All ETDs from UAB

Advisory Committee Chair

Nengjun Yi

Advisory Committee Members

Nianjun Liu

Upender Manne

Boris C Pasche

Kui Zhang

Document Type


Date of Award


Degree Name by School

Doctor of Philosophy (PhD) School of Public Health


This dissertation research focuses on genetic association analysis based on haplotypes in the context of both population-based and family-based studies. Haplotype-based association analysis is powerful in the discovery and characterization of the genetic basis of complex human diseases. However, statistical models