Deciphering The Genetic Basis For Complex Trait Variation

Within any population, complex trait variation can be attributed to an impressive number of genetic factors. Identification of such factors has been made possible, in part, by large biomedical datasets comprised of genotypes and phenotypes for hundreds of thousands of individuals. Furthermore, understanding the biological mechanisms through which genetic variation creates complex trait variation has been facilitated by high-throughput sequencing technology, used to quantify molecular, intermediate phenotypes. Despite such datasets being widely available, we lack understanding of the full spectrum of genetic effects, including gene-by-sex (GxS) interactions. We also have yet to uncover various molecular phenotypes that may "link" genetic variation to complex trait variation. To address these gaps in knowledge, the following chapters will 1) develop and utilize statistical methodology for mapping GxS interactions among human traits, and 2) utilize a pig model to characterize RNA editing-a relatively understudied form of transcriptional regulation- and evaluate its potential to link genetic variation with complex trait variation.Growing evidence from genome-wide parameter estimates suggest males and females from human populations possess differing genetic architectures. Despite this, mapping GxS interactions remains challenging, suggesting that the magnitude of a typical GxS interaction is exceedingly small. We have developed a local Bayesian regression (LBR) approach to estimate sex-specific single nucleotide polymorphism (SNP) marker effects after fully accounting for local linkage-disequilibrium (LD) patterns. This provided means to infer GxS interactions either at the SNP level, or by aggregating multiple sex-specific SNP effects to make inferences at the level of small, LD-based regions. In simulations, LBR provided greater power and resolution to detect GxS interactions than the traditional approach to genome-wide association (GWA), single-marker regression (SMR).When using LBR to analyze human traits from the UK Biobank (N ∼ 250,000) including height, BMI, bone-mineral density, and waist-to-hip ratio, we find evidence of novel GxS interactions where sex-specific effects explain a very small proportion of phenotypic variance (R2