Issues in Human PhenomicsUse of genetic studies to better understand

common complex disease

Stuart MacGregorStatistical Genetics Laboratory,

QIMR Berghofer, Brisbane, Australia

© Queensland Institute of Medical Research | 2

Overview

• Goal• Phenomic studies in humans• Efficacy for gene mapping• Genetic correlation• Causal inference

© Queensland Institute of Medical Research | 3

Statistical genetics @ QIMR-BGoal

• Understand genetic basis of human disease

• Dissect genetic architecture of diseases and traits

• Identify specific genes• Use knowledge to improve health

Human phenomics studies

• Queensland Twin Registry

• >10,000 individuals with GWAS

• Brisbane Adolescent Twin Study

12 yrs 14yrs 16yrs 12 yrs 14yrs 16yrs 12 yrs 14yrs 16yrsOTHER SERUM BIOCHEMISTRY MELANOMA RISKBlood pressure x x x Cholesterol, HDL, LDL x x x Mole counts and locations x xHeight, weight x x x Triglyceride x x x Eye, hair and skin colour x x xFingerprints, handprints x x x Apolipoproteins A1,A2.B,E x x x Melanoma family history x xLaterality (hand, eye, foot) x x x Lp(a) x x x Photoaging (skin mould) x x xHand preference (peg board) x x x Glucose, Insulin x x x Sun exposure x x

ENT (grommets, T&A) x x x Ca, PO4 x x x Sun protective behaviour x xTaste (PTC, bitter, sweet) x x x Creatinine x x x HAEMATOLOGYSmell (BSIT, NatGeo) x Urea, Uric acid x x x Haemoglobin x x xCOGNITION & MRI Alkaline phosphatase x x x Red blood cell count x x xCognitive Ability (IQ – MAB) x Albumin, Bilirubin x x x Packed cell volume x x x

Information Processing (IT) x AST, ALT, GGT x x x Mean corpuscular volume x x xWorking Memory (nBack) x Fe, Ferritin, Transferrin x x x Platelet count x x xReading Ability (CCRT) x Heavy metals (Pb, As etc) x x x White blood cell count x x xAcademic achievement (QCST) x OPHTHALMOLOGY Neutrophils x x x

Computer Use x Visual acuity x x x Monocytes x x xEEG (power, coherence) x AutoRefractometry (myopia) x x x Eosinophils x x xERPs (DRT) x Stereopsis x Basophils x x xMRI scans - structural x x Eye dominance x Total lymphocytes x x x

MRI scans - functional x x Conjunctival UV auto-fluorescence x T-cell measures x x x

Diffusion Tensor Imaging x x Intraocular pressure x Blood groups (ABO, MNS, Rh) x

PSYCHIATRY Central corneal thickness x Asthma, eczema x x xPsychiatric signs (SPHERE) x x x Axial length x Acne x x xPersonality (NEO-PI) x x x Anterior chamber depth x Mosquito bite susceptibility x xBinocular rivalry (bipolar) x x Corneal curvature xRelationships x Retinal vessel diameters xLeisure activity x Optic disc and optic cup size x

Human phenomics studies

Brisbane Adolescent Twin Study

– ~800 families – Array data - SNP,

Expression, Methylation– eQTL, methylation

Human phenomics studies

• National, international– Raine (Perth), TwinsUK, ALSPAC (UK), Framingham

(USA)– UK Biobank N~500,000

• GxE

• Personal genomics– 23andMe

• Total N~850,000– melanoma N~380,000– reflux N~53,000– myopia N~45,000

– recontactibility

Human phenomics studies

• Data sharing – Kaiser-Permanente (N~100,000)– TCGA (N~500 per cancer)– dbGaP

• Focus on gene mapping efforts using GWAS

How do phenomics studies fare for GWAS?

• How do they compare with ascertainment of clinical samples (case-control)?

• Phenotyping quality - can you cut corners in your phenomic study phenotyping?

