UCLA

A major finding from the last decade of genome-wide association studies (GWAS) is that variant-phenotype associations are significantly enriched in noncoding regulatory regions of the genome. This result suggests that GWAS associations localize variants that modulate phenotype via gene regulation as opposed to alterations in protein structure/function. However, for most complex traits, most aspects of genetic architecture—the number of causal variants/genes for a trait and the degree to which causal effect sizes are coupled with genomic features such as minor allele frequency (MAF) and linkage disequilibrium (LD)—remain actively debated. In this dissertation, I introduce three new methods to explore and quantitatively characterize complex-trait genetic architecture. First, I derive an unbiased estimator of genome-wide SNP-heritability under a very general random effects model that makes minimal assumptions on the underlying (unknown) genetic architecture of the trait. Second, I introduce a method for estimating the number of causal variants that are shared between two ancestral populations for a given trait, and I discuss the implications of the method and real-data results for improving polygenic risk prediction in ethnic minority populations. Third, I propose methods for partitioning the heritability of individual genes by MAF to identify disease-relevant genes, with the hypothesis that some disease-relevant genes may have relatively large heritability contributions from rare and low-frequency variants while still having low total gene-level heritability.