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Objectives

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.

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Objectives

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  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

  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 (χ2) • H0: HWE • Ha: no HWE • Compare observed frequency for a class with that expected if the null hypothesis were true

  4. χ2 = 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?

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