1. 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

2. 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 GenotypeProbability AAP AA = p 2 AaP Aa = 2 p q aaP aa = q 2

3. 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 (χ 2 ) H0: HWE Ha: no HWE Compare observed frequency for a class with that expected if the null hypothesis were true

4. GenotypeAAAaaaTotal Number obs. 364723106 Frequency exp. p2p2 2pqq2q2 1 Number exp. 33.452.220.4106 Deviation 2.6-5.22.6 χ2χ2 0.200.520.331.05 χ 2 = 1.05 d.f. =1; P≥0.05 Fail to reject H0: HWE holds

5. 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

6. Meta-Analysis Results are Displayed as Forest Plots Castaldi et al, Human Molecular Genetics 2010

7. 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

8. Imputation Works by Inferring Haplotypes and Comparing to a Reference Marchini et al, Nature Reviews Genetics 2011

9. 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

10. What is PCA?