• Assessment of genetic correlation

• Case study 1 - GWAS of open angle glaucoma (severe cases)– Estimated 300,000 Australians have glaucoma

(half undiagnosed)– Leading cause of irreversible blindness

• First GWAS 2009 – CAV1• Second GWAS 2010 – TMCO1, CDKN2BAS• Third, Fourth – SIX6, GMDS, ABCA1, AFAP1, ARHGEF12

• Sample sizes ~few thousand cases, controls

Phenomics vs ascertainment of clinical samples?

Eye quantitative traitsHealthy individuals

Optic Nerve Cup Disc RatioN=28,000 GWAS – Brisbane twins et al

Springelkamp et al, Nat Comm, 2014

Intraocular pressure (IOP)N=35,000 GWAS – Brisbane twins et al

Naturally, provides insight into glaucomaAlso now combining information across study designs

Hysi et al, Nat Genetics, 2014

Can you cut corners in your phenomic study phenotyping?

• Case study 2: Myopia• Accurately measured

refractive error (dioptres) in 45,000 sample GWAS meta-analysis - CREAM

• 23andMe: “At what age did you start wearing glasses?” – survival analysis, N=46,000

• Remarkable overlap in findings – even by strict criteria, >50% loci replicate

Nature.com

Genetic correlation

• Can estimate using multiply phenotyped individuals in phenomic studies

• Houle et al “Phenomic-level data are necessary to understand pleiotropy”

• Power an issue, particularly for disease traits• In some circumstances, don’t need traits

measured on the same individual• Germline genetic contributions to cancer

variance and covariance

Houle et al, Nat Rev Genetics, 2010

Estimating genetic contribution – array heritability

Univariate analysis

TRAIT TRAIT

Distant Relatedness (e.g. 0.01, 0.02)

Covariance

GREML: genomic-relatedness-matrix restricted maximum likelihoodSoftware GCTA Visscher, Yang and colleagues

Two traits

TRAIT 1

PhenotypicCovariance

TRAIT 2

BEACON data

TRAIT 1Barrett’s esophagus(case/control)

TRAIT 2Esophageal Cancer(case/control)

Barrett’s esophagus and esophageal adenocarcinoma consortium (BEACON)

Genetic covariance

TRAIT 1 TRAIT 1Covariance

TRAIT 2 TRAIT 2

Distant Relatedness (e.g. 0.01, 0.02)

Genetic covariance

TRAIT 1Barrett’s esoph(case/control)

TRAIT 1Barrett’s esoph.(case/control)

Covariance

TRAIT 2Esophageal cancer(case/control)

TRAIT 2Esophageal cancer(case/control)

Distant Relatedness (e.g. 0.01, 0.02)

Ek et al, JNCI, 2013

Bulik-Sullivan et al, bioRxiv 2015

Causal Inference• Obesity is associated with esophageal cancer

risk in observational studies– Cannot rule out confounding, recall bias

• Randomized clinical trial

Weight loss program

Controls

Esophageal cancer?

Esophageal cancer?

Obesity SNP genotype AC

Obesity SNP genotype AA

X Y obesity esophageal cancer

CConfounders e.g. education

Zsnps

Mendelian Randomization

• 29 SNPs associated with body mass index (BMI)

• Genetically derived 1 unit increase in BMI increases esophageal cancer risk by 16%

• 23andMe reflux data

Thrift et al, JNCI, 2013

Summary

• Phenomic studies offer many opportunities– Powerful for GWAS– Agreement with clinical findings– Flexibility in phenotyping quality

• Estimating genetic correlation• Causal inference

AcknowledgmentsStatistical Genetics Lab

Matthew Law, Puya Gharahkhani, Gabriel Cuellar, Michael Quinn

Yi Lu, Aniket Mishra, Weronica Ek,Henriet Springelkamp

Collaborators

QIMR BerghoferNick Martin, Grant Montgomery, David Whiteman, Aaron Thrift, Graham Radford-Smith, Nick Hayward, Georgia Chenevix-Trench

BEACON, 23andMe

OphthalmologyDavid Mackey, PerthJamie Craig, AdelaideAlex Hewitt, MelbourneKathryn Burdon, HobartIGGC, CREAM

FundingARC, NHMRC, Cancer Australia, NIH