Introduction to GWAS: Understanding Genetic Association Studies
Explore the world of Genome-Wide Association Studies (GWAS) in this informative introduction covering hypothesis-free genetic variation analysis, quantitative trait regression, case-control logistic regression, relatedness considerations, and their importance in understanding genetic associations with diseases and traits.
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Presentation Transcript
Introduction to GWAS (part I) Katrina Grasby and Lucia Colodro Conde
What is it? A hypothesis free study of genetic variation across the entire human genome Tests for genetic associations with continuous traits or with the presence / absence of disease With a focus on low penetrance & high frequency loci Tests indirect association Hirschhorn & Daly. Nat Rev Genet (2014)
Why do it? McCarthy et al. Nat Rev Genet (2008)
Quantitative Trait Linear Regression = + X + = score on phenotype X = 0, 1 or 2 copies of allele ( G ) = 0 no association > 0 G allele associated with higher score on trait < 0 G allele associated with lower score on trait Balding. Nat Rev Genet (2006)
Case-Control Logistic Regression Controls Cases A/A G/A A/A ln(P/1-P) = + X + G/G G/A A/A G/A G/A = difference in log odds for cases vs. controls G/G A/A G/G G/A A/A A/A A/A e( ) = difference in odds = Odd Ratio (OR) G/A A/A A/A G/G G/G Allelic effect is an OR: OR > 1 increased risk OR < 1 decreased risk The G allele is associated with disease
Relatedness Only a few in the total sample = drop Random Effects Model = + X + G + = fixed effect of the allele G = genetic relationship random effect Genetic Relationship Matrix (GRM) Sub-sample of SNPs Leave One Chromosome Out (LOCO)
