagnosis of 58.1. In the presence of MC1R variants, this penetrance

increased to 0.84 with a lower mean age of diagnosis of 37.8. Additionally, Gold-

stein et al. [127] suggested that in individuals with CDKN2A mutations, multiple

MC1R variants may be associated with the development of multiple melanomas.

Therefore, MC1R appears to not only be a melanoma risk gene, but it may also

interact with CDKN2A, increasing melanoma risk and number of melanomas, and

reducing the age at diagnosis.

Epidermal Growth Factor (EGF) located on chromosome 4q25-q27 has been im-

plicated as a melanoma-susceptibility gene. Shahbazi et al. [128] found a signif-

icant association between the rs4444903 SNP in EGF and melanoma risk, with

homozygous carriers of the rare allele having an increased odds of melanoma of

4.9 (95% CI = 2.3, 10.2). However, subsequent studies failed to detect any such

associations [129–132]. Some of these studies did detect significant associations


between the EGF variant and melanoma progression and this is discussed further

in Section 2.2.6 (please see page 50).

The recent advent of GWAS technology has enabled melanoma researchers to take

an unbiased approach in identifying melanoma-risk genes. To date, a number of

GWAS for melanoma risk, naevi count and pigmentation have been conducted.

A GWAS of naevi count by Falchi et al. [105] identified SNPs on the Methylth-

ioadenosine Phosphorylase (MTAP) gene (adjacent to CDKN2A on 9p21) that

were associated with naevi count. The strongest signal was from rs4636294 which

explained 1.5% of naevi count variance in the GWAS and 3.0% of naevi count

variance in the replication study (combined GWAS and replication P = 3.4 x

10−15).

In the same study, this SNP was also significantly associated with melanoma risk

in two independent samples, with each risk allele conferring a combined odds ratio

of 1.21 (95% CI = 1.14, 1.28; combined P = 3.7 x 10−8).

The MTAP rs4636294 SNP was also independently identified by Bishop et al. [106]

in a GWAS for melanoma risk, with each risk allele conferring a similar combined

odds ratio of 1.16 (95% CI = 1.09, 1.23; combined P = 1.97 x 10−6). Several

other SNPS in the same region were associated with both melanoma risk and

naevi count, including rs7023329, rs10757257 and rs2218220.

Falchi et al. [105] also identified the Phospholipase A2, Group VI (cytosolic,

calcium-independent) (PLA2G6) gene on chromosome 22q13.1 as a gene asso-



ciated with both naevi count and melanoma risk. Four SNPs in this region

(rs2284063, rs6001027, rs132985, rs73822) were significantly associated with naevi

count and melanoma risk. The strongest signal from these SNPs for naevi count

was rs2284063. Together, the two top naevi count SNPs – rs2284063 and rs4636294

– accounted for 9% and 20% of naevi count variance in the GWAS and replication

sample, respectively. The strongest signal for melanoma risk was from rs132985,

with each risk allele conferring a combined odds ratio of 1.23 (95% CI = 1.15,

1.30). Two of the four SNPs identified – rs2284063 and rs6001027 – were also

identified as melanoma-susceptibility loci in a GWAS by Bishop et al. [106].

In a GWAS by Brown et al. [107] using pooled DNA, two SNPs on chromosome

20q11.22 – rs910873 and rs1885120 – were identified as melanoma-susceptibility

loci, and these were replicated in two independent samples (combined P < 1 x

10−15). These SNPS were highly correlated and the risk allele conferred combined

odds ratios of 1.75 (95% CI = 1.53, 2.01) and 1.78 (95% CI = 1.54, 2.04) for

rs910873 and rs1885120, respectively.

Several studies have identified SNPs in the Tyrosinase (oculocutaneous albinism

IA) (TYR) gene located on chromosome 11q14-q21 that are associated with pig-

mentation and melanoma risk [106, 108, 109]. In a GWAS by Sulem et al. [109],

the risk allele of the rs1393350 SNP conferred an increased odds ratio for blue

eyes compared to green eyes of 1.52 (95% CI = 1.28, 1.81). This risk allele was

also associated with an increased odds of melanoma (odds ratio of 1.29; 95% CI

= 1.21, 1.38) in a GWAS by Bishop et al. [106]. The rs1393350 SNP is highly

correlated with another TYR SNP, rs1126809, and this was found to be associ-

ated with increased melanoma odds (odds ratio of 1.21; 95% CI = 1.13, 1.30) in


a candidate-gene study by Gudbjartsson et al. [108].

GWAS have therefore been successful in identifying melanoma risk, pigmentation

and naevi count loci. However, these SNPs may only be statistical markers for

causal variants, and fine-mapping of these regions is required to identify the causal

variant(s).

2.2.5 The diagnosis and treatment of melanoma

Melanoma is often identified as a lesion on the skin which has either changed (in

shape, outline, size or colour) or looks different to other naevi. The Australian

National Health and Medical Research Council (NHMRC) clinical practice guide-

lines state that ‘the diagnosis of melanoma should be considered in all cases of

pigmented or changing lesions on the skin’ [133].

A suspected melanoma is examined by a clinician, and if the lesion is deemed

suspicious, then the lesion may be fully or partially excised. This excised tissue

is then examined by a histopathologist to confirm diagnosis of melanoma. If the

lesion is melanoma and an adequate margin of normal tissue has been removed,

no further action is required.

Excision is often the only required treatment for thin melanoma. Metastatic

melanoma may also be treated by radiation or chemotherapy, however these treat-

ments are generally not a cure, and may often only relieve symptoms and extend

survival time [134].



2.2.6 Breslow thickness

Breslow thickness is a measure of the microscopic primary tumour depth of the

melanoma, and is measured in millimetres. Breslow thickness is measured from the

granular layer of the epidermis to the point of deepest invasion by the tumour cells.

Its value as a prognostic factor for melanoma was first reported by Dr A. Breslow

in 1970 [135], although as early as 1953, Allen and Spitz [136] reported that

patients with superficial melanomas had greater survival rates than those with

more invasive melanoma. Today, Breslow thickness is considered by clinical bodies

such as the Australian NHMRC and the American Joint Committee on Cancer to

be the most important predictor of survival in localised melanoma [133,137].

2.2.6.1 Breslow thickness and prognosis

Numerous studies in different countries have determined Breslow thickness to be

the best known predictor for prognosis of localised melanoma [138–146]. In gen-

eral, thinner melanomas have the best prognosis, whereas thicker melanomas are

more likely to lead to metastatic disease and concomitant poorer prognosis.

In a large international study of approximately 23,000 individuals with melanoma

by Balch et al. [137], Breslow thickness was significantly associated with sur-

vival probabilities (P < 0.001). Ten-year survival was 92% in individuals with

melanomas less than 1 mm thick, 80% in individuals with melanoma 1.01 to 2

mm thick, 63% in individuals with melanoma 2.01 to 4 mm thick, and 50% in

individuals with melanoma more than 4 mm thick. However, amongst the 17,841

individuals with melanoma less than 1 mm thick, survival rates ranged from 85%

to 99%. Further analysis indicated that in addition to Breslow thickness, mitotic


rate and presence of ulceration were important prognostic factors for these thin

melanomas.

A Western Australian study of individuals with melanoma by Heenan et al. [147]

found that five-year melanoma survival rates were significantly associated with

Breslow thickness. Survival after five years decreased from 98% for the thinnest

melanomas, to only 58% for melanomas over 4 mm thick (Table 2.2.6.1.1).

Thickness (mm) 5-year survival rates, % (95% CI)

0.00 - 0.75 98 (94 - 99)

0.76 - 1.49 96 (89 - 99)

1.50 - 2.99 86 (75 - 92)

3.00 - 3.99 79 (54 - 92)

> 4.00 58 (36 - 75)

Table 2.2.6.1.1: Estimated 5-year survival rates by Breslow thickness (mm) in West-

ern Australia [147]

A more recent study by Downing et al. [148] of approximately 30,000 individuals

with melanoma from New South Wales also found a significant association between

Breslow thickness and melanoma survival. While Breslow thickness categories are

not directly comparable between this study and the Western Australian study due

to the different breakpoints used, a similar trend can be seen, with the thinnest

melanomas having a survival rate of 99%, decreasing to 54% for melanomas over

4 mm thick (Table 2.2.6.1.2).

The consistency of associations between Breslow thickness and survival rates in-



Thickness (mm) 5-year survival rates, % (95% CI)

0.00 - 1.00 99 (98-99)

1.01 - 2.00 89 (88-91)

2.01 - 4.00 73 (70-75)

> 4.00 54 (51-58)

Table 2.2.6.1.2: Estimated 5-year survival rates by Breslow thickness (mm) in New

South Wales [148]

dicate that the identification of the risk factors associated with Breslow thickness

may also lead to a greater understanding of prognosis, which in turn may result

in improved prognosis and survival of melanoma patients.

2.2.6.1.1 Breslow thickness and melanoma risk factors

Breslow thickness has been shown to be associated with host factors, some of

which are also associated with melanoma risk, such as age at diagnosis, sex and

family history of melanoma.

Breslow thickness is associated with increasing age [149–153]. A review of melanoma

cases from the United Kingdom spanning 14 years by Osborne et al. [153] found

thicker melanomas were strongly associated with increasing age (P < 0.001), with

a greater proportion of thicker melanomas in individuals older than 60 years of

age. This may be due to the different behaviour of melanomas in older individuals,

or more likely, older individuals having less frequent skin examinations, leading

to delays in tumour excision.

An association between Breslow thickness and male sex has been reported in nu-


merous studies [150, 152, 154, 155]. In the review by Osborne et al. [153], males

had a greater proportion of thicker melanomas when compared to females (P =

0.05), with thick melanomas accounting for 22.5% of male melanomas, compared

to 14.2% in females. In an Australian study by Heenan and Holman [150], mean

Breslow thickness for males was 2.27 mm compared to a mean Breslow thickness

of 1.85 mm in females. The differences in Breslow thickness between males and

females may be due to some underlying biological process resulting in melanomas

growing faster in males. However, there has been no published evidence to sup-

port this. Instead, it may be that males are less likely to examine their skin or

have regular skin examinations, and therefore have their melanomas diagnosed or

excised later than women, after the tumour has had more time to grow.

Some studies have reported associations between Breslow thickness and family

history, with thinner melanomas diagnosed in individuals with a family history of

melanoma [156, 157]. However, other studies have not found any evidence of this

association [158,159]. Family history was only verified in the two studies that did

not find any associations, and Fisher et al. suggested that the observed association

may be due to confounding factors. For example, individuals who have a greater

concern over skin examinations may falsely believe they have a family history of

melanoma, and therefore may be more likely to present for skin examinations and

have thinner tumours excised.

To the author’s knowledge, no studies to date have estimated heritability in Bres-

low thickness, that is, the proportion of variation in Breslow thickness that can

be explained by genetic factors. In fact, there have only been a limited number

of studies which have investigated the genetics of Breslow thickness. The few



studies that have investigated the genetics of Breslow thickness have concentrated

on the EGF gene which, in addition to its role as a possible melanoma risk gene,

has also been associated with Breslow thickness [128]. In a study by Shahbazi et

al., individuals who were common homozygotes at the rs4444903 SNP were more

likely to have melanomas greater than 3.5 mm (odds ratio of 3.7; 95% CI = 1.0,

13.2). However, the odds ratio was reduced to 2.3 when pigmentary factors were

also included in the logistic regression model. Two subsequent studies [129, 130]

replicated this finding by Shahbazi et al., however several other studies reported

conflicting results [131,132,160].

Breslow thickness has been associated with several melanoma risk factors, includ-

ing male sex, increasing age at diagnosis and melanoma-risk loci. Therefore, it

is plausible that other melanoma risk factors, in particular other melanoma-risk

loci, are also associated with Breslow thickness and melanoma prognosis. Further

investigations into these associations may enable identification of individuals at

increased risk of poorer melanoma prognosis.

2.2.7 Literature Review Summary

This section has described the incidence and mortality of melanoma, and the

known environmental, host and genetic risk factors for both melanoma risk and

melanoma prognosis (i.e. Breslow thickness). From the literature presented,

it is evident that there has been substantial research into the development of

melanoma. In particular, numerous studies over the past 20 years have investi-

gated the role of risk factors for melanoma development, such as sun exposure,

skin pigmentation, family history and naevi. This has led to a better understand-

ing of the risk factors associated with melanoma development.


In more recent years, the role of genetic variants in melanoma development have

been investigated; beginning with the early candidate gene studies in genes such

as CDKN2A and CDK4, to the current GWAS of melanoma risk. While these

genetic studies have identified new genetic loci for melanoma risk, it is probable

that there are still many unidentified risk loci. In addition, it is possible that

these unidentified genetic loci may interact with known melanoma risk factors to

increase melanoma risk.

Associations between environmental and host factors with Breslow thickness have

not been studied as widely. However, as Breslow thickness is considered the best

proxy for melanoma prognosis, it is imperative that further research is undertaken

to identify environmental and host risk factors associated with Breslow thickness.

To date, there have been no GWAS for Breslow thickness and only one gene (EGF)

has been identified as a possible risk variant for melanoma prognosis. It is due to

this gap in melanoma research, that this thesis investigates associations between

Breslow thickness and genetic variants.

From Section 2.2.6.1.1, it is evident that melanoma development and prognosis

have risk factors in common (i.e. age, sex, and family history of melanoma).

Therefore, it is proposed in this thesis, that melanoma development and prognosis

may also be affected by the same genetic variants. Facilitated by the development

of the WAMHS, this thesis investigates associations between previously identified

melanoma-risk genetic variants and melanoma prognosis, with the aim of further

understanding the genetics of melanoma prognosis.



2.3 The Western Australian Melanoma Health Study

The aim of this section is to describe the establishment and development of the

WAMHS cohort and to summarise the characteristics of the WAMHS participants.

In addition, this section describes the phenotypic and genotypic variables of the

sub-sample of the WAMHS which is used for analyses in later sections. The gen-

eralisability of this sub-sample to the entire population diagnosed with melanoma

in Western Australia over the WAMHS data collection period is also investigated.

2.3.1 Author’s contribution

The author assisted in establishing the WAMHS from its beginning in July 2006

until the end of data collection in March 2010. This included assisting in de-

veloping the questionnaires for both the pilot and full-launch studies, and other

materials and protocol. The author also attended the Western Australian Cancer

Registry (WACR) on a weekly basis to contact doctors and recruit subjects. After

data were collected, the author had primary responsibility for cleaning the data

and for making the questionnaire data available for use.

2.3.2 Introduction

The WAMHS database is a population-based database and linked biospecimen

resource of melanoma cases. The aim of the study was to collect a range of phe-

notypic, clinical and biospecimen data on a representative sample of all cases of

invasive melanoma diagnosed in Western Australia over the period 2006 to 2009,

and to establish a research resource that would enable a range of research projects.

Initial envisioned research directions included investigations into the genetic and

environmental factors associated with melanoma and their interactions in the sus-

2.3. The Western Australian Melanoma Health Study 57

ceptibility, progression and prognosis of melanoma.

In this thesis, the WAMHS database was used to investigate the role of known

environmental and genetic risk factors in melanoma susceptibility and a measure

of prognosis, Breslow thickness.

2.3.3 WAMHS population

The WAMHS database is a population-based resource established and maintained

by the Centre for Genetic Epidemiology and Biostatistics (CGEB) and funded by

the Scott Kirkbride Melanoma Research Centre. The database consists of all con-

senting adult incident cases of invasive cutaneous melanoma diagnosed in Western

Australia from January 2006 until September 2009 (the ‘diagnosis time frame’).

Subjects were identified through the WACR, which is notified of all incident cases

of cancer in the Western Australian population. Information collected by the

WACR includes the name, address, and date of birth of individuals diagnosed

with cancer. In addition, pathology reports are collected which include the loca-

tion and type of each cancer, and the name and address of the referring doctor.

Melanoma diagnoses notified to the WACR include both invasive and in situ

melanomas.

Melanoma diagnosis was defined as having an invasive melanoma of the skin diag-

nosed which was recorded on the WACR under an ICD-9 code ranging from C440

to C449 (Table 2.3.3.1). All eligible incident cases of melanoma diagnosed within

the diagnosis time frame were invited to participate in the WAMHS. Cases were



ICD-9 Code Description

C440 Skin of the lip

C441 Eyelid

C442 External ear

C443 Skin of other and unspecified parts of the face

C444 Skin of scalp and neck

C445 Skin of trunk

C446 Skin of upper limb and shoulder

C447 Skin of lower limb and hip

C448 Overlapping lesion of skin

C449 Skin, not otherwise specified

Table 2.3.3.1: Eligible ICD-9 codes for WAMHS eligibility

contacted regarding the melanoma which was diagnosed during the diagnosis time

frame. This may or may not have been the first melanoma diagnosed during their

life time.

Eligibility was defined as cases of invasive melanoma (excluding in situ melanomas)

notified to the WACR from 1st January 2006 to 30th September 2009. Eligible

subjects were all aged between 18 and 80 at the time their melanoma was diag-

nosed, and had to be residents of Western Australia at the time their melanoma

was diagnosed and during data collection.

Of the 3,420 individuals diagnosed with invasive melanoma within the diagnosis

time frame, 92.16% (3,152) were invited to participate in the WAMHS and 52.13%


(1,643) of these consented to some or all components of the study. Of these ini-

tially consenting subjects, 5.23% (86) of these subjects later withdrew from the

study.

The study protocol and all subsequent amendments were approved by the Uni-

versity of Western Australia Human Ethics Research Committee (UWA HREC)

(reference RA14/1/5552) and the Health Department of Western Australia’s Con-

fidentiality of Health Information Committee (reference 200633). All analysis con-

ducted in this thesis also had approval from UWA HREC (reference RA4/1/2308).

2.3.3.1 Recruitment

Subjects were recruited from the WACR primarily by the author and the WAMHS

study coordinator, Ms Sarah Ward. The recruitment process through the WACR

is outlined in Figure 2.3.3.1.1.

Before any doctor or subject contact, the most recent death register was checked

so that the doctor or family members of subjects who had died were not con-

tacted inadvertently. The doctor who requested the subject’s pathology report

was contacted initially. A letter from Dr Timothy Threlfall, Medical Director of

the WACR, was sent to each subject’s doctor and they were asked whether there

was any reason not to contact their patient regarding the study (Doctor letter in

Appendix A, please see page 271). A doctor may have deemed the subject un-

suitable for contact about the study for a number of reasons, such as, ill-health,

poor knowledge of English or anxiety regarding their diagnosis.



Subject diagnosed with melanoma on WACR database

Update date of death to confirm subject’s status

No further action taken

Write to treating clinician (listed on pathology report) regarding

suitability to contact subject

No response from clinician within 5 weeks

Response from clinician within 5 weeks


Advice from clinician that subject is suitable to contact

regarding the study

Clinician recommends subject is not suitable for contact

regarding the study



Write a letter to invite subject to participate in the

study

DEAD

DEAD ALIVE

ALIVE

No response from subject within 14 days



Send reminder letter to subject

DEAD ALIVE

Response from subject within 14 days

No response from subject within 5 weeks


Subject response

Refusal to participate in study


Consent to participate in study

Subject details taken to CGEB

Figure 2.3.3.1.1: Recruitment process through the WACR, from subject appearing

on the WAMHS database, to consenting subject details taken to CGEB


If the doctor was being contacted for the first time about the study, they were also

sent a WAMHS information sheet (Appendix B, please see page 273), a WAMHS

information poster to display in their clinics and a Western Australian Melanoma

Advisory Service information brochure.

Initially, reminder letters were sent to doctors if they had not replied to the WACR

indicating patient suitability within a two-week period. Following this reminder,

if the doctor had not replied within five weeks of initial contact, it was assumed

that the subject was suitable to be contacted and they were subsequently invited

to participate in the study. In early 2009, the protocol was amended so that doc-

tors would not receive reminder letters. Instead, if the doctor did not reply to the

WACR within four weeks of the initial contact, it was assumed the subject was

suitable to be contacted.

If the requesting doctor deemed their patient not suitable to be contacted about

participation in the study, the reason given was recorded and their patient was

not contacted.

If the doctor deemed their patient suitable to be contacted regarding the study,

their patient was sent a letter (Appendix C, please see page 275), an information

brochure (Appendix D, please see page 277) and a consent form (Appendix E,

please see page 281) inviting them to participate in the study. The subject also

received a copy of the consent form to keep for their own records, information

about the WACR and an information sheet – DNA Storage and Testing – which



contained details on the storage and testing of DNA in medical research, provided

to the WAMHS by UWA HREC. Subjects were asked to complete and return

the consent form in the reply-paid envelope, indicating whether they wished to

participate in the study.

Reminder letters were also sent to subjects if they had not replied within a two-

week period. If the subject did not respond to their reminder letter, a telephone

call was placed. If the subject still did not return their consent form, no further

follow up occurred. During the latter stages of the study, the telephone reminder

calls ceased as they were time-consuming and did not significantly increase the

rate of return of consent forms.

Once subjects consented to be part of the study, their contact details were taken

to CGEB where they were sent the study materials which are described further in

Sections 2.3.3.2, 2.3.3.3 and 2.3.3.4 (please see pages 62, 64 and 66, respectively).

2.3.3.2 WAMHS pilot study

The WAMHS pilot study began in October 2006 after the development of subject

and doctor information brochures, consent forms and questionnaires. Consenting

subjects were sent a self-completion questionnaire and a mole-counting chart to

assist in their recognition of moles for several questions in the questionnaire.

The questionnaire was designed and compiled by the author and Ms Sarah Ward,

in collaboration with Professor Lyle Palmer, Dr Judith Cole, Dr Liz Milne, Profes-

sor Lin Fritschi and Professor Michael Millward. It was harmonised with the Aus-


tralian Melanoma Family Health Study, the Genes, Environment and Melanoma

Study and the Queensland Institute for Medical Research’s Melanoma Study ques-

tionnaires to facilitate possible future collaboration. The questionnaires were

printed and scanned by an independent research facilities company.

Subjects were also sent blood collection forms for PathWest blood services. Sub-

jects were asked to donate DNA and serum, with the additional option to donate

RNA, via blood samples. It was explained to subjects that if they wished to

donate RNA, they would need to attend the Queen Elizabeth II (QEII) Medical

Centre, Perth, as it was not practicable to send the PAXGene RNA tubes to all

PathWest Centres in Western Australia. If the participant was unable to travel to

Perth, or if they did not wish to give a sample of RNA, they were able to attend

any of the approximately 50 PathWest blood collection centres located through-

out Western Australia. Biospecimen collection and storage is described in more

detail in Section 2.3.3.5 (please see page 67).

Between October 2006 and January 2007, 235 subjects were contacted about par-

ticipating in the WAMHS (after the doctor on their pathology report confirmed

suitability for contact). Of these subjects, 137 (58.30%) consented and were sent

questionnaires and blood collection forms. However, 16 subjects withdrew or died

prior to completion of any subject components. By March 2007, 96 questionnaires

and 89 blood samples had been received. Of these, 87 subjects had returned both

the questionnaire and donated a blood sample. Based on the 121 subjects who

remained enrolled in the study, 67.97% of subjects completed both components

of the study.



The low completion rate of the questionnaire and blood samples highlighted that

the questionnaire was too time-consuming and complex which made subjects re-

luctant to complete and return it. As a result of this low response rate, the pro-

tocol was altered for the full-scale implementation of the study. Major changes

included shortening the questionnaire and changing part of the questionnaire from

self-completion to a telephone interview.

2.3.3.3 WAMHS full-scale implementation I

The WAMHS full-scale implementation I ran from October 2007 to December

2008. During this phase of the study, subjects were asked to complete two parts

of the questionnaire - Part 1, which was a written questionnaire taking approx-

imately 15 minutes to complete, and Part 2, which was a telephone interview

taking approximately 40 minutes to complete.

As subjects found the pilot study questionnaire difficult to complete, the ques-

tionnaires for the full-scale implementation were shortened and simplified. Part 1

of the questionnaire contained questions which were deemed easy to answer, such

as questions relating to hair and skin pigmentation. It also included questions

that required the subject to view a diagram, such as the degree of freckling and

naevi questions and also questions that required measurement, such as height and

weight.

Part 2 of the questionnaire contained more in-depth questions which required the

subject to recall details about their family history, past sun exposure, residence

history and ethnicity. The reasoning behind this was that with a trained inter-


viewer asking these questions, subjects could be assisted in a neutral manner to

remember details. It also ensured the whole questionnaire was completed and

that the data collected was of a higher quality compared to the self-completion

questionnaire.

Part 1 of the questionnaire was designed in the software package Teleforms by the

author. Returned questionnaires were scanned and verified by members of the

WAMHS team, including the author.

Part 2 of the questionnaire was designed in the software package LiquidOffice by

the author. Sections of the questionnaire were automated based on the age and

sex of the subjects so that only relevant questions would appear in the online

form for the interviewer to ask. Upon submission of the online form, data was

sent to an Oracle database and then extracted periodically. Part 1 and Part 2

questionnaire data was then cleaned and uploaded into the Western Australian

Genetic Epidemiology Resource (WAGER) by the author.

Upon consenting, subjects were sent blood collection forms, Part 1 of the ques-

tionnaire and a revised mole-counting chart (Appendix F, please see page 285).

Part 1 was completed by the subject at home and was returned to the CGEB in

a reply-paid envelope with the subject indicating their preferred day and time for

Part 2 of the questionnaire. Once the completed Part 1 questionnaire was received

by members of the WAMHS team, the questionnaire was reviewed and trained

interviewers conducted Part 2 of the questionnaire in a telephone interview.



Blood collection locations altered slightly for the full-scale implementation of the

study compared to the pilot study. In the full-scale implementation I, subjects

were also able to consent to a scar examination (the Natural History of Scarring

project) which took place at Telstra Burns Unit at Royal Perth Hospital (RPH)

under the direction of Professor Fiona Wood. If a participant consented to the

scar examination, they were then able to donate RNA at RPH, as well as the

QEII Medical Centre. Subjects who did not wish to donate RNA continued to

attend any PathWest centre throughout Western Australia. The author assisted

with establishing the Natural History of Scarring project, however this project did

not form part of this thesis, and will not be discussed further.

2.3.3.4 WAMHS full-scale implementation II

In September 2008, a review of recruitment and completion data highlighted some

issues related to data collection, in particular the role of the scar examination and

donation of RNA. Availability of resourcing at the scar examination clinics meant

that only a limited number of scar examinations could be conducted each week.

Subjects who consented to the scar examination gave their blood sample at the

time of examination, resulting in a limited number of blood samples donated

weekly. Therefore, the study protocol was altered and subjects were no longer

invited to participate in the scar examinations for a period of time. The removal

of the scar examination meant that subjects no longer had the option of donating

RNA at RPH. Instead, RNA donation was only possible at the QEII Medical

Centre and DNA and serum donation was possible at any PathWest, as per the

aforementioned protocol.

The questionnaire delivery methods were also altered at this time. The questions


asked remained the same, however Parts 1 and 2 of the questionnaire were merged

and were all delivered via telephone interview (full questionnaire in Appendix G,

please see page 287). The use of the telephone interview for the entire question-

naire meant that subjects were not required to return Part 1 of the questionnaire

before the rest of the questionnaire could be completed. It was also done to

save the time spent scanning, verifying and uploading the data using Teleforms,

and to reduce the time spent following up on questionnaire completion. Instead,

data collected from the telephone interview could be saved directly into an Ora-

cle database via the online form, cleaned by the author, and then uploaded into

WAGER. Subjects were sent a questionnaire brochure before their interview con-

taining the diagrams required for answering questions (Appendix H, please see

page 329). This also contained instructions regarding questions subjects would

need to complete before their interview, such as height, weight and naevi counts.

2.3.3.5 Biospecimens

Subjects in the study attended one of either QE-II Medical Centre, RPH or any

PathWest centre to donate DNA and serum. RNA donation was available at the

QEII Medical Centre and for a limited time, at RPH. In the pilot study, a blood

sample for DNA was collected in one 9mL Vacutainer EDTA tube. This was

changed in the full-launch study, when samples for DNA were collected in two

9mL Vacutainer EDTA tubes. These tubes were stored at 4C and then trans-

ported to the Western Australian DNA Bank (WADB) within 24 to 48 hours.

At the WADB, the blood was centrifuged and the buffy coat removed, and then

dual-site banked at -40C at both RPH and the QE-II Medical Centre.

Blood samples for serum were collected in either one 10mL or two 5mL Vacutainer



SST serum tubes. After blood collection, the serum tubes sat at room tempera-

ture for 30 minutes prior to being centrifuged within 2 hours of collection. These

tubes were stored at 4C and then transported to the WADB within 24 to 48

hours. At the WADB, the collected sample was processed and the serum was

dual-site banked at -40C at both RPH and QE-II Medical Centre.

Blood samples for RNA were collected in two 2.5mL PAX Gene Blood RNA

tubes. After blood collection, the RNA tubes were inverted 8–10 times and stored

upright at room temperature. The RNA tubes were transported to the WADB

within 24–48 hours. At the WADB, the RNA tube was centrifuged and the PAX

Gene Blood RNA kit was used to extract RNA. The RNA was dual-site banked

at -80C at both RPH and the QE-II Medical Centre.

2.3.3.6 Study variables

As only the full-scale implementation data was used in this thesis, the following

descriptions of questionnaire, demographic and clinical data refers to only the

data collected as part of the full-scale implementation.

2.3.3.6.1 Phenotypic variables

The questionnaire-based variables formed the majority of the phenotypic variables

collected as part of the WAMHS. These included pigmentation variables such as

skin colour, hair colour, eye colour, and the skin’s ability to tan and propensity to

burn. Questions related to sun exposure were also collected, including freckling

during childhood and adulthood, and the number of episodes of sunburn. Some

of the phenotypic variables collected are described in further detail below.


Self-reported ethnicity was collected, as well as the ethnicity of the subject’s par-

ents and grandparents. More than one answer was allowed to be given if a subject

considered themselves to have a mixed ethnic background.

Country of birth was also collected, and if not born in Australia, the age of the

subject when they moved to Australia was recorded.

Height in centimetres and weight in kilograms were collected via the self-report

questionnaire. These were used to calculate Body Mass Index (BMI).

Three measures of naevi were collected. Subjects were shown a diagram of four

degrees of naevi ranging from ‘None’ to ‘Very Many’ and were asked to select the

diagram which most closely resembled themselves (Figure 2.3.3.6.1.1).

Two naevi counts were also performed which required the subject to count the

naevi in two sections of their back - from the base of their neck to the top of their

armpit and from the top of their armpit to the top of their underpants (Figure

2.3.3.6.1.2).

Degree of freckling during childhood and adulthood was ascertained from a di-

agram of six degrees of freckling, ranging from ‘None’ to ‘Very Many’ (Figure

2.3.3.6.1.3).



Figure 2.3.3.6.1.1: Degrees of naevi from the WAMHS questionnaire

Figure 2.3.3.6.1.2: Number of naevi in two sections of the back from the WAMHS

questionnaire


Figure 2.3.3.6.1.3: Degree of freckling from the WAMHS questionnaire

Subjects were asked about their eye colour, skin colour and hair colour at age 18.

Skin reaction to the sun was also collected from subjects. These questions were

used as an indicator of how uncovered skin reacted to sun exposure in terms of

sunburn and tanning and were based on the Fitzpatrick scale [161].

Smoking history was collected, with ‘smoking’ defined as having smoked at least

one cigarette per day for a period of six months or more. Subjects were asked if

they had ever smoked, how long they smoked for, and how many cigarettes per

day they smoked.

A subject’s history of melanoma was ascertained through both the questionnaire

and WACR records. Subjects who identified themselves during the questionnaire

as having had more than one melanoma were classified as having a history of



melanoma. Similarly, subjects who had more than one primary melanoma diag-

nosis notified to the WACR were classified as having a history of melanoma. In

both instances, the number of melanomas diagnosed was also recorded. A sub-

ject’s history of other cancers, including other skin cancers, was also recorded.

Sunbed use was collected and was defined as having used a sunbed on more than

one occasion. The overall number of sunbed sessions and the locations of these

sessions were recorded.

A subject’s family history of melanoma was also collected. A family history of

melanoma was defined as having more than one first-degree relative (i.e. mother,

father, brother, sister) diagnosed with melanoma, as reported by the subject. In

addition, a family history of other skin cancers, and other cancers was also col-

lected.

Subjects were asked to recall the number of times they had been sunburnt during

three periods of their lives – from 5-12 years, from 13-19 years and from 20 years

onwards. Sunburn was separated into two questions – sunburn to cause pain for

more than two days and sunburn severe enough to cause blisters. These variables

had four levels – 0, 1-5, 6-10 and more than 10 times.

Time spent outdoors in warmer and cooler months during various age periods was

also collected. This included the number of hours spent outside and not under

any shade between 9am and 5pm.


2.3.3.6.2 Demographic and clinical variables

The majority of demographic and clinical variables were collected from the WACR.

The WACR collects extensive details regarding an individual’s melanoma diagno-

sis from pathology reports, including Breslow thickness, Clark’s level, sex, WACR

history of melanoma and date of melanoma diagnosis.

Breslow thickness was collected for the excised melanoma and was measured in

millimetres. In addition, another measure of the thickness of the excised melanoma

was collected – Clark’s level. The possible levels were II, III, IV and V, indicating

the layers of skin the melanoma had invaded. Clark’s level I refers to an in situ

melanoma and as subjects were recruited into the study based on diagnosis of an

invasive melanoma, no subjects were recruited into the study who had only been

diagnosed with a Clark’s level I melanoma.

Sex was collected from the WACR. Age at diagnosis was also collected and was

calculated as the date of diagnosis minus date of birth. Date of diagnosis is the

date at which the melanoma was fully or partially excised by a clinician.

Age at data collection was calculated as the date of the telephone questionnaire

minus date of birth. WACR history of invasive and in situ melanoma was also

collected, where WACR history of melanoma was defined as the subject having

had more than one primary melanoma notified to the WACR.



2.3.3.7 Sample size and response rates

At the conclusion of the data collection period, the WAMHS consisted of 1,643

consenting cases of melanoma. This consisted of 144 subjects who enrolled in

the pilot study, and 1,499 subjects who enrolled in the full-scale implementation

of the study. 73 of the 1,499 subjects withdrew or died prior to participation in

the study. Of the 1,426 subjects who remained enrolled in the full-scale study,

91.87% (n=1,310) completed Part 1 of the questionnaire, 91.87% (n=1,310) com-

pleted Part 2 of the questionnaire and 84.43% (n=1,204) gave a blood sample for

DNA and serum. 81.14% (n=1,157) of the subjects completed all parts of the

study.

Figure 2.3.3.7.1 depicts the final figures and rates for the study, including both

the pilot study and the full-scale implementations.

2.3.3.7.1 Consent, non-consent and non-response rates

Statistical analysis was performed to compare the subject characteristics between

consenting subjects and non-consenting subjects, and also consenting subjects and

non-responders. Non-consenting subjects were defined as those who returned a

consent form indicating they did not wish to participate in the study, and non-

responders were defined as subjects who did not return a consent form indicating

if they wished to participate.

In total, of the 3,420 individuals diagnosed with melanoma within the diagnosis

timeframe, 1,643 (48%) subjects consented to participate in the study, 644 (19%)


Figure 2.3.3.7.1: Consent and participation figures and rates for the WAMHS

did not consent to participate, and 713 (21%) subjects failed to return their con-

sent form indicating consent or non-consent.

Fisher’s exact test [162] was used to identify any differences in distributions be-

tween these groups for sex, Breslow thickness, age at diagnosis, Clark’s level and

melanoma site, and these results are presented in Table 2.3.3.7.1.1. Fisher’s exact

test was used in place of Pearson’s Chi-Square test as Fisher’s test allows for zero

or small cell counts.



