STATISTICAL METHODS IMPROVING THE CLINICAL UTILITY OF OMICS DATA

Abstract

Variants identified via genome-wide association studies (GWAS) have ushered in an era of deep interest in omics data. Early adopters have used GWAS discoveries to inform drug targets and establish causal relationships using genetic instruments, yet more research must be done to bring the initial boons of GWAS to clinical practice. My dissertation presents three novel statistical methods which could bridge this gap by correcting biases when analyzing omics data and addressing methodological disparities affecting non-European populations. In my first project, I present THUNDER, a novel deconvolution method tailored to the unique challenges of chromatin conformation capture. Prior to our research, differential analysis of chromatin organization was confounded by underlying cell type proportions. Therefore, analyzing across individuals for differential chromatin activity has been of limited utility. THUNDER accurately estimates cell type proportions, allowing for their inclusion as a confounder in future association studies of Hi-C phenotypes. In my second project, I present GAUDI, a fused lasso approach to estimate polygenic risk scores (PRS) in admixed individuals. Our method addresses the decreases in performance of PRS methods in non-European populations, in part due to previously unaccounted for patterns of genetic admixture. Finally, in my third project, I extend polygenic risk score estimation techniques to the variable copy number setting to identify carriers for Spinal Muscular Atrophy (SMA) for which no standard test to identify these carriers exists.Doctor of Philosoph