Statistical Methods for Identifying X-linked Genes Associated with Complex Phenotypes.

摘要

Genetic association studies aim to detect association between one or more genetic polymorphisms and complex traits, which might be some quantitative characteristic or a qualitative attribute of disease. In Chapter 1, we introduce the development of methods for association mapping in the past decades and present the rationale behind our X-linked method development. Family-based association methods have been well developed for autosomes, but unique features of X-linked markers have received little attention. In Chapter 2, we propose a likelihood approach (X-LRT) to estimate genetic risks and test association using a case-parents design. The method uses nuclear families with a single affected proband, and allows additional siblings and missing parental genotypes. We also extend X-LRT from a single-marker test to a multiple-marker haplotype analysis. Our X-LRT offers great flexibility for testing different penetrance relationships within and between sexes. In addition, estimation of relative risks provides a measure of the magnitude of X-linked genetic effects on complex disorders. In Chapter 3 and 4, we fill the methodological gaps by developing two approaches (X-QTL and X-HQTL) to test association between X-linked marker alleles/haplotypes and quantitative traits in nuclear family design. We adopt the orthogonal decomposition which provides consistent estimates of the additive genetic values of marker alleles/haplotypes. Joint estimation of the linkage variance component in the association model reduces type I errors to nominal expectations. Dosage compensation models provide a simple relationship of X-linked additive effects between sexes. In Chapter 2, 3, and 4, our simulation results demonstrate the validity and substantially higher power of our approaches compared with other existing programs. We also apply our methods to MAOA & MAOB candidate-gene studies of family data with Parkinson disease. In Chapter 5, we discuss some issues relevant to the design and execution of our X-linked family-based association studies.