Molecular and Genetic Epidemiology
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Transcript of Molecular and Genetic Epidemiology
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Molecular and Genetic Epidemiology
Kathryn Penney, ScDJanuary 5, 2012
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Definitions Genetic Epidemiology
‘a science which deals with the etiology, distribution, and control of disease in groups of relatives and with inherited causes of disease in populations’ - Morton, 1982
Molecular Epidemiology (www.aacr.org) seeks to identify human (cancer) risk and (carcinogenic)
mechanisms to improve (cancer) prevention strategies is multi-disciplinary and translational, going from the
bench to the field and back uses biomarkers and state-of-art technologies to gain
mechanistic information from epidemiological studies
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Genetic and Molecular Epidemiology
Genetic variation
Disease
Disease
Exposure
Biological Factors/ Mechanism
Association?
Association?
Association?
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Genetic Studies
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Twin studies Determine if a disease has a genetic component Estimate the genetic contribution to disease
(heritability) Genetics (heritable component) Shared environment Unique environment
Twins Monozygotic (MZ) share 100% of their genes Dyzygotic (DZ) share ~50% of their genes
Use correlation of trait/disease RMZ = genetics + shared environment RDZ = ½ genetics + shared environment Genetics = 2 x (RMZ – RDZ)
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Heritability
Lichtenstein et al, 2000
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Association studies Family based
Parent-child trios, siblings Population based
Case-control Types of studies
Candidate gene/SNPs Genome-wide association study (GWAS)
Single nucleotide polymorphisms (SNPs) vs. mutations/rare variants Germline variation SNPs > 1% population frequency
A/A
A/C
A/C
cases
controls
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Samples Blood
DNA, RNA, biomarkers (dietary, hormones)
Tissue Tumor and normal DNA, RNA, proteins
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Candidate genes Select a gene of interest Select SNPs to genotype
Literature tagSNPs
Haplotype tagSNPs
C G A A C GC G A A C GC G A C C GC T A C C AC T A C C A
G/T A/C G/AC G A A C GC G A A C GC G A C C GC T A C C AC T A C C A
G/T A/C G/A12345
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Candidate genes The International HapMap Project
Catalog of common genetic variants Describes what these variants are, where they
occur, and how they are distributed among people within populations and among populations
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www.hapmap.org Haploview – visualize correlations between SNPs in
HapMap or study data Tagger – method to select tagSNPs in HapMap or
study data
Candidate genes
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Are the SNPs associated with outcome?
Are the SNPs associated with intermediate phenotypes/biomarkers/tumor markers?
Candidate genes
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Genotyping technology Taqman
PCR-based fluorescent assay Single SNP assay
Sequenom PCR-based single-base extension MALDI-TOF (Matrix-Assisted Laser
Desorption/Ionization – Time Of Flight) Multi-plex (≤36-40 SNPs) assay
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Genome-wide Association Study (GWAS) Estimated 10 million SNPs in the genome
Genotype 350k – 1 million SNPs across entire genome
Test association of each SNP with outcome
Adjust for the number of tests performed p < 5x10-8 considered “genome-wide” significant
Replicate findings in a different population Same SNP, same direction, approximate same magnitude of
effect
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GWAS results
Amundadottir et al, 2009
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Published Genome-Wide Associations through 6/2010, 904 published GWA at p<5x10-8 for 165 traits
NHGRI GWA Catalogwww.genome.gov/GWAStudies
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Genotyping technology Illumina
1 million SNP chip tagSNPs selected from
HapMap data Affymetrix
1 million SNP chip Selected based on
distance
http://www.illumina.com/Documents/products/technotes/technote_intelligent_snp_selection.pdf
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Whole Genome Sequencing Human Genome Project
First genome sequenced in 2000; project completed 2003 1000 Genomes Project
Goal: to create a complete and detailed catalogue of human genetic variation
Knome (founded by George Church and Harvard University) knomeDiscovery – sequencing (30x) and interpretation for
~$5,000 The Personal Genome
Interpretation (counseling?) Screening? High-risk groups? Drug efficacy? May help individuals alter behavior – but for now, we can’t do
anything about our genes!
