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 ProbabilityAA PAA= p2

Aa PAa = 2 p q

aa 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 Total

Number obs. 36 47 23 106

Frequency exp. p2 2pq q2 1

Number exp. 33.4 52.2 20.4 106

Deviation 2.6 -5.2 2.6

χ2 0.20 0.52 0.33 1.05

χ2 = 1.05 d.f. =1; P≥0.05 Fail to reject H0: HWE holds

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?