Variable Consent Non-consent p-value No reply p-value

(n=1,643) (n=644) (n=713)

% [n] % [n] % [n]

Sex

Male 58.37 [959] 59.78 [385] 64.09 [457]

Female 41.63 [684] 40.22 [259] 0.57 35.91 [256] 0.0091

Breslow thickness, mm

0–1.00 71.94 [1182] 71.43 [460] 72.37 [516]

1.01–2.00 14.49 [238] 12.42 [80] 14.31 [102]

2.01–4.00 7.12 [117] 9.47 [61] 0.20 6.59 [47] 0.87

>4.00 3.77 [62] 3.26 [21] 4.49 [32]

Unknown 2.68 [44] 3.42 [22] 2.24 [16]

Age at diagnosis

18–29 2.31 [38] 1.70 [11] 8.56 [61]

30–44 13.94 [229] 8.85 [57] 25.95 [185]

45–59 32.56 [535] 25.47 [164] <0.0001 35.34 [252] <0.0001

60–74 40.54 [666] 42.24 [272] 23.98 [171]

75–80 10.65 [175] 21.74 [140] 6.17 [44]

Clark’s Level

II 36.76 [604] 37.42 [241] 38.71 [276]

III 25.63 [421] 22.98 [148] 23.56 [168]

IV 31.89 [524] 32.30 [208] 0.10 31.98 [228] 0.85

V 2.74 [45] 2.17 [14] 2.66 [19]

Unknown 2.98 [49] 5.12 [33] 3.09 [22]

Site

Head and neck 16.43 [270] 17.86 [115] 13.74 [98]

Trunk 34.04 [625] 39.60 [255] 37.03 [264]

Upper limb 23.25 [382] 22.05 [142] 0.63 25.95 [185] 0.39

Lower limb 21.85 [359] 20.34 [131] 22.86 [163]

Unknown 0.43 [7] 0.15 [1] 0.42 [3]

Table 2.3.3.7.1.1: Comparison of characteristics between consenting, non-consenting

and non-responding subjects

Analysis of consent versus non-consent found that the distributions of Breslow

thickness, sex, Clark’s level and site did not differ significantly. The frequency

distributions of these variables in consenters and non-consenters were similar.

However, the age of diagnosis distribution was significantly different (P<0.0001).


Analysis of consent versus non-response found that the distributions of Breslow

thickness, Clark’s level and site did not differ significantly. However, the sex

distribution was significantly different (P=0.0091), with a greater proportion of

males not responding (64.09%) versus consenting to participate (58.37%). The

age of diagnosis distribution also differed significantly between consenters and

non-responders (P<0.0001).

The differences in age distribution between consenters and non-consenters, and

consenters and non-responders was marked. In consenting individuals, the age

group 18 to 29 accounted for 2.31% of consenters. This was similar to the 1.70%

of non-consenters in this age category, however 8.56% of the non-responders were

aged between 18 to 29. This is a much larger proportion than would be expected.

Similar trends can be seen for the next two age groups – 30 to 44, and 45 to 59 –

with these age groups accounting for a smaller proportion of non-consenters, and

a larger proportion of non-responders. In the older age groups, the opposite trend

was observed, with the two age groups 60 to 74 and 75 to 80 accounting for a

larger proportion of non-consenters and a smaller proportion of non-responders.

These results suggest that individuals who did not respond tended to be younger

than individuals who consented. In addition, non-consenters appeared to be older

than consenting individuals. This may indicate that younger individuals who did

not wish to participate in the Study were less likely to respond, where as older

individuals who did not wish to participate were more likely to respond indicating

their non-consent.



2.3.3.8 Characteristics of participants

Table 2.3.3.8.1 shows demographic, clinical and questionnaire characteristics of

WAMHS participants who completed either Part 1 or Part 2 of the questionnaire.

These characteristics are stratified by sex and compared using Fisher’s exact test.

Participants who consented to participate in the study, but did not complete ei-

ther of the questionnaires are excluded from this table.

The sex distribution was skewed, with 58.17% of the sample male. Approximately

three-quarters of subjects were aged over 45, and the age distribution differed be-

tween sexes (P<0.0001). There were more females aged between 18 and 59 and

more males aged between 60 and 80.

Approximately 70% (n=944) of subjects had melanomas diagnosed less than 1 mm

thick, and only 3.55% (n=47) had melanomas thicker than 4 mm. Breslow thick-

ness differed between the sexes, with 69.57% (n=535) of males having melanomas

less than 1 mm thick, compared to 73.96% (n=409) of females. In addition, males

were more than twice as likely (4.55% compared to 2.17%) of having melanomas

more than 4 mm thick.

The most common melanoma site was the trunk, with 38.05% (n=203) of melanoma

diagnoses. The least common melanoma site was the head and neck region

(15.36%). Site distributions differed significantly between sexes (P<0.0001), with

male participants more likely to have melanomas diagnosed on their head, neck

and trunk, and females on their upper and lower limbs. The greatest difference

due to sex was observed at the lower limb site, with females approximately 2.4


times more likely to be diagnosed with melanomas on their lower limbs compared

to males.

The majority of subjects considered themselves to be Caucasian, with 50% (n=813)

considering themselves to be Caucasian of English background, and 27% (n=442)

Caucasian with Scottish, Irish or Welsh background. Approximately three-quarters

of subjects were born in Australia, with approximately 14% (n=182) born in Eng-

land, Ireland, Scotland, or Wales.

Over 90% (n=1,198) of subjects had some ability to tan, while only 8.47% (n=112)

reported that their skin never tanned, and only freckled. Men appeared to have

a greater ability to tan (P<0.0001) and also had a lower propensity to burn

(P<0.0001). This may be a result of self-reporting errors, with men more likely to

under-report their burning tendency, however there have not been any published

studies which support this theory.

Approximately 18% (n=237) of subjects considered their skin colour to be very

fair, 10.59% (n=140) had red hair at age 18, and 42.51% (n=562) had blue eyes.

Both hair (P<0.0001) and eye colour (P<0.0001) differed between sexes, with

men more likely to have black hair, and women more likely to have red hair. Men

were also more likely to have blue or brown eyes, with women more likely to have

hazel or green eyes. There is no scientific evidence for men and women having

different coloured eyes or hair, and this difference may be a result of reporting bias.

Degree of freckling decreased from childhood to adulthood. 23.45% (n=310) of



subjects had no freckling during childhood, which increased to 29.05% (n=384)

during adulthood. Similarly, 9.91% (n=131) of subjects considered themselves to

have ‘Many’ or ‘Very many’ freckles during childhood, which more than halved to

4.91% (n=65) in adulthood. Sex differences for childhood and adulthood freckling

also existed, with males less likely to have freckling compared to females for both

periods (P<0.0001). It is possible that this is due to differences in reporting by

males and females.

Another measure of sun exposure - blistering sunburn - also decreased from child-

hood to adolescence. 8.09% (n=107) of subjects reported more than 10 episodes of

blistering sunburn during childhood, which decreased to 5.98% (n=79) in adoles-

cence. Similarly, the percentage of subjects who reported no episodes of blistering

sunburn increased from childhood to adolescence, from 38.28% (n=506) in child-

hood, to 43.72% (n=578) in adolescence.

VariableWAMHS Males Females p-value

(n=1,322) (n=769) (n=553)

% [n] % [n] % [n]

Breslow thickness, mm

0–1.00 71.41 [944] 69.57 [535] 73.96 [409]

1.01–2.00 15.13 [200] 15.60 [200] 14.47 [80]

2.01–4.00 7.11 [94] 8.07 [94] 5.79 [32] 0.03

>4.00 3.55 [47] 4.55 [47] 2.17 [12]

Unknown 2.80 [37] 2.21 [17] 3.61 [20]

Age at diagnosis

18–29 2.19 [29] 1.43 [11] 3.26 [18]

30–44 14.14 [187] 9.75 [75] 20.25 [112]

45–59 33.06 [437] 30.94 [238] 35.99 [199] <0.0001

60–74 40.47 [535] 45.90 [353] 32.91 [182]

75–80 10.14 [134] 11.96 [92] 7.59 [42]

Clarks Level

II 36.23 [479] 36.02 [277] 36.53 [202]

III 25.72 [340] 24.58 [189] 27.31 [151]

Continued on Next Page. . .


Table 2.3.3.8.1 – Continued


(n=1,322) (n=769) (n=553)

IV 32.45 [429] 34.59 [266] 29.48 [163] 0.44

V 2.34 [31] 2.21 [17] 2.53 [14]

Unknown 0.45 [6] 0.52 [4] 0.26 [2]

Site

Head and neck 15.36 [203] 18.73 [144] 10.67 [59]

Trunk 38.05 [503] 46.69 [359] 26.04 [144]

Upper limb 23.68 [313] 19.90 [153] 28.93 [160] <0.0001

Lower limb 22.62 [299] 14.20 [110] 34.18 [189]

Unknown 0.30 [4] 0.39 [3] 0.18 [1]

Self-reported ethnicity

Caucasian - English 50.03 [813] 50.58 [478] 49.26 [335]

Caucasian - Scottish, Irish, Welsh 27.20 [442] 27.83 [263] 26.32 [179]

Caucasian - Northern European 7.69 [125] 8.47 [80] 6.62 [45]

Caucasian Southern European 2.52 [41] 1.69 [16] 3.68 [25]

Caucasian Eastern European 0.86 [14] 0.95 [9] 0.74 [5] 0.02

Caucasian Other 11.08 [180] 9.73 [92] 12.94 [88]

Indigenous 0.12 [2] 0.21 [2] 0.00 [0]

Middle Eastern 0.18 [3] 0.32 [2] 0.00 [0]

Pacific Islander 0.25 [4] 0.11 [1] 0.44 [3]

African 0.06 [1] 0.11 [1] 0.00 [0]

Place of Birth

Australia 74.58 [986] 75.68 [582] 73.06 [404]

New Zealand 4.31 [57] 3.77 [29] 5.06 [28]

England 10.82 [143] 10.40 [80] 11.39 [63]

Scotland/Ireland/Wales 2.95 [39] 2.99 [23] 2.89 [16] 0.78

Northern Europe 2.12 [28] 2.08 [16] 2.17 [12]

Other 4.31 [57] 4.42 [34] 4.16 [23]

Missing 0.91 [12] 0.65 [5] 1.27 [7]

Naevi

None 10.67 [141] 10.40 [80] 11.03 [61]

Few 35.02 [463] 34.20 [263] 36.17 [200]

Some 37.97 [502] 37.97 [292] 37.97 [210] 0.62

Many 15.20 [201] 15.99 [123] 14.10 [78]

Missing 1.14 [15] 1.43 [11] 0.72 [4]

Ability to tan

Very brown 12.55 [166] 15.47 [119] 8.50 [47]

Moderate brown 44.33 [586] 49.15 [378] 37.61 [208]

Slight tan 33.74 [446] 29.78 [229] 39.24 [217] <0.0001

No tan / freckle only 8.47 [112] 4.55 [35] 13.92 [77]

Missing 0.91 [12] 1.04 [8] 0.72 [4]






(n=1,322) (n=769) (n=553)

Propensity to burn

Never burn, always tan 2.12 [28] 2.73 [21] 1.27 [7]

Sometimes burn, usually tan 50.76 [671] 55.40 [426] 44.30 [245]

Usually burn, sometimes tan 22.69 [300] 22.24 [171] 23.33 [129] <0.0001

Always burn, never tan 23.52 [311] 18.60 [143] 30.38 [168]

Missing 0.91 [12] 1.04 [8] 0.72 [4]

Skin Colour

Olive/brown 7.72 [102] 8.45 [65] 6.69 [37]

Fair 73.37 [970] 74.51 [573] 71.79 [397] 0.14

Very fair 17.93 [237] 15.99 [123] 20.61 [114]

Missing 0.98 [13] 1.04 [8] 0.90 [5]

Hair colour at age 18

Black 5.45 [72] 7.93 [61] 1.99 [11]

Dark brown 26.85 [355] 26.66 [205] 27.12 [150]

Grey 1.82 [24] 2.99 [23] 0.18 [1]

Light/mouse brown 33.59 [444] 31.73 [244] 36.17 [200] <0.0001

Fair/blonde 20.73 [274] 21.07 [162] 20.25 [112]

Red 10.59 [140] 8.58 [66] 13.38 [74]

Missing 0.98 [13] 1.04 [8] 0.90 [5]

Eye Colour

Brown 14.75 [195] 16.12 [124] 12.84 [71]

Hazel 22.77 [301] 19.51 [150] 27.31 [151]

Green 13.62 [180] 12.09 [93] 15.73 [87] <0.0001

Grey 5.37 [71] 5.07 [39] 5.79 [32]

Blue 42.51 [562] 46.16 [355] 37.43 [207]

Missing 0.98 [13] 1.04 [8] 0.90 [5]

Freckling, Childhood

None 23.45 [310] 27.57 [212] 17.72 [98]

Very Few 31.39 [415] 33.42 [257] 28.57 [158]

Few 20.42 [270] 19.90 [153] 21.16 [117]

Some 13.92 [184] 11.44 [88] 17.36 [96] <0.0001

Many/Very Many 9.91 [131] 6.63 [51] 14.47 [80]

Missing 0.91 [12] 1.04 [8] 0.72 [4]

Freckling, Adulthood

None 29.05 [384] 35.50 [273] 20.07 [111]

Very Few 37.90 [501] 39.27 [302] 35.99 [199]

Few 16.49 [218] 13.39 [103] 20.80 [115] <0.0001

Some 10.74 [142] 7.41 [57] 15.37 [85]

Many/Very Many 4.91 [65] 3.38 [26] 7.05 [39]

Missing 0.91 [12] 1.04 [8] 0.72 [4]





(n=1,322) (n=769) (n=553)

Blistering Sunburn, 5-12 years

0 times 38.28 [506] 35.24 [271] 42.50 [235]

1–5 times 40.54 [536] 42.26 [325] 38.16 [211]

6–10 times 10.06 [133] 11.05 [85] 8.68 [48]

> 10 times 8.09 [107] 8.84 [68] 7.05 [39] 0.07

Don’t know 1.74 [23] 1.43 [11] 2.17 [12]

Missing 1.29 [17] 1.17 [9] 1.45 [8]

Blistering Sunburn, 13-19 years

0 times 43.72 [578] 44.73 [344] 42.32 [234]

1–5 times 40.32 [533] 40.45 [311] 40.14 [222]

6–10 times 8.09 [107] 8.06 [62] 8.14 [45] 0.65

> 10 times 5.98 [79] 5.07 [39] 7.23 [40]

Don’t know 0.60 [8] 0.52 [4] 0.72 [4]

Missing 1.29 [17] 1.17 [9] 1.45 [8]

Table 2.3.3.8.1: Questionnaire, demographic and clinical characteristics of WAMHS sample, strat-

ified by sex

2.3.3.9 WAMHS sample summary

The WAMHS is a population-based case collection comprised of individual melanoma

cases recruited from the WACR. Phenotypic, demographic, clinical, and biospeci-

men data were collected as part of this study, and form a comprehensive resource

for melanoma researchers.

The WAMHS sample consisted of 1,643 consenting cases of melanoma diagnosed

between January 2006 and September 2009. Of these cases, 144 (8.76%) par-

ticipated in the pilot study, and 1,499 (91.24%) participated in the full-scale

implementation. Of consenting participants from the pilot study, 87 (67.97%)

completed both the questionnaire and donated a blood sample. After changes to

the study, and the implementation of the full-launch study, 1,157 (81.14%) partic-



ipants completed the questionnaire and donated a blood sample. This increase in

completion rates may be attributed to the shorter questionnaire and the change

to administering the questionnaire by telephone interview.

Analysis of consent versus non-consent revealed that the age distributions were

significantly different, with a greater proportion of subjects aged between 45 and

59 consenting to the study, and a lower proportion of subjects aged between 75

and 80 consenting. The lower proportion of older subjects consenting may be as

participation in a study can be particularly difficult for older individuals for many

reasons, e.g. they may be less mobile and suffering from poorer health.

Analysis of consent versus non-response found that the sex distribution was signif-

icantly different, with a greater proportion of males failing to respond, compared

to males who consented to participate. In addition, the age distribution differed

significantly, with more subjects aged less than 60 failing to respond, and more

individuals over 60 responding.

Analysis of the WAMHS sample revealed that the demographic and clinical vari-

ables, age at diagnosis, Breslow thickness, and site distributions, differed between

sexes. There were more females aged between 18 and 59 and more males aged be-

tween 60 and 80 in the study. Females were more likely to have thinner melanomas

(< 1 mm), while males were more likely to have thicker melanomas (> 4 mm).

Male subjects were more likely to have melanomas diagnosed on their head, neck

and trunk, while women were more likely to have melanomas diagnosed on their

upper and lower limbs. This site distribution is similar to those observed in pre-


vious studies [90,163].

Several questionnaire variables differed between sexes, including ability to tan,

propensity to burn, hair and eye colour. Males appeared to have a greater ability

to tan, and a lower propensity to burn. In addition, males were more likely to

have black hair, compared to females, and females were more likely to have red

hair, compared to males. Eye colour also differed between the sexes, with males

more likely to have brown or blue eyes, and females more likely to have hazel or

green eyes.

The imbalance in self-reported variables in the WAMHS sample between men and

women, such as hair and eye colour, does not appear to have a scientific basis.

These differences may be explained by systematic self-report bias. For example,

women in this sample may be more likely than men to select their eye colour as

green. This may have implications if these self-reported variables were included

as covariates in analyses.

2.3.4 WAMHS sub-sample used in this thesis

2.3.4.1 Introduction

The genetic association analyses presented in this thesis, which are found in Sec-

tions 2.4 (please see page 111) and 2.5 (please see page 131), use a subset of

the WAMHS sample. This subset consists of 800 unrelated individuals who had

completed both study questionnaires by February 2010, and had donated the

blood sample by September 2009. In addition, these subjects were chosen as they

had a Breslow thickness recorded and were of Caucasian descent. A subject was



categorised as Caucasian if they described both themselves and their parents as

Caucasian.

Blood samples from these 800 participants were genotyped for 42 SNPs, and the

selection of these SNPs is described in this section. This section also includes

methods for the descriptive analyses of both study variables and genotypic data,

and also presents the results of these descriptive analyses. In addition, the gener-

alisability of this sample to the entire population of melanoma diagnoses within

the diagnosis timeframe is investigated.

2.3.4.2 SNP selection

Forty two melanoma risk SNPs were chosen as they had been identified as vali-

dated melanoma-risk SNPs, or were in genes which have been validated as melanoma-

risk genes. Nineteen SNPs were tagged from three genes – MC1R, BRAF and

EGF. Variation in all three genes was captured by choosing tag-SNPs using a

pairwise tagging approach in the Haploview software package [164]. Gene coordi-

nates were determined by using the University of California Santra Cruz Genome

Browser Gateway [165], with the coordinates used for tagging including 10 kb

upstream and 10 kb downstream of each gene. Haploview tagger [166] was used

to select a set of tag-SNPs which captured maximal variation in the gene. The

minimum minor allele frequency for selected SNPs was set at 5% and the thresh-

old for LD was set to r2=0.80.

Table 2.3.4.2.1 shows the three genes tagged in Haploview, the coordinates used

for tagging, and the tag-SNPs successfully genotyped in the WAMHS sample.


Gene Chromosome SNP Tagging Coordinates

rs1805005

rs1805007

MC1R 16 rs3212363 88,502,527 – 88,524,885

rs3212369

rs1267635

rs1733826

rs10487888

BRAF 7 rs17161747 140,070,754 – 140, 281,033

rs2365151

rs17623382

rs4726020

rs6944385

rs7655579

rs929446

rs11568993

EGF 19 rs882471 111,053,499–111,152,868

rs4698803

rs6533485

rs11569121

Table 2.3.4.2.1: Tagged SNPs from MC1R, BRAF and EGF genes

Several SNPs initially chosen were not able to be genotyped as their SNP score

was too low (< 0.4), or they were in close proximity to an already selected SNP.

The remaining 23 SNPs were chosen as they had been identified as melanoma-risk

SNPs in GWAS or candidate gene studies. Table 2.3.4.2.2 lists these SNPs, the

risk variant, and the observed associations. Thirteen of these SNPs had been

associated with melanoma risk in earlier studies, while 18 of these SNPs had been

associated with melanoma risk factors, including pigmentation, freckling, naevi

count, and skin sensitivity to the sun.

88

Chapter

2.G

eneticE

pidemiology

ofM

alignantM

elanoma:

Susceptibility

andP

rognosisin

theW

AM

HS

Gene SNP Risk Allele Association

CDC91L1 rs910873 A Increased melanoma risk [106,107]

MC1R rs258322 A Increased melanoma risk [106,124]

MYH7B rs1885120 C Increased melanoma risk [106,107]

TYR rs1042602 C Freckling [109], increased melanoma risk [106]

TYR rs1393350 A Blonde hair [109], green eyes [109], increased skin sensitivity to sun [109], increased melanoma risk [106]

TYRP1 rs1408799 C Blue eyes [109], increased skin sensitivity to sun [109], increased melanoma risk [108]

KITLG rs12821256 C Blonde hair [109]

SLC2A4 rs12896399 T Blonde hair [109], increased skin sensitivity to sun [109]

IRF4 rs1540771 A Brown hair [109], increased freckling [109], increased skin sensitivity to sun [109]

IRF4 rs12203592 T Blonde hair [124], lighter eye and skin colour [124], reduced tanning ability [124]

TPCN2 rs35264875 T Blonde hair [167]

TPCN2 rs3829241 A Blonde hair [167]

OCA2 rs1800407 G Blue eyes [168]

OCA2 rs1800401 A Black or brown eyes [169]

OCA2 rs7495174 A Blue or green eyes [109,168]

HERC2 rs12913832 G Blue eyes [170,171]

TP53 rs1042522 C Increased melanoma risk [172]

MTAP rs4636294 A Increased melanoma risk [105,106], increased naevi count, [105]

MTAP rs10757257 G Increased melanoma risk [105,106], increased naevi count [105]

MTAP rs7023329 A Increased melanoma risk [105,106], increased naevi count [105]

MTAP rs1011970 T Increased melanoma risk [106]

PLA2G6 rs2284063 A Increased melanoma risk [105,106], increased naevi count [105]

PLA2G6 rs132985 C Increased melanoma risk [105], increased naevi count [105]

Table 2.3.4.2.2: Reported melanoma-risk associations between genotyped SNPs and melanoma-risk factors


2.3.4.3 Laboratory methods

The methods to prepare the DNA for genotyping and the genotyping methods are

described below.

2.3.4.3.1 DNA preparation

DNA extraction and sample preparation was performed by the WADB, Perth.

DNA was extracted at room temperature from the buffy coat using the phenol-

chloroform method, and then suspended in TE buffer. DNA was quantitated using

a ND-1000 Nanodrop Spectrophotometer to ensure that sufficient quantities of un-

contaminated DNA were present. DNA samples of > 20 ng/µL concentration were

diluted to approximately 2 ng/µL using the PerkinElmer Multiprobe II pipetting

robot. Two microlitres of each DNA sample was dispersed into each well of a

96-well plate using the Beckman Coulter biomek FX machine, resulting in 4 ng

of DNA sample per well. Plates were left to dry at room temperature overnight

prior to use.

2.3.4.3.2 Genotyping

DNA samples were genotyped by the PathWest Molecular Genetics Service, Perth.

All genotyping was performed using Illumina GoldenGate Assays. The DNA sam-

ple was first activated, and the DNA was added to the oligonucleotides and then

hybridised. Three oligonucleotides were used for each SNP locus. Two oligonu-

cleotides were specific to each allele of the SNP (‘allele specific oligo’), and the

third hybridised at a site downstream from the SNP site (‘locus specific oligo’).

Following hybridisation, wash steps were performed to reduce excess and mis-

hybridised oligonucleotides. The allele specific oligo joined with the locus specific



oligo, to provide information regarding the genotype present at the SNP site. A

set of fluorescent Polymerase Chain Reaction (PCR) primers were added and PCR

occurred, generating fluorescent products. These products were combined with

Illumina beads on the Sentrix Array Matrix, combining with their matching bead.

The fluorescence of each bead was analysed on the BeadArray Reader, generating

automated genotype clustering and calling.

Genotyping quality control was performed in several steps. Each plate contained

a negative control sample (water) and a positive control CEPH trio sample. If

these controls failed, then the plate was re-genotyped. The controls did fail on one

plate and this was re-genotyped with no further errors. Additionally, genotyping

was repeated on 10% of the samples to ensure accuracy which resulted in 100%

concordance.

2.3.4.4 Methods for descriptive analyses

This section describes the methods for descriptive statistics of both study variables

and genotypic data.

2.3.4.4.1 Descriptive statistics of study variables

Descriptive statistics for the phenotypic and genotypic data were calculated us-

ing the statistical package, R. Means and standard deviations were calculated for

Normally distributed continuous variables, and medians and interquartile range

(IQR) were calculated for skewed continuous variables. Counts and percentages

were calculated for categorical and dichotomous variables. A histogram of Bres-

low thickness was examined to identify any deviations from Normality. Breslow


thickness was transformed by taking the natural log, loge(Breslow thickness) and

the resulting histogram was examined. Due to the skewness of Breslow thickness,

both the geometric mean and and 95% confidence intervals were calculated.

2.3.4.4.2 Descriptive statistics of genotype data

Genotype and allele frequencies were calculated using R. Consistency of genotype

distributions with HWE in the WAMHS sub-sample were examined at each SNP

locus using Fisher’s exact test.

2.3.4.5 Results for descriptive analyses

2.3.4.5.1 Phenotypic data

This sub-sample consisted of 800 individuals who were diagnosed with melanoma

from the WAMHS. Questionnaire-based characteristics and demographic and clin-

ical characteristics of this sub-sample are presented in Table 2.3.4.5.1.1 and Table

2.3.4.5.1.2 respectively.

Variable Study Population

(n=800)

Continuous variable, mean (SD)

BMI, kg/m2 27.45 (5.22)

Continuous variables, median (IQR)

Number of naevi - upper back 4.00 (9.00)

Number of naevi - lower back 5.00 (13.00)

Smoking, pack years 11.63 (21.95)

Dichotomous and categorical variables, % [n]

Place of Birth

Australia 76.00 [608]

England 10.50 [84]

New Zealand 4.25 [34]




Table 2.3.4.5.1.1 – Continued


(n=800)

Scotland/Ireland/Wales 3.25 [26]

Africa 2.38 [19]

Northern Europe 2.12 [17]

Other 1.50 [12]

Smoking

Never smoked 53.75 [430]

Ex-smokers 40.25[322]

Current Smokers 6.00 [48]

Naevi

None 11.88 [95]

Few 36.00 [288]

Some 38.50 [308]

Many 13.50[108]

Missing 0.02[1]

Sunbed use

Yes 11.38 [91]

No 88.25 [706]

Don’t know 0.37 [3]

Self-reported history of melanoma

Yes 27.37 [219]

No 72.63 [581]

WACR reported history of melanoma

Yes 21.13 [169]

No 78.87 [631]

Self-reported history of SCC or BCC

Yes 45.38 [363]

No 45.50 [364]


Ability to tan

Very brown 12.37 [99]

Moderate tan 44.50 [356]

Slight tan 34.00 [272]

No tan/freckle only 9.13 [73]

Propensity to burn





(n=800)

Never burn, always tan 2.25 [18]

Sometimes burn, usually tan 51.12 [409]

Usually burn, sometimes tan 24.13 [193]

Always burn, never tan 22.50 [180]

Skin colour

Olive/brown 7.00 [56]

Fair 74.30 [594]

Very fair 18.70 [150]


Black 5.50 [44]

Dark brown 29.38 [235]

Grey 2.25 [18]

Light/mouse brown 33.62 [269]

Fair/blonde 20.37 [163]

Red 8.88 [71]

Eye colour

Brown 14.12 [113]

Hazel 24.25 [194]

Green 14.00 [112]

Grey 6.13 [49]

Blue 41.50 [332]

Degree of freckling - childhood

None 24.38 [195]

Very few 33.00 [264]

Few 20.25 [162]

Some 12.87 [103]

Many/Very many 9.50 [76]

Degree of freckling - adulthood

None 30.38 [243]

Very few 38.25 [306]

Few 16.75 [134]

Some 10.62 [85]

Many/Very many 4.00 [32]

Family history of melanoma

Yes 23.25 [186]






(n=800)

No 76.75 [614]

Sunburn causing blisters, 5–12 years

0 times 39.75 [318]

1–5 times 39.63 [317]

6–10 times 10.50 [84]

> 10 times 8.00 [64]

Don’t know 2.12[17]

Sunburn causing blisters, 13–19 years

0 times 43.25 [346]

1–5 times 42.00 [336]

6–10 times 8.88 [71]

> 10 times 5.37 [43]


Sunburn causing blisters, > 20 years

0 times 69.38 [555]

1–5 times 25.37 [203]

6–10 times 2.63 [21]

> 10 times 2.37 [19]


Table 2.3.4.5.1.1: Questionnaire-based characteristics of subjects in the WAMHS sub-

sample

All 800 subjects used in this analysis were of self-reported Caucasian descent.

Individuals were classified as Caucasian if they considered their ethnicity to be

‘Caucasian’ and if they considered both their parents to be ‘Caucasian’. The

sex distribution was skewed with 56.12% males and 43.88% females. The median

age at diagnosis was 62.67, while the average age at data collection was 63.84

years. This corresponds to a median lag-time of approximately 14 months be-

tween melanoma diagnosis and completion of the study questionnaire.


VariableStudy Population

(n=800)

Continuous variable, geometric mean (95% CI)

Breslow thickness, mm 0.73 (0.69, 0.77)


Age at melanoma diagnosis, years 62.67 (16.86)

Age at data collection, years 63.84 (16.77)


Sex

Male 56.12 [449]

Female 43.88 [351]

Clark’s Level

II 36.25 [290]

III 26.00 [208]

IV 33.87 [271]

V 2.75 [22]

Unknown 1.13 [9]

Melanoma Site

Head and neck 17.00 [136]

Trunk 38.25 [306]

Upper limb 23.37 [187]

Lower limb 21.38 [171]

Table 2.3.4.5.1.2: Demographic and clinical characteristics of subjects in the

WAMHS sub-sample

Mean Breslow thickness was 1.11 mm; the distribution of which was highly skewed

(see Figure 2.3.4.5.1.1), with a median of 0.65 mm, indicating that half of WAMHS

subjects had melanomas less than 0.65 mm. A natural log transformation of

Breslow thickness resulted in a more Normal-appearing histogram (see Figure

2.3.4.5.1.2). When back-transformed, the geometric mean of Breslow thickness

was 0.73 (95%CI = 0.69, 0.77).

Melanomas were approximately evenly distributed through Clark’s Levels II, III



Figure 2.3.4.5.1.1: Histogram of the distribution of Breslow thickness in the

WAMHS.

and IV, while only a very small percentage of melanomas (2.75%) had a Clark’s

level of V. A large percentage (38.25%) of melanomas were located on the trunk,

with the lowest percentage of melanomas located on the head and neck regions

(17.00%).

Approximately three-quarters of subjects were born in Australia, with 10.50%

(n=84) born in England, and 4.25% (n=34) born in New Zealand. 53.75% (n=430)

of subjects had not smoked for a period of greater than six months, with approx-


Figure 2.3.4.5.1.2: Histogram of the distribution of log-transformed Breslow thick-

ness in the WAMHS

imately 6% (n=48) of all subjects currently smoking. Mean BMI was calculated

as 27.45 kg/m2.

Fewer naevi were present on the upper back (median=4), compared to the lower

back (median=5). The majority of subjects (74.59%) considered themselves to

have ‘Few’ or ‘Some’ naevi, while only 11.88% (n=95) had ‘None’. The degree

of freckling from childhood to adulthood decreased, with 30.38% (n=243) of sub-

jects recording no freckling during adulthood, decreasing to 24.38% (n=195) of



subjects in childhood. Similarly, 9.50% (n=76) of subjects considered themselves

to have ‘Many’ or ‘Very Many’ freckles in childhood, which more than halved by

adulthood to 4.00% (n=32).

27.37% (n=219) of individuals reported to have had more than one melanoma

diagnosed, however, records from the WACR indicated that only 21.13% (n=169)

of individuals had more than one melanoma diagnosis notified to the WACR. The

concordance between these two histories was moderate (Cohen’s kappa of 0.54).

Of the 169 individuals with a WACR history of melanoma, 44 (26%) individuals

did not report to the WAMHS that they had been diagnosed with more than

one melanoma. This may be as they were unaware of additional diagnoses, par-

ticularly if more than one melanoma was excised on the same day. Similarly, of

the 219 individuals who reported to have a history of melanoma, the WACR did

not have 94 (43%) of these melanomas recorded. This may be due to individuals

falsely stating they had a history of melanoma, or the WACR may not have been

notified of all melanoma diagnoses, which may occur if the melanomas were diag-

nosed outside of Australia. However, it appears more likely that individuals may

have over- and under-stated their melanoma diagnoses.

45.38% (n=363) of individuals reported to have been diagnosed with either an

SCC or BCC, 45.50% (n=364) reported not to have been diagnosed with an SCC

or BCC, with the remaining 9% (n=71) unable to recall this information. It has

been estimated that by the age of 70 years, 69% of males and 60% of females in

Australia will have been diagnosed with an SCC or BCC [281]. Therefore, with a

mean age of 63 at data collection, this rate of SCC and BCC diagnoses appears

to be a reasonable estimate.


Approximately 23% of individuals reported to have a first degree relative diag-

nosed with melanoma. This was self-reported and not validated. This is consid-

erably higher than other estimates of family history [93] and may be due to in-

dividuals with melanoma falsely believing they have a family history of melanoma.

Nearly half of all subjects had blue eyes (41.50%), and approximately 9% had red

hair at age 18. The majority of subjects considered their skin colour to be ‘Fair’

or ‘Very fair’ (93.00%), with the remaining 7.00% having ‘Olive’ or ‘Brown’ skin.

Approximately one quarter of subjects responded that they ‘Always burn, never

tan’, while only 2.25% (n=18) of subjects indicated that they ‘Never burn, always

tan’. Approximately 75% (n=628) of subjects indicated that after repeated sun

exposure, they develop a ‘Slight or moderate tan’, while 12.37% (n=99) develop

a ‘Very brown tan’, with 9.13% (n=73) indicating that they never tan and only

develop freckles.

Approximately 40% (n=318) of individuals did not have any incidents of sunburn

causing blisters when aged 5 to 12 years old. This increased to 43.25% (n=346)

and 69.38% (n=555) for the periods 13 to 19, and over 20 years of age, respectively.

The number of individuals who were sunburnt in each age group decreased as the

number of times sunburnt increased. 8.00% (n=64) of individuals were sunburnt

more than 10 times between the ages of 5 and 12, and this decreased to 5.37%

(n=43) and 2.37% (n=19) for the periods 13 to 19, and over 20 years of age,

respectively. In addition, 11.38% (n=91) of subjects had used a sunbed at some

time in their life.



2.3.4.5.2 Genotypic data

Genotype and allele frequencies and counts for the 42 genotyped SNPs are pre-

sented in Table 2.3.4.5.2.1, along with HapMap-CEU minor allele frequencies.

Included in Table 2.3.4.5.2.1 is the exact p-value for the HWE test for each SNP.

Thirty seven of the 42 genotype distributions were consistent with HWE. How-

ever, five genotypic distributions (IRF4 rs12203592, BRAF rs12676365, BRAF

rs6944385, EGF rs929446 and EGF rs11569121) were not in HWE at the 5% level

of significance.