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Bias in Genetic Studies
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Bias in Genetic Studies
Genetic polymorphism Disease
???
CONFOUNDING
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Bias in Genetic Studies
Genetic polymorphism Disease
Race/Ethnicity
CONFOUNDING
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Population Stratification
Example: Prostate cancer is more common in African
Americans than in Caucasians Frequency of many SNPs is different in African
American and Caucasian populations If we ignored race/ethnicity, what might
happen in our study?
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Population Stratification
Figure 1. The effects of population structure at a SNP locus.If the study population consists of subpopulations that differ genetically, and if disease prevalence also differs across these subpopulations, then the proportions of cases and controls sampled from each subpopulation will tend to differ, as will allele or genotype frequencies between cases and controls at any locus at which the subpopulations differ. The figure shows an example of this scenario with two populations in which the cases have an excess of individuals from population 2 and population 2 has a lower frequency of allele A than population 1. In this example, the structure mimics the signal of association in that there is a significant difference in allele and genotype frequencies between cases and controls.
Marchini, 2004
Caucasian
African American
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Adjusting for Ethnicity Defining & measuring ethnicity
Self-report Ancestry (where are you grandparents from?) Genotype many (hundreds) “ancestry
informative markers” Control for ethnicity
In design Restrict to one ethnicity Match on ethnicity
In analysis Stratify by ethnicity Include ethnicity in regression model
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Misclassification Non-differential
Of exposure: the degree of misclassification is the same according to disease status Likelihood that exposure is wrong is similar among
those who do and do not develop disease Differential
Of exposure: The degree of misclassification varies according to the disease status
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Misclassification Laboratory tests do not always work perfectly –
some % of samples may fail genotyping Missing or incorrect exposure information
Non-differential or differential misclassification? What can we do to ensure that the misclassification is
non-differential?
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Gene x Environment Interaction: An Example of Effect Modification
Given equal exposure to the same risk factor, individuals may have different risk of disease depending on their genetic background The effect of an exposure on a disease outcome is modified by genotype
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Gene-environment interaction
D+ D-
E+ 40 20
E- 80 40
D+ D-
E+ 60 80
E- 20 60
D+ D-
E+ 100 100
E- 100 100
OR = 1
AA genotype
AT/TT genotype
OR = 1
OR = 2.25
Stratify on genotype
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Effect Modification is Biological
DNA damage Lung Cancer
CYP1A1 GSTM1
Metabolism
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GWAS follow-up
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GWAS follow-up-Dozens of GWAS for many diseases have now been performed
-Thousands of samples and hundreds of thousands of SNPs
-Replication is necessary to determine which significant results are real
-Once we know the results are real, then what???
Eeles RA et al. (2008)
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GWAS follow-up Risk prediction model development
Understand biological function candidate genes/regions!
Some associated SNPs are not in gene regions Many types of biological data and techniques
can be employed to determine the function of the risk SNPs Fine mapping Expression (RNA and protein) Enhancer activity
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GWAS follow-up – 8q24 story
Ghoussaini et al.
A) Haploview output of the 1.18-Mb 8q24 "desert" showing the five cancer-specific regions reported to date
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GWAS follow-up – 8q24 story
Pomerantz et al, 2009
8q24 variation not associated with MYC mRNA expression in prostate tumor or normal tissue
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(a) ChIP assay on Colo205, demonstrating a pattern consistent with enhancer activity. (b) Luciferase reporter assay demonstrating enhancer activity in two CRC lines. Error bars denote one standard deviation from the mean of replicate assays. (c) Representative luciferase assay showing increased enhancer activity of G over T alleles, performed on a total of 18 clones (nine G and nine T over 3 d) (P = 0.024). Error bars denote one standard deviation from the mean of assays performed in triplicate. (d) Mass spectrometry plots from Sequenom analysis showing preferential binding of TCF7L2 to risk allele (G) in immunoprecipitated DNA, as evidenced by differential peak heights (right panel) compared to control input DNA (left panel) (P = 1.1 10-5).
GWAS follow-up – 8q24 story
Pomerantz et al, 2009
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GWAS follow-up (and beyond)
GWAS results
mRNA expression
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Thank you! Questions?