Genetic Architecture of Common Diseases and Complex Traits

Abstract

Over the last decade, genome-wide association studies (GWAS) have revolutionized the study of disease. For the first time, the genetic architectures of common diseases and complex traits can be observed, their associations mapped and mined; and with these advances have come new mysteries: where heritability resides, how it is mediated, and why it is as it is. In this thesis, I introduce statistical methods to probe the genetic architecture of common diseases and complex traits. In chapter I, I ask why it is that complex traits are so polygenic. I show that negative selection flattens the distribution of heritability across the genome relative to the underlying biology. In chapter II, I describe the landscape of pleiotropy across complex traits. I show that pleiotropy is strongest among highly polygenic traits with shared causal cell types. In chapter III, I develop method to infer causal relationships between complex traits. This method harnesses the entire polygenic signal for each trait and quantifies the asymmetry in their pleiotropic effect-size distribution, and I show that it recapitulates known biology and suggests intriguing new hypotheses.