Objectives • Cover some of the essential concepts for GWAS that have not yet been covered • Hardy-Weinberg equilibrium • Meta-analysis • SNP Imputation • Review what we have learned about the genetics of common disease from GWAS • Where do we go from here? What do we go with GWAS results. • functional characterization of GWAS loci • clinical applications Hardy-Weinberg Law In a large, randomly mating population, genotypes at a given locus will be in Hardy Weinberg Equilibrium (HWE) Aa : alleles at a single locus; p = relative frequency of A; q = relative frequency of a; p+q=1 Under random mating Genotype AA Aa aa Probability PAA= p2 PAa = 2 p q Paa = q2 HWE and genotyping HWE provides useful check for genotyping errors For a rare disease (or no/modest genetic effects), genotype frequencies in controls should (nearly) follow HWE HWE test: Chi-square test (χ2) H0: HWE Ha: no HWE Compare observed frequency for a class with that expected if the null hypothesis were true Genotype AA Aa aa Number obs. 36 47 23 106 Frequency exp. p2 2pq q2 1 33.4 52.2 20.4 106 2.6 -5.2 2.6 0.20 0.52 0.33 Number exp. Deviation χ2 Total χ2 = 1.05 d.f. =1; P≥0.05 Fail to reject H0: HWE holds 1.05 Meta-Analysis • Most current GWAS studies actually combine the results of multiple distinct cohorts • mega-analysis versus meta-analysis • How does meta-analysis work? combine the association results ORs/Betas and standard errors fixed effects – assume one true effect for SNP random effects – account for a range of possible true effects • heterogeneity – Cochrane’s Q or I-squared • • • • Meta-Analysis Results are Displayed as Forest Plots Castaldi et al, Human Molecular Genetics 2010 Imputation – Using LD and Hapmap/1000 Genomes to Impute Untyped SNPs • Most current GWAS studies take their genotyped SNPs and then impute SNPs from the HapMap project or the 1,000 Genomes project (~8 million SNP). • This is very computationally intensive • Mach • Beagle • Basic principle is to use a densely genotyped reference panel, compare it to your study sample, and infer untyped SNPs. • Imputation allows for combining studies that used different genotype chips Imputation Works by Inferring Haplotypes and Comparing to a Reference Marchini et al, Nature Reviews Genetics 2011 Using Principal Components Analysis (PCA)as a Surrogate for Genetic Ancestry • DNA contains a tremendous amount of information about evolutionary history. • It is common practice to adjust for population stratification in GWAS studies by adjusting for principal components of genetic ancestry. • Price et al, “Principal components analysis corrects for stratification in genome-wide association studies”, Nature Genetics 2006 What is PCA?