1/6/11. Definitions. Molecular and Genetic Epidemiology. Genetic Studies. Genetic and Molecular Epidemiology. Twin studies.

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1 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 and Genetic Epidemiology Kathryn Penney, ScD January 6, 2011 Molecular Epidemiology ( 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 Genetic and Molecular Epidemiology Genetic Studies Genetic variation Association? Association? Association? Exposure Biological Factors/ Mechanism 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 R MZ = genetics + shared environment R DZ = ½ genetics + shared environment Genetics = 2 x (R MZ R DZ ) Heritability Lichtenstein et al,

2 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 Samples Blood DNA, RNA, biomarkers (dietary, hormones) Tissue Tumor and normal DNA, RNA, proteins Select a gene of interest Select SNPs to genotype Literature tagsnps Haplotype tagsnps G/T A/C G/A C G A C C G G/T A/C G/A C G A C C G 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 Haploview visualize correlations between SNPs in HapMap or study data Tagger method to select tagsnps in HapMap or study data Are the SNPs associated with outcome? Are the SNPs associated with intermediate phenotypes/ biomarkers/tumor markers? 2

3 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 ( SNPs) assay 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 GWAS results Published Genome-Wide Associations through 6/2010, 904 published GWA at p<5x10-8 for 165 traits NHGRI GWA Catalog Amundadottir et al, 2009 Genotyping technology Illumina 1 million SNP chip tagsnps selected from HapMap data Affymetrix 1 million SNP chip Selected based on distance Bias in Genetic Studies technote_intelligent_snp_selection.pdf 3

4 Bias in Genetic Studies Bias in Genetic Studies CONFOUNDING CONFOUNDING Genetic polymorphism Genetic polymorphism??? Race/Ethnicity 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? Population Stratification Caucasian African American 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 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 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 4

5 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 nondifferential? 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 Gene-environment interaction D+ D- Effect Modification is Biological E E OR = 1 Stratify on genotype Metabolism AA genotype D+ D- E AT/TT genotype D+ D- E CYP1A1 GSTM1 DNA damage Lung Cancer E E OR = 1 OR = 2.25 Gene-environment interaction Compared to those with low quercetin and GG genotype, those with high quercetin or the GA/AA genotype had a lower risk of lung cancer GWAS follow-up While this p-value is significant, it would not remain significant after adjustment for multiple comparisons -- common problem with gene-environment interaction studies Lam et al,

6 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??? 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 Eeles RA et al. (2008) GWAS follow-up 8q24 story A) Haploview output of the 1.18-Mb 8q24 "desert" showing the five cancer-specific regions reported to date GWAS follow-up 8q24 story 8q24 variation not associated with MYC mrna expression in prostate tumor or normal tissue Ghoussaini et al. Pomerantz et al, 2009 GWAS follow-up 8q24 story GWAS follow-up (and beyond) (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 = ). Lethal PCa GWAS ~500,000 SNPs 600 cases mrna expression 24,000 genes 400 cases Pomerantz et al,

7 Thank you! Questions? 7