UCLA

Variance component models are a fundamental topic in statistical genetics. These models enable us to estimate the underlying heritability of a phenotype, adjust for confounding in association testing, and assess the strength of effects of a set of genetic markers on a phenotype. Under the overarching theme of variance component models, this dissertation aims to elucidate the genetic architecture of complex diseases and traits by developing and applying variance component model-based methods to analyze high-dimensional genomic data. In the first half of the dissertation, we propose a variance component selection framework that jointly models and prioritizes a set of genetic markers that are associated with quantitative traits. The second half of the dissertation is devoted to quantifying the heritability of diabetes complications. We use various heritability estimation methods, some of which are based on variance component models.