Beyond summary statistics

Jie Yuan

Over the past 20 years, Genome-Wide Association Studies (GWAS) have identified thousands of variants in the genome linked to genetic diseases. However, these associations often reveal little about underlying genetic etiology, which for many phenotypes is thought to be highly heterogeneous. This work investigates statistical methods to move beyond conventional GWAS methods to both improve estimation of associations and to extract additional etiological insights from known associations, with a focus on schizophrenia. This thesis addresses the above aim through three primary topics: First, we describe DNA.Land, a web platform to crowdsource the collection of genomic data with user consent and active participation, thereby rapidly increasing sample sizes and power required for GWAS. Second, we describe methods to characterize the latent genomic contributors to heterogeneity in GWAS...