Only three of the 42 SNPs had MAF less than 5%, and 12 SNPS had MAF less

than 10%, indicating that the majority of the SNPS were fairly common.

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Major Homozygote Heterozygote Minor Homozygote Minor Allele

SNP n Gene Gen % n Gen % n Gen % n SMAF1 hMAf2 HWE3

rs910873 798 CDC91L1 GG 77.19 616 AG 21.43 171 AA 1.38 11 A 12.09 8.4 1.00

rs1885120 798 MYH7B GG 78.32 625 CG 20.68 165 TT 1.00 8 C 11.34 7.1 0.60

rs1805005 793 MC1R GG 75.16 596 GT 23.33 185 TT 1.51 12 T 13.18 7.6 0.76

rs1805007 798 MC1R CC 74.06 591 CT 24.69 197 TT 1.25 10 T 13.60 12.0 0.18

rs3212363 798 MC1R AA 59.52 475 AT 33.96 271 TT 6.52 52 T 23.50 32.5 0.12

rs258322 800 MC1R CC 72.50 580 CT 25.87 207 TT 1.63 13 T 14.56 13.7 0.32

rs3212369 797 MC1R AA 67.13 535 AG 30.49 243 GG 2.38 19 G 17.63 13.7 0.18

rs1042602 799 TYR CC 38.92 311 AC 46.93 375 AA 14.15 113 A 37.61 43.0 1.00

rs1393350 796 TYR GG 47.49 378 AG 40.70 324 AA 11.81 94 A 32.16 22.6 0.06

rs1408799 793 TYRP1 CC 48.80 387 CT 41.87 332 TT 9.33 74 T 30.26 3.0 0.80

rs12821256 799 KITLG TT 80.98 647 CT 18.02 144 CC 1 .00 8 C 10.01 15.0 1.00

rs12896399 799 SLC24A4 GG 28.45 228 GT 51.69 413 TT 19.77 158 T 45.62 43.0 0.25

rs1540771 799 IRF4 AA 32.29 258 AG 49.56 396 GG 18.15 145 G 42.93 44.0 0.77

rs12203592 793 IRF4 CC 55.36 439 CT 34.93 277 TT 9.71 77 T 27.17 16.0 0.001

rs35264875 799 TPCN2 AA 69.09 552 AT 28.03 224 TT 2.88 23 T 16.90 17.5 1.00

rs3829241 797 TPCN2 GG 36.64 292 AG 45.92 366 AA 17.44 139 A 40.35 43.0 0.19

rs1800407 788 OCA2 GG 80.10 632 AG 18.76 148 AA 1.14 9 A 10.52 8.0 0.85

rs1800401 798 OCA2 CC 90.85 725 CT 9.15 73 TT 0.00 0 T 4.57 6.5 0.40

rs7495174 799 OCA2 AA 90.25 722 AG 9.63 77 GG 0.12 1 G 4.94 5.0 0.72

rs12913832 797 HERC2 GG 64.12 511 AG 30.74 245 AA 5.14 41 A 20.51 21.0 0.10

rs1042522 794 TP53 GG 58.57 465 CG 36.52 290 CC 4.91 39 C 23.17 23.0 0.55

rs4636294 791 MTAP AA 27.94 221 AG 48.93 391 GG 22.63 179 G 47.35 49.6 0.83

rs10757257 797 MTAP GG 38.27 305 AG 48.18 374 AA 14.80 118 A 38.36 37.6 0.88

rs7023329 797 MTAP AA 29.99 239 AG 48.18 384 GG 21.83 174 G 45.92 49.1 0.39

rs1011970 795 MTAP GG 65.54 521 GT 31.82 253 TT 2.64 21 T 18.55 17.4 0.16


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Major Homozygote Heterozygote Minor Homozygote Minor Allele

SNP n Gene Gen % n Gen % n Gen % n SMAF1 hMAf 2 HWE3

rs2284063 797 PLA2G6 AA 45.18 360 AG 43.79 349 GG 11.04 88 G 32.94 31.9 0.81

rs132985 791 PLA2G6 CC 31.60 250 CT 49.56 392 TT 18.84 149 T 43.62 44.2 0.89

rs1267635 776 BRAF GG 71.52 555 AG 27.19 211 AA 1.29 10 A 14.88 12.8 0.05

rs1733826 792 BRAF GG 89.39 708 AG 10.10 80 AA 0.51 4 A 5.56 6.4 0.29

rs10487888 798 BRAF AA 29.70 237 AG 46.74 373 GG 23.56 188 G 46.93 47.8 0.08

rs17161747 783 BRAF GG 91.70 718 CG 8.30 65 CC 0.00 0 C 4.15 8.0 0.64

rs2365151 793 BRAF TT 88.02 698 CT 11.85 94 CC 0.13 1 C 6.05 5.0 0.35

rs17623382 800 BRAF TT 77.37 619 GT 21.50 172 GG 1.13 9 G 11.87 13.3 0.61

rs4726020 800 BRAF AA 55.87 447 AG 37.88 303 GG 6.25 50 G 25.19 21.9 0.93

rs6944385 800 BRAF AA 71.75 574 AT 27.00 216 TT 1.25 10 T 14.75 10.9 0.05

rs7655579 798 EGF AA 42.98 343 AG 43.99 351 GG 13.03 104 G 35.03 30.8 0.35

rs929446 798 EGF CC 35.84 286 CT 44.36 354 TT 19.80 158 T 41.98 39.7 0.01

rs11568993 799 EGF CC 84.11 672 CT 15.52 124 TT 0.37 3 T 8.13 9.0 0.47

rs882471 798 EGF GG 37.84 302 AG 46.62 372 AA 15.54 124 A 38.85 38.69 0.60

rs4698803 793 EGF TT 62.72 497 AT 33.04 262 AA 4.29 34 A 20.81 22.5 1.00

rs6533485 799 EGF GG 28.54 228 CG 49.44 395 CC 22.03 176 C 46.75 48.7 0.83

rs11569121 800 EGF GG 89.12 713 AG 10.13 81 AA 0.75 6 A 5.81 7.1 0.04

Table 2.3.4.5.2.1: Genotype frequencies of SNPs in the WAMHS sub-sample, including

HapMap-CEU frequencies and a test for HWE

1WAMHS minor allele frequency2HapMap-CEU minor allele frequency3P-value for exact test for HWE


2.3.4.6 Generalisation of sub-sample to population and participating

cases

2.3.4.6.1 Introduction

It is important that, as much as possible, a selected sub-sample represents the

eligible population and the participating cases so that results can be generalised

back to the participating sample, and in turn, the whole eligible population. The

eligible population in this thesis were all adult invasive cases of melanoma which

were diagnosed between January 2006 and September 2009 and were notified to

the WACR. WAMHS participating cases were all subjects who consented to par-

ticipate in the WAMHS, and who fully or partly completed questionnaires and/or

provided blood samples for DNA in either the pilot or the full-launch studies.

Subjects were included in the sub-sample on the basis that they had a recorded

Breslow thickness, therefore the eligible population and WAMHS participating

cases sample include only those subjects with a Breslow thickness recorded.

2.3.4.6.2 Methods

Comparisons between the selected sub-sample and both the WAMHS cases and

the eligible population were calculated for Breslow thickness, sex, age at diagnosis,

melanoma site and Clark’s level distributions. Differences in mean logged Breslow

thickness between the sub-sample and two other samples was calculated by a one-

sample t-test, with the null hypothesis that the sub-sample mean was equal to

both the WAMHS participating cases mean and population mean. Differences in

sex, age at diagnosis, Clark’s level and melanoma site distributions were calculated

using Fisher’s exact tests on contingency tables.



2.3.4.6.3 Results

Differences in Breslow thickness, sex, age at diagnosis, Clark’s level and melanoma

site distributions are shown in Table 2.3.4.6.3.1. The eligible population consisted

of 3,284 individuals who were eligible for inclusion in the WAMHS. The participat-

ing cases consisted of 1,418 individuals, and is a subset of the eligible population.

The sub-sample of 800 Caucasian subjects used in this thesis is a subset of the

WAMHS participating cases sample.

No significant differences were detected between the sub-sample used in this thesis

and the WAMHS case sample. This indicates that the sample of 800 subjects used

in this thesis is representative of the sample of 1,418 subjects who participated in

the WAMHS.

There were no significant differences in Breslow thickness, Clark’s level or melanoma

site between the WAMHS sub-sample used in this thesis and the eligible popula-

tion, with Fisher p-values of 0.75, 0.95 and 0.76, respectively.

Significant differences were observed between the distributions of sex (P=0.02)

and age (P<0.001). There was a higher proportion of females represented in the

WAMHS sub-sample compared to the eligible population, with females account-

ing for 43.88% (n=351) of the sample and only 39.37% (n=1,293) of the eligible

population. There was a higher proportion of individuals aged between 60 and

74 at the time of melanoma diagnosis in the WAMHS sub-sample, and a lower

proportion of all other age groups in the WAMHS sub-sample.


These differences in sex and age at diagnosis means that the WAMHS sub-sample

may not be representative of the eligible population.

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VariableSub–sample WAMHS cases p-value Eligible Population p-value

(n=800) (n=1,418) (n=3,284)

Continuous variable, geometric mean (95% CI)

Breslow thickness, mm 0.73 (0.69, 0.77) 0.70 (0.67, 0.73) 0.24 0.73 (0.71, 0.75) 0.75


Sex

Male 56.12 [449] 58.89 [835] 60.60 [1991]

Female 43.88 [351] 41.11 [583] 0.22 39.37 [1293] 0.02

Age at diagnosis

18–29 1.38 [11] 2.05 [29] 3.87 [127]

30–44 12.13 [97] 13.61 [193] 15.99 [525]

45–59 30.37 [243] 32.79 [465] 0.28 31.18 [1024] <0.001

60–74 45.25 [362] 41.11 [583] 36.87 [1211]

75–80 10.87 [87] 10.44 [148] 12.09 [397]

Clark’s Level

II 36.25 [290] 37.73 [535] 37.42 [1229]

III 26.00 [208] 26.16 [371] 25.12 [825]

IV 33.88 [271] 32.93 [467] 0.28 33.43 [1098] 0.95

V 2.75 [22] 2.40 [34] 2.98 [98]

Unknown 1.12 [9] 0.78 [11] 0.12 [34]

Melanoma Site

Head and neck 17.00 [136] 15.73 [223] 16.60 [545]

Trunk 38.25 [306] 38.36 [544] 38.61 [1268]

Upper limb 23.37 [187] 23.70 [336] 0.79 23.54 [773] 0.76

Lower limb 21.38 [171] 22.00 [312] 20.98 [89]

Unknown 0.00 [0] 0.21 [3] 0.27 [9]

Table 2.3.4.6.3.1: Comparisons between the WAMHS sub-sample (n=800), WAMHS participating cases (n=1,418), and the

eligible population (n=3,284)


2.3.5 Summary

In this section, the establishment of the WAMHS resource has been described,

including the pilot study and full-scale launch of the study. Analysis of comple-

tion rates revealed that the completion rates of study components increased after

changes to the study protocol. Analysis of consenting, non-consenting and non-

responding subjects found that consenting and non-consenting subjects differed

in their age of diagnosis, while consenting and non-responding subjects differed in

both their age of diagnosis and sex.

Eligible subjects were identified through the WACR. Initially, each subject’s doc-

tor was contacted regarding their patient’s suitability to be contacted about the

study. If deemed suitable, subjects were subsequently contacted. This method

had both its benefits and limitations. Contacting the subject’s doctor initially

allowed the doctor to indicate whether their patient was suitable to be contacted

about participation in the study. Possible reasons not to contact their patient

included an inability to speak English, or the presence of co-morbidities which

meant contact or participation in the study was not suitable. Therefore, it is pos-

sible that the WAMHS sample has less non-English-speaking participants than

in the entire eligible population. There is also a possibility that individuals with

co-morbidities, such as dementia, or advanced melanoma were not invited to par-

ticipate in the study after their doctor deemed them not suitable to be contacted.

However, potential biases are likely to be small, as out of the 3,347 doctor contacts,

only 3% (n=112) of patients were not suitable to be contacted regarding the study.

Aside from these possible biases, one benefit of the study recruitment is that there



was limited selection bias when subjects were first identified through the WACR.

This meant that individuals contacted should have been representative of the en-

tire eligible population, and should not have differed in terms of demographic or

phenotypic variables. If recruiting subjects through a clinic, many selection biases

are possible, for example, it is possible that only subjects who live within a set

geographical distance from the clinic may be approached about participating in

the study. However, recruitment through the WACR meant that the WAMHS

was able to contact subjects from all over Western Australia, such that rural and

urban areas should be evenly represented. Also, as blood donation was possible

at many PathWest locations throughout Western Australia, there should not have

been an over-representation of any such geographical area. Analyses (not shown)

found that 70% of WAMHS subjects lived in urban areas, and 30% lived in rural

areas. These rates are comparable with the state geographical distributions, of

74% urban, and 26% rural [173].

Descriptive analyses of study variables identified several key variables which dif-

fered between sexes, including age at diagnosis, site, and pigmentation traits. The

WAMHS sub-sample used in this thesis was also described, with descriptive anal-

yses of phenotypic and genotypic variables performed.

Five of the 42 genotyped SNPs were not in HWE. These SNPs were IRF4 rs12203592,

BRAF rs12676365, BRAF rs6944385, EGF rs929446 and EGF rs11569121. Depar-

tures from HWE may be explained by genotyping errors, selection bias, violation

of the underlying assumptions, or chance [30]. However, departures from HWE

in cases does not necessarily represent a flawed study. Instead, departures from

HWE in cases may indicate an association between these SNPs and melanoma. In


fact, it has been proposed that deviations from HWE can be used as a screening

process to detect gene-disease associations [174].

Qualitative analysis of the key variables available from the WACR and the WAMHS

sub-sample – Breslow thickness, Clark’s level, age at diagnosis, sex and melanoma

site – revealed that Breslow thickness, Clark’s level, and site were similar between

the sub-sample used in this thesis and the eligible population for this thesis (sub-

jects with a recorded Breslow thickness).

The sub-sample of 800 subjects used in this thesis were the first 800 Caucasian

subjects with a recorded Breslow thickness who completed their questionnaire and

blood sample before November 2009. Therefore, there is a potential that these

early-responders may not have been similar to the late-responders. However, com-

parisons between the sub-sample and the WAMHS cases who participated in at

least one component of the study did not detect any significant differences between

these two samples for Breslow thickness, sex, age, Clark’s level and melanoma site.

In addition, the inclusion of only live subjects into the study may introduce bias.

Deceased subjects may have been different compared to live subjects, in particular

for characteristics which may have affected mortality, such as Breslow thickness.

However, this is not likely to have impacted greatly on the analyses as only 118

out of 3,420 (3.45%) individuals died prior to contact.

The sex distribution was skewed, with approximately 56% of the sub-sample male.

This a lower than expected number, given that it is expected that males are ap-



proximately 1.5 times more likely to be diagnosed with melanoma [63]. Therefore,

approximately 60% of the sub-sample would be expected to be male. In fact, anal-

ysis of the entire population diagnosed with melanoma in the diagnosis time frame

(n=3,284) revealed that 60.60% were male. The sex distribution of the eligible

population and the sub-sample used in this thesis differed, with 61% of the eligi-

ble population male, compared to only 56% males in the sub-sample. This may

indicate that women were more likely to participate in the study, or were more

likely to be early-responders, and therefore included in the sub-sample for this

thesis.

Age at diagnosis also differed between the sub-sample used in this thesis and the

eligible population. There was a higher proportion of individuals aged between 60

and 74 years in the sub-sample, and a lower proportion of individuals in all other

age groups. This may indicate that individuals aged between 60 and 74 were more

likely to participate in the study, or be early-responders.

With the exception of sex and age, analyses revealed that the WAMHS sub-

sample used in this thesis was generally representative of the eligible population.

Therefore, analyses which follow in the following sections should be generalisable

back to the entire melanoma population. These results may not be generalisable

if they are sex- or age-specific association results.

2.4. Association of Candidate Loci with Melanoma Susceptibility 111

2.4 Association of Candidate Loci with Melanoma Susceptibility

2.4.1 Introduction

The aim of this section is to investigate the genetic association of 42 candidate

SNPs in 16 candidate loci with melanoma risk using WAMHS cases and relevant

controls. The 42 genotyped SNPs described in Section 2.3.4.2 were chosen as they

had been identified as melanoma-risk SNPs, therefore replication of their associa-

tions with melanoma-risk in the WAMHS sample was investigated. Although not

a primary aim of this thesis, this was thought to be important to inform both

the generalisability of the WAMHS genetic association results and the interpre-

tation of any genetic associations with melanoma prognostic phenotypes. In this

section, SNP allelic frequencies in WAMHS cases were compared to two general

population (unselected) control samples: the Busselton Health Study (BHS) and

HapMap-CEU populations. The results of these analyses are then presented and

discussed.

2.4.2 Methods

2.4.2.1 Study populations

Data from two independent populations, one from the United States of America,

and one from Western Australia, were used to investigate the role of candidate

SNPs with melanoma risk.

The WAMHS sample (i.e. cases) used in these analyses was the sub-sample of 800

Caucasian individuals described earlier in Section 2.3.4.



The two non-melanoma populations (i.e. controls) were the HapMap-CEU and

the BHS populations. HapMap-CEU is a population of Caucasian individuals

from Utah in the United States of America, with Northern and Western Euro-

pean ancestry [175]. HapMap tag-SNPs have been shown to perform well in the

Australian population [176]. The HapMap-CEU sample size differs depending on

which SNPs are examined. For the SNPs investigated in this thesis, the HapMap

sample size ranged from 55 to 113 individuals. No phenotypic data, including age

or sex, were available for these individuals.

The BHS is a longitudinal epidemiological study of participants residing in Bus-

selton, a coastal town in the south-west of Western Australia. Since 1966, a series

of health surveys has collected epidemiological data on over 16,000 men, women

and children [177, 178]. For the current study, genotypic data from a GWAS un-

dertaken using the Illumina 610-Quad BeadChip was used. Phenotypic data was

collected as part of the 1994-1995 collection period. The BHS sample used in this

thesis consisted of 1,366 unrelated individuals, however not all individuals were

successfully genotyped at each SNP. For the SNPs investigated in this thesis, the

BHS sample size ranged from 1,322 to 1,366 individuals

Table 2.4.2.1.1 shows some characteristics of the BHS sample used in this thesis.

The sample consisted of 57.98% females and the median age of the sample was

53.05 years.


VariableBHS Sample

(n=1,366)


Age at data collection, years 53.05 (28.1)

BMI, kg/m2 25.97 (5.12)

Dichotomous variable, % [n]

Sex

Male 42.02 [574]

Female 57.98 [792]

Table 2.4.2.1.1: Participant characteristics of the BHS sample

2.4.2.2 Statistical methods

Genotype frequency distributions were compared between the WAMHS sample

and Hapmap-CEU frequencies using Fisher’s exact test for contingency tables.

As additional covariate data were available in the BHS sample, multivariate case-

control analyses were undertaken for the WAMHS-BHS comparisons. WAMHS

genotype frequency distributions were compared to BHS genotype frequencies

using a generalised linear model, adjusted for any covariates common to both data

sets and significantly associated (P< 0.05) with melanoma risk. P-values were

presented as Likelihood Ratio Test (LRT) p-values. The LRT-statistic, D, was

calculated by D = −2 loge(likelihood [pheno]) − 2 loge(likelihood [pheno+SNP]),

where the model ‘pheno’ contains only the significant phenotypic variables and the

‘pheno+SNP’ model contains both phenotypic variables and the SNP. D is then

compared to a Chi-squared distribution with df1-df2 degrees of freedom, where

df1 and df2 are the degrees of freedom for the ‘pheno’ and ‘pheno+SNP’ models

respectively.



2.4.3 Results

Genotype frequency distributions between the WAMHS sample and HapMap-

CEU and BHS are presented in Table 2.4.3.1. Two SNPs, OCA2 rs1800401 and

TPCN2 rs35264875, were not genotyped in either the HapMap-CEU or the BHS

samples, and are therefore not shown in the table.

Genotype distributions were available for 40 of the 42 SNPs in the HapMap-CEU

sample. Statistical analysis revealed that seven genotypic distributions were sta-

tistically different from the HapMap genotypic distributions at the 5% level of sig-

nificance (MC1R rs3212363, TYR rs1042602, TYR rs1393350, KITLG rs12821256,

SLC2A4 rs12896399, IRF4 rs12203592, BRAF rs17161747), indicating their pos-

sible role as melanoma-risk SNPs. Three genotypic distributions showed marginal

significance (MYH7B rs1885120, MC1R rs1805005, MTAP rs1011970). When ad-

justed for multiple testing using FDR, no SNPs were significantly associated with

melanoma-risk compared to the HapMap-CEU sample.

Twenty seven of the 48 SNPs were available as genotyped or imputed SNPs in the

BHS GWAS. Multivariate modelling between WAMHS cases and BHS controls

found that the phenotypes sex, BMI, age, age2, age3 and an age:sex interaction

were significantly associated with melanoma-risk (Table 2.4.3.2).

After adjustment for these phenotypic variables, comparison of genotypic dis-

tributions between the WAMHS cases and BHS controls found nine distribu-

tions which were statistically different at the 5% level of significance (CDC91L1

rs910873, MC1R rs258322, TYR rs1393350, HERC2 12913832, IRF4 rs12203592


MTAP rs4636294, MTAP rs7023329, MTAP rs1011970, BRAF rs6944385). In ad-

dition, five genotypic distributions were marginally significant (P<0.10), MYH7B

rs1885120, MTAP rs10757257, PLA2G6 rs2284063, PLA2G6 rs132985, BRAF

rs10487888). After adjustment for multiple testing, six SNP associations us-

ing the BHS sample remained significant with q-values < 0.05 (MC1R rs258322,

TYR rs1393350, IRF4 rs12203592, MTAP rs7023329, MTAP rs1011970, BRAF

rs6944385).

At the 5% level of significance, two SNPs, TYR rs1393350 and IRF4 rs12203592,

were both significantly associated with melanoma risk compared to both the

HapMap-CEU and BHS populations. However, after adjustment for multiple

testing, no SNPs were significantly associated with melanoma risk across both

populations.

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WAMHS HapMap-CEU BHSGene SNP Genotype % [n] % [n] p-value q-value % [n] p-value1 q-value

GG 77.19 [616] 84.07 [95] 81.89 [1099]CDC91L1 rs910873 AG 21.43 [171] 15.04 [17] 0.25 0.62 16.99 [228] 0.05 0.06

AA 1.38 [11] 0.89 [1] 1.12 [15]

GG 78.32 [625] 86.73 [98] 82.45 [1109]MYH7B rs1885120 CG 20.68 [165] 12.39 [14] 0.08 0.35 16.65 [224] 0.06 0.06

CC 1.00 [8] 0.89 [1] 0.89 [12]

GG 75.16 [596] 84.82 [95] -MC1R rs1805005 GT 23.33 [185] 15.18 [17] 0.06 0.28 - - -

TT 1.51 [12] 0 [0] -

CC 74.06 [591] 76.79 [86] -MC1R rs1805007 CT 24.69 [197] 22.32 [25] 0.87 0.85 - - -

TT 1.25 [10] 0.89 [1] -

AA 59.52 [475] 40.00 [24] -MC1R rs3212363 AT 33.96 [272] 55.00 [33] 0.005 0.06 - - -

TT 6.52 [52] 5.00 [3] -

CC 72.50 [581] 73.45 [83] 79.73 [1082]MC1R rs258322 CT 25.87 [207] 25.66 [29] 1.00 0.87 18.79 [255] 0.00003 0.0001

TT 1.63 [13] 0.89 [1] 1.47 [20]

AA 67.13 [535] 72.57 [82] -MC1R rs3212369 AG 30.49 [243] 27.43 [31] 0.19 0.56 - - -

GG 2.38 [19] 0.00 [0] -

CC 38.92 [311] 37.17 [42] 39.82 [544]TYR rs1042602 AC 46.93 [376] 39.82 [45] 0.05 0.25 47.00 [642] 0.80 0.37Continued on Next Page. . .

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Table 2.4.3.1 - Continued

WAMHS HapMap BHSGene SNP Genotype % [n] % [n] p-value q-value % [n] p-value1 q-value

AA 14.15 [113] 23.01 [26] 13.18 [180]

GG 47.55 [379] 62.83 [71] 52.12 [689]TYR rs1393350 AG 40.65 [324] 29.20 [33] 0.01 0.07 40.32 [533] 0.003 0.01

AA 11.79 [94] 7.97 [9] 7.56 [100]

CC 48.80 [387] 47.79 [54] 47.84 [653]TYRP1 rs1408799 CT 41.87 [332] 43.36 [49] 0.97 0.86 41.25 [563] 0.83 0.38

TT 9.33 [74] 8.85 [10] 10.92 [149]

TT 80.98 [647] 71.68 [81] 77.58 [1059]SLC24A4 rs12821256 CT 18.02 [144] 27.43 [31] 0.05 0.25 20.59 [281] 0.14 0.10

CC 1.00 [8] 0.89 [1] 1.83 [25]

GG 28.54 [228] 16.81 [19] 30.31 [414]KITLG rs12896399 GT 51.69 [413] 52.22 [59] 0.004 0.06 49.41 [675] 0.22 0.14

TT 19.77 [158] 30.97 [35] 20.28 [277]

AA 32.29 [258] 27.03 [111] -IRF4 rs1540771 AG 49.56 [396] 57.66 [64] 0.30 0.66 - - -

GG 18.15 [145] 15.31 [17] -

GG 36.64 [292] 32.74 [37] 35.16 [480]TPCN2 rs3829241 AG 45.92 [366] 49.56 [56] 0.71 0.82 49.45 [675] 0.37 0.21

AA 17.44 [139] 17.70 [20] 15.38 [210]

GG 80.10 [632] 85.72 [96] -OCA2 rs1800407 AG 18.76 [148] 13.39 [15] 0.35 0.70 - - -

AA 1.14 [9] 0.89 [1] -


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AA 90.25 [722] 90.27 [102] 89.04 [1210]OCA2 rs7495174 AG 9.63 [77] 9.73 [11] 1.00 0.87 10.52 [143] 0.66 0.33

GG 0.12 [1] 0.00 [0] 0.44 [6]

GG 64.12 [512] 61.95 [70] 57.25 [782]HERC2 rs12913832 AG 30.74 [245] 34.51 [39] 0.65 0.81 35.80 [489] 0.05 0.06

AA 5.14 [41] 3.54 [4] 6.95 [95]

CC 55.36 [439] 70.80 [80] 62.42 [832]IRF4 rs12203592 CT 34.93 [277] 26.55 [30] 0.002 0.06 32.71 [436] 0.00002 0.0009

TT 9.71 [77] 2.65 [3] 4.88 [65]

GG 58.57 [465] 61.67 [37] -TP53 rs1042522 CG 36.52 [290] 30.00 [18] 0.31 0.67 - - -

CC 4.91 [39] 8.33 [5] -

AA 27.94 [221] 24.78 [28] 23.87 [326]MTAP rs4636294 AG 49.43 [391] 51.33 [58] 0.48 0.76 48.76 [666] 0.05 0.06

AA 22.63 [179] 23.89 [27] 27.38 [374]

GG 38.27 [305] 35.40 [40] 33.06 [451]MTAP rs10757257 AG 46. 93 [374] 53.98 [61] 0.30 0.66 49.41 [674] 0.07 0.07

AA 14.80 [118] 10.62 [12] 12.52 [239]

AA 29.99 [239] 26.13 [29] 24.74 [337]MTAP rs7023329 AG 48.18 [384] 49.55 [55] 0.66 0.81 49.05 [668] 0.04 0.05

GG 21.83 [174] 24.32 [27] 26.21 [357]

GG 65.54 [521] 70.54 [79] 71.23 [973]MTAP rs1011970 GT 31.82 [253] 24.11 [27] 0.09 0.39 26.57 [363] 0.02 0.04Continued on Next Page. . .

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TT 2.64 [21] 5.35 [6] 2.20 [30]

AA 45.18 [360] 46.02 [52] 40.26 [550]PLA2G6 rs2284063 AG 43.79 [349] 44.25 [50] 0.96 0.86 46.49 [635] 0.09 0.07

GG 11.04 [88] 9.73 [11] 13.25 [181]

CC 31.60 [250] 30.09 [34] 27.11 [370]PLA2G6 rs132985 CT 49.56 [392] 51.33 [58] 0.95 0.86 50.04 [683] 0.09 0.07

TT 18.84 [149] 18.58 [21] 22.86 [312]

GG 71.52 [555] 76.11 [86] 75.11 [1026]BRAF rs1267635 AG 27.19 [211] 22.12[25] 0.44 0.74 22.99 [314] 0.19 0.13

AA 1.29 [10] 1.77 [2] 1.90 [26]

GG 89.39 [708] 87.27 [96] -BRAF rs1733826 AG 10.10 [80] 12.73 [14] 0.65 0.81 - - -

AA 0.51 [4] 0.00 [0] -

AA 29.70 [237] 23.89 [27] 31.77[434]BRAF rs10487888 AG 46.74 [373] 56.64 [64] 0.16 0.51 49.27 [673] 0.07 0.07

GG 23.56 [188] 19.47 [22] 18.96 [259]

GG 91.70 [718] 84.07 [95] -BRAF rs17161747 CG 8.30 [66] 15.93 [18] 0.01 0.70 - - -

CC 0.00 [0] 0.00 [0] -

TT 88.02 [698] 90.00 [54] -BRAF rs2365151 CT 11.85 [94] 10.00 [6] 0.85 0.85 - - -

CC 0.13 [1] 0.00 [0] -


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TT 77.37 [619] 75.22 [85] 74.89 [1023]BRAF rs17623382 GT 21.50 [172] 23.01 [26] 0.68 0.82 23.28 [318] 0.31 0.19

GG 1.13 [9] 1.77 [2] 1.83 [25]

AA 55.87 [447] 61.61 [69] -BRAF rs4726020 AG 37.88 [303] 33.03 [37] 0.55 0.78 - - -

GG 6.25 [50] 5.36 [6] -

AA 71.75 [574] 80.00 [44] 82.25 [1098]BRAF rs6944385 AT 27.00 [216] 18.18 [10] 0.28 0.65 16.70 [223] 0.000001 0.0001

TT 1.25 [10] 1.82 [1] 1.05 [14]

AA 42.98 [343] 48.33 [29] 43.18 [589]EGF rs7655579 AG 43.99 [351] 41.67 [25] 0.71 0.82 45.09 [615] 0.66 0.33

GG 13.03 [104] 10.00[6] 11.73 [160]

CC 35.84 [286] 39.65 [23] 36.48 [494]EGF rs929446 CT 44.36 [354] 41.38 [24] 0.85 0.85 47.56 [644] 0.11 0.08

TT 19.80 [158] 18.97 [11] 15.95 [216]

CC 84.11 [672] 81.98 [91] 84.11 [1149]EGF rs11568993 CT 15.52 [124] 18.02 [20] 0.65 0.81 15.30 [209] 0.93 0.41

TT 0.37 [3] 0.00 [0] 0.59 [8]

GG 37.84 [302] 36.28 [41] 39.63 [541]EGF rs882471 AG 46.62 [372] 49.56 [56] 0.86 0.85 45.79 [625] 0.68 0.33

AA 15.54 [124] 14.16 [16] 14.58 [199]

TT 62.67 [497] 61.67 [37] -EGF rs4698803 AT 33.04 [262] 31.67 [19] 0.62 0.80 - - -Continued on Next Page. . .

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AA 4.29 [34] 6.67 [4] -

GG 28.53 [228] 26.55 [30] -EGF rs6533485 CG 49.44 [395] 49.56 [56] 0.86 0.85 - - -

CC 22.03 [176] 23.89 [27] -

GG 89.12 [713] 86.72 [98] 90.30 [1220]EGF rs11569121 AG 10.13 [81] 12.39 [14] 0.57 0.79 9.25 [125] 0.43 0.24

AA 0.75 [6] 0.89 [1] 0.44 [6]

Table 2.4.3.1: Comparison between WAMHS and HapMap and BHS genotype frequencies

1P-value adjusted for age, age2, age3, sex, BMI and age:sex interaction



Variable Odds ratio 95% CI p-value

Intercept 1.24 (0.04, 36.78) 0.90Sex 0.15 (0.07, 0.36) <0.001BMI 1.05 (1.03, 1.07) <0.001Age 0.75 (0.62, 0.92) <0.001Age2 1.01 (1.00, 1.01) <0.001Age3 1.00 (1.00, 1.00) <0.001Age:Sex 1.04 (1.03, 1.06) <0.001

Table 2.4.3.2: Phenotypic variables multivariately associated with melanoma risk

in the WAMHS, compared to the BHS controls and cases

Overall, 14 SNPs were associated with melanoma risk in the WAMHS at the 5%

level of significance. However, after adjusting for multiple testing using the FDR,

only six SNPs were significantly associated with melanoma risk. A summary of

these results can be seen in Table 2.4.3.3.

HapMap BHS

Gene SNP p-value q-value p-value q-value

CDC91C1 rs910873 0.25 0.62 0.05 0.06

MC1R rs3212363 0.005 0.06 - -

MC1R rs258322 1.00 0.87 0.00003 0.0001

TYR rs1042602 0.05 0.25 0.80 0.37

TYR rs1393350 0.01 0.07 0.003 0.01

KITLG rs12821256 0.05 0.25 0.14 0.10

SLC24A4 rs12896399 0.004 0.06 0.22 0.14

HERC2 rs12913832 0.65 0.81 0.05 0.06

IRF4 rs12203592 0.002 0.06 0.00002 0.00009

MTAP rs4636294 0.48 0.76 0.05 0.06

MTAP rs7023329 0.66 0.81 0.04 0.05

MTAP rs1011970 0.09 0.37 0.02 0.04

BRAF rs17161747 0.01 0.70 - -

BRAF rs6944385 0.28 0.65 0.00001 0.0001

Table 2.4.3.3: Summary of associations between significant SNPs and melanoma

risk in the WAMHS


2.4.3.1 Summary of results

Seven SNPs (from six genes) were associated with melanoma risk when com-

pared to HapMap-CEU, and nine SNPs (from seven genes) were associated with

melanoma risk when compared to the BHS controls. Only two SNPs were sig-

nificantly associated with melanoma risk across both HapMap-CEU and BHS

samples. After adjustment for multiple testing, no SNPs were significantly asso-

ciated with melanoma risk when compared to the HapMap-CEU, and six SNPS

(from five genes) were significantly associated with melanoma risk compared to

the BHS controls. It is likely that the results using the BHS sample are more

reliable than those using the HapMap-CEU sample, as the BHS sample is larger,

and is also sampled from a Western Australian population. The significant gene

and SNP associations, both before and after adjustments for multiple testing, are

described below.

2.4.3.1.1 MTAP

Compared to the BHS controls, three SNPs in the MTAP gene were signifi-

cantly associated with melanoma risk - rs4636294, rs7023329 and rs1011970. The

melanoma-risk alleles for these SNPs were consistent with the melanoma-risk SNP

alleles identified in previous studies. In particular, the rare variants of rs4636294

and rs7023329 were both associated with increased melanoma risk and increased

naevi count in GWAS by Falchi et al. [105] and Bishop et al. [106], while the

rare variant of the rs1011970 SNP was associated with naevi count only [105].

The fourth MTAP SNP genotyped, rs10757257, which had been identified as a

melanoma risk and naevi count SNP [105, 106] was marginally associated with

melanoma risk, with a p-value of 0.07.



After adjusting for multiple testing, the associations between melanoma risk and

rs7023329 (q=0.05) and rs1011970 (q=0.04) remained significant.

Compared to the HapMap-CEU sample, no MTAP SNPs were significantly asso-

ciated with melanoma risk in this study.

2.4.3.1.2 CDC91C1

The rs910873 SNP in the CDC91C1 gene was significantly associated with melanoma

risk when compared to the BHS controls. In particular, the ‘A’ variant of this SNP

was associated with melanoma risk in this study. A similar association between

this variant and melanoma risk was also observed in the two GWAS studies by

Brown et al. [107] and Bishop et al. [106]. Compared to the HapMap sample, this

SNP was not associated with melanoma risk. However, the frequency of the ‘G’

allele in the HapMap and BHS samples was substantially lower when compared

to the WAMHS sample. The frequency of the ‘G’ allele in the HapMap and BHS

samples was 8.41% and 9.61% respectively, compared to the WAMHS sample fre-

quency of 12.09%.

After adjusting for multiple testing, the rs910873 SNP was marginally associated

with melanoma risk when compared to the BHS sample (q=0.06).

2.4.3.1.3 TYR

Two SNPs in the TYR gene were associated with melanoma risk in this study.

The ‘A’ variant of the rs1393350 SNP was significantly associated with melanoma


risk in this study when compared to the HapMap-CEU and BHS samples. The

genotypic distributions differed greatly between samples, with 63% and 52% of

individuals in the control populations having the common homozygote, compared

to 48% in the WAMHS sample. Similarly, 8% of individuals in the control popu-

lations were rare homozygous, compared to 12% in the WAMHS sample.

This SNP was previously associated with melanoma risk in a study by Bishop et

al. [106]. In addition, the ‘A’ allele was also associated with blonde hair, green

eyes and increased skin sensitivity to the sun in a study by Sulem et al. [109],

which are all known melanoma risk factors.

After adjustment for multiple testing, the rs1393350 SNP remained significantly

associated with melanoma risk when compared to the BHS controls (q=0.01), and

marginally associated (q=0.07) when compared to the HapMap-CEU sample.

The ‘C’ variant of the rs1042602 SNP, also in TYR, was associated with melanoma

risk, compared to the HapMap sample. However, when compared to the BHS con-

trols, the rs1042602 SNP was not associated with melanoma risk, with a p-value

of 0.80. The ‘C’ variant has been associated with melanoma risk in the GWAS by

Bishop et al. [106], and was also associated with freckling in a study by Sulem et

al. [109] which is a known risk factor for melanoma.

After adjustment for multiple testing, the rs1042602 SNP was no longer associated

with melanoma risk when compared to the HapMap sample (q=0.25).



2.4.3.1.4 KITLG

The rs12821256 SNP in the KITLG gene was significantly associated with melanoma

risk when compared to the HapMap sample, however no association was detected

between this SNP and melanoma risk using the BHS controls (P=0.14). The ‘T’

variant was associated with melanoma risk in this study, however this association

is in direct contrast to a previous study by Sulem et al. [109]. In the study by

Sulem et al., the ‘C’ variant was associated with blonde hair, which is a melanoma

risk factor, therefore suggesting that the ‘C’ variant is associated with melanoma

risk.

I am unable to explain this difference. However, it may be that the association ob-

served in this study was a false positive, which is supported by the non-significance

of the association when adjusted for multiple testing (q=0.25).

2.4.3.1.5 SLC24A4

The rs12896399 SNP from the SLC24A4 gene was significantly (P=0.05) asso-

ciated with melanoma risk when compared to the HapMap sample, however no

association was detected between this SNP and melanoma risk when compared

to the BHS controls (P=0.22). The ‘G’ variant was associated with melanoma

risk in this study. In a previous study by Sulem et al. [109], the ‘T’ variant was

associated with melanoma risk factors, including blonde hair and increased skin

sensitivity to the sun. The results from the study by Sulem et al. are therefore in

direct contrast from the results of this study.

I am unable to explain this difference. However, as with rs12821256 from KITLG,


it may be that the association observed in this study was a false positive. This is

supported by the only marginal significance of the association when adjusted for

multiple testing (q=0.06).

2.4.3.1.6 HERC2

The ‘G’ variant of the rs12913832 SNP in the HERC2 gene was associated with

melanoma risk in this study, when compared to BHS controls. 61.96% of the

WAMHS sample were homozygous for the ‘G’ allele, compared to 57.25% of the

BHS sample. No association was detected between this SNP and melanoma risk

compared to the HapMap sample.

The ‘G’ allele of this SNP has been associated with blue eyes in previous stud-

ies [170,171], and melanoma risk in a recent study by Duffy et al. [179].

After adjustment for multiple testing, the rs12913832 SNP was marginally asso-

ciated with melanoma risk when compared to the BHS controls (q=0.06).

2.4.3.1.7 IRF4

The ‘T’ variant of the rs12203592 SNP in the IRF4 gene was associated with

melanoma risk in this study, compared to both BHS controls and the HapMap

sample. In particular, 71% and 62% of individuals in the control populations had

the common homozygote, compared to 55% in the WAMHS sample. Similarly, 3%

and 5% of individuals in the control populations were rare homozygotes, compared

to 10% in the WAMHS sample. This variant has been found to be associated with

melanoma risk factors, such as blonde hair, lighter skin colour, lighter eye colour



and reduced tanning ability in a study by Han et al. [124].

After adjustment for multiple testing, the rs12203592 SNP was marginally associ-

ated with melanoma risk compared to the the HapMap sample (q=0.06), however

this SNP was strongly associated with melanoma risk when using the BHS con-

trols (q=0.0009). HWE analysis from Section 2.3.4.5 revealed that this SNP was

not in HWE in the WAMHS cases, which may have been due to its role as a

melanoma-risk SNP.

The rs1540771 SNP also in the IRF4 gene was not associated with melanoma risk

when compared to the HapMap sample, and this SNP was not available in the

BHS study.

2.4.3.1.8 MC1R

Two SNPS in the MC1R gene were associated with melanoma risk. The rs3212363

SNP was associated with melanoma risk when compared to the HapMap sample,

however this SNP was not available in the BHS sample.

The ‘T ’ variant of the rs258322 SNP was associated with melanoma risk, when

using BHS controls. This variant allele was also identified by Bishop et al. [106]

and Han et al. [124] as a melanoma risk variant. Compared to the HapMap sam-

ple, this SNP was not associated with melanoma risk.

After adjustment for multiple testing, rs3212363 was marginally associated with

melanoma risk compared to the HapMap controls (q=0.06). However, the asso-


ciation between rs258322 and melanoma risk was significant when using the BHS

controls (q=0.0001).

2.4.3.1.9 BRAF

Two SNPs on the BRAF gene were significantly associated with melanoma risk.

The ‘G’ variant of the rs17161747 SNP was associated with melanoma risk using

the HapMap sample, however this SNP was not available in the BHS sample. The

rs6944385 SNP was associated with melanoma risk when using the BHS controls,

however there was no association between this SNP and melanoma risk when us-

ing the HapMap sample. HWE analysis from Section 2.3.4.5 revealed that this

SNP was not in HWE in the WAMHS cases, which may have been due to its role

as a melanoma-risk SNP.

After adjustment for multiple testing, both the rs17161747 and the rs6944385

SNPs were no longer associated with melanoma risk when using the HapMap

sample, with q-values of 0.70 and 0.65, respectively. However, when using the

BHS controls, the rs6944385 SNP remained strongly associated with melanoma

risk (q=0.0001).

2.4.3.2 Discussion

The SNPs used in this analysis were chosen as they had been identified as melanoma-

risk SNPs, or were on melanoma candidate genes. This analysis has found 14 SNPs

which were significantly associated with melanoma risk, out of a possible 67 SNP

associations. More specifically, 25 SNPs were not associated with melanoma risk

using either the HapMap sample or BHS controls. These results may be due to a



number of reasons which are discussed below.

The WAMHS sample (n=800) is relatively small, and it is likely that the analyses

were underpowered. Similarly, the HapMap sample is small, generally consisting

of approximately 100 individuals. For the rarer SNPs, the rare homozygote count

is very small, often with less than 20 individuals. Therefore, the power to de-

tect differences in genotypic distributions was likely to be low. This was further

evidenced by the substantial differences in genotype distributions between the

WAMHS and the HapMap samples which may have been significant differences if

the HapMap sample size was larger.

In addition, the WAMHS sample did not have many phenotypic variables in com-

mon with both the HapMap and BHS samples. In fact, the HapMap sample did

not contain any phenotypic variables, therefore it was not possible to adjust for

any possible covariates when analysing melanoma risk. There was a limited num-

ber of common phenotypic variables collected as part of the WAMHS and BHS

samples; these were age, sex and BMI. Therefore only these three variables were

considered as covariates. It would be preferable to adjust for more phenotypic

variables, including sun exposure and naevi counts.

There is also a discrepancy between the methods of obtaining the SNP data in

cases and controls. The case (WAMHS) SNPs were all obtained through geno-

typing, while the control SNPs from the BHS consisted of both genotyped and

imputed SNPs. For the imputed SNPs, the program MACH [266] was used to de-

termine the ‘most likely’ genotype for each individual based on a genotype prob-

2.5. Association of Candidate Loci with Breslow thickness 131

ability greater than 0.50. This may have led to biases within the analyses and a

reduction in power, as some genotype uncertainty will remain unaccounted [267].

If possible, it would be preferable to use only genotyped or imputed SNPs in a

case-control study; and if using imputed data, to incorporate the genotype uncer-

tainty into the analysis.

One particular advantage of the BHS sample over the HapMap-CEU sample is

that the BHS individuals are from Western Australia, and are likely to have experi-

enced a similar level of sun exposure, therefore limiting biases due to confounding.

However, the sun exposure or other exposures experienced by the HapMap-CEU

sample are likely to be different to the WAMHS sample.

This is, to the author’s knowledge, the first study which has used Western Aus-

tralian individuals to investigate the role of genetic variants and melanoma risk.

As such, large-scale replication is required in larger Australian samples. This

is particularly relevant for SNPs in MC1R (rs3212363) and BRAF (rs17161747,

rs6944385) which have not been identified previously as melanoma-risk SNPs.

2.5 Association of Candidate Loci with Breslow thickness

2.5.1 Introduction

The aim of the research described in this section was to investigate the associ-

ation between the primary melanoma prognosis phenotype - Breslow thickness

- and genetic variants in candidate genes within the WAMHS population. The

SNPs genotyped (n=42 in 16 genes) were chosen as they had been previously

identified as variants associated with melanoma risk or were from melanoma-risk



genes. The associations between these SNPs and melanoma risk in the WAMHS

was reported in Section 2.4 (please see page 111).

In this section, a genetic association study between these SNPs and the best in-

dependent predictor of melanoma prognosis, Breslow thickness, was undertaken.

The results of these genetic association analyses are presented, and their implica-

tions are discussed.

2.5.2 Statistical methods

The methods used for univariate and multivariate association analyses for both

phenotypic and genotypic variables are described in this section. All analyses were

performed in Rv2.10.1. The sub-samples used in these analyses was the WAMHS

sub-sample (n=800) described earlier in Section 2.3.4.

2.5.2.1 Association analyses

2.5.2.1.1 Epidemiological analyses

2.5.2.1.1.1 Univariate analyses

Possible associations were investigated between Breslow thickness and several di-

chotomous, categorical and continuous phenotypic variables, using linear regres-

sion. Due to its skewed distribution, Breslow thickness was transformed using the

formula loge(Breslow thickness) for all analyses. The magnitude of the association

of variables with Breslow thickness were calculated by back-transforming the co-

efficients using the formula 100 ∗ (expcoefficient−1). For continuous phenotypes,

model coefficients were presented as a percentage change for a one unit increase in


the phenotype. For dichotomous and categorical phenotypes, model coefficients

were presented as a percentage change for each level of the phenotype, compared

to the baseline.

2.5.2.1.1.2 Multivariate analyses

Multivariate models were fitted using generalised linear models (linear regres-

sion) [180]. Independent predictors of Breslow thickness were determined by using

a stepwise variable selection procedure. Starting with the full model of all possible

predictors, a backwards elimination approach was used, based on the Akaike Infor-

mation Criterion (AIC). The AIC attempts to find the model which best explains

the data, while penalising the model for the number of parameters estimated. AIC

is calculated as AIC=2p − 2 loge(L), where p is the number of parameters, and

L is the maximised value of the likelihood function for the estimated model. In

these analyses, the model with the smallest AIC was chosen as the preferred model.

Quadratic effects of age at diagnosis and phenotypic interactions, in particular sex

interactions, were also investigated. Model diagnostic plots, including residual and

Normal quantile-quantile plots were used to check that the final model did not

violate linear regression assumptions.

2.5.2.1.2 Genotypic analyses

2.5.2.1.2.1 Univariate analyses

All 42 SNPs were tested for univariate linear associations with loge(Breslow thick-

ness). These SNPs were tested under a codominant model, with the common ho-

mozygote set as the reference category. A codominant model was chosen initially



as inspection of codominant model coefficients can be used to identify the best fit-

ting model (i.e. co-dominant, additive, dominant, or recessive). ANOVA p-values

derived from the F-distribution were used to determine statistical significance,

which was defined at the 5% level of significance.

2.5.2.1.2.2 Multivariate analyses

Multivariate analysis of loge(Breslow thickness) and the 42 SNPs were undertaken.

Each SNP was added to the multivariate phenotypic models derived in Section

2.5.2.1.1.2 (please see page 133). Multivariate analysis was performed to reduce

the variability in the outcome which may enhance the ability to recognize statis-

tically significant effects. Initially each SNP was modelled codominantly. If the

SNP was significantly (P<0.05) or marginally (P<0.10) associated with Breslow

thickness, the model coefficients were investigated to determine if the SNP was

more predictive under either a codominant, additive, dominant or recessive model.

P-values are presented for each genotype factor relative to the baseline (common)

homozygote.

SNP:phenotype interactions were also considered to investigate any non-additive

effect of the SNPs and phenotypes on Breslow thickness. A final model of all

significant SNPs adjusted for significant phenotypes was fitted, including possi-

ble SNP:phenotype interactions. Residual analysis and Normal quantile-quantile

plots were used to check that the final model did not violate linear regression

assumptions.


2.5.2.2 Statistical power

In statistical analyses, the probability of a ‘false negative’ or Type II error is the

probability of failing to reject the null hypothesis when the null hypothesis is

false. Power is defined as 1 − β, where β is the Type II error rate. Therefore as

the Type II error rate decreases, power increases. Conventionally, power of 0.8 is

considered to be adequate. This means that if 100 tests are performed, when the

null hypothesis is not true, 80 of these tests would reject the null hypothesis and

detect significant differences.

Power in genetic analyses is dependent on the sample size, MAF, size of the

effect and the type of disease model being tested, such as dominant, additive or

recessive. Statistical power was calculated for both genetic main effects and gene-

environment interactions. An alpha level of 0.0012 was used for the genetic main

effects, calculated by 0.0542

. For the interaction effects, 42 x 2 = 84 interaction tests

were performed, and therefore the alpha level used was 0.0006 (0.0584

). In this thesis,

power calculations were performed in the statistical software package Quanto [181]

for a sample size of 800, MAF ranging from 0.1 to 0.5, effect sizes of 0.1, 0.2,

0.3 and 0.5 standard deviations (SD). Main effect power was calculated under

dominant, additive and recessive models. Interaction effect power was calculated

for an additive model, assuming main effects for the gene and environment of

0.5SD, and an environment prevalence of 0.2 and 0.8.

2.5.3 Results

Descriptive statistics for the phenotypic variables and genotyped SNPs were pre-

sented in Section 2.3.4.5 (please see page 91). Results of phenotypic and genotypic



associations with Breslow thickness are presented in the following section.

2.5.3.1 Epidemiological analyses

2.5.3.1.1 Univariate analyses

Four phenotypic variables were significantly associated with Breslow thickness

in the WAMHS population when analysed univariately. Univariate associations

between Breslow thickness and questionnaire-based, demographic, and clinical

variables are presented in Table 2.5.3.1.1.1.

VariableMean change in p-value

logged Breslow thickness1

Continuous variables

BMI, kg/m2 0.009 0.09

Age at melanoma diagnosis, years 0.009 <0.001

Age at data collection, years 0.009 <0.001

Number of naevi - upper back 0.0006 0.71

Number of naevi - lower back -0.001 0.47

Smoking, pack years 0.0008 0.71

Dichotomous and categorical variables

Sex

Males reference 0.64

Females -0.027

Clark’s Level

II reference

III 0.569

IV 1.307 <0.001

V 2.699

Unknown 1.139

Melanoma Site

Head and neck reference

Trunk -0.142 0.32



Table 2.5.3.1.1.1 - Continued



Upper limb -0.096

Lower limb -0.037

Ever Smoked

No reference

Yes 0.112 0.06

Presence of Naevi

No reference

Yes -0.392 <0.001

Self-reported history of melanoma

No reference

Yes -0.054 0.41

Family history of melanoma

No reference

Yes -0.027 0.69

Skin colour

Very fair reference

Fair -0.021 0.31

Olive/brown 0.155


Red reference

Fair/blonde -0.119

Light/mouse brown -0.039 0.75

Grey -0.201

Dark brown -0.031

Black -0.149

Eye colour

Blue reference

Grey 0.179

Green 0.089 0.42

Hazel 0.117

Brown 0.035

Sunburn causing blisters, 5-12 years

0 times reference

1-5 times -0.0618

6-10 times -0.029 0.81







> 10 times 0.046

Don’t know 0.0607

Sunburn causing blisters, 13-19 years

0 times reference

1-5 times 0.0376

6-10 times -0.090 0.61

> 10 times -0.134

Don’t know 0.067

Sunburn causing blisters, > 20 years

0 times reference

1-5 times -0.014

6-10 times 0.048 0.79

> 10 times 0.186

Don’t know 0.467

Table 2.5.3.1.1.1: Univariate analysis between phenotypic variables with Breslow thick-

ness in the WAMHS

Significant associations were observed between Breslow thickness and age at di-

agnosis (P<0.001), age at data collection (P<0.001), Clark’s level (P<0.001) and

the presence of naevi (P<0.001). Older age at diagnosis, older age at data collec-

tion and increasing Clark’s level were associated with increased Breslow thickness,

while the presence of naevi was associated with decreased Breslow thickness. BMI

and smoking status were marginally associated with Breslow thickness, with p-

values of 0.09 and 0.06 respectively. Both BMI and smoking status were marginally

associated with increased Breslow thickness.

1For continuous variables, this is the mean change in logged Breslow thickness per unit of

phenotype. For dichotomous and categorical variables, this is the mean change in logged Breslow

thickness for each level of the variable, in relation to the reference or baseline category.


2.5.3.1.2 Multivariate analyses

Multivariate modelling suggested significant associations between Breslow thick-

ness and age at diagnosis and the presence of naevi in the WAMHS population

(Table 2.5.3.1.2.1).

Variable Mean change in % change 95% CI for % change p-value

logged Breslow

thickness (SE)

Intercept -0.016 (0.085) - - -

Age at diagnosis 0.007 (0.002) 0.71 (0.25, 1.17) 0.003

Naevi -0.343 (0.090) -29.01 (-15.25, -40.55) 0.0001

Table 2.5.3.1.2.1: Multivariate model of phenotypic variables associated with Bres-

low thickness in the WAMHS

Clark’s level was not considered as a possible predictor for Breslow thickness as

Clark’s level is determined at the same time as the Breslow thickness from the

excised melanoma and therefore is not able to predict melanoma thickness. Age

at diagnosis was associated with an increase in Breslow thickness of 0.71% per

year increase in age of diagnosis. The presence of naevi was associated with a

decrease in Breslow thickness of 29.01% compared to no naevi. The multivariate

phenotypic model accounted for 3.2% of variation in Breslow thickness.

Model diagnostic plots (Figure 2.5.3.1.2.1) show a fairly straight Normal Q-Q

plot, indicating the residuals appear to be Normally distributed. The residuals

also appear to be homoscedastic, independent and without pattern. Therefore the

major assumptions underlying the linear regressions do not appear to be violated.



Figure 2.5.3.1.2.1: Model diagnostic plots from the multivariate phenotypic model

for Breslow thickness in the WAMHS

2.5.3.2 Genotypic analyses

2.5.3.2.1 Univariate analyses

Univariate analyses revealed that when modelled codominantly, four SNPs were

significantly associated with Breslow thickness (Table 2.5.3.2.1.1). These were

IRF4 rs12203592 (P=0.008), TP53 rs1042522 (P=0.03), BRAF rs1733826 (P=0.05)

and EGF rs6533485 (P=0.04).


Gene SNP ANOVA p-value

CDC91L1 rs910873 0.32

MYH7B rs1885120 0.80

MC1R rs1805005 0.15

MC1R rs1805007 0.21

MC1R rs3212363 0.34

MC1R rs258322 0.46

MC1R rs3212369 0.48

TYR rs1042602 0.23

TYR rs1393350 0.32

TYRP1 rs1408799 0.31

KITLG rs12896399 0.64

SLC24A4 rs12821256 0.75

IRF4 rs1540771 0.11

TPCN2 rs35264875 0.14

TPCN2 rs3829241 0.94

OCA2 rs1800401 0.11

OCA2 rs1800407 0.51

OCA2 rs7495174 0.51

HERC2 rs12913832 0.37

IRF4 rs12203592 0.008

TP53 rs1042522 0.03

MTAP rs4636294 0.79

MTAP rs10757257 0.62

MTAP rs7023329 0.21

MTAP rs1011970 0.36

PLA2G6 rs2284063 0.41

PLA2G6 rs132985 0.64

BRAF rs1267635 0.69

BRAF rs1733826 0.05

BRAF rs10487888 0.57

BRAF rs17161747 0.82

BRAF rs2365151 0.87

BRAF rs17623382 0.62

BRAF rs4726020 0.37

BRAF rs6944385 0.76

EGF rs7655579 0.93

EGF rs929446 0.74





Gene SNP ANOVA p-value

EGF rs11568993 0.43

EGF rs882471 0.74

EGF rs4698803 0.86

EGF rs6533485 0.04

EGF rs11569121 0.80

Table 2.5.3.2.1.1: Univariate associations between Breslow thickness and SNPs modelled codom-

inantly in the WAMHS

2.5.3.2.2 Multivariate analyses

The model fitted in Section 2.5.3.1.2 formed the basis of the multivariate analysis

of the 42 genotyped genetic variants. Table 2.5.3.2.2.1 shows the coefficients for

each genotype and likelihood ratio p-values for all the SNPs, adjusted for age at

diagnosis and the presence of naevi. When modelled codominantly, three SNPs

were significantly associated with Breslow thickness at the 5% level of significance,

OCA2 rs1800401 (P=0.04), TP53 rs10425422 (P=0.009) and BRAF rs1733826

(P=0.03). Two SNPs were marginally associated with Breslow thickness, IRF4

rs12203592 (P=0.09) and EGF rs6533485 (P=0.07).

Also presented in Table 2.5.3.2.2.1 is the q-value for the FDR. After adjustment

for multiple testing, no SNPs were significantly associated with Breslow thickness.

Gene SNP Genotypes Mean change in LRT p-value q-value


GG

CDC91L1 rs910873 AG 0.049 0.21 0.89

AA 0.408

GG






MYH7B rs1885120 CG 0.064 0.61 0.89

CC 0.138

GG

MC1R rs1805005 GT -0.004 0.18 0.89

TT 0.432

CC

MC1R rs1805007 CT -0.078 0.24 0.89

TT 0.301

AA

MC1R rs3212363 AT 0.013 0.59 0.89

TT -0.113

CC

MC1R rs258322 CT -0.020 0.62 0.89

TT 0.208

AA

MC1R rs3212369 AG -0.035 0.59 0.89

GG -0.178

CC

TYR rs1042602 AC 0.095 0.29 0.89

AA 0.081

GG

TYR rs1393350 AG -0.023 0.42 0.89

AA -0.124

CC

TYRP1 rs1408799 CT 0.031 0.52 0.89

TT -0.086

TT

SLC24A4 rs12821256 CT -0.049 0.81 0.89

CC -0.021

GG

KITLG rs12896399 GT 0.012 0.74 0.89

TT -0.046







AA

IRF4 rs1540771 AG -0.124 0.16 0.89

GG -0.080

AA

TPCN2 rs35264875 AG 0.104 0.27 0.89

GG 0.024

GG

TPCN2 rs3829241 AG 0.002 1.0 1.00

AA -0.007

GG

OCA2 rs1800407 AG 0.038 0.57 0.89

AA 0.262

CC

OCA2 rs1800401 CT -0.211 0.04 0.56

TT

AA

OCA2 rs7495174 AG 0.062 0.51 0.89

GG -0.791

GG

HERC2 rs12913832 AG -0.003 0.30 0.89

AA 0.201

CC

IRF4 rs12203592 CT 0.107 0.09 0.76

TT 0.183

GG

TP53 rs1042522 CG 0.174 0.009 0.38

CC 0.208

AA

MTAP rs4636294 AG -0.050 0.65 0.89

AA 0.007

GG

MTAP rs10757257 AG -0.007 0.52 0.89






AA 0.088

AA

MTAP rs7023329 AG -0.060 0.32 0.89

GG 0.046

GG

MTAP rs1011970 GT -0.034 0.53 0.89

TT -0.188

AA

PLA2G6 rs2284063 AG 0.027 0.63 0.89

GG -0.066

CC

PLA2G6 rs132985 CT 0.047 0.73 0.89

TT 0.002

GG

BRAF rs1267635 AG 0.036 0.68 0.89

AA 0.186

GG

BRAF rs1733826 AG -0.150 0.03 0.56

AA -0.865

AA

BRAF rs10487888 AG -0.056 0.56 0.89

GG 0.012

GG

BRAF rs17161747 CG 0.044 0.67 0.89

CC

TT

BRAF rs2365151 CT -0.009 0.94 0.99

CC -0.267

TT

BRAF rs17623382 GT 0.005 0.50 0.89

GG -0.317

AA







BRAF rs4726020 AG 0.038 0.36 0.89

GG 0.167

AA

BRAF rs6944385 AT 0.024 0.75 0.89

TT 0.179

AA

EGF rs7655579 AG 0.015 0.90 0.97

GG -0.025

CC

EGF rs929446 CT 0.040 0.68 0.89

TT -0.023

CC

EGF rs11568993 CT 0.127 0.25 0.89

TT -0.206

GG

EGF rs882471 AG 0.040 0.76 0.89

AA -0.009

TT

EGF rs4698803 AT 0.003 0.99 1.00

AA -0.013

GG

EGF rs6533485 CG 0.140 0.07 0.73

CC 0.022

GG

EGF rs11569121 AG -0.037 0.80 0.89

AA -0.184

Table 2.5.3.2.2.1: Multivariate associations between Breslow thickness and SNPs in the WAMHS

modelled codominantly

1This is the mean change in logged Breslow thickness for each genotype level, compared to

the baseline genotype.


Analysis of the genotype coefficients determined that two of the significantly as-

sociated SNPs (rs6533485 and rs1800401) were best modelled codominantly (see

Table 2.5.3.2.2.2). The association between Breslow thickness and rs6533485 was

marginally significant (P=0.07), however only the heterozygote was significantly

associated with Breslow thickness (P=0.04).

The rs1800401 SNP did not have any rare homozygotes, however when compared

to the common homozygote, the heterozygote was significantly associated with

decreased Breslow thickness (P=0.04). This SNP accounted for 0.70% of vari-

ation in Breslow thickness. Model diagnostic plots (Figure 2.5.3.2.2.1) indicate

that the residuals appear Normally distributed, and also appear independent and

with constant variance.

SNP Genotype Mean change in % change 95% CI for % change p-value

logged Breslow

thickness1 (SE)

GG - - - -

rs6533485 CG 0.140 (0.068) 15.04 (0.72, 31.39) 0.04

CC 0.022 (0.082) 2.22 (-12.90, 19.97) 0.79

rs1800401 CC - - - -

CT -0.211 (0.100) -19.00 (-33.47, -1.37) 0.04

Table 2.5.3.2.2.2: Association between codominant SNPs and Breslow thickness in

the WAMHS, adjusted for age at diagnosis and presence of naevi

Two SNPs were best modelled additively, BRAF rs1733826 and IRF4 rs12203592,

and these results are presented in Table 2.5.3.2.2.3. Each copy of the minor allele

1This is the mean change in logged Breslow thickness for each genotype level, compared to

the baseline genotype.



Figure 2.5.3.2.2.1: Model diagnostic plots for the association between OCA2

rs1800401 and Breslow thickness in the WAMHS, adjusted for age at diagnosis

and presence of naevi

of the rs1733826 SNP decreased Breslow thickness, on average, by 18.06% (95%

CI = -2.70, -30.99). This SNP accounted for 0.44% of the variation in Breslow

thickness.

Conversely, each variant allele of the IRF4 rs12203592 SNP increased Breslow

thickness, on average, by 10.22% (95% CI = 1.08, 20.18). This SNP accounted

for 0.58% of variation in Breslow thickness.


Model diagnostics for the two additive SNPs are presented in Figures 2.5.3.2.2.2

and 2.5.3.2.2.3. The residuals appear to be Normally distributed and do not ap-

pear to be violating any of the major assumptions underlying linear regression.

SNP Allele Mean change in % change 95% CI for % change p-value

logged Breslow

thickness1 (SE)

rs1733826 G - - - -

A -0.199 (0.088) -18.06 (-2.70, -30.99) 0.02

rs12203592 C - - -

T 0.097 (0.044) 10.22 (1.08, 20.18) 0.03

Table 2.5.3.2.2.3: Association between additive SNPs and Breslow thickness in the

WAMHS, adjusted for age at diagnosis and presence of naevi

One SNP was best modelled dominantly - TP53 rs1042522 - and was subject to a

significant interaction with the presence of naevi (Table 2.5.3.2.2.4). Inclusion of

the SNP and the SNP:(naevi count) interaction accounted for 1.87% of the varia-

tion in Breslow thickness. When the SNP:(naevi count) interaction was included

in the model, the presence of naevi was no longer significantly associated with

Breslow thickness (P=0.10). However, the SNP and the SNP:(naevi count) inter-

action were both significantly associated with Breslow thickness, with p-values of

0.0004 and 0.007, respectively. The interaction between TP53 rs1042522 and the

presence of naevi can be seen graphically in Figure 2.5.3.2.2.4.

Compared to a baseline model of no naevi and no variant alleles, an individual of

average age with no naevi and one of the CG or GG genotypes had, on average,

1This is the mean change in logged Breslow thickness for each variant allele.



Figure 2.5.3.2.2.2: Model diagnostic plots for the association between BRAF



a melanoma 85% thicker. An individual with naevi and the GG genotype had

a melanoma 17% thinner, and an individual with both naevi and the GG geno-

type had a melanoma approximately 6% thicker. Therefore, there is evidence to

suggest that the effect of the TP53 rs1042522 SNP on Breslow thickness differs

according to whether an individual has naevi.

1For naevi, this is the mean change in logged Breslow thickness for individuals with naevi,

compared to individuals with no naevi. For rs1042522, this is the mean change in logged Breslow

thickness for CG+CC, compared to the baseline genotype. For rs1042522:Naevi, this is the mean

change in logged Breslow thickness for individuals with naevi and CG+CC, compared to the


Figure 2.5.3.2.2.3: Model diagnostic plots for the association between IRF4



SNP Genotype Mean change in % change 95% CI for % change p-value

logged Breslow

thickness1 (SE)

Naevi - -0.181(0.111) -16.59 (-32.93, 3.74) 0.10

rs1042522 GG - - - -

CG + CC 0.617 (0.174) 85.41 (31.88, 160.69) 0.0004

rs1042522:Naevi GG - - - -

rs1042522:Naevi CG+CC -0.495 (0.184) -39.04 (-57.54, -12.48) 0.007

Table 2.5.3.2.2.4: Association between rs1042522, modelled dominantly, and Bres-

low thickness in the WAMHS, adjusted for age at diagnosis and presence of naevi



Figure 2.5.3.2.2.4: Interaction between rs1042522 modelled dominantly and the

presence or absence of naevi in the WAMHS. CG+CC represents one or two copies

of the variant allele. The combination GG and no naevi forms the baseline.

Model diagnostics for the the rs1042522 model are presented in Figure 2.5.3.2.2.5.

The residuals appear to be Normally distributed and do not appear to be violating

any of the major assumptions underlying linear regression.

Modelling of Breslow thickness, with the four significant SNPS (from four genes)

and the rs1042522:(naevi count) interaction, adjusted for age at diagnosis and

the presence of naevi, found that all SNPs remained significantly associated with

Breslow thickness (P<0.05). This indicated that, before adjustment for multiple

baseline genotype and no naevi.


Figure 2.5.3.2.2.5: Model diagnostic plots for the association between rs1052522

and Breslow thickness in the WAMHS, adjusted for age at diagnosis and presence

of naevi

testing, each SNP was an independent predictor of Breslow thickness. This final

model, including age at diagnosis, the presence of naevi and the four significant

SNPs, accounted for 6.8% of variation in Breslow thickness. However, after ad-

justment for multiple testing, these SNP associations were not significant.



2.5.3.3 Statistical power

2.5.3.3.1 Genetic main effects

Post-hoc statistical power analyses for an additive model with n=800 and alpha

level of 0.0012 demonstrated sufficient power (>0.80) to detect a 0.3SD change or

greater in Breslow thickness for most MAF (Figure 2.5.3.3.1.1). However, changes

in Breslow thickness of 0.1SD are not likely to have been detected by the current

study (maximum power for 0.1SD = 0.11).

Under a dominant model with n=800, there was sufficient power to detect a 0.3SD

change or greater in Breslow thickness for most MAF (Figure 2.5.3.3.1.2). How-

ever, changes in Breslow thickness of less than 0.2SD are not likely to have been

detected by the current study.

Under a recessive model with n=800, there was sufficient power to detect a 0.5SD

change in Breslow thickness for MAF > 0.30. (Figure 2.5.3.3.1.3). However,

changes in Breslow thickness of less than 0.5SD would likely not be detected.

2.5.3.3.2 Gene-environment interactions

Post–hoc statistical power analyses for an additive model with n=800, alpha level

of 0.0006, and environment exposure prevalence of 0.2 demonstrated sufficient

power (>0.80) to detect a 0.5SD change or greater in Breslow thickness for MAF

0.3 and greater (Figure 2.5.3.3.2.1). However, other combinations of MAF and

changes in Breslow thickness are not likely to have been detected by the current


Figure 2.5.3.3.1.1: Estimated main effects statistical power under an additive model

for n=800, under varying MAF and SD

study.

For an additive model with n=800, alpha level of 0.0006, and environment expo-

sure prevalence of 0.8, there was sufficient power (>0.80) to detect a 0.5SD change

or greater in Breslow thickness for MAF 0.2 and greater (Figure 2.5.3.3.2.2). How-

ever, other combinations of MAF and changes in Breslow thickness are not likely



Figure 2.5.3.3.1.2: Estimated main effects statistical power under a dominant model


to have been detected by the current study.

2.5.3.4 Summary of results

Four SNPs (from four different genes - IRF4, TP53, OCA2 and BRAF) were as-

sociated with Breslow thickness when modelled multivariately (Table 2.5.3.4.1).


Figure 2.5.3.3.1.3: Estimated main effects statistical power under a recessive model


The final multivariate model with all four SNPs accounted for 7.3% of variation

in Breslow thickness. Of this explained variance, 3.6% was due to these SNPs

and interactions, and 3.2% of the variance was due to age at diagnosis, and the

presence of naevi.

One SNP (OCA2 rs1800401) was modelled codominantly, and the heterozygote



Figure 2.5.3.3.2.1: Estimated interaction effects statistical power under an addi-

tive model for n=800, under varying MAF and SD for environmental exposure

prevalence of 0.2

genotype was associated with a decrease in Breslow thickness. Two SNPs were

modelled additively, with the variant allele associated with a thinner melanoma for

BRAF rs1733826, and a thicker melanoma for IRF4 rs12203592. The remaining

SNP, TP53 rs1042522, was modelled dominantly, with the variant allele associ-

ated with a thicker melanoma, which was moderated by the presence of naevi.


Figure 2.5.3.3.2.2: Estimated interaction effects statistical power under an addi-

tive model for n=800, under varying MAF and SD for environmental exposure

prevalence of 0.8

However, after adjusting for multiple testing using the FDR, none of these asso-

ciations were significant at the 5% significance level.

The rs6533485 SNP was significant (without adjustment for multiple testing) when

modelled univariately, however it was not significant when adjusted for age at di-



agnosis and naevi, and is therefore not discussed further.

2.5.A

ssociation

ofC

andidateL

oci

with

Breslow

thickness161

SNP Model Genotype Mean change in % change 95% CI for % change p-value R2

logged Breslow

thickness (SE)

GG - - - - -

rs6533485 Codominant CG 0.140 (0.068) 15.04 (0.72,31.39) 0.04 0.04

CC 0.022 (0.082) 2.22 (-12.90,19.97) 0.79 -

rs1800401 Codominant CC - - - - -

CT -0.211 (0.100) -19.00 (-33.47,-1.37) 0.04 0.70

rs1733826 Additive GG - - - - -

AG+AA -0.199 (0.088) -18.06 (-2.70, -30.99) 0.02 0.44

rs12203592 Additive CC - - - -

CT+TT 0.097 (0.044) 10.22 (1.08, 20.18) 0.03 0.58

rs1042522 Dominant GG - - - - -

CG + CC 0.617 (0.174) 85.41 (31.88, 160.69) 0.0004 -

rs1042522:Naevi GG - - - - 1.87

rs1042522:Naevi CG+CC -0.495 (0.184) -39.04 (-57.54, -12.48) 0.007 -

Table 2.5.3.4.1: Summary of associations between significant SNPs and Breslow thickness in the WAMHS, adjusted for age

at diagnosis and presence of naevi



2.5.4 Discussion

2.5.4.1 Introduction

The aim of the analyses presented in this section was to investigate associations

between 42 identified melanoma-risk candidate SNPs and melanoma prognosis

in the WAMHS sample. In this study, four loci were significantly associated

with melanoma prognosis, however these associations become non-significant after

adjustment for multiple testing. These associations and their implications are

discussed further in Section 2.5.4.3 (please see page 171).

2.5.4.2 Summary of population characteristics

The WAMHS sub-sample used in this analysis were 800 Caucasian individuals who

were diagnosed with an invasive melanoma between January 2006 and September

2009. To be eligible for inclusion in this analysis, subjects were required to have a

Breslow thickness recorded, as this was the outcome variable. Breslow thickness

and associations between phenotypic variables and Breslow thickness are discussed

in this section.

2.5.4.2.1 Breslow thickness

Breslow thickness was the outcome variable in these analyses and was used as a

surrogate for melanoma prognosis. There is continued debate over whether Bres-

low thickness is a measure of the time from melanoma development to examination

and diagnosis by a physician (‘diagnosis delay’), a measure of the biological ag-

gressivity of the tumour, or a combination of both [182–188].


The relationship between Breslow thickness, diagnosis delay and aggressiveness

of the tumour is complex. It is difficult to accumulate sufficient evidence to re-

ject the hypotheses that Breslow thickness is not a proxy for either the biological

aggressiveness of the melanoma tumour, or diagnosis delay, as prospective stud-

ies to evaluate the natural history of a melanoma tumour from its onset raises

both practical and ethical issues. Studies investigating these relationships must

rely on patients to recall when they first noticed the new lesion or the change in

a pre-existing lesion. These studies also assume that when the melanoma first

developed, there was some noticeable change to the skin that was recognised by

the patient. However, for melanomas arising on areas of the body that are not

frequently viewed, such as the back, the tumour may remain undetected for some

time.

Within these framework limitations, several studies have attempted to investigate

the relationship between tumour thickness, delay in diagnosis and the aggres-

siveness of the tumour. However, these studies have failed to identify a clear

association between thicker melanomas and a longer delay of diagnosis [182–188].

The largest of these studies, by Richard et al. [187], investigated the correlation

between Breslow thickness and diagnosis delay in a sample of 590 French indi-

viduals (57.6% female, mean age of 51.2 years, and median Breslow thickness of

1.13 mm). Richard et al. found some evidence for a positive correlation (r=0.17,

P=0.009) between Breslow thickness and the time the subject noticed the lesion

to the time the subject became suspicious of the lesion. However, this association

depended greatly on the thickness of the melanoma. For tumours less than 3mm

thick, thicker melanoma tumours were associated with an increase in diagnosis



delay, while for tumours greater than 3mm thick, thicker melanoma tumours were

associated with a decrease in diagnosis delay. Additionally, a negative correlation

was observed between the time the subject became suspicious of the lesion and

the first examination of the lesion by a physician, and Breslow thickness (r=-0.20,

p<0.001). These results suggest that Breslow thickness may be a result of both

diagnosis delay, and also the aggressiveness of the tumour.

Furthermore, Richard et al. suggested that in populations uninformed about

melanoma and the importance of skin examinations, diagnosis delay is possibly

the main factor responsible for tumour thickness, while in informed populations,

the biological behaviour of the tumour may be the major factor affecting tumour

thickness. Australia has had a large number of public education campaigns, par-

ticularly over the past 25 years [189]. These numerous campaigns, primarily run

by the Cancer Council of Australia, would suggest that Australia is an informed

population. As such, in Australian communities of informed individuals, Breslow

thickness is more likely to be a measure of the agressiveness of the melanoma

tumour.

The most recent study by Lui et al [188] investigated tumour rate of growth (es-

timated by Breslow thickness divided by delay in diagnosis) in a sample of 404

Australian individuals (45.0% female, mean age of 54.2 years, and median Bres-

low thickness of 1.14 mm). Liu et al. found that the rate of growth was highly

associated with thicker melanomas, providing evidence to suggest that thicker

melanomas are thicker as they have grown faster, not because there has been a

delay in diagnosis.


If Breslow thickness is solely a measure of diagnosis delay, then the observed as-

sociations in this study between Breslow thickness, age at diagnosis, naevi and

several SNPs would represent associations between these variables and diagnosis

delay. However, if Breslow thickness is a measure of the aggressiveness of the

tumour, then these observed associations would represent associations between

these variables and melanoma prognosis. If the latter is true, then this study will

allow the identification of individuals who are at higher risk of melanoma with a

poorer prognosis.

Without the use of prospective, longitudinal cohort studies, it is difficult to assess

the relationship between Breslow thickness and the aggressiveness of the tumour

with any certainty. However, there is some evidence to suggest that Breslow thick-

ness is a sensible measure for melanoma growth and prognosis, and is largely de-

pendent on the aggressiveness of the tumour. Therefore, the identification of phe-

notypic variables and genetic variants that are associated with Breslow thickness

is important as it may help to identify individuals at risk of poor melanoma prog-

nosis, and may also elucidate the biological mechanisms underpinning melanoma

growth.

In the current study, Breslow thickness was analysed as a continuous variable. In

previous studies, convention has been to categorise Breslow thickness using cut-

points which represent staging classifications [153]. A review of melanoma studies

by Vollmer et al. [190] found that in these studies, Breslow thickness was cate-

gorised with as few as one cutpoint, and as many as seven. However, it is unclear

if natural cutpoints exist and if so, which cutpoints are optimal for modelling

Breslow thickness. Analyses by Vollmer et al. and Stadelmann et al. [191] found



that using Breslow thickness as a continuous variable more clearly modelled sur-

vival, than using various different cutpoints to create arbitrary categories.

In addition, the use of continuous outcomes in genetic association studies is of-

ten more powerful and informative than categorising outcomes [192]. However,

Breslow thickness is still often categorised [112, 129, 158, 193, 194]. This may be

due to various reasons, such as the ability to easily match thickness categories to

treatment. It may also be categorised due to the high positive skewness of Breslow

thickness measurements, with a large number of thin melanomas (< 1mm). In this

thesis, the natural log transformation sufficiently transformed Breslow thickness,

and modelling Breslow thickness this way did not appear to violate any linear

regression assumptions.

Median Breslow thickness in this study was 0.65. The comparison of this median

thickness with median thicknesses from other studies is difficult due to the conven-

tion of reporting Breslow thickness as a categorical variable. In addition, median

Breslow thickness has decreased over the past few decades due to the increase

in diagnosis of thin melanomas [195], which further compounds the difficultly in

comparing Breslow thickness in this study with earlier studies. However, the few

recent studies which have published median Breslow thickness found similar me-

dians of 0.55 mm [112], 0.6 mm [196] and 0.56 mm [112]. Therefore, the median

Breslow thickness in this study appears comparable to other studies.

Furthermore, analysis of Breslow thickness between the eligible population and

the WAMHS sub-sample from Section 2.3.4 found that median Breslow thickness


in the sample did not differ significantly from the median Breslow thickness of all

adult individuals diagnosed with melanoma in Western Australia from January

2006 and September 2009.

Therefore, the distribution of Breslow thickness in this study appears compa-

rable to Breslow thickness described in Australian and international studies of

melanoma cases. This indicates that the associations observed in this study be-

tween Breslow thickness and age at diagnosis, the presence of naevi and several

genetic variants may be generalisable back to melanoma cases in Caucasian indi-

viduals world-wide.

2.5.4.2.2 Sex

No association was observed between Breslow thickness and sex in this study.

Median Breslow thickness for males was 0.63, compared to a median thickness

for females of 0.65, however this association was not statistically significant. This

thicker Breslow thickness for females contradicts other studies which have found

males tend to have thicker melanomas [150, 152, 154, 155]. Further analysis (not

shown) found that for the eligible population, the median Breslow thickness was

0.67 mm (range of 0.04 mm to 35.00 mm) for males and 0.60 mm (range of 0.10

mm to 23.00 mm) for females. Therefore, it appears that the sub-sample used

in this analysis was not representative of the eligible population, and included

a greater proportion of women with thicker melanomas, when compared to the

eligible population. However, it is unlikely that this had a great effect on the

analysis as sex was not significantly associated with Breslow thickness, and no

sex interactions with either age at diagnosis, the presence of naevi, or any genetic

variants were identified.



2.5.4.2.3 Age at diagnosis

Age at diagnosis was significantly associated with Breslow thickness. In this study,

each year of age resulted in an average increase in Breslow thickness of 0.71%,

when adjusted for the presence of naevi. Breslow thickness has been associated

with age at diagnosis in previous studies [149–153], in particular, with older indi-

viduals being diagnosed with thicker melanomas. One reason for this, suggested

by Hanrahan et al. [197], is that older individuals are less likely to notice changes

in melanoma, and are therefore less likely to have their tumours detected and

subsequently diagnosed.

However, as previously described, evidence suggests that melanoma thickness is

not solely related to diagnosis delay, and therefore this argument does not appear

to be wholly valid. In their study of rate of growth of melanoma, Liu et al. [188]

found that individuals aged over 70 years had higher rates of melanoma growth,

when compared to individuals aged under 70 years. As rate of growth is believed

to be an independent risk factor for Breslow thickness, it is therefore possible that

older individuals would have thicker melanomas. However, the evidence is not

clear and older individuals being diagnosed with thicker melanomas may be due

to a combination of these higher rates of tumour growth and a reduced ability for

older individuals to detect changes in melanoma.

2.5.4.2.4 Naevi

The presence of multiple naevi is the principle risk-factor for melanoma. In this

study, 75% of subjects considered their bodies to have ‘Few’ or ‘Some’ naevi, 14%

had ‘Many’ naevi and only 11% had ‘None’. The median number of naevi on the


upper back was four, while the median number of naevi on the lower back was five.

The number of naevi on either section of the back was not significantly associated

with Breslow thickness. The four categories of naevi counts, ranging from ‘Few’

to ‘Many’ were also not significantly associated with Breslow thickness. This vari-

able was then transformed into a binary trait - the presence of naevi - which was

recorded as ‘No’ or ‘Yes’, and this new variable was significantly associated with

Breslow thickness.

In this study, having no naevi resulted in an increase of Breslow thickness of 29%,

when compared to having naevi, adjusted for age at diagnosis. While this supports

the hypothesis that melanoma risk factors will also be associated with melanoma

prognosis, the direction of the association is contrary to what may be expected.

That is, it seems more plausible that thicker melanomas would be associated with

more naevi, not that thicker melanomas are associated with fewer naevi.

In their study of Breslow thickness, Liu et al. [188] found a significant association

between the rate of growth of melanoma, and naevi. In particular, individuals

with fewer naevi had a faster melanoma growth rate and individuals with more

naevi had a slower melanoma growth rate.

One reason for the inverse relationship between naevi count and melanoma growth

rate may be that thicker melanomas tend to be the nodular type [198] and these

are associated with fewer naevi. In this study, the types of melanoma were not

available directly from the WACR as they are not uniformly recorded on pathology

forms, however it may be possible to obtain this information. If it was possible



to adjust for the type of melanoma, then the association between fewer naevi and

thicker melanomas may no longer be observed.

Another reason for this inverse relationship may be that individuals with many

naevi may be more likely to visit their doctor and be screened for melanoma. This

may result in individuals with naevi having thinner melanomas diagnosed, which

would result in naevi appearing to be associated with thinner melanomas.

The number of naevi on the back was not associated with melanoma thickness,

while the presence of naevi was associated with melanoma thickness. This discrep-

ancy may be explained by the way naevi were reported. In this study, subjects or

a family member of the subject were asked to count the number of naevi on two

sections of the back. The subject was sent an information sheet explaining how

to identify naevi, however anecdotal evidence suggested that WAMHS subjects

found it difficult to identify naevi. This may be as the back is a difficult area to

examine, and there may also be a large amount of freckling which may be confused

with naevi. Therefore, the naevi counts on the back may not be wholly reliable.

However, when the subject examined four different diagrams representing degrees

of naevi on the whole body, ranging from ‘None’ to ‘Many’, their responses may

have been more accurate and less likely to be subjected to biases. In particular,

it may have been easier for an individual to identify themselves as having ‘No’

naevi, while the categories ‘Few’, ‘Some’ or ‘Many’, may have been more difficult

to choose between.


The only way to determine if the naevi counts were accurate would be for a

trained individual to count the number of naevi on subjects’ backs for a subset

of the sample. The concordance between the counts performed by an expert and

those performed by the subject could be measured to identify how accurately naevi

were counted. As part of the scar examination which some subjects underwent

as part of the WAMHS, each subject’s back was photographed. Therefore, it is

possible to estimate the concordance between self-reported naevi counts and those

identified by a trained individual. This was beyond the scope of the present study,

however it would make for interesting further research.

2.5.4.3 Summary of genetic association analyses

2.5.4.3.1 Review of genetic variants studied

Forty two genetic polymorphisms in 16 candidate genes and their associations with

Breslow thickness were analysed in the current study. Twenty three of these SNPs

were chosen as they had been identified as melanoma-risk SNPs in earlier GWAS

and candidate-gene studies, or were associated with melanoma-risk factors, such

as light pigmentation and naevi. The remaining nineteen SNPs were tag-SNPs

derived from HapMap [175] in the EGF, BRAF and MC1R genes.

2.5.4.3.2 Details of associations

After adjustment for age at diagnosis and naevi, four SNPs provided some evidence

for association with Breslow thickness. These SNPs were rs1800401 in the OCA2

gene, rs1042522 in the TP53 gene, rs12203592 in the IRF4 gene and rs1733826 in

the BRAF gene. After adjustment for multiple testing, none of these associations

remained significant. Therefore, it is possible that the associations detected were



false positives. However under different conditions, such as a larger sample size,

these associations may have remained significant after multiple testing.

Prior to this study, associations between these SNPs and Breslow thickness had

not been investigated. Therefore, the results of the current association study are

novel and warrant further investigations, including replication of these associations

in a larger sample.

2.5.4.3.2.1 OCA2

The rs1800401 SNP was modelled codominantly, with the heterozygote associ-

ated with a 19% thinner melanoma, compared to the major homozygote. The

rs1800401 SNP is located in the OCA2 (oculocutaneous albinism) gene on chro-

mosome 15q11. Mutations in OCA2 have been associated with albinism [199,200]

and pigmentation [169,201,202]. In particular, the ‘A’ allele of the rs1800401 SNP

was associated with greater odds of having brown or black eyes [169], or reduced

odds of having lighter eye colours. Lighter eyes are thought to be a melanoma-risk

factor, in particular, blue eyes have been identified as a melanoma risk factor [89],

therefore the ‘A’ allele would likely be associated with reduced melanoma risk. In

this thesis, the complementary DNA strand was analysed (‘T’ allele), and this ‘T’

allele was associated with thinner melanoma. Therefore the risk allele (‘T’ or ‘A’)

is associated with lower melanoma risk, and also thinner melanomas.

It is interesting to note that no subjects in this thesis were rare homozygotes for

this SNP. This was also observed in a study by Duffy et al. [203] of Australian

twin families without melanoma, where 1,268 were common homozygous, 136 were


heterozygous, and none were rare homozygous.

2.5.4.3.2.2 TP53

The rs1042522 SNP in the TP53 gene was modelled dominantly, with one or two

copies of the variant C allele associated with thicker melanoma. This relationship

was moderated by the presence of naevi, with the ‘C’ variant associated with 85%

thicker melanomas if no naevi, and 6% thicker melanomas with some naevi, when

compared to having no variant alleles and no naevi.

The TP53 gene is found on chromosome 17p31.1, and encodes the tumour sup-

pressor protein p53. P53 plays a critical role in preventing cancer development, in

particular by preventing tumour cell growth [204]. Mutations in TP53 have been

associated with a wide spectrum of cancers, including Li-Fraumeni syndrome [205],

breast cancer, and leukemia, and is also associated with early onset cancer [206].

The most common polymorphism is rs1042522, and this has been found to be

associated with melanoma prognosis in several studies [172, 207–209]. However,

these associations have been contradictory, with two studies identifying the ‘C’ al-

lele as the risk allele [207,208], and the other two studies identifying the ‘A’ allele

as the risk allele [172, 209]. Further replication is therefore required to reconcile

these findings.

Few investigations have studied the relationship between naevi and TP53, however

there is some evidence of somatic mutations of TP53 in naevi [210]. In addition,

a linkage study of naevi by Zhu et al. [211] identified a region of chromosome 17



which was linked with naevi, which may have been due to TP53.

The association between Breslow thickness and the rs1042522 SNP is unclear, and

further research is required to unravel the relationship between Breslow thickness,

naevi and TP53.

2.5.4.3.2.3 IRF4

The rs12203592 SNP in the IRF4 gene was modelled additively, with each ‘T’

variant allele associated with a 10% thicker melanoma. The ‘T’ variant of the

rs12203592 SNP has been associated with many pigmentation factors, such as

blonde hair, light skin colour, light eye colour and an inability to tan [124], which

are known melanoma-risk factors. Therefore, it appears that the ‘T’ variant is

associated with both melanoma risk and also thicker melanomas.

The IRF4 (Interferon Regulatory Factor 4) gene is located on chromosome 6p25.3,

and encodes the IRF4 protein. This protein regulates the transcription of inter-

feron proteins which are made and released in response to pathogens, including

tumour cells [124].

The IRF4 gene was first shown to be associated with pigmentation factors in a

2007 GWAS by Sulem et al. [109]. Subsequent to this GWAS, a second GWAS

of pigmentation in 2008 [124] found the rs12203592 SNP on the IRF4 gene to be

associated with pigmentation factors and skin tanning response to sunlight. In

their paper, Han et al. [124] reported that this association had been replicated

in several studies. IRF4 encodes the B-cell protein which has been proposed as


a marker for both primary and metastatic melanoma and benign naevi [212].

However, the role of IRF4 in pigmentation and therefore melanoma risk, is still

generally not understood.

2.5.4.3.2.4 BRAF

The rs1733826 SNP in the BRAF gene was associated with a decrease in Breslow

thickness, in particular, each copy of the ‘A’ allele decreased Breslow thickness by

18%.

The BRAF gene is located on chromosome 7q34 and encodes the B-RAF protein.

This protein is associated with serine/threonine kinase activities, and mediates

signals involving cell growth, transformation and differentiation [272]. Somatic

mutations in BRAF have been identified in many cancers, including melanoma,

lung cancer, and colorectal cancer [213]. Associations between germline mutations

in BRAF have been detected [130,214], however other studies have not found any

association [215,216], and therefore the role of BRAF as a melanoma-susceptibility

gene is still largely unclear.

No associations have been detected between somatic mutations in BRAF and

Breslow thickness [217]. However, to the author’s knowledge, no associations

between germline mutations and Breslow thickness have been investigated before

this study.



2.5.4.4 Causality

The set of criteria described by Hill and explained earlier in Section 1.1.1 can

be used to investigate if the associations observed in this thesis may be causal.

These criteria include strength, consistency and temporality. The strength of the

associations between genetic variants and Breslow thickness were not strong, with

each variant only associated with a small change in Breslow thickness.

Consistency of these associations is difficult to determine as the genetic vari-

ants identified in this study as associated with Breslow thickness have not been

replicated in other studies to date. Temporality is easier to identify in genetic

association studies, as genotypes are assigned to offspring from parental germline

DNA at conception, and therefore the genotype must predate the development of

melanoma.

The observed associations do not appear to violate Hill’s criteria. However, due

to the millions of common SNPs in the human genome, it is unlikely that any of

the variants found to be associated with Breslow thickness are themselves causal

variants. If the observed associations are true, it is more likely that these variants

are simply markers in LD with nearby causal variants.

2.5.4.5 Potential limitations

As with all genetic association studies, this study has potential limitations, in-

cluding the use of self-reported variables, low statistical power, and the possibility

of population stratification.


All variables collected as part of the WAMHS questionnaire were self-reported.

This included relatively easy to measure data, such as height and weight, and also

more difficult to recall measures such as sun exposure. The use of self-reported

data has been extensively studied [169,218–222]. Sun exposure is notoriously dif-

ficult to measure, and there is debate surrounding the reliability and validity of

sun exposure recollection [70,219,221]. It is possible that sun exposure, sometimes

from decades earlier, cannot be reliably recalled. In this study, we attempted to

minimise recall bias due to sun exposure measures by using events such as blis-

tering sunburn, which may be recalled more easily than total time spent in the

sun. One way to reduce recall bias would be to conduct a prospective study of a

sample from the general population, where a group of individuals is followed, and

their sun exposures are measured. This would negate the need for individuals to

recall their sun exposure from years earlier. However, as melanoma is a relatively

rare disease, thousands of individuals would need to be followed-up in order to

obtain an adequate sample size of melanoma cases.

Many other variables were also self-reported, including height, weight, naevi

counts and pigmentation traits. It is possible that all of these were not mea-

sured reliably. Measurement error could be reduced if trained individuals were

employed by the study to measure these traits, however this would likely be more

costly, and time-consuming for the study.

The measure of freckling used in this thesis was taken from an estimate of freckling

on the face. Freckling on the face has been shown to be highly correlated with

freckling on other anatomic sites, including the arms and back [280]. Therefore,

freckling on the face, which is easy to measure, was used as a proxy for freckling



on other anatomic sites.

Ethnicity collected as part of the WAMHS was also self-reported. Subjects were

asked their ethnicity and their parents’ ethnicity. If either of these was non-

Caucasian, the subject was not considered Caucasian and was not included in

the analyses for this thesis. In a study of self-reported ethnicity, Mez et al. [223]

found self-reported ethnicity was a reasonable proxy for genetic ancestry [223].

In addition, any bias due to population stratification is likely to be small [224],

particularly for well conducted and analysed studies. However, there is a possibil-

ity that self-reported ethnicity, along with other self-reported variables were not

reliably measured.

The inclusion of only Caucasian individuals in the study also means that the

genetic association results are only relevant to Caucasian populations. Further

replication in other ethnic groups is required in order to generalise these results

back to different ethnic groups.

Another limitation of this study is the relatively small sample size, resulting in

modest statistical power. At this start of this thesis, it was estimated that with

1,000 melanoma cases diagnosed in Western Australia each year, approximately

2,000 individuals would be recruited and available for use in this thesis. However,

due to a number of issues with data collection, recruitment took longer than

expected and therefore only 800 individuals were able to be included in this thesis

which will have reduced statistical power substantially. Power analyses showed

that for an additive genetic model, for SNPs with a MAF greater than 0.2, this


sample would only be sufficiently powered to detect a change in Breslow thickness

of more than 0.2SD (1.3% of variance). Therefore, the study was unable to detect

small genetic effects. Replication is therefore required in larger sample sizes which

would be more adequately powered.

2.5.5 Summary

In this section, possible associations between Breslow thickness and phenotypic

variables and genotypic variants were investigated. Two phenotypic variables

were significantly associated with Breslow thickness. These were age at diagnosis,

where older age of diagnosis was associated with thicker melanomas, and the pres-

ence of naevi, which was associated with thinner melanomas. The use of Breslow

thickness as a proxy for progression of the tumour means that these factors would

likely also be associated with melanomas which tend to grow faster.

In addition, four SNPs were found to be associated with Breslow thickness. Fur-

ther replication of these results is necessary, as after adjustment for multiple test-

ing, none of these associations remained significant. However, this study has

provided some evidence for the role of four genes in melanoma prognosis, which

may lead to an increased understanding of the mechanisms by which melanomas

grow. The results of this study will also assist with identifying individuals who

are at a higher risk of thicker melanomas, and therefore, poorer prognosis.

In total, age at diagnosis, the presence of naevi, and the four SNPs accounted

for 6.8% of variation in Breslow thickness. This means there is a large amount

of variation which is as yet unaccounted for. This is likely to include many other



SNPs, and also environment and host factors. Further studies of other genes,

and collection of more phenotypic data in larger samples may assist in elucidating

the role of these phenotypic and genetic variables in melanoma development and

prognosis.

2.6 Chapter Summary

Melanoma is a significant public health issue in Australia, particularly in Western

Australia. In Western Australia, more than 1,000 individuals are diagnosed with

melanoma each year, accounting for approximately 10% of all cancer diagnoses.

Invasive melanoma is an aggressive form of skin cancer, and it is expected that

Australian incidence and mortality rates from this disease will continue to rise [51].

The main environmental risk factor for melanoma is sun exposure, and the main

host risk factor is the presence of naevi, however the association of other risk-

factors with melanoma risk is uncertain and many associations have not been

consistently replicated. Several genetic variants which contribute to melanoma

risk have been identified, however, these account for only a small proportion of

melanoma diagnoses. In addition, the role of specific genetic and environmental

factors in melanoma prognosis are largely unknown. Therefore, the collection of

genetic epidemiological data from melanoma cases is required, along with further

investigations in order to elucidate the role of environmental, host and genetic

risk factors in melanoma-susceptibility and prognosis.

This chapter described the establishment of the WAMHS, and the use of these

data to conduct genetic epidemiological associations into melanoma susceptibility

2.6. Chapter Summary 181

and prognosis.

The WAMHS is a population-based case-collection and biospecimen resource com-

prised of consenting adults diagnosed with melanoma in Western Australia be-

tween January 2006 and September 2009. WAMHS subjects completed a ques-

tionnaire and also gave a blood sample for DNA, serum, and an additional blood

sample for RNA. The final sample consisted of 1,643 participants, with 1,157 hav-

ing complete questionnaire data and blood samples. This is the largest population-

based study of melanoma cases in Western Australia, and one of the largest in

the world. As such, the WAMHS is an ideal resource for genetic epidemiological

investigations into melanoma susceptibility and prognosis.

As part of this PhD, 42 SNPs in 16 genes were genotyped in the WAMHS sample.

The SNPs were chosen on the basis that they were in or close to loci which have

been previously identified as melanoma-risk genes. The association of these SNPs

and melanoma-susceptibility were investigated by comparing the genotypic dis-

tributions of the WAMHS cases, with the genotypic distributions of two general

population samples - the HapMap-CEU and the BHS samples. After adjustment

for multiple testing, the association of six SNPs in five genes with melanoma

risk was replicated, with only two SNPs associated when compared to both the

HapMap-CEU and BHS control populations.

In this chapter, possible associations between these 42 melanoma-risk SNPs and

Breslow thickness were also investigated. Melanoma prognosis was determined by

Breslow thickness, with a thicker Breslow thickness indicating a poorer progno-



sis. Phenotypic analyses found increasing age at diagnosis was associated with

thicker Breslow thickness, and therefore poorer prognosis, while the presence of

naevi was associated with thinner melanomas, and therefore a better prognosis.

Four SNPs were associated with Breslow thickness, however after adjustment for

multiple testing, none of these associations remained significant.

One SNP - rs12203592 in the IRF4 gene - was associated with melanoma risk

in the WAMHS sample, compared to both the HapMap and BHS control sam-

ples, and was also associated with thicker melanomas in the WAMHS sample. In

particular, the ‘T’ allele was associated with both increased melanoma risk, and

thicker melanomas. However, the latter association was not significant after ad-

justment for multiple testing. No other SNPs were consistent in their associations.

It is possible that genetic variants which increase melanoma-susceptibility are

also associated with thicker Breslow thickness and therefore poorer prognosis.

However, the current study had several limitations, and was not able to provide

sufficient evidence to support this hypothesis. Replication of these genetic results,

particularly in larger samples, is required.

183CHAPTER 3

Mendelian Randomisation: An Application of

Instrumental Variable Techniques

3.1 Introduction

‘Mendelian randomisation’ is an application of a technique known as instrumental

variables (IVs), which can be used to better understand and model causality in

epidemiological studies. In this chapter I provide a background for IV methods

and Mendelian randomisation, including their advantages and disadvantages. I

also describe a novel implementation of Mendelian randomisation analyses incor-

porating haplotypes in a software library for R, MRsnphap.

When this thesis was being planned, I intended to investigate causal pathways

underlying melanoma. Mendelian randomisation approaches were therefore at-

tractive as they would have allowed me to investigate such pathways assuming the

conditions for Mendelian randomisation analyses were met (these are described

further in Section 3.4.2). One potential analysis was to investigate the effect of

serum vitamin D levels on Breslow thickness in the WAMHS cases, and if an as-

sociation was detected, to investigate whether this association was causal. The

rationale for this is discussed further in Section 3.2. Due to financial constraints,

vitamin D levels in the WAMHS were not measured, and therefore this analysis

was not able to be performed. However, this example is used throughout this

section to illustrate the possible use of Mendelian randomisation studies using

melanoma cases from the WAMHS.

184Chapter 3. Mendelian Randomisation: An Application of Instrumental Variable

Techniques

3.2 Vitamin D Levels and Breslow Thickness

Vitamin D is a fat-soluble secosteroid and is a term generally used to refer to

vitamin D2 and/or vitamin D3. Vitamin D3 is produced when the skin is ex-

posed to sunlight, in particular, ultraviolet-B radiation. In recent years, vitamin

D levels, in particular vitamin D deficiency has been linked to increased risks of

chronic disease, including osteoarthritis, diabetes, prostate cancer, breast cancer,

and melanoma [277].

Vitamin D levels have been associated with both melanoma susceptibility and

prognosis [263]. In particular, increased vitamin D levels are associated with a

lower risk of melanoma recurrence, and thinner melanomas. Vitamin D levels are

often higher in individuals with substantial sun exposure, as exposure to ultra-

violet radiation leads to an increased production of vitamin D. Therefore, this

association suggests that individuals with higher vitamin D levels (and higher

ultraviolet radiation exposure) are at a lower risk of melanoma recurrence, and

have better melanoma prognosis. This is particularly interesting as high ultravio-

let radiation exposure is the main known environmental risk factor for melanoma,

therefore the association between high vitamin D levels and improved melanoma

prognosis and reduced recurrence warrants further investigation.

In a study of 872 patients with melanoma in the United Kingdom, Newton-Bishop

et al. [263] found that vitamin D protected against the recurrence of melanoma.

In addition, higher vitamin D levels were associated with thinner Breslow thick-

ness (P=0.002). For individuals with a melanoma less than 0.75mm, mean serum

vitamin D levels were 55.8 nmol/L and this decreased linearly to 48.5 nmol/L for

3.3. Epidemiological Studies 185

individuals with Breslow thickness of more than 3mm.

However, it is possible that vitamin D levels are not causally associated with

Breslow thickness or melanoma recurrence, rather these associations may be con-

founded by other factors, such as socioeconomic status or a healthy lifestyle. If

this relationship is causal, then an intervention to increase vitamin D levels may

reduce Breslow thickness and therefore improve melanoma prognosis, and may

also reduce melanoma recurrence.

3.3 Epidemiological Studies

One aim of epidemiological studies is to detect associations between disease-

associated outcomes and modifiable exposures so that interventions can be per-

formed on the exposure to reduce the impact on disease outcomes. Disease-

associated outcomes may include quantitative measures associated with disease,

such as Breslow thickness as a close correlate of melanoma prognosis, or a binary

measure, such as diagnosed melanoma. A modifiable exposure is some environ-

mental factor to which an individual is exposed, such as vitamin D levels. An

example of this would be a positive association detected between vitamin D levels

and Breslow thickness, where an intervention to increase vitamin D levels leads

to a decrease in the Breslow thickness of excised melanomas.

For an intervention to make a meaningful impact on disease outcomes, the ex-

posure must be causally associated with these outcomes. However, a causal as-

sociation may be difficult to determine. Broadly speaking, there are two types

of epidemiological studies – experimental and observational studies. The benefits


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and limitations of these studies and their ability to infer causality are discussed

in the following section.

3.3.1 Experimental Studies

Experimental studies are epidemiological studies where the investigator is able to

manipulate who is exposed and who is unexposed to some treatment or exposure.

Randomised controlled trials (RCTs) are a type of experimental study which al-

locates individuals randomly into exposed and non-exposed groups, or treatment

and non-treatment groups, and are considered the ‘gold-standard’ for evidence

of causality [9, 231, 233, 243]. RCTs have the advantage that, as individuals are

randomly allocated into treatment and non-treatment groups, there should not be

any confounding between unobserved factors and the treatment received. There-

fore if the treatment and non-treatment groups groups exhibit different disease

characteristics, this should be wholly due to the treatment received. It is then

plausible to suggest that the treatment has a causal effect on the disease outcome.

As such, RCTs are recognised as the gold-standard for epidemiological studies.

However, it is not always possible to perform a RCT as it may not be ethical

or feasible to allocate individuals to a particular exposure. For example, if it is

hypothesised that smoking causes high blood pressure, it would not be ethical to

randomize individuals into smoking and non-smoking groups due to the known

harmful effects of smoking. In this example, an observational study may be pre-

ferred, where an individual’s blood pressure is measured and their smoking history

is collected either prospectively or retrospectively. In a study of vitamin D lev-

els, it may be difficult to allocate individuals into low and high vitamin D level

groups as it may be difficult to restrict an individual’s exposure to UVB in the


low-vitamin D level group. Therefore, in situations such as these, observational

studies may be utilised.

3.3.2 Observational Studies

Observational epidemiological studies are often a precursor to RCTs as they are

cheaper and easier to conduct, and are unlikely to violate ethical standards.

The three main types of observational epidemiological studies, cross-sectional,

case-control and cohort studies, and their abilities to infer causality in epidemio-

logical studies are described below [13].

Cross-sectional studies involve collecting information (usually through a ques-

tionnaire) regarding an individual’s disease status and possible risk exposures at

a point in time. Researchers then use this information to determine if there is

an association between risk exposures and some disease outcome. For example,

a study to investigate the effect of vitamin D levels on Breslow thickness may

involve the recruitment of individuals recently diagnosed with melanoma. Indi-

viduals may then have their vitamin D levels measured as soon as possible after

melanoma diagnosis, and their Breslow thickness recorded to investigate whether

vitamin D levels are associated with Breslow thickness, and therefore melanoma

prognosis.

Case-control studies involve recruiting two groups of individuals: cases, who have

some disease, and controls, who do not. These two groups are then compared


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based on their past exposure to suspected disease risk factors. If the risk factor

is more prevalent in the cases, then it may be that this risk factor contributes to

disease. For example, two groups of individuals may be recruited – those diag-

nosed with melanoma, and those who have not been diagnosed with melanoma.

Individuals may then have their vitamin D levels measured to determine if the

levels of vitamin D are different amongst cases and controls. If vitamin D lev-

els are higher in controls, this suggests that vitamin D levels are associated with

melanoma susceptibility.

Cohort studies follow groups of healthy individuals through time to record ex-

posure to risk factors and incidence of disease or disease outcomes. This allows

researchers to determine if those individuals who develop the disease or disease

outcome had increased exposures to risk factors before the disease occurred. For

example, a cohort study may recruit a group of individuals all aged 20. These

individuals may then be followed for many decades, with their vitamin D levels

recorded annually. If melanoma diagnoses are also recorded, researchers will be

able to identify individuals who develop melanoma and compare their vitamin D

level histories with the individuals who have not developed melanoma. If individ-

uals with diagnosed melanoma also have low vitamin D levels, this suggests that

low vitamin D levels are associated with melanoma susceptibility.

These studies all have respective advantages and disadvantages. Data collection

in cross-sectional and case-control studies is often easier and less time-intensive

compared to cohort studies, however these studies are more likely to be subject

to recall bias, as participants with disease (cases) may be more likely to recall

exposures that they believe caused disease, compared to controls. In addition,


cohort studies are also able to demonstrate a temporal sequence between the ex-

posure and the occurrence of disease. The disadvantage of cohort studies lies in

the cost and time of conducting such studies – a great number of subjects may

need to be followed for a long period of time to accrue enough cases, particularly

when the disease is rare, or if there is a long lag-time between exposure and dis-

ease occurrence [225]. All three types of observational studies may be subject to

confounding, as an individual is self-selecting their exposure to risk factors which

may be related to unobserved factors, such as socioeconomic status or diet. For

example, individuals who have higher levels of vitamin D may have a healthier

diet and be more educated. These factors may also affect melanoma susceptibility

and prognosis, therefore confounding the observed association between vitamin D

levels and melanoma.

These potential biases mean that observational studies may not always be able to

detect true associations. The potential disparity in results between observational

studies and subsequent RCTs are discussed in the next section.

3.3.2.1 Limitations of observational epidemiology

Observational epidemiology has had many successes at identifying risk exposures

for disease, including perhaps the most high-profile, establishing the link between

smoking and cancers, in particular, lung cancer [226, 227]. However, there have

also been many notable failures; that is, associations reported from observational

studies which have not been replicated in ‘gold-standard’ RCTs.

One such failure is the putative association between hormone replacement therapy


Techniques

(HRT) and CVD. An initial meta-analysis of observational epidemiological studies

showed that the use of HRT halved a woman’s risk of CVD, with a relative risk of

0.56 (95% CI = 0.50, 0.61) [228]. Similarly, a subsequent meta-analysis conferred

a relative risk of 0.65 (95% CI= 0.59, 0.71), prompting the authors to state that

women with CVD or at high-risk of CVD should probably be recommended for

HRT [229].

To test the hypothesis generated from these observational studies, a series of

RCTs were performed to investigate the effect of HRT on CVD risk. In a pooled

analysis of these subsequent RCTs, women given HRT were not found to have a

reduced risk of CVD; in fact, if any change was detected, it was an increased risk

of CVD [230].

Other reported associations from observational studies that have not been repli-

cated by RCTs include the association between elevated homocysteine level and

increased CVD risk, the use of antioxidant vitamins and reduced CVD and cancer

risk, and the use of beta-carotene and reduced smoking-related cancers [231–234].

The disparity between associations detected in epidemiological studies and RCTs

suggest that the initially reported associations in observational epidemiological

studies were not in fact causal. Instead, these observations are likely to be ex-

plained by a number of biases, which were removed or reduced in the RCT. These

possible biases include confounding by lifestyle, behavioural and socioeconomic

factors, reverse causation, and selection bias [235].


Regression dilution bias is another bias which may effect associations in observa-

tional epidemiological studies [235]. These biases and their impact on observa-

tional epidemiological studies are described in the following section.

3.3.2.1.1 Confounding

Confounding occurs when the exposure of interest is not associated directly with

a disease outcome, but rather is observed to be associated due to its association

with some other unidentified (‘confounding’) factor. For instance, the association

between higher vitamin D levels and decreasing Breslow thickness, may be due to

confounding. That is, vitamin D levels may be higher in individuals who exercise

more outdoors, take dietary supplements, and are generally in good health. These

individuals may also be more likely to examine their skin, and have regular visits

to the doctor. This may result in earlier melanoma diagnosis, and therefore thin-

ner tumours excised. In this example, the association between vitamin D levels

and Breslow thickness is confounded by a healthy lifestyle.

Healthy lifestyle

$$zzBreslow thickness Vitamin D levelsoo

Figure 3.3.2.1.1.1: Confounding – the observed association between Breslow thick-

ness and vitamin D levels is due to confounding by a healthy lifestyle


Techniques

3.3.2.1.2 Reverse causation

Reverse causation occurs when instead of the exposure of interest influencing some

disease outcome, the disease outcome influences the exposure. This is a violation

of the concept of temporality which was used to define causality in Section 1.1.1.

Continuing the Breslow thickness and vitamin D example from earlier, reverse

causation may occur if it is the presence of a thicker melanoma tumour (thicker

Breslow thickness) which actually reduces vitamin D levels, rather than lower vi-

tamin D levels increasing Breslow thickness. If the reported association between

vitamin D levels and Breslow thickness was due to reverse causation, then indi-

viduals with thicker tumours would have lower vitamin D levels and it may be

wrongly assumed that lower vitamin D levels actually increase melanoma thick-

ness.

3.3.2.1.3 Selection bias

Selection bias occurs when study participants have been selected based on their

exposure and disease combination in case-control studies, or to their exposure and

disease outcome in association studies. An example of this was seen in the Cancer

Prevention Study II which consisted of 1.2 million volunteers [238]. This study

found that in a middle-aged and elderly population, heavy alcohol consumption

was associated with a reduced risk of death from stroke. Alcohol consumption in-

creases blood pressure - a major risk factor for stroke - so this association seemed

counterintuitive. Instead, Ebrahim and Davey Smith suggested [239] that this as-

sociation may have been reported as healthy individuals who drank heavily were

more likely to volunteer for and participate in a health study, compared to heavy


drinkers who had poor health. Therefore, study participants may have been se-

lected with bias, and associations that indicated heavy drinking reduced stroke

mortality may have been an artefact of the selection.

Similarly, for the Breslow thickness and vitamin D level example, selection bias

may occur if healthy individuals (with higher vitamin D levels) who have thicker

melanomas are more likely to volunteer for a study, compared to individuals who

are less healthy (with lower vitamin D levels) with thicker melanomas. In this

situation, it may appear that high vitamin D levels are associated with thicker

melanomas, but it may just be that individuals with low vitamin D levels and

thicker melanomas did not volunteer for the study.

3.3.2.1.4 Regression dilution bias

Regression dilution bias, or attenuation by errors, is the dilution of the associa-

tion between the exposure and disease risk, due to measurement error, or random

noise in the exposure. This dilution will result in a smaller reported effect of the

exposure on disease [240,241].

This commonly occurs in observational epidemiological studies where exposures

are often only measured once, but are used as a proxy for an individual’s long-

term average value. For example, in a study of vitamin D levels and Breslow

thickness, vitamin D levels may only be measured once during the study, and

these one-off measurements may be unrepresentative of an individual’s lifetime

vitamin D levels. Therefore, the effect of vitamin D levels on Breslow thickness

may be diluted and the estimated effect of vitamin D levels on Breslow thickness


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may be underestimated.

3.4 Statistical Methods for Analysing Observational Epidemiological

Studies

In this section the statistical methods for observational epidemiological studies,

in particular cross-sectional studies, are described.

3.4.1 Ordinary least squares

Ordinary least squares (OLS), in particular linear OLS, is often used in regression

analysis to model relationships between measurements collected in studies, for ex-

ample, associations between some disease outcome and exposures in observational

epidemiological studies.

Consider a linear regression model,

y = Xβ + u,

where y is a column vector of observed response variables, X is the design matrix

for the intercept and regressors (explanatory variables), β is the column vector of

regression parameters, and u is the column vector of random errors.

The estimated regression parameter βOLS is given by,

βOLS = (XTX)−1XTy,

3.4. Statistical Methods for Analysing Observational Epidemiological Studies 195

where T and −1 indicate the transpose and inverse of a matrix, respectively.

The variance of βOLS is given by,

V (βOLS) = σ2OLS(XTX)−1,

where σ2OLS =

(uTOLS uOLS)

n, where uOLS is the OLS residual, uOLS = y − XβOLS,

and n is the number of observations.

3.4.1.1 Assumptions of ordinary least squares

There are three assumptions underlying the use of OLS:

1. Explanatory variables and residuals are independent, such that E(Xiui) = 0,

2. residuals are Normally distributed with expected value 0 and a common

variance, u ∼ N(0, σ2OLS), and

3. residuals, u, are independent.

If the above assumptions are not violated, the model is valid to both predict values

of y given X and to make inferences about the causal nature of the modelled asso-

ciation. For example, if there is an observed association between Breslow thickness

and vitamin D levels, and the assumptions of OLS are not violated (implying the

association is causal), then an intervention in the community to increase vitamin


Techniques

D levels will have an effect on the Breslow thickness of melanomas.

However, if Assumption 1 is violated, that is, when E(Xiui) 6= 0, βOLS will be a

biased and inconsistent estimator of β. This correlation between the explanatory

variables and residuals may occur if the association between Breslow thickness

and vitamin D levels is due to confounding; that is, there is an omitted variable

which is associated with both Breslow thickness and vitamin D levels, such as a

healthy lifestyle.

In this situation, OLS would not be an appropriate technique to model the data,

and a different technique may be required. Instrumental Variables (IVs) is one

such method, and is described in the following section.

3.4.2 Instrumental variable methods

IV methods are a statistical technique which can be used to estimate the possi-

ble nature and direction of a causal relationship between two variables. Imagine

a researcher has modelled a relationship between Breslow thickness and vitamin

D levels from a cross-sectional observational study. They may be interested in

whether vitamin D levels are associated with the Breslow thickness, and if vita-

min D levels also have a causal effect on Breslow thickness.

As before, consider the linear regression model,

y = Xβ + u,


where each term is as described in Equation 3.4.1.

Under conditional homoskedasticity, the error, u, is distributed as follows : u ∼ (0,Ω),

where Ω is the covariance matrix, Ω = σ2IV I, where I is the identity matrix. As

before, σ2IV =

(uTIV uIV )

n, where uIV is the IV residual, uIV = y−XβIV ; the formula

for βIV is described in Section 3.4.2.2 (please see page 198).

IV methods can be utilised when some regressors are endogenous; that is, when

they are correlated with the error term, so that Assumption 1 is violated, or

E(Xiui) 6= 0. In this case, by using IV methods, βIV will be consistent. En-

dogeneity of the regressors may occur if the association modelled is not causal.

Instead, the association may be subject to some bias as discussed in Section 3.3.2.1

(please see page 189), leading to correlation between the regressor and the error

term, resulting in endogeneity of the regressor.

The IV method can be modelled in the following way (adapted from Baum

[242]). Define X to be the nxk design matrix and partition X into two parts,

X = [X1, X2], where X1 consists of the k1 endogenous regressors (including the

intercept term) which are assumed to be correlated with the error term and X2

are the k − k1 exogenous regressors which are not assumed to be correlated with

the error term.

Define Z to be the nxl matrix of instrumental variables, which are the vari-

ables assumed to be exogenous, or not correlated with the error term, such that

E(Ziui) = 0. Z can also be partitioned into two parts, Z = [Z1, Z2], where Z1


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consists of the l1 excluded instruments and Z2 are the l− l1 included instruments.

These included instruments are also the exogenous regressors described above.

3.4.2.1 Assumptions of instrumental variables

An excluded instrument, Z1, is defined as a variable that satisfies the following

assumptions [243]:

1. Z1 is strongly correlated with X1,

2. Z1 is independent of U (the factors that confound the association of X and

Y ),

3. Z1 is independent of outcome Y , given X1 and U , and

4. all of the associations between Z1, X1, Y , and U are linear and are unaffected

by statistical interactions.

These assumptions are depicted in the directed acyclic graph (DAG) in Figure

3.4.2.1.1.

3.4.2.2 Instrumental variable estimator of β

The IV estimator of β can be calculated by,

βIV = (XTPZX)−1XTyPZ ,


Z1

X1

U

oo

Y

Figure 3.4.2.1.1: DAG for the instrumental variables model. Z1, instrumental vari-

able; X1, exposure of interest; Y, outcome of interest; and U, unmeasured con-

founders.

where PZ is the projection matrix and is defined by Z(ZTZ)−1ZT .

3.4.2.3 Variance-covariance matrix estimator of βIV

The variance-covariance matrix of the IV estimator, βIV , is,

V (βIV ) = σ2IV (XTPZX)−1,

where σ2IV and PZ are as defined earlier.

3.4.3 Instrumental variable diagnostic tests

There are several diagnostic tests which should be performed either during or after

IV analysis to ensure that the assumptions of IV analysis have not been violated.

These tests are described below.

3.4.3.1 Strength of excluded instruments

The excluded instruments must be strongly correlated with the endogenous re-

gressor (Assumption 1 in Section 3.4.2.1). The strength of this correlation can


Techniques

be calculated by first regressing the endogenous regressor on both the exogenous

regressor and excluded instruments, and also regressing the endogenous regressor

on only the exogenous regressor. Comparison of these two models by analysis of

variance techniques yields an F-statistic for the relationship between the excluded

instruments and the endogenous regressor.

It has been shown [244, 268] that an F-statistic ≥ 10 indicates a strong correla-

tion. When the F-statistic < 10, a weak correlation exists and the instrument is

referred to as weak. While this is a convenient rule of thumb, the strength of the

F statistic will depend on the number of instruments used. For example, Stock et.

al. [268] estimated that for 1 instrument, bias was minimised with an F-statistic

of 8.96. However, with 15 instruments, an F-statistic of 26.80 would be required.

The use of weak instruments increases both the bias and inconsistency in the IV

coefficient. In general, IV estimates are biased towards the OLS estimates. As

the correlation between the instrument and endogenous regressor decreases, the

bias of βIV increases. Therefore, the stronger the correlation, and therefore the

strength of the instrument, the smaller the bias in βIV [279].

3.4.3.2 Overidentification test

If there are more instruments than endogenous regressors, L1 > K1 (which is

equivalent to L > K), it is possible to test the assumption that the excluded

instruments are independent of the unobservable error process (Assumption 2

in Section 3.4.2.1). As this can only occur when the equation is overidentified

(or when L > K), this is referred to as the overidentification test, and can be


performed by calculating Sargan’s test statistic [245],

S =(y −X ˆβIV )TZ(ZTZ)−1ZT (y −X ˆβIV )

(y−X ˆβIV )T y−X ˆβIVn

.

Under the null hypothesis that all instruments are independent of the error, S

∼ χ2l−k. If there is sufficient evidence to reject the null hypothesis, this suggests

the instruments are not truly exogenous (independent of the error term) and there-

fore are violating Assumption 2 in Section 3.4.2.1.

This test may also provide evidence that each instrument is providing a consistent

estimate of the exposure-outcome association, which increases confidence regard-

ing the causal nature of the exposure [243].

3.4.3.3 Endogeneity test

When using IV estimation, the asymptotic variance of the IV estimator is al-

ways larger than the asymptotic variance of the OLS estimator [246]. This loss

of efficiency is necessary if the OLS estimator would be biased and inconsistent.

However, if IV estimation is used when not required, for example, if regressors

are treated as endogenous when they are truly exogenous and not correlated with

the error, the loss of efficiency is unnecessary. Therefore, the test for endogeneity

tests whether the endogenous regressors are truly endogenous. This is done by

essentially testing the null hypothesis that the OLS coefficient and IV coefficient

are consistent with each other.


Techniques

An endogeneity test can be performed by calculating the Durbin–Wu–Hausman

(DWH) statistic [247–249]. The hypothesis being tested are:

H0: OLS estimator, βOLS, is efficient

H1: OLS estimator, βOLS, is not efficient.

The DWH statistic can be calculated in the following steps:

1. Calculate OLS coefficient,

βOLS = (XTX)−1XTy.

2. Calculate OLS variance,

σ2OLS =

(y −XβOLS)T (y −XβOLS)

n.

3. Calculate DWH statistic,

DWH = n(βIV − βOLS)TD−(βIV − βOLS)T ,

where D− is the generalised inverse of D, defined as,

D = (σ2OLS(XTPZX)−1 − (XTX)−1).

Under the null hypothesis, DWH ∼ χ2K1

. If there is sufficient evidence to reject

the null hypothesis, this suggests the tested endogenous regressors are correlated

with the error term and IV methods should be employed.

3.5. Mendelian Randomisation 203

3.4.4 Instrumental variable methods for observational epidemiological

studies

IV methods can be employed for determining causal associations from observa-

tional cross-sectional epidemiological studies. In this framework the response, y,

is some disease outcome and the endogenous regressor, X1, is the exposure which

is believed may be associated with the error term. The excluded instrument, Z1,

is typically a genetic variant, and any adjusting explanatory variables are the in-

cluded instruments, or exogenous variables, X2 and Z2. When used in this way,

the IV method is referred to as Mendelian randomisation which is introduced and

discussed in the following section.

3.5 Mendelian Randomisation

3.5.1 Introduction

Mendelian randomisation is a term given to observational studies that use ge-

netic variants known to be associated with an outcome to make causal inferences

about modifiable risk factors for disease and health-related outcomes [233]. This

is an application of IV methods, where the genetic variant is the excluded instru-

ment for the non-genetic exposures which may be correlated with the error term.

Mendelian randomisation studies are based upon the principle of Mendel’s 2nd

law, or the Law of Independent Assortment, which states that the inheritance

pattern of one trait is independent of the inheritance pattern of other traits. That

is, alleles on paired chromosomes are transmitted by parents to their offspring

at random (i.e. 50:50 probability), and the transmission of these alleles will be

independent of other traits at a population level.


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The concept of Mendelian randomisation and using genetic variants as instruments

has recently been attributed to Katan [233]. Katan commented on an observa-

tional study which showed that low serum cholesterol levels were associated with

cancer. Katan questioned whether this relationship was causal, or if it may have

been due to a hidden tumour which had decreased cholesterol levels (reverse cau-

sation), or if the association was confounded by other factors related to both low

cholesterol and cancer risk, for example, smoking [250].

Katan suggested the authors of the study may wish to use another method to

determine if this relationship is causal; this method would later be referred to as

Mendelian randomisation. Katan’s idea was as follows: the E2 allele of the Apo E

polymorphism has been shown to be associated with lower cholesterol levels, com-

pared to the E3 and E4 alleles. These alleles, which are transmitted from parent to

offspring at conception, will not be affected by cancer status (reverse causation),

nor will they be associated with lifestyle or socioeconomic status (confounding).

Therefore, a comparison of Apo E alleles in cancer cases and controls may help

explain the relationship between cholesterol levels and cancer.

If the association was causal and low cholesterol causes cancer, then in Katan’s

design, there would be more E2 alleles amongst the cases, compared to the con-

trols. If the association was not causal, then the E2, E3 and E4 alleles would be

distributed equally amongst the cancer cases and controls.

This Mendelian randomisation study was recently investigated by Trompet et


al. [251], who found no evidence to suggest that low cholesterol levels were causally

associated with increased cancer risk. This is just one of the numerous Mendelian

randomisation studies which have been conducted [252–256].

3.5.2 Genetic variants as excluded instruments

The genetic variants used in MR analysis may be either SNPs or haplotypes. The

use of both of SNPs and haplotypes is discussed in Sections 3.5.2.1 and 3.5.2.2

respectively.

3.5.2.1 SNPs as excluded instruments

In Mendelian randomisation analyses, the excluded instruments may be one or

more SNPs. This situation is straightforward and can be modelled easily based

on the IV methods described earlier in Section 3.4.2 (please see page 196).

3.5.2.2 Haplotypes as excluded instruments

In Mendelian randomisation analyses, the excluded instruments may be one or

more haplotypes. One advantage of haplotype analysis over single SNP analysis

is the greater statistical power of using haplotypes compared to single SNP analy-

ses [274,275]. In addition, as complex diseases may be caused by multiple variants

in the same gene or genetic region, haplotypes represent the combined effects of

these SNPs (which may not be detected when analysing one SNP at a time).

However, the use of haplotypes in statistical analyses is not as straightforward as

the SNP case, as when an individual is heterozygous at more than one loci, their

haplotype pair, or diplotype, cannot be known with certainty using conventionally

genotyped data.


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As described in Section 1.3.2.3, the EM algorithm can be utilised to calculate the

probability of an individual having certain haplotypes given their genotype set.

In Mendelian randomisation studies, when using haplotypes as instruments, one

analytic option which has been employed by Timpson et al. [257], is to perform

the analysis using the most probable haplotype (i.e. the realisation of the hap-

lotype with the highest posterior probability) for each individual. However, this

may fail to account appropriately for the variability within the data. This would

be particularly troublesome if the analysis was sensitive to the haplotype cho-

sen. Instead, it would be preferable to incorporate this uncertainty directly into

the Mendelian randomisation analysis. The inclusion of haplotype uncertainty

in Mendelian randomisation analysis, is the method employed by the R library

MRsnphap which was created by the author. This is discussed further in Section

3.5.7 (please see page 214).

3.5.3 Mendelian randomisation example

To further clarify the use of Mendelian randomisation analysis for epidemiologi-

cal studies, consider a researcher is investigating the possible causal relationship

between high vitamin D levels and Breslow thickness, such that if there exists a

casual relationship, an intervention which increases vitamin D levels will result in

thinner melanomas excised.

In the first stage of this analysis, an OLS regression model can be fitted, with Bres-

low thickness as the response, vitamin D levels as the main explanatory variable,

or exposure, adjusting for the confounders, age and sex, such that,


Breslow Thickness = β0 + β1Age + β2Sex + β3Vitamin D level.

Having fitted the above linear regression model, a significant negative association

between vitamin D levels and Breslow thickness may be observed, such that high

vitamin D levels are associated with lower Breslow thickness. However, the re-

searcher may also wish to investigate whether this relationship is causal and that

an increase in vitamin D levels will have the effect of reducing Breslow thickness.

It may be suspected that this relationship is not causal, and that both vitamin D

levels and Breslow thickness may be associated with unmeasured confounders, or

that melanomas which have penetrated deeper into the skin (with thicker Breslow

thickness) may be influencing vitamin D levels, such that β3 is correlated with the

error term. If this is the case, vitamin D levels may be treated as an endogenous

regressor (X1).

The next stage of the analysis, and possibly the most vital, is to identify a genetic

variant which is strongly associated with vitamin D levels. One possible genetic

variant may be the rs2282679 SNP on the Vitamin D Receptor gene which has

been shown to be associated with vitamin D levels [278]. Therefore, this SNP may

be used as the excluded instrument (Z1). The rs2282679 SNP may be modelled

additively, and coded as 0, 1 and 2, where 0, 1, and 2 indicates the number of

variant alleles. The confounders age and sex are the included instruments, or the

exogenous regressors (X2 and Z2).


Techniques

This can be modelled using a DAG shown in Figure 3.5.3.1.

Z1 : rs2282679

))))X1 : Vitamin D

**

Ue.g.

healthylifestyle

tt

oo

Y : Breslow thickness

Figure 3.5.3.1: DAG for the Mendelian Randomisation model. Z1:rs2282679, in-

strumental variable; X1:Vitamin D levels, exposure of interest; Y:Breslow thick-

ness, outcome of interest; and U, unmeasured confounders. [Adapted from Lawlor

et al. [243]]

The strength of the excluded instrument-exposure relationship can be tested using

ANOVA to compare the model of vitamin D levels regressed on all instruments,

Vitamin D level = β0 + β1rs2282679 + β2Age + β3Sex,

with the model of vitamin D regressed only on the exogenous regressors,

Vitamin D level = β0 + β2Age + β3Sex.

The F-statistic for the strength of the association between rs2282679 and vita-

min D levels, may be calculated as F = 11.5. As this is > 10, the association

between vitamin D levels and the rs2282679 SNP is considered strong, and so the

Mendelian randomisation analysis may continue.

If the F-statistic calculated from the ANOVA was < 10, the recommendation

would be to not continue with the Mendelian randomisation analysis, as the esti-

mates would likely be biased.


In order to test for overidentification, there must be more instruments than expo-

sures, such that L > K. In this example, k = 3 consisting of k1 = 1 endogenous

regressors and k − k1 = 2 exogenous regressors. There are also l = 3 instruments

consisting of l1 = 1 excluded instruments, l − l1 = 2 included instruments. As

l = k, the analysis is referred to as exactly identified. As such, it is not possible

to test for overidentification.

To test for endogeneity, a Durbin-Wu-Hausman test can be performed. After per-

forming this test, there may be sufficient evidence to reject the null hypothesis

(DWH statistic p-value < 0.05). This would indicate that vitamin D levels are

endogenous and the OLS methods are not efficient, therefore IV methods should

be used. If the DWH p-value > 0.05, vitamin D levels would not be endogenous,

therefore OLS methods should be employed.

Finally, the p-value for the vitamin D/rs2282679 coefficient of the Mendelian ran-

domisation analysis must be interpreted. Interpretation of the analysis should

involve the careful consideration of the OLS and IV coefficients, confidence inter-

vals and p-values. In this case, the OLS and IV coefficients, confidence intervals

and p-values are presented in Table 3.5.3.1.

Model Vitamin D/rs2282679 Coefficient Confidence Interval p-value

OLS 1.05 (1.01, 1.09) 0.01

IV 0.98 (0.95, 1.01) 0.08

Table 3.5.3.1: IV and OLS coefficients and confidence intervals

This difference between OLS and IV coefficient estimates and confidence intervals


Techniques

(along with the DWH test) suggest that the observational epidemiological associ-

ation may be subject to bias. That is, that vitamin D levels may not be causally

associated with Breslow thickness and that the different OLS and IV estimates

may be due to unmeasured confounding or reverse causation. Therefore in this

situation, Mendelian randomisation analysis suggests that a public health inter-

vention to reduce the severity of melanoma by encouraging individuals to increase

their vitamin D levels through sun exposure or diet would not be beneficial.

3.5.4 Benefits of Mendelian randomisation

The use of genetic variants as excluded instruments in Mendelian randomisation

analysis has major advantages. It may help overcome the potential limitations of

observational epidemiological associations which were discussed in Section 3.3.2.1,

namely confounding, reverse causation, selection bias, and regression dilution.

3.5.4.1 Confounding

Exposures may be associated with socioeconomic, behavioural and lifestyle fac-

tors, however individual genetic variants are unlikely a priori to be associated

with these factors at a population level, due to their random transmission at con-

ception. This means that any reported association between a disease outcome and

the genetic variant will generally not be due to confounding.

3.5.4.2 Reverse causation

Presence of, and severity of disease may influence exposures in epidemiological

studies. This may be due to behavioural changes made by individuals, or through

unobserved chemical processes. However, genetic variants will not be influenced

by disease and will remain unchanged regardless of disease status.


3.5.4.3 Selection bias

While individuals may be selected or volunteer for a study dependent on their

exposure and its influence on some disease measure, an individual is unlikely to

volunteer for or be selected either as a case or a control based solely on their

genetic variant.

3.5.4.4 Regression dilution bias

Unlike exposures, genetic variants only need to be measured once as they will

remain unchanged, and if these variants are associated with some exposure, the

genetic variant should be indicative of these exposure levels across the individual’s

lifetime.

3.5.5 Limitations of Mendelian randomisation

Along with the many advantages of Mendelian randomisation analysis, there

are several limitations [233, 243] which need to be considered when undertak-

ing Mendelian randomisation studies. The most significant of these, namely the

identification of a suitable genetic variant known with some certainty to be as-

sociated with the trait of interest, population stratification, and pleiotropy are

discussed in the following section.

3.5.5.1 Identification of suitable genetic variant for exposure

In order to use Mendelian randomisation, there must be a known genetic variant

that is clearly and consistently associated with the trait of interest [7]. Such a

variant may itself be directly functional or may be a marker in strong linkage

disequilibrium with one or more functional genetic variants. Historically, these


Techniques

variants have not been easy to identify, however with the increase in genome-wide

association studies and functional genomic work, more such genetic variants are

becoming available with each passing month [8, 243].

3.5.5.2 Reliable gene associations

As with any genetic epidemiological study, Mendelian randomisation studies re-

quire reliable associations between the genetic variant and exposure and also be-

tween the genetic variant and the disease outcome. Reliability of associations can

be determined by ensuring that the associations have been replicated in several

independent studies, and should also be confirmed within the current Mendelian

randomisation study [243]. However, genetic association studies are renowned

for producing statistical associations that are not able to be subsequently repli-

cated. In fact, an examination of 301 published studies of 25 different reported

associations found less than half of these associations had strong evidence of repli-

cation [259]. This lack of replication may be due to publication bias, small power

to detect associations and the reporting of spurious associations.

3.5.5.3 Population stratification

Population stratification occurs when a population consists of subgroups who have

systematic differences in disease rates and allele frequencies [224]. This can result

in associations between genetic variants and disease in the whole population which

are related to, and confounded, by ethnicity. This may result in confounding in a

Mendelian randomisation study, particularly when gene-exposure associations are

performed in different study populations to the gene-outcome associations.


3.5.5.4 Pleiotropy

Mendelian randomisation analysis assumes that the genetic variant is associated

with the disease outcome only through the exposure. It is possible that this genetic

variant is pleiotropic and actually effects multiple traits. However, this is only a

problem if one of these additionally affected traits influence the outcome [243].

For example, in the vitamin D levels and Breslow thickness example discussed

earlier: if the rs2282679 SNP affects not only vitamin D levels, but also skin

pigmentation, then skin pigmentation may also affect Breslow thickness. This

would be violating the assumption (Section 3.4.2.1) that rs2282679 affects Breslow

thickness only through vitamin D levels. However, if rs2282679 is pleiotropic and

also affects height, it is unlikely that Breslow thickness will be affected by this.

3.5.6 Current software for Mendelian randomisation analysis

To the author’s knowledge, there are two software packages currently available

for Mendelian randomisation analysis; however neither were designed specifically

for use in the epidemiological framework. IVREG2 is a comprehensive module

for Stata which can perform Mendelian randomisation analysis using SNPs and

known haplotypes as the excluded instrument [242]. However, IVREG2 is only

able to use the most probable haplotype for each individual, which as discussed

in Section 3.5.2.2 may introduce bias. IVREG2 also performs comprehensive di-

agnostic tests described in Section 3.4.3 (please see page 199).

TSLS is a function in the R library sem [260] which, like IVREG2, can perform

Mendelian randomisation analysis using SNPs and the most probable haplotype

for each individual as excluded instruments. However, this function does not per-


Techniques

form any of the diagnostic tests described in Section 3.4.3, which are integral in

interpreting the results of the Mendelian randomisation analysis.

3.5.7 Software implementation

As there are no software packages available which model haplotype uncertainty in

Mendelian randomisation studies, the author designed a library within the soft-

ware package R [261], MRsnphap. MRsnphap has been designed to work within

the current SimHap [262] framework. SimHap is a statistical analysis program

designed to analyse associations between SNPs and haplotypes and various out-

comes. Two functions are available in MRsnphap – these are mrsnp.quant which

models Mendelian randomisation studies with the SNP as the excluded instru-

ment, and mrhap.quant which models these studies using phase-ambiguous hap-

lotypes. These functions also perform the testing described in Section 3.4.3, which

includes a test of overidentification, a test of endogeneity and an F-test for weak

instruments.

The methods employed in MRsnphap for Mendelian randomisation analyses are

described further in Sections 3.5.7.1 and 3.5.7.2 respectively.

3.5.7.1 Using SNPs as instruments: mrsnp.quant

The R function mrsnp.quant enables the user to perform Mendelian randomisa-

tion analysis, with a SNP as the excluded instrument. The mrsnp.quant function

is able to model continuous outcomes and exposures, while the SNP may be mod-

elled under additive, dominant or recessive assumptions, using a generalised linear


model.

3.5.7.1.1 Example: mrsnp.quant

Referring back to the Mendelian randomisation example in Section 3.5.3 (please

see page 206), a researcher is interested in determining if there exists a causal

association between Breslow thickness and vitamin D levels. As they believe vita-

min D is an endogenous regressor, they use the rs2282679 SNP as an instrument

for vitamin D levels.

In order to use mrsnp.quant function, the MRsnphap library must be loaded.

When loading the MRsnphap library, several other libraries, including the SimHap

library are loaded simultaneously. In addition, the SimHap function SNP2Geno

must be used to generate the necessary SNP file. The code to load these pack-

ages, and run the necessary SimHap functions are included in the MRsnphap

manual in Appendix I.

The appropriate R input is:

mymodel < − mrsnp.quant(outcome = “BT”, exposure= “VitD”, covariates =

“AGE”, geneticmodel = “rs2282679 add”, geno=geno.dat, pheno=pheno.dat),

where BT (Breslow thickness) is a continuous outcome, VitD (vitamin D) is the

endogenous regressor and age is the exogenous regressor. The genetic model is

the rs2282679 SNP modelled additively, taking the values 0, 1 or 2, where these

indicate the number of variant alleles. The files containing the genotypic and


Techniques

phenotypic data are geno.dat and pheno.dat, respectively.

The output produced by mrsnp.quant can be seen in Appendix J.

3.5.7.2 Using haplotypes as instruments: mrhap.quant

The mrhap.quant function enables the user to perform Mendelian randomisa-

tion analysis, with a haplotype as the excluded instrument. Like mrsnp.quant,

mrhap.quant models continuous outcomes and exposures, and the haplotype can

be modelled as either additive, dominant or recessive. The mrhap.quant func-

tion uses the same maximum likelihood method as SimHap to infer diplotypes

for phase-ambiguous individuals; that is, for individuals whose diplotypes are not

known for certain. Each phase-ambiguous individual is assigned possible diplo-

types consisting of two haplotypes, where each haplotype has a probability at-

tached to it derived from the EM algorithm. This is because for phase-ambiguous

individuals, it is not clear which haplotypes each individual has within their diplo-

type. All that can be calculated (through the EM algorithm) is the probability of

an individual having certain haplotypes given their genotype set. As these hap-

lotypes occur with a probability and are not known for certain, this uncertainty

can be included within the modelling framework.

In SimHap, this is done by simulating data sets in which individuals are assigned

haplotypes with the appropriate probabilities. Particular statistical models are

then fitted to each simulated data set and the results are aggregated.

For example, in mrhap.quant, a Mendelian randomisation model is fitted to each


simulated data set and coefficients and p-values for the Mendelian randomisa-

tion analysis are averaged. In addition, the mean F-statistic is presented for the

test for weak instruments, and the mean p-values are presented for the tests for

endogeneity and overidentification.

3.5.7.2.1 Example: mrhap.quant

The mrsnp.quant example described in Section 3.5.7.1.1 can be extended to in-

clude haplotypes in mrhap.quant. The researcher is interested in determining if

there exists a causal association between increased Breslow thickness and lower

vitamin D levels, and wishes to use a haplotype to test this. They choose a haplo-

type of several SNPs from the Vitamin D Receptor gene, CCTG, which has been

found to be associated with vitamin D levels.

As before, to use the mrhap.quant function, the MRsnphap library must first

be loaded. In addition, the SimHap functions SNP2Haplo, infer.haplos and

make.haplos must be used to generate the necessary haplotype files. The code

to load these packages, and run the necessary SimHap functions are included in

the MRsnphap manual in Appendix I.

The appropriate R input is:

mymodel < − mrhap.quant(outcome = “BT”, exposure= “VitD”, covariates

= “AGE”, geneticmodel = “CCTG”, haplo=myhaplo.dat, effect=“add”, sim=

1000, pheno=pheno.dat),


Techniques

where BT, VitD, and age are as described earlier. The genetic model is a haplo-

type modelled as an additive effect. In this example, the Mendelian randomisation

is simulated 1000 times. The files containing the haplotype and phenotypic data

are myhaplo.dat and pheno.dat, respectively.

The output produced by mrhap.quant can be seen in Appendix K.

3.5.8 Summary

This chapter has described an overview of epidemiological studies, and their failure

to identify causal associations between disease outcomes and modifiable exposures.

IV methods were proposed as a method to infer causality in epidemiological stud-

ies when the use of RCTs are not possible. An application of this technique –

Mendelian randomisation – was also proposed, and the benefits and limitations

of this technique were described. The remainder of this chapter described the

implementation of a novel extension to MR –the inclusion of imputed haplotypes

and the construction of an R library, MRsnphap, which performs Mendelian ran-

domisation using both SNPs and haplotypes as instruments.

219CHAPTER 4

Summary and Suggestions for Further Research

This final chapter provides a discussion of the findings of this thesis, and suggests

areas for further research.

This thesis has described the establishment of a population-based collection of

melanoma cases in Western Australia. The WAMHS facilitated investigation into

the genetic epidemiology of both melanoma susceptibility and prognosis. In addi-

tion, this thesis described the implementation of Mendelian randomisation tech-

niques in an R library, MRsnphap, which can be used to infer causal associations

in epidemiological studies.

4.1 The Western Australian Melanoma Health Study

The WAMHS was established with the aim of investigating the genetic epidemi-

ology of melanoma susceptibility and prognosis. The WAMHS is one the single

largest population-based studies of melanoma cases in the world, and the analysis

in this thesis has shown that the WAMHS is a valuable resource for investigating

genetic risk factors associated with melanoma susceptibility and progression.

The results of the association analyses between candidate genetic variants and

melanoma susceptibility replicated some of the associations identified by earlier

studies. However, not all associations were replicated in the WAMHS. This may

be due to one of four reasons: (1) the initial associations were spurious; (2) the

genetic variants associated with melanoma in other populations are not the same

as in the WAMHS; (3) the WAMHS sample (n=800) was underpowered to detect

these associations; or (4) the controls used in these analyses were not suitable. The

220 Chapter 4. Summary and Suggestions for Further Research

latter reason may be particularly relevant in the use of HapMap controls. Several

SNPs were also relatively rare (MAF < 0.15), which may have contributed to

low power as described in (3). As such, further investigation of these associations

in a larger WAMHS sample would help elucidate the potential role these genetic

variants have in melanoma susceptibility in the Western Australian population.

The analysis of Breslow thickness, as a surrogate for melanoma prognosis, pro-

vided evidence for novel associations between genetic variants and Breslow thick-

ness, which had not been widely studied previously. In particular, the current

study was the first of its kind to test the hypothesis that genetic variants which

predispose an individual to melanoma, are also associated with a poorer prog-

nosis. These associations, however, did not remain significant after adjustment

for multiple testing. This may have been due to two reasons: (1) the associa-

tions were spurious; or (2) this study had low statistical power to detect small

differences in Breslow thickness due to low MAF or small sample size (n=800).

As such, replication in larger samples is required. If the associations observed

in the current study are replicated, this may lead to an improved understanding

of the genetic mechanisms underlying both melanoma susceptibility and prognosis.

However, further studies should not be restricted only to testing associations be-

tween Breslow thickness and genetic variants which are associated with melanoma

susceptibility. While the analyses presented in this thesis provide some evidence

for the association of genetic variants with both increased melanoma susceptibility

and increased Breslow thickness, it is probable that other genetic variants exist

which affect only Breslow thickness. A GWAS of Breslow thickness would allow

an unbiased approach in identifying genetic variants associated with melanoma


prognosis, however this would need to be performed on a large sample for suffi-

cient power.

The analyses presented in this thesis identified genetic variants which were associ-

ated with melanoma susceptibility and prognosis in the WAMHS. However, these

analyses had several limitations. The WAMHS sample consisted of only Western

Australian, adult, Caucasian individuals diagnosed with melanoma, and therefore

these results are generalisable only to Western Australian, adult, Caucasian indi-

viduals. In addition, the WAMHS sample used in this thesis was small (n=800),

therefore it would be preferable to attempt to replicate these associations in larger

samples. Furthermore, these analyses identified genetic variants associated with

melanoma susceptibility and prognosis, however these variants may not be causal,

and may only be markers for causal variants. Functional genetic investigations

are required to determine whether these associations are causal, and this may also

lead to an enhanced understanding of melanoma susceptibility and prognosis.

4.2 Mendelian Randomisation

Mendelian randomisation has been shown to overcome some of the problems with

observational epidemiological studies, by its ability to identify causal relationships.

The statistical methodological work to model imputed haplotypes in Mendelian

randomisation analyses is, to the best of my knowledge, novel. Prior to the imple-

mentation of Mendelian randomisation methods in the R library MRsnphap, to

the author’s knowledge, no software designed specifically for use in the setting of

genetic association analysis was available to perform these analyses. As such, the

uncertainty surrounding haplotype assignment had not previously been able to be

included in Mendelian randomisation analyses. The development of MRsnphap


will therefore allow researchers to perform Mendelian randomisation analyses us-

ing both SNPs and imputed haplotypes as instruments.

The MRsnphap library does have some limitations, and further development of

MRsnphap would make it more useful to epidemiological researchers. Possible

future developments of MRsnphap are described in the following section.

4.2.1 Future development of MRsnphap

In the current release of MRsnphap, the functions mrsnp.quant and mrhap.quant

assume conditional homoskedasticity of the errors. However, it is possible, partic-

ularly for family studies and longitudinal studies, that heteroskedasticity will be

present. As such, the methods described in Section 3.4.2 will no longer be valid;

the IV coefficient estimate will be unaffected by heteroskedasticity, however the

standard error estimates will be inconsistent [264] and therefore tests of statistical

significance will be invalid. Additionally, diagnostic tests, including the test for

endogeneity and overidentification will be affected.

Therefore, heteroskedasticity-consistent standard errors and statistics need to be

calculated. One method for this is Generalised Methods of Moments (GMM). In

fact, IV and OLS are special cases of GMM estimation. GMM is robust to het-

eroskedasticity, however its use does require very large samples [264]. Therefore,

a later release of MRsnphap could be developed to allow for both homoskedastic

and heteroskedastic errors, including the Pagan and Hall test [265] to determine

if heteroskedasticity is in fact present.


Currently Mendelian randomisation analysis in MRsnphap requires the outcome

to be continuous, otherwise Assumption 4 in Section 3.4.2.1 will be violated. To

date, no methods have been published that allow the inclusion of binary outcomes,

however when available, these could be incorporated into MRsnphap. This would

allow for Mendelian randomisation analysis to determine causal associations be-

tween both binary and continuous measures of disease.

MRsnphap could also be extended to include imputed SNP data in Mendelian

randomisation studies that has been generated from GWAS. One commonly used

imputation program is IMPUTE [276]. After running the IMPUTE imputation

procedure, an output file is created which contains three probabilities for each

individual at each SNP. These probabilities represent the probability of each in-

dividual having each of the three genotypes, i.e. individual 1 at SNP A may have

the probabilities 0.2, 0.5, and 0.3 which represent the probability of having the

AG, GG, and GG genotypes, respectively. These probabilities could be used in

MRsnphap using the same modelling framework which is currently used for hap-

lotype analysis.

Some limitations of Mendelian randomisation described earlier in Section 3.5.5,

such as population stratification and pleiotropy, may be reduced by the use of

multiple instruments. That is, the use of two or more genetic variants which are

both associated with the exposure. At the current time, methods to incorporate

multiple genetic variants in Mendelian randomisation analysis are not available.

However, when available, these methods could be incorporated into MRsnphap.


4.3 Conclusion

The aims of this thesis, as described in Chapter 1, were to establish the WAMHS

database and to use these data to investigate the environmental, host and genetic

risk factors associated with melanoma susceptibility and prognosis. The main

hypothesis of these analyses was that factors associated with increased melanoma

susceptibility are also associated with poorer melanoma prognosis. One genetic

variant on the IRF4 gene was associated with both increased melanoma risk, and

thicker melanomas, resulting in a poorer melanoma prognosis.

In addition, this thesis aimed to extend a statistical technique for modelling causal

associations in epidemiological studies, known as Mendelian randomisation, to in-

clude the use of imputed haplotypes as instruments, and to implement this tech-

nique for use by other researchers in a library for R, MRsnphap.

The collection and use of high-quality genetic and epidemiological data is integral

in identifying risk factors for disease susceptibility and prognosis. The discovery

of genetic variants associated with melanoma susceptibility has the potential to

identify individuals who are high risk genetically of developing melanoma. These

identified individuals would then be able to modify their behaviour, for example,

by limiting their sun exposure, with the hope that the disease does not develop.

Similarly, the discovery of genetic variants associated with melanoma prognosis

may help identify individuals at increased risk of poorer melanoma prognosis,

and may also potentially lead to the development of new treatments. The de-

velopment of the R library MRsnphap may enable future melanoma (and other

disease) researchers to determine if environmental risk factors are causally asso-

4.3. Conclusion 225

ciated with disease. These lifestyle and behavioural risk factors for disease may

then be modified in order to reduce the impact of disease in the community.

227

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271APPENDIX A

Letter to Doctor

Western Australian Cancer Registry Information Collection and Management, DOH (WA)

1st floor C Block, 189 Royal St EAST PERTH WA 6004

Ph. 08 9222 4022 Fax 08 9222 4236 Website: http://www.health.wa.gov.au/wacr

e-mail: [email protected]

«DOCNAME» «Doctor_Mail_Add1» «Doctor_Mail_Add2» «Doctor_Mail_Add3» «Doctor_Mail_Town» «Doctor_Mail_PCode»

Dear Dr «DOCSNAM»

I am writing to you about the Western Australian Melanoma Health Study (WAMHS) which has recently been established in order to develop a database of information and blood specimens to assist in research into this disease, which has significant mortality and morbidity.

The researchers wish to approach persons diagnosed with melanoma, via the Western Australian Cancer Registry (WACR), to seek their consent and participation. Participation involves the patient completing a questionnaire containing health and lifestyle information, as well as donating a blood sample for storage and use in medical research.

I am attaching details of persons whom WACR information suggests were your patients. I would appreciate it if you would indicate on the list whether there is any reason not to approach particular people about this project and return it to us in the reply paid envelope provided. If we do not receive a reply from you within four weeks, we will assume that you have no concerns about the researchers contacting any of the people listed.

Should you have any questions or would like further information about the WACR, please do not hesitate to contact me using the details above. For more information about the Study, please contact the WAMHS study coordinator, Ms Sarah Ward, on (08) 9346 1119.

This study has been approved by The University of Western Australia Ethics Committee and the Confidentiality of Health Information Committee of the Department of Health.

Yours sincerely,

Dr Timothy Threlfall Principal Medical Officer/Manager

<<Date>>

273APPENDIX B

Doctor Information Sheet

1

WESTERN AUSTRALIAN MELANOMA

HEALTH STUDY

Information for Doctors

BACKGROUND AND PURPOSE

Researchers from the Centre for Genetic Epidemiology and Biostatistics at The University of Western Australia have established the Western Australian Melanoma Health Study (WAMHS) in an effort to investigate the genetic and environmental factors that may cause malignant melanoma. This study has been approved by the University of Western Australia’s Human Research Ethics Committee and the Department of Health’s Human Research Ethics Committee (formerly Confidentiality of Health Information Committee).

Your patient/s are being asked to take part in this study because they have recently been diagnosed with melanoma, and have had this noted in the Western Australian Cancer Registry (WACR). We are asking all patients who have been diagnosed with melanoma in Western Australia to consider participating in this study.

Taking part in this study is completely voluntary. This information sheet will give you information regarding the study that your patient/s are being invited to participate in.

WHAT DO YOU HAVE TO DO?

You are asked to indicate on the enclosed list of your patients that have been diagnosed with malignant melanoma, if there is any reason, medical or other, not to approach particular patients about this project. You are then asked to return the list to the WACR in the reply paid envelope provided. Those patients that you consider suitable to participate in the study will be contacted by the WACR and can then decide if they wish to participate.

How will consent be handled? Once approval to contact the patient has been indicated by you, information about the WAMHS and a consent form will be sent directly to them by the WACR, on behalf of the WAMHS research staff. They will have ample time to discuss their decision with you, their family or WAMHS research staff and will be asked to return the consent form indicating their decision either way to the WACR. If the patient consents to participate in the study, the WACR will notify the WAMHS research staff.

WHAT IS THE PATIENT GIVING CONSENT FOR?

If patients choose to take part in the study, they will be asked to:

• Complete a questionnaire: This would involve the patient answering questions on their health, lifestyle, background and family history. The questionnaire will be conducted as a telephone interview and will take approximately 45 minutes.

• Provide a blood sample: This would be collected for serum and DNA at a PathWest collection centre, with the option of providing an additional sample for RNA.

2

• Health information access: In addition we will ask for consent to access health information kept about the patient. This information may come from hospital case notes, GP records or may be information kept about them by the Department of Health as part of its regular function.

DOES THE PATIENT HAVE TO TAKE PART IN THE STUDY?

No. Participation in this study is entirely up to the individual and their care will not be affected in any way by their decision. We will not inform you as the treating doctor whether your patient chose to participate. Patients can also withdraw their consent at any time by communicating this to WAMHS staff in writing.

WHO CAN ANSWER QUESTIONS ABOUT THE STUDY?

If you or your patients have any concerns or questions about the Western Australian Melanoma Health Study, please contact the WAMHS staff:

Website: http://www.wamhs.org.au

Phone: (08) 6488 6753 or 1800 145 511 (Sarah Ward - Study Coordinator)

Mail: Western Australian Melanoma Health Study Centre for Genetic Epidemiology and Biostatistics

The University of Western Australia M409, 35 Stirling Highway CRAWLEY WA 6009

OTHER ISSUES

If you or your patients have any queries about the Western Australian Cancer Registry, please contact the registry directly:

Mail: Dr. Timothy Threlfall Principal Medical Officer and Manager Western Australia Cancer Registry Department of Health (WA) 1st Floor C Block, 189 Royal Street East Perth WA 6004

Phone: (08) 9222 4022

If you or your patients have any complaints about any aspect of the study, or any questions about your patients’ rights as participants, then you may contact this committee:

Mail: Secretary, Human Research Ethics Committee Registrar’s Office - University of Western Australia 35 Stirling Highway Crawley WA 6009

Phone: (08) 6488 3703

275APPENDIX C

Letter to Patient

«crn»

Western Australian Cancer Registry Information Collection and Management, DOH (WA)

1st floor C Block, 189 Royal St EAST PERTH WA 6004

Ph. 08 9222 4022 Fax 08 9222 4236 Website: http://www.health.wa.gov.au/wacr

e-mail: [email protected]

«title» «FORENAME1» «FORENAME2» «SURNAME» «Person_Mail_Address» «Person_Mail_Town» WA «Person_Mail_PostCode»

Dear «title» «SURNAME» I am writing to ask for your assistance in an important state wide research project, interested in identifying the factors that cause malignant melanoma. This study is called the Western Australian Melanoma Health Study (WAMHS), and is being conducted by researchers at the Centre for Genetic Epidemiology and Biostatistics at The University of Western Australia. Currently, melanoma is the third most common form of cancer in Australia. The researchers wish to collect a blood sample and information on health, lifestyle and family history from people recently diagnosed with melanoma. Please find an information brochure about the study enclosed. Your name has been notified to the Western Australian Cancer Registry (WACR) as a result of medical tests, as required by law, and I am writing to invite you to participate in this study (please also find an information brochure about the WACR enclosed). If we could identify your doctor, we have contacted them as a courtesy prior to writing to you. However, your doctor will not be notified as to whether you chose to participate in this study or not. Further, no personal information will be given to any researchers without your consent. If you would like to participate in this study, I would be grateful if you would read and complete the enclosed consent form and post it back to me, using the reply paid envelope provided. No stamp is required (free postage). An extra copy of the consent form is also enclosed for you to keep for your records. If you do agree to participate, I will forward your name and consent to the WAMHS team at the Centre for Genetic Epidemiology and Biostatistics. They will then send you a questionnaire and a blood collection form to take with you if you have a blood sample taken. I will send a reminder letter about this study in 2 weeks, as it is most important to the success of the research that we get information from everyone we contact about whether they want to take part or not. If you do NOT wish to participate, you can indicate this on the consent form, so I can avoid sending you another letter and your name will NOT be given to the researchers. I appreciate your time in considering this request, and would be happy to discuss any problems or other WACR matters with you. Alternatively, you can call the Study Coordinator, Ms Sarah Ward, on 1800 145 511 (free-call number) or (08) 9346 1119 about the study itself. Yours sincerely,

Dr Timothy Threlfall Principal Medical Officer/Manager

«DATE»

277APPENDIX D

Patient Information Brochure

Participant Information Brochure

What is the purpose of this study?

Researchers at the Centre for Genetic Epidemiology and Biostatistics

(CGEB) at the University of Western Australia are investigating why people get malignant melanoma, a type of skin cancer, as part of the Western

Australian Melanoma Health Study (WAMHS).

Causes of melanoma include genetic (hereditary factors passed on to you

from your parents) and environmental factors (such as lifestyle differences

between people).

You are being asked to take part in this study because you have recently

been diagnosed with melanoma, and this has been notified to the Western Australian Cancer Registry (WACR). All patients who have been diagnosed

with malignant melanoma in Western Australia are being asked if they would

like to be a part of this study.

This information brochure will give you information to help you decide

whether or not you would like to be a part of the study. Please read this

carefully and discuss with members of your family and friends, as necessary.

Do I have to take part in this study?

No. Taking part in this study is completely voluntary and entirely up to you. Your decision will not be revealed to your doctor and will not affect your care

in any way.

What does this study involve?

There are three parts to this study:

1. Questionnaire (necessary)

2. Blood samples (two options)

a. One type of blood sample (DNA and serum)

b. Two types of blood samples (DNA, serum and RNA)

3. Health information access

You may participate in one part, more than one part, or all parts. However, if you participate in any parts of the study, you will need to

complete the questionnaire (Part 1) so that researchers can better

understand any environmental factors that are relevant to you.

1

Part 1. Questionnaire

What is this for?

The questionnaire provides information about your health and lifestyle, your

background and family history, and your melanoma. This information will help

us to understand environmental factors that cause melanoma.

What does this involve?

The questionnaire will be completed in a telephone interview with WAMHS

staff at a time convenient to you. This should take approximately 45 minutes.

Before the interview, some questions will be sent to you, so that you can

complete them beforehand (e.g. height) and have the answers ready to give

the interviewer.

What will be done with this information?

This information will be used by medical researchers interested in

environmental or lifestyle factors which may be involved in melanoma, e.g.

sun exposure. In addition, information regarding your family history will help

to see if melanoma runs in families.

Part 2. Blood Samples

What is this for?

Blood samples are very important to help in understanding malignant

melanoma. Specifically, blood samples can be used to identify possible

causes, improve diagnosis and potentially lead to new treatments. Your

sample will greatly assist melanoma research in the following ways:

1. A number of blood tests are currently performed to detect certain types

of disease, such as the cholesterol test for high cholesterol or the blood

sugar test for diabetes.

New tests are currently being developed for other forms of skin cancer

(basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)). Your

blood sample would be very valuable in helping to evaluate new tests

that may be able to be used for melanoma.

2. Genes or DNA carry the information in your body that determines many

2

of your characteristics, including the colour of your hair or eyes. RNA is

produced from genes and in turn makes all the proteins in your body.

Your DNA and RNA will be used in research looking at the genes that

affect melanoma.

Some researchers may wish to perform tests to see if you are carrying

genes that might predict risk of melanoma or that may be involved in

whether or not you respond to a particular type of therapy.


There are two options for the type of blood sample that you can donate:

a. One type of blood sample (DNA and serum)

This blood sample will provide a sample of your DNA (along with serum - the

proteins and chemicals in your blood).

If you would only like to donate this blood sample, it can be collected at any

PathWest centre in Western Australia (both metropolitan and regional

centres).

You will be asked to give approximately 30ml (about 2 and a half

tablespoons) of blood with this option.

b. Two types of blood samples (DNA, serum and RNA)

An extra blood sample is needed to collect RNA. This is collected at the

same time as the sample for DNA and serum, as part of the same blood test.

As a special tube is used to collect the RNA, it can only be done at certain

PathWest centres.

If you would like to donate both types of blood samples, this will be collected

at the PathWest centre at QEII Medical Centre in Nedlands, Perth.

You will be asked to give approximately 35ml (about 3 tablespoons) of blood

with this option.


Serum, DNA and RNA will be extracted from your blood sample and stored in

small plastic tubes in a secure freezer located at the Western Australian DNA

Bank (WADB), within the CGEB.

3

The samples will be used by medical researchers in biochemical and genetic

studies of melanoma and other types of skin cancer such as basal cell

carcinoma (BCC) and squamous cell carcinoma (SCC).

These types of skin cancer occur more often than melanoma but are not as

dangerous, as they are less likely to spread to other parts of the body or

spread as quickly. All three types of skin cancer develop in different types of

cells in the skin and differ in their appearance.

Your blood will not be used for research that involves reproductive

cloning. Researchers will not be permitted to work out a genetic profile of

you as an individual.

With your permission, we would like to store this blood indefinitely for future

research into skin cancer. As such, your blood will be stored until it has all

been used, unless you contact the WAMHS to request its destruction

(contact details on the back page of this brochure).

What are the future implications of donating blood?

Feedback to participants

Your donated samples are not intended to be used in your diagnosis or

treatment. Feedback will not normally be given to participants regarding

personal genetic information. This is because melanoma is not thought to

be caused by just one gene (e.g. like cystic fibrosis is), but a complex

disease with many genes contributing to its development.

These genes will have small effects by themselves for each individual and

will generally combine together with other factors (such as other genes, the

environment, or lifestyle factors) to increase disease risk.

The disease risk attributable to each factor will be small and we do not yet

know exactly how all these elements interact, so releasing genetic

information would not be clinically meaningful or useful to you or your family.

However, it is possible that future DNA testing may result in new information

about diseases or potential diseases that you carry. Such information will

require extensive testing and validation before it can be determined to be

clinically useful but some of this information may have health implications for

yourself and your family, including your descendants.

4

Contact with you about any potentially important findings will only be made

where there is clear evidence of the medical importance to you or your

family. Each situation will be carefully reviewed by a Human Research Ethics

Committee before you are informed of the availability of results.

At that time, you may choose whether you would like to receive information

and what further information and tests you would like to proceed with.

Information will be provided through approved medical channels. Please note

it is your responsibility to keep the WAMHS up to date with your contact

details.

Disclosure to family members

You should consider whether or not any significant health information

obtained as a result of DNA testing should be disclosed to your family

members or descendents in the future. We will ask whether you consent to

the disclosure of your information to your family members or descendents (on

the attached consent form).

Family members would have to supply a written request to the Chief

Investigator and a Human Research Ethics Committee would review and

consider the request to disclose information.

Part 3. Health Information Access

What is this for?

The discovery of factors important in understanding disease may also require

additional knowledge of other relevant medical information about you,

beyond what is already stored in the WACR in the Department of Health of

Western Australia .


We will ask for your consent for the WAMHS to access medical information

kept about you that is relevant to medical research, for example specific

information about other illnesses you may have had in the past. This

information may come from hospital case notes, GP records or from the

Department of Health as part of its routine data collection.


This information will be used for research into melanoma and related health

areas.

5

281APPENDIX E

Patient Consent Form

1

WESTERN AUSTRALIAN MELANOMA HEALTH STUDY

CONSENT FORM I have read the information given to me and have had all of my questions answered. I have also been given a copy of the:

• Information Brochure entitled “DNA (Genetic) Testing and Storing”;

• Patient Information Brochure for the Western Australian Melanoma Health Study and

• Consent Form for my records

There are 8 parts to this consent form, Parts A to H. If you WOULD LIKE to participate in the WA Melanoma Health Study, please complete parts A to H of this consent form including completing and signing the declaration in Part H. You may consent to any or all parts of the study. However, it is necessary to consent to participate in the questionnaire (Part A) in order to consent to participate in the other parts. If you WOULD NOT LIKE to participate in the WA Melanoma Health Study, please go straight to Part H: Declaration, tick the box stating that you do not agree to participate and sign the form.

Part A: Consent to participate in questionnaire

I consent to complete a questionnaire about the lifestyle and environmental factors associated with melanoma. The questionnaire will be completed by phone interview at a time convenient to me. Some questions will be mailed to me before the interview, so that I can complete them beforehand and have the answers ready at the time of the interview (e.g. height).

I Do Do Not (Please continue below) (Please go to Part H)

Part B: Consent for Blood Sample and Storage for Medical Research

I consent to blood being taken and donate that blood absolutely for testing and research into melanoma and related health areas. In making my donation of blood, I understand and agree that: (a) the blood (which in this consent form includes its constituents and any genetic material or any

cell lines derived from the blood) will be stored and used for research into melanoma and related health areas;

(b) storage of blood samples will be conducted in accordance with the National Health & Medical Research Council’s1* Guidelines for Genetic Registers and Associated Genetic Material. Storage will be conducted under a coded system, to ensure that my confidentiality is maintained;

(c) samples of blood held will be discarded upon my written request;

1 *The National Health & Medical Research Council advises the Australian community and Commonwealth and the

State Governments on standards of individual and public health, and supports research to improve these standards.

2

(d) access to my blood donation for research will be managed by an Advisory Committee and only released where the research project that wishes to use my blood donation has been approved by a Human Research Ethics Committee;

(e) feedback will not normally be given to me regarding my personal genetic research information as these results would not be clinically meaningful or useful to me;

(f) I can ask to know more specific details of any studies that used my blood sample by contacting the WA Melanoma Health Study;

(g) the results of the research may be of interest to my immediate family, including my descendants, and I may decide whether or not the information may be disclosed to my family in accordance with ‘Options for disclosure to family members’ detailed in Part C;

(h) international research collaborations using my blood will only take place where researchers abide by equal or more stringent regulations of privacy and ethics as those in Australia, as assessed by a Human Research Ethics Committee;

(i) the Chief Investigator of this project and his or her associates involved with this project as well as The University of Western Australia will not be liable for any loss or damage to the blood/tissue taken or used in accordance with this form.

I consent to the collection of a blood sample for DNA extraction and biochemical analyses (from serum) and understand that this donation is given under the conditions (a) to (i) stated above. I understand that this sample may be taken at any PathWest centre in WA.

I Do Do Not (Please continue below) (Please go to Part C)

IN ADDITION:

I also consent to the collection of an additional blood sample for RNA to be taken at the same time as the sample for DNA and serum is collected. I understand that this donation is also given under the conditions (a) to (i) stated above. I understand that this sample must be taken at the QE-II Medical Centre, Nedlands.

I Do Do Not (Please go to Part C) (Please go to Part C)

Part C: Options for disclosure to family members

Please only complete this section if you have consented to any part of Part B.

I understand that future DNA testing may result in new information about diseases or potential diseases that may have health implications for my family, including my descendants. I understand that extensive testing and validation would be required before the information can be determined to be useful and that information can only be provided through approved medical channels when there is clear evidence of the medical importance to my family.

I consent to genetic information obtained from my DNA, as a result of this study, being revealed to my family members, upon their written request to the Chief Investigator, in the event of my death.

I Do Do Not (Please go to Part D) (Please go to Part D)

N.B: Part D is no longer applicable

3

Part E: Consent to access health information

I consent to allow access to health information about me relevant to melanoma-related research, such as is kept in a medical record or by the WA Department of Health, to assist medical research only where the research proposal has been approved by a Human Research Ethics Committee.

I Do Do Not (Please go to Part F) (Please go to Part F)

Part F: Future clinical trials for new treatments for melanoma

I consent to allow WAMHS staff to contact me in the future if there are any new treatments for melanoma which I may be suitable for (for example a clinical trial for a new drug or other treatment).

I Do Do Not (Please go to Part G) (Please go to Part G)

Part G: General

I understand that: (a) If a researcher wishes to obtain additional information or samples from me, my name will not be

divulged to that researcher without my written permission; (b) Any research results about me, and the fact that I have taken part in this study, will not be

revealed to any third party without my written consent, except if required by law; (c) The researchers will not reveal my identity and personal information about this project if

published in any public form; (d) I will not receive, or be entitled to, any reward or remuneration for providing my blood sample for

this project. I understand that the sample being donated could be used for commercial development and acknowledge the public interest in the research and donate the sample absolutely;

(e) I understand the potential benefits and risks involved in taking part in this study which have been

explained to me and I accept the risks involved. I have had the opportunity to ask questions and am satisfied with the explanation and the answers to my questions;

(f) I may withdraw from the study at any time, no questions asked, and without it negatively

affecting my future medical treatment; (g) If I choose to withdraw from the study, I understand that any information about me already

collected by the researchers will be retained unless I request otherwise in writing; (h) I understand that my information will only be collected and used for the study of melanoma and

related conditions. Related conditions are defined as other types of skin cancer; basal cell carcinoma (BCC) and squamous cell carcinoma (SCC).

4

Part H: Declaration

(Please tick the option that applies)

I agree to participate in the WA Melanoma Health Study and consent to the parts of the study that I have indicated in Part A through F. I have also read and understood Part G.

OR

I do not agree to participate in the WA Melanoma Health Study. Please sign:

……………………………... …………...………… …………………...

Name of Participant Signature Date ……………………………... …………...………… …………………...

Name of Witness Signature Date

The Human Research Ethics Committee at The University of Western Australia requires that all participants are informed that, if they have any complaint regarding the manner in which a research project is conducted, it may be given to the researcher or, alternatively, to the Secretary, Human Research Ethics Committee, Registrar’s Office, The University of Western Australia, Nedlands, WA 6009 (telephone number 6488-3703, fax number 6488-3861, email [email protected]). Name:

Address: CRN:

5

IF YOU HAVE CONSENTED TO PARTICIPATE

PLEASE COMPLETE THE FOLLOWING:

QUESTIONNAIRE INTERVIEW TIMES The questionnaire part of the study is completed by telephone interview. Could you please place a √ next to the day you would prefer us to call you for this interview?

Monday Tuesday Wednesday Thursday Friday

Could you please place a √ next to the time you would prefer us to call you for this interview:

Morning (9am-12pm) Afternoon (12pm-5pm) Evening (5pm – 8pm)

We will try to contact you on your home number first to complete the interview. If we are unable to contact you on this number, could you please place a √ next to the phone number you would prefer us to try contacting you on.

Work phone Mobile

CONTACT DETAILS

Please complete the following details: Home Phone Number Work Phone Number

Mobile Number

Please check the following details that we have for you and amend any that are incorrect. Name: Address: Date of Birth:

285APPENDIX F

Mole-Counting Chart

MOLE COUNTING CHART This chart will help you to complete Section D of the questionnaire

What are moles? Moles are collections of the skin’s “colour” cells lying just below the surface of the skin. Moles: • Vary from pale brown to almost black in colour. • Vary in size from about 1mm to 10mm. • May be completely flat but are usually raised above the surrounding

skin and can be felt if you run your fingers over them. • Usually appear during childhood or the teenage years. • Do not come and go with sunlight.

Freckles The hardest part of counting moles is telling the difference between them and freckles.

• Moles are usually darker and more regular in shape than freckles. • Freckles have a ‘splotchier’ appearance and come and go with sunlight. • Freckles are never raised above the surrounding skin and usually occur

together in patches, particularly on the upper part of the body and especially on the face and shoulders.

• Freckles on the back will mainly occur on the upper part of the back and the shoulders.

• Usually raised and occur in a range of colours, often light to dark brown. • Seem to be “stuck on” and may flake a bit at the edges. • Look dull and waxy. • Mostly on the face, neck and trunk but can

be anywhere on the body. • More common with increasing age but can

appear in a person’s 20’s or 30’s.

Sebborhoeic keratoses (‘Senile warts’) It can be hard to tell the difference between moles and senile warts (sebborhoeic keratoses). Senile warts are:

PLEASE COUNT THESE DO NOT COUNT THESE

287APPENDIX G

Questionnaire

IdId

EntryEntry

EntryEntry

EntryEntry

IDID Start TimeStart TimeDateDate

Good morning/afternoon, could I please speak to

Please enter your ID, today's date and the participant's sex.

Participant's AgeParticipant's Age SexSex

MHS No.MHS No.

EntryEntry

My name is ......... and I am calling about the telephone interview for the Western Australian Melanoma Health Study. Would now be a convenient time to do the interview and do you have your brochure with you?

If:- asked who is calling/why you are calling- someone else answers- the participant is not there- you get an answering machinePlease refer to your questionnaire guide for standard responses.

Once speaking to the participant:

If YES - continue to next page (they MUST have the brochure to continue)If NO - refer to questionnaire guide

Have you had a chance to complete the height and weight questions and do your mole count ?

If NO, arrange time to call back today, or another day [refer to interviewing roster], for these answers.

Now go to page 3a.

If YES, ask questions below and continue to page 3a.

cm

What is your weight in kilograms? kg

We would also like to know the number of moles on your back. Please refer to the diagram in question D2.

How many moles do you have in section A, or from the base of your neck to the top of your armpits ?

D2

How many moles do you have in section B, or from the top of your armpit to the top of your underpants ?

moles

What is your height in centimetres?A2

A3

moles

Before we start, I’d like to ask you to get a pen and paper, as I’ll ask you to note down a few things a bit later on in the interview to help you with your answers. [Wait whilst items obtained].

SECTION B : COLOURING AND SKIN TYPE

SECTION A : PERSONAL DETAILS

Single De facto Married Divorced WidowedSingle De facto Married Divorced Widowed

Red (including auburn)

Fair or blonde (including white)

Light or mouse brown

Grey

Dark brown

Black

Red (including auburn)

Fair or blonde (including white)

Light or mouse brown

Grey

Dark brown

Black

Blue

Grey

Green

Hazel

Brown

Black

Blue

Grey

Green

Hazel

Brown

Black

Very fair

Fair

Olive or brown

Dark

Very fair

Fair

Olive or brown

Dark

Which colour best describes your skin before tanning or on areas never exposed to the sun, such as the inside of your upper arm?

Could you confirm your date of birth is :

B3

Which colour best describes the colour of your eyes?B2

Which colour best describes your natural hair colour at age 18?

B1

Page 3Version 4

What is your marital status?

B4 Which statement best describes what would happen to your skin if it were exposed to bright sunlight for the first time in summer, for one hour in the middle of the day, without any protection (e.g. sunscreen, clothing)?

Get a severe sunburn with blistering

Have a painful sunburn for a few days followed by peeling

Get mildly burnt followed by some tanning

Go brown without any sunburn

Get a severe sunburn with blistering

Have a painful sunburn for a few days followed by peeling

Get mildly burnt followed by some tanning

Go brown without any sunburn

B5 Which of the following best describes what would happen to your skin if it were repeatedly exposed to bright sunlight in summer without any protection (e.g. sunscreen, clothing)?

Which statement best describes what would happen to your skin if it were exposed to bright sunlight for the first time in summer, for one hour in the middle of the day, without any protection (e.g. sunscreen, clothing)?

B6

Always burns, never tans

Usually burns, never tans

Sometimes burns, usually tans

Never burns, always tans

Always burns, never tans

Usually burns, never tans

Sometimes burns, usually tans

Never burns, always tans

Go very brown and deeply tanned

Get moderately tanned

Get mildly or occasionally tanned

Get no suntan at all or only get freckled

Go very brown and deeply tanned

Get moderately tanned

Get mildly or occasionally tanned

Get no suntan at all or only get freckled

Please look at the faces above questions B7 and B8 in your brochure. Each of the faces shows some degree of freckling, from none to very many. For the following questions, we are looking for your best estimate.

B7

None Very few Few Some Many Very ManyNone Very few Few Some Many Very Many

Which of these faces best describes how many freckles you had at the end of summer as an adult; that is, when you were over 18 ?

B8

None Very few Few Some Many Very manyNone Very few Few Some Many Very many

Page 5

Which of these faces best describes how many freckles you had at the end of summer during childhood; that is, up until you were 17 years old?

How old were you when you first started smoking cigarettes or tobacco at least once a day?

Have you ever smoked cigarettes or tobacco once a day for six months or more?

C1

No

Yes

No

Yes

SECTION C : LIFESTYLE HISTORY

C2

years old

C3 Do you smoke cigarettes or tobacco at least once a day now?

No

Yes

No

Yes

C4 How old were you when you stopped smoking cigarettes or tobacco at least once a day?

years old

C5 Over your life time, how many total years have you smoked?

years

C6 On average, over the entire time that you smoked, how much did you smoke each day?

cigarettes per day (manufactured cigarettes only, not self rolled cigarettes)

of loose tobacco per day

cigars per day50g = 1 pouch of tobacco 1g = 1 self rolled cigarette

grams

ounces

grams

ounces (includes loose tobacco smoked in pipes or self rolled cigarettes)

Page 6

SECTION D : MOLES

The following question is about the number of moles that you have. Again, we are looking for your best estimate.

Please look at the diagrams in question D1. They show various numbers of moles. Which picture best describes how many moles you have now ?

D1

None Few Some ManyNone Few Some Many

Page 7

F2

In which country were you born ?

I would now like to ask you some questions about your background and family history.

DO NOT READ OPTIONS TO PARTICIPANT, CODE FROM RESPONSE

If other selected, please type country's name in the box.

What year did you first arrive in Australia?What year did you first arrive in Australia?

Where did you live for the majority of the time from when you were born to when you were 12 years of age ?

Country Country

State/County/Province/RegionState/County/Province/Region

Where did you live for the majority of the time between the ages of 13 and 19 ?

Where did you live for the majority of the time from the age of 20 to now ?

CountryCountry

State/County/Province/Region State/County/Province/Region

CountryCountry

State/County/Province/Region State/County/Province/Region

F1

F3

F4

F5

What is the highest level of education you have completed?

F6

City/TownCity/Town

City/TownCity/Town

City/TownCity/Town

I'd now like to ask you some questions about your ethnic background and the ethnic background of your family.

How would you describe your ethnic background ?

Indigenous Australian (Aboriginal, Torres Strait Islander)Indigenous Australian (Aboriginal, Torres Strait Islander)

South East Asian (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar/Burma, Philippines,

Singapore, Thailand, Vietnam)



North-East Asian (China, Hong Kong, Japan, Korea, Macau, Taiwan)North-East Asian (China, Hong Kong, Japan, Korea, Macau, Taiwan)

South-Asian (Afghanistan, Bangladesh, India, Nepal, Pakistan, Sri Lanka)South-Asian (Afghanistan, Bangladesh, India, Nepal, Pakistan, Sri Lanka)

Middle Eastern (Israel, Iran, Iraq, Lebanon, Turkey, Egypt)Middle Eastern (Israel, Iran, Iraq, Lebanon, Turkey, Egypt)

Pacific Islander (New Zealand Maori or originated from Pacific Islands, Hawaii, New Guinea)Pacific Islander (New Zealand Maori or originated from Pacific Islands, Hawaii, New Guinea)

African (Originating from North Africa, Sub-Saharan Africa, Zimbabwe, Indigenous South African)African (Originating from North Africa, Sub-Saharan Africa, Zimbabwe, Indigenous South African)

F7A

DO NOT READ OPTIONS TO PARTICIPANT, CODE FROM RESPONSEIf participant wishes to select two answers, they may, however do not suggest this option.

Caucasian - English (Orginated from England)Caucasian - English (Orginated from England)

Caucasian - Scottish, Irish or Welsh (Orginated from Scotland, Ireland or Wales)Caucasian - Scottish, Irish or Welsh (Orginated from Scotland, Ireland or Wales)

Caucasian - Other Northern European (Austria, Latvia, Lithuania, Estonia, Denmark, France, Germany, Luxembourg, Netherlands/Holland, Sweden, Norway, Finland, Switzerland, other Western/Northern European countries)


Caucasian - Southern European (Greece, Italy, Portugal, Spain, Former Yugoslavia, Malta, Cyprus, other Southern European countries)Caucasian - Southern European (Greece, Italy, Portugal, Spain, Former Yugoslavia, Malta, Cyprus, other Southern European countries)

Caucasian - Eastern European (Bulgaria, Former Czechoslovakia, Hungary, Poland, Romania, Former USSR, other Eastern European countries)Caucasian - Eastern European (Bulgaria, Former Czechoslovakia, Hungary, Poland, Romania, Former USSR, other Eastern European countries)

Caucasian - Other CaucasianCaucasian - Other Caucasian Please specify - Please specify -

B How would you describe the ethnic background of your biological mother?


















How would you describe the ethnic background of your biological father?











C








D How would you describe the ethnic background of your mother's mother?


















How would you describe the ethnic background of your mother's father?











E








F How would you describe the ethnic background of your father's mother?


















G How would you describe the ethnic background of your father's father?


















I’d now like to ask you some questions about your family history.

F8

Yes

No

Don't know

Has your biological mother ever been told by a doctor that she has any type of skin cancer?Has your biological mother ever been told by a doctor that she has any type of skin cancer?

No

Don't know

F9A

Yes

No

Don't know

Did she have a melanoma?Did she have a melanoma?

No

Don't know

F9B

Yes

No

Don't know

Did she have another type of skin cancer that wasn’t a melanoma?Did she have another type of skin cancer that wasn’t a melanoma?

No

Don't know

F10

Yes - Please specify

No

Don't know

Has your biological mother ever been told by a doctor that she has any other type of cancer?Has your biological mother ever been told by a doctor that she has any other type of cancer?

No

Don't know BreastBreast

BladderBladder

ColorectalColorectal

KidneyKidney

StomachStomach

PancreasPancreas

OvarianOvarian

LeukemiaLeukemia

ThyroidThyroid

LungLung

LymphomaLymphoma

UterusUterus

Other - Please SpecifyOther - Please Specify

F11

Yes

No

Don't know

Has your biological father ever been told by a doctor that he has any type of skin cancer ?Has your biological father ever been told by a doctor that he has any type of skin cancer ?

No

Don't know

F12A

F12B

Yes

No

Don't know

Did he have a malignant melanoma?Did he have a malignant melanoma?

No

Don't know

Yes

No

Don't know

Did he have another type of skin cancer that wasn’t a melanoma?Did he have another type of skin cancer that wasn’t a melanoma?

No

Don't know

Yes - Please specify

No

Don't know

Has your biological father ever been told by a doctor that he has any other type of cancer?Has your biological father ever been told by a doctor that he has any other type of cancer?

No

Don't know

F13

BladderBladder


KidneyKidney

LeukemiaLeukemia

LungLung

LymphomaLymphoma

Other - Please SpecifyOther - Please Specify

ThyroidThyroid

StomachStomach

PancreasPancreas

ProstateProstate

F14

Yes

No

Don't know

Do you have any full brothers? That is, brothers with the same mother and father as you.Do you have any full brothers? That is, brothers with the same mother and father as you.

No

Don't know

How many full brothers do you have? How many full brothers do you have?

Yes

No

Don't know

Have any of your full brothers ever been told by a doctor that they had any type of skin cancer?Have any of your full brothers ever been told by a doctor that they had any type of skin cancer?

No

Don't know

How many brothers were told this? How many brothers were told this?

Yes

No

Don't know

Did any of them have a malignant melanoma?Did any of them have a malignant melanoma?

No

Don't know

Yes

No

Don't know

Did any of them have another type of skin cancer that wasn’t a melanoma?Did any of them have another type of skin cancer that wasn’t a melanoma?

No

Don't know

How many brothers were told this?How many brothers were told this?


Yes - Please Specify How Many

No

Don't know

Have any of your full brothers ever told by a doctor that they had any other type of cancer?Have any of your full brothers ever told by a doctor that they had any other type of cancer?

No

Don't know

BladderBladder


KidneyKidney

LungLung

LymphomaLymphoma

PancreasPancreas

F21F21

F15F15

F16F16

F17F17

F18AF18A

F19F19

F18BF18B

F20F20

LeukemiaLeukemia ProstateProstate

StomachStomach

ThyroidThyroid

OtherOther

F23

Yes

No

Don't know

Do you have any half brothers? That is, brothers with the same mother OR father as you.Do you have any half brothers? That is, brothers with the same mother OR father as you.

No

Don't know

How many half brothers do you have? How many half brothers do you have?

Yes

No

Don't know

Have any of your half brothers ever been told by a doctor that they have any type of skin cancer?Have any of your half brothers ever been told by a doctor that they have any type of skin cancer?

No

Don't know

How many brothers were told this? How many brothers were told this?

Yes

No

Don't know


No

Don't know

Yes

No

Don't know


No

Don't know




No

Don't know

Were any of your half brothers ever told by a doctor that they had any other type of cancer?Were any of your half brothers ever told by a doctor that they had any other type of cancer?

No

Don't know

F24F24

F25F25

F26F26

F27AF27A

F28F28

F27BF27B

F29F29

F30F30

BladderBladder


KidneyKidney


PancreasPancreas

LymphomaLymphoma

LungLung StomachStomach

ThyroidThyroid

OtherOther

F32

Yes

No

Don't know

Do you have any full sisters? That is, sisters with the same mother and father as you.Do you have any full sisters? That is, sisters with the same mother and father as you.

No

Don't know

How many full sisters do you have? How many full sisters do you have?

Yes

No

Don't know

Have any of your full sisters ever been told by a doctor that they have any type of skin cancer?Have any of your full sisters ever been told by a doctor that they have any type of skin cancer?

No

Don't know

How many sisters were told this? How many sisters were told this?

Yes

No

Don't know


No

Don't know

Yes

No

Don't know


No

Don't know

How many sisters were told this?How many sisters were told this?



No

Don't know

Were any of your full sisters ever told by a doctor that they had any other type of cancer?Were any of your full sisters ever told by a doctor that they had any other type of cancer?

No

Don't know

F33F33

F34F34

F35F35

F36AF36A

F37F37

F36BF36B

F38F38

F39F39

OtherOther

ThyroidThyroidLungLung

LymphomaLymphoma

PancreasPancreas

StomachStomach

BladderBladder


KidneyKidney

LeukemiaLeukemia

BreastBreast

OvarianOvarian

UterusUterus

F41

Yes

No

Don't know

Do you have any half sisters? That is, sisters with the same mother or father as you.Do you have any half sisters? That is, sisters with the same mother or father as you.

No

Don't know

How many half sisters do you have? How many half sisters do you have?

Yes

No

Don't know

Have any of your half sisters ever been told by a doctor that they had any type of skin cancer?Have any of your half sisters ever been told by a doctor that they had any type of skin cancer?

No

Don't know

How many sisters were told this? How many sisters were told this?

Yes

No

Don't know


No

Don't know

Yes

No

Don't know


No

Don't know




No

Don't know

Were any of your half sisters ever been told by a doctor that they had any other type of cancer?Were any of your half sisters ever been told by a doctor that they had any other type of cancer?

No

Don't know

F42F42

F43F43

F44F44

F45AF45A

F46F46

F45BF45B

F47F47

F48F48

OtherOther

LymphomaLymphoma

PancreasPancreas

StomachStomach

OvarianOvarian

UterusUterus

ThyroidThyroidLungLungBladderBladder


KidneyKidney

LeukemiaLeukemia

BreastBreast

F50 Are you a twin, triplet or other multiple birth?

Yes, a twin

Yes, a triplet

Yes, other multiple birth

No

Don't know

Are you a twin, triplet or other multiple birth?Are you a twin, triplet or other multiple birth?

Yes, a triplet

Yes, other multiple birth

No

Don't know

F51

Yes

No

Don't know

Do you have any biological children? Do you have any biological children?

No

Don't know

How many children do you have? How many children do you have?

Yes

No

Don't know

Have any of your children ever been told by a doctor that they have any type of skin cancer?Have any of your children ever been told by a doctor that they have any type of skin cancer?

No

Don't know

F52F52

F53F53

How many children have been told this? How many children have been told this?

This only refers to biological children, not step-children.

F54F54

Yes

No

Don't know


No

Don't know

F55AF55A

How many children were told this?How many children were told this?

Yes

No

Don't know


No

Don't know

F56F56

F55BF55B

How many children were told this?How many children were told this?F57F57

Yes

No

Don't know

Were any of your children ever told by a doctor that they had any other type of cancer?Were any of your children ever told by a doctor that they had any other type of cancer?

No

Don't know

F58F58

BreastBreast

OtherOtherPancreasPancreas

OvarianOvarianColorectalColorectal

KidneyKidney

LymphomaLymphoma

BladderBladder LungLung


StomachStomach

UterusUterus

ThyroidThyroid

I am now going to ask you some questions about time that you've spent outdoors at various ages. By 'outdoors', I mean the time you spent outdoors and not under any shade between 9am and 5pm.

Please remember we are looking for your best estimate.

To help you with the next set of questions, it might be useful to write down ‘not under any shade’ and ‘between 9am and 5pm’, to act as a reminder when answering the questions.”

I'd like to start by asking you about time outdoors in the warmer months, that is, spring and summer, when you were 5-12 years of age or in primary school.

How many hours per day did you usually spend outdoors and not under any shade, between 9am and 5pm in the warmer months on:

School days School days

WeekendsWeekends

ORDon't knowDon't know


G1.3

Mainly indoorsSplit between indoors and outdoorsMainly outdoors

On average, in the warmer months, during the hours that you were at school, were your activities:On average, in the warmer months, during the hours that you were at school, were your activities:

Split between indoors and outdoorsMainly outdoors

G1.4


On average, were these activities:On average, were these activities:


I'd now like you to think about recreational activities that you did during the warmer months (for example, playing sport, going to the beach, playing musical instruments) between 9am and 5pm.

G1.1,1.2

hrs

hrs

I'd now like to ask you about time outdoors in the cooler months, that is, autumn and winter, when you were 5-12 years of age.

How many hours per day did you usually spend outdoors and not under any shade, between 9am and 5pm in the cooler months on:

School days School days

WeekendsWeekends



G1.7


On average, in the cooler months, during the hours that you were at school, were your activities:On average, in the cooler months, during the hours that you were at school, were your activities:


G1.8




I'd now like you to think about recreational activities that you did during the cooler months (for example, playing sport, going to the beach, playing musical instruments) between 9am and 5pm.

G1.5,1.6

hrs

hrs

I'd now like to ask you some questions about when you were 13-19 years of age.

How many hours per day did you usually spend outdoors and not under any shade, between 9am and 5pm in the warmer months on:

School or week days School or week days

WeekendsWeekends



G2.4


On average, in the warmer months, during the hours that you were at school or working, were your activities:On average, in the warmer months, during the hours that you were at school or working, were your activities:


G2.5




I'd now like you to think about recreational activities that you did during the warmer months (for example, playing sport, going to the beach, playing musical instruments) between 9am and 5pm.

G2.1

During these years, what was your main occupation?

I'd now like to ask you about time outdoors in the warmer months, that is, spring and summer, when you were 13-19 years of age.

I'd now like you to think about all the jobs that you had during these years (including studying).

G2.2,2.3

hrs

hrs

OtherOther

How many hours per day did you usually spend outdoors and not under any shade, between 9am and 5pm in the cooler months on:

School or week days School or week days

WeekendsWeekends



G2.8


On average, in the cooler months, during the hours that you were at school or working, were your activities:On average, in the cooler months, during the hours that you were at school or working, were your activities:


G2.9

I'd now like you to think about recreational activities that you did during the cooler months (for example, playing sport, going to the beach, playing musical instruments) between 9am and 5pm.

I'd now like to ask you about time outdoors in the cooler months, that is, autumn and winter, when you were 13-19 years of age.





G2.6,2.7

hrs

hrs


G3.2


On average, during the hours that you were working between 9am and 5pm, were your activities:On average, during the hours that you were working between 9am and 5pm, were your activities:


G3.3


In the warmer months, on average, were these activities:In the warmer months, on average, were these activities:


I'd now like you to think about recreational activities that you did (for example, playing sport, going to the beach, playing musical instruments) between 9am and 5pm.

G3.1



G3.4


In the cooler months, on average, were these activities:In the cooler months, on average, were these activities:


OtherOther


G4.3




G4.4





G4.1



G4.5




Year Retired ?Year Retired ?

OtherOther

I'd now like to ask you some questions about when you were 65 years of age till now.

G5.3




G5.4





G5.1



G5.5




Year Retired ?Year Retired ?

OtherOther

I'd now like to ask you some questions about sunburn for various ages. Before we start, can I ask you to write down the following options, which might help you with your responses: "0, 1-5, 6-10, more than 10".

G6.1

0Between 1 and 5 timesBetween 6 and 10 timesMore than 10 timesDon't know

How many times were you sunburnt so as to cause pain for 2 or more days when you were between 5-12 years of age?


Between 1 and 5 timesBetween 6 and 10 timesMore than 10 timesDon't know


How many times were you sunburnt so severely as to cause blisters between 5-12 years of age?How many times were you sunburnt so severely as to cause blisters between 5-12 years of age?


G6.2

G7.1






How many times were you sunburnt so severely as to cause blisters between 13-19 years of age?How many times were you sunburnt so severely as to cause blisters between 13-19 years of age?


G7.2


How many times were you sunburnt so severely as to cause blisters since you were 20 years of age? How many times were you sunburnt so severely as to cause blisters since you were 20 years of age?



How many times were you sunburnt so as to cause pain for 2 or more days since you were 20 years of age?How many times were you sunburnt so as to cause pain for 2 or more days since you were 20 years of age?


G8.1G8.1

G8.2G8.2

I'd now like to ask you about sunbed use.

G9

YesNoDon't know

Have you ever used a sunlamp or sunbed for any reason on more than one occasion?Have you ever used a sunlamp or sunbed for any reason on more than one occasion?

NoDon't know

How old were you when you first used one?How old were you when you first used one?

How old were you when you last used one?How old were you when you last used one?

About how many sunlamp or sunbed sessions have you had in total, over your lifetime?

About how many sunlamp or sunbed sessions have you had in total, over your lifetime?

G13 In what types of locations have you used a sunlamp or sunbed?

Tanning salonTanning salon

Hairdressers, beauty salonsHairdressers, beauty salons

Gymnasium, health club/spa or fitness club/spaGymnasium, health club/spa or fitness club/spa

Hospital or medical facilityHospital or medical facility

Private homePrivate home

Other - Please specifyOther - Please specify

Don't knowDon't know

DO NOT READ OPTIONS TO PARTICIPANT.

The participant may select more than one answer.

G10G10

G11G11

G12G12

I'd now like to ask you some questions about your medical history.

How many melanomas have you been told you've had by a doctor?How many melanomas have you been told you've had by a doctor?

H1.1

How many non-melanoma skin cancers have you been told you've had by a doctor? How many non-melanoma skin cancers have you been told you've had by a doctor?

H1.2a

H1.3

How many skin cancers are you unsure about?How many skin cancers are you unsure about?

Yes

No

Have you had any skin cancers and are unsure what they were?Have you had any skin cancers and are unsure what they were? No

H1.4H1.4

Yes

No

Don't know

Have you had any non-melanoma skin cancers, that is basal cell carcinomas or squamous cell carcinomas? These are often called BCC's or SCC's.

Have you had any non-melanoma skin cancers, that is basal cell carcinomas or squamous cell carcinomas? These are often called BCC's or SCC's.

No

Don't know

H1.2b

When did a doctor tell you that you had a melanoma for the X time?

Where on your body was your X melanoma situated?

Where were you living when you were told by a doctor about your X melanoma?

How large is the scar where your X melanoma was removed?

Please fill in the table for each melanoma.

You should then ask the questions above the table, replacing X with first, second, third, etc.

Number of melanomasNumber of melanomas

YearYear


MonthMonth

1st

AnatomicalAnatomical

SideSide

StateState

Please specify countryPlease specify country

Size of scarSize of scar

MonthMonth

2nd


SideSide

AnatomicalAnatomical StateState



MonthMonth

3rd


SideSide

AnatomicalAnatomical StateState



YearYear

YearYear



YearYear

MonthMonth AnatomicalAnatomical

SideSide




StateState


5th


SideSide

StateState



6th SideSide

StateState







YearYear

YearYear

MonthMonth

YearYear



SideSide

StateState



MonthMonth

YearYear



SideSide

StateState



4th

7th

8th



YearYear


SideSide




StateState


10th


SideSide

StateState



11th SideSide

StateState







YearYear

YearYear

MonthMonth

YearYear



SideSide

StateState



MonthMonth

YearYear



SideSide

StateState



9th

12th

13th

How many have you been told you've had?How many have you been told you've had?H16H16

H17H17 What year were you first told you had an SCC?What year were you first told you had an SCC?

H15H15Yes

No

Don't know

Of the non-melanoma skin cancers that you've been told about, were any of them squamous cell carcinomas or SCC?

Of the non-melanoma skin cancers that you've been told about, were any of them squamous cell carcinomas or SCC?

No

Don't know

Yes

No

Don't know

Of the non-melanoma skin cancers that you've been told about, were any of them basal cell carcinomas or BCC?

Of the non-melanoma skin cancers that you've been told about, were any of them basal cell carcinomas or BCC?

No

Don't know

H12H12

How many have you been told you've had?How many have you been told you've had?H13H13

H14H14 What year were you first told you had a BCC?What year were you first told you had a BCC?

H18 Yes

No

Don't know

Have you ever been told by a doctor that you've had any other type of cancer?Have you ever been told by a doctor that you've had any other type of cancer? No

Don't know

H19H19

H20 Yes

No

Don't know

Has a doctor ever told you that you have any other medical problems apart from cancer?

Has a doctor ever told you that you have any other medical problems apart from cancer?

No

Don't know

BreastBreast

BladderBladder


KidneyKidney

LungLung

LymphomaLymphoma

Ovarian Ovarian

PancreasPancreas

ThyroidThyroid

UterusUterus


StomachStomach

What type of cancer were you told you had?What year were you told you had that cancer?

OtherOther

OtherOther

YearYear

YearYear

YearYear

YearYear

YearYear

YearYear

YearYear

YearYear

YearYear

YearYear

YearYear

YearYear

YearYear

YearYear

YearYear

AsthmaAsthma

H21 What medical conditions were they?

DiabetesDiabetes

Heart attackHeart attack

High blood pressureHigh blood pressure

StrokeStroke

DepressionDepression

OtherOther

OtherOther

OtherOther

OtherOther

I1 Do you have any comments, or other information that you think would be useful?Do you have any comments, or other information that you think would be useful?

I2 For the interviewer: do you have any comments regarding this interview?For the interviewer: do you have any comments regarding this interview?

End TimeEnd Time

Please check with participant whether or not they have done their blood sample yet, if they consented to give one.

If YES - please thank themIf NO - please let them know it would be greatly appreciated if they could do it ASAP.

Please see QxQ for details on types of blood forms.

328 Appendix G. Questionnaire

329APPENDIX H

Questionnaire Brochure

330

App

endixH

.Q

uestionnaireB

rochure

Appendix H. Questionnaire Brochure 331

332 Appendix H. Questionnaire Brochure

333APPENDIX I

MRsnphap R Package User Manual

Package ‘MRsnphap’

May 18, 2010

Type Package

Title A modeling framework for Mendelian Randomisation studies of single SNPsand haplotypes

Version 1.0.0

Date 2010-05-05

Depends MASS, stats, SimHap

Author Gemma Cadby

Maintainer Gemma Cadby <[email protected]>

Description MRsnphap is a package for modelling Mendelian Randomisation studies. Itcan perform single SNP and haplotype association analyses for continuousNormal outcomes measured in population-based samples. MRsnphap usesthe estimation maximisation techniques for inferring haplotypic phase, andincorporates a multiple-imputation approach to deal with the uncertainty ofimputed haplotypes in Mendelian Randomisation analyses.

License GPL2

R topics documented:

mrhap.quant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1mrsnp.quant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

mrhap.quant Mendelian Randomisation using Haplotypes as Instruments

Description

mrhap.quant is used for Mendelian Randomisation modelling of epidemiological data withcontinuous outcomes, where one or more haplotypes are used as instruments for somemodifiable exposure.

Usage

mrhap.quant(outcome, exposure, covariates = NULL, geneticmodel, pheno,

haplo, sim, effect="add",small = NULL)

1

Arguments

outcome a continuous outcome.

exposure a modifiable exposure

covariates a list of covariates in the form"x1+...+xn". Defaults to NULL.

geneticmodel the haplotype/s used as the instrument. May be coded as either additive,recessive or dominant.

haplo a haplotype object made by make.haplo.rare.

pheno a dataframe containing phenotype data.

sim the number of simulations from which to evaluate the results.

effect the haplotypic effect type: "add" for additive, "dom" for dominant and"rec" for recessive. Defaults to additive.

small a small sample correction. Defaults to NULL.

Details

If an instrument is considered weak, a warning file with the current date will be stored inthe working directory. Use getwd() to locate this directory.

Value

mrhap.quant returns an object of class MRhap.

The summary function can be used to obtain and print a summary of the results.

An object of class MRhap is a list containing the following components:

instruments formula containing instruments and covariates

structual structural formula containing the outcome regressed on exposure and co-variates

results a table containing the coefficients, standard errors, p-values and 95% con-fidence limits of the parameter estimates, averaged over the sim modelsperformed

empiricalResults

a list containing the coefficients, standard errors and p-values calculatedat each simulation.

observations number of observations

weakfstat mean F-Statistic for the first stage regression of exposure on instrument/s,averaged over the sim models performed

sarganp mean P-values for the Sargan test statistic, averaged over the sim mod-els performed. The sarganp will only be shown when there are moreinstruments than exposures

DWHp mean P-values for the Durbin-Wu-Haussman statistic, averaged over thesim models performed

effect the haplotypic effect modelled, ‘ADDITIVE’, ‘DOMINANT’ or ‘RECES-SIVE’.

2

Author(s)

Gemma Cadby

References

Carter, K.W., McCaskie, P.A., Palmer, L.J. (2008). SimHap GUI: An intuitive graphicaluser interface for genetic association analysis. BMC Bioinformatics 2008 Dec 25;9(1):557.

McCaskie, P.A., Carter, K.W, Hazelton, M., Palmer, L.J. (2007) SimHap: A comprehen-sive modeling framework for epidemiological outcomes and a simulation-based approach tohaplotypic analysis of population-based data, [online] www.genepi.org.au/simhap.

Baum, C.F., Schaffer, M.E., Stillman, S. (2007) Enhanced routines for instrumental vari-ables/GMM Estimation and testing, Stata Journal, 7 (4), 465-506.

See Also

mrsnp.quant

Examples

data(SNP.dat)

# convert SNP.dat to format required by infer.haplos

haplo.dat <- SNP2Haplo(SNP.dat)

data(pheno.dat)

# generate haplotype frequencies and haplotype design matrix

myinfer<-infer.haplos(haplo.dat)

# print haplotype frequencies generated by infer.haplos

myinfer$hap.freq

# generate haplo object where haplotypes with a frequency

# below min.freq are grouped as a category called "rare"

myhaplo<-make.haplo.rare(myinfer,min.freq=0.05)

mymodel.hap<- mrhap.quant(outcome="BT", exposure="VitD", covariates="AGE", geneticmodel="h.N1AA",

pheno=pheno.dat, haplo=myhaplo, sim=10, effect="add")

summary(mymodel.hap)

mrsnp.quant Mendelian Randomisation using Single SNPs as Instruments

Description

mrsnp.quant is used for Mendelian Randomisation modelling of epidemiological data withcontinuous outcomes, where one or more SNPs are used as instruments for some modifiableexposure.

3

Usage

mrsnp.quant(outcome, exposure, covariates = NULL, geneticmodel,

geno, pheno, small = NULL)

Arguments

outcome a continuous outcome.

exposure a modifiable exposure

covariates a list of covariates in the form"x1+...+xn". Defaults to NULL.

geneticmodel the SNP used as the instrument. May be coded as either additive, reces-sive or dominant.

geno a dataframe containing genotype data.

pheno a dataframe containing phenotype data.

small a small sample correction. Defaults to NULL.

Details

If an instrument is considered weak, a warning message will be printed.

Value

mrsnp.quant returns an object of class MRsnp.

The summary function can be used to obtain and print a summary of the results.

An object of class MRsnp is a list containing the following components:

instruments formula containing instruments and covariates

structual structural formula containing the outcome regressed on exposure and co-variates

results a table containing the coefficients, standard errors, p-values and 95% con-fidence limits of the parameter estimates

observations number of observations

weakfstat F-Statistic for the first stage regression of exposure on instrument/s

sarganstat Sargan test statistic for the overidentification test of all instruments. Thesarganstat will only be shown when there are more instruments thanexposures

sarganp P-value for the Sargan test statistic. The sarganp will only be shownwhen there are more instruments than exposures

DWHstat Durbin-Wu-Haussman statistic for the test of endogeneity.

DWHp P-value for the Durbin-Wu-Haussman statistic

residualerror Residual error of the model

Author(s)

Gemma Cadby

4

References

Carter, K.W., McCaskie, P.A., Palmer, L.J. (2008). SimHap GUI: An intuitive graphicaluser interface for genetic association analysis. BMC Bioinformatics 2008 Dec 25;9(1):557

McCaskie, P.A., Carter, K.W, Hazelton, M., Palmer, L.J. (2007) SimHap: A comprehen-sive modeling framework for epidemiological outcomes and a simulation-based approach tohaplotypic analysis of population-based data, [online] www.genepi.org.au/simhap.

Baum, C.F., Schaffer, M.E., Stillman, S. (2007) Enhanced routines for instrumental vari-ables/GMM Estimation and testing, Stata Journal, 7 (4), 465-506.

See Also

mrhap.quant

Examples

data(SNP.dat)

#convert SNP.dat to format required by mrsnp.quant

geno.dat<- SNP2Geno(SNP.dat, baseline=c("MM", "11", "GG", "CC"))

data(pheno.dat)

mymodel.snp<- mrsnp.quant(outcome="BT", exposure="VitD", covariates="AGE", geneticmodel="SNP_1_add",

geno=geno.dat, pheno=pheno.dat)

summary(mymodel.snp)

5

339APPENDIX J

Example output using mrsnp.quant

The output in this appendix was produced by modelling the association between

a prognostic measure of melanoma, Breslow thickness (BT), and Vitamin D levels

(VitD), adjusted for age, using the mrsnp.quant function in MRsnphap. The

instrument is a SNP which is modelled additively, and was produced by the

SNP2Geno function in SimHap.

mymodel.snp<- mrsnp.quant(outcome="BT", exposure="VitD", covariates="AGE",

geneticmodel="SNP_1_add", geno=geno.dat, pheno=pheno.dat)

summary(mymodel.snp)

Instruments:

~SNP_1_add + AGE

Structural Model:

BT ~ VitD + AGE

Coefficient Std.Error Lower 95% CI Upper 95% CI P.Value

(Intercept) 90.0988 29.8852 31.1194 149.078 0.00295 **

VitD 1.7064 25.2587 -48.1424 51.555 0.94621

AGE 0.7266 0.1326 0.4650 0.988 1.45e-07 ***

---

Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1

Number of Observations:

179

Weak Instrument F Statistic:

5.6199

Sargan F Statistic:

NA: equation exactly identified

Sargan P-Value:

NA: equation exactly identified

Durbin-Wu-Hausman Statistic:

0.15521

Durbin-Wu-Hausman P-Value:

0.69361

Residual Error:

19.141

The F-statistic for the weak instrument test (F=5.6199) indicates that the SNP

340 Appendix J. Example output using mrsnp.quant

is a weak instrument for Vitamin D and should not be used. The Durbin-Wu

Hausman p-value of 0.69361 indicates that Vitamin D should not be treated as

an endogeneous regressor.

341APPENDIX K

Example output using mrhap.quant

The output in this appendix was produced by modelling the association between

a prognostic measure of melanoma, Breslow thickness (BT), and Vitamin D levels

(VitD), adjusted for age, using the mrhap.quant function in MRsnphap. The in-

strument is a haplotype, h.N1AA, which is modelled additively, and was produced

by the haplo.dat and infer.haplos functions in SimHap.

The Mendelian randomisation model is run 100 times, and the average of the co-

efficients, statistics and p-values are reported.

mymodel.hap<- mrhap.quant(outcome="BT", exposure="VitD", covariates="AGE",

geneticmodel="h.N1AA", pheno=pheno.dat, haplo=myhaplo, sim=100, effect="add")

[1] "* Finding highest individual frequency ..."

[1] " Done"

[1] "* Populating individual haplotypes and posterior probabilities ..."

[1] " Done"

[1] "* Distributing individual occurrences across the simulations by posterior probability ..."

[1] " 25%"

[1] " 50%"

[1] " 75%"

[1] " 90%"

[1] " Done"

[1] "* Generating a random pattern of individuals for each simulation ..."

[1] " Done"

[1] "* Constructing datafiles and performing model for each simulation ..."

[1] " Done"

summary(mymodel.hap)

Instruments:

~h.N1AA + AGE

Structural Model:

BT ~ VitD + AGE

Coefficients:

Coefficient Std.error Lower 95% CI Upper 95% CI P.Value

(Intercept) 204.0052 580.5689 -941.7227 1349.733 0.1655

VitD -97.0130 502.4302 -1088.5376 894.511 0.4170

AGE 0.9529 1.1717 -1.3594 3.265 0.0815 .

---

Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1

Number of Observations:

180

Weak Instrument F Statistic:

342 Appendix K. Example output using mrhap.quant

[1] 1.4697

Sargan P-Value:

[1] NA

Durbin-Wu-Hausman P-Value:

[1] 0.25544

The F-statistic for the weak instrument test (F=1.4697) indicates that the h.N1AA

haplotype is a weak instrument for Vitamin D levels and should not be used. The

Durbin-Wu Hausman p-value of 0.25544 indicates that Vitamin D should not be

treated as an endogeneous regressor.

Gemma Cadby - UWA Research Repository€¦ · The genetic epidemiology of melanoma susceptibility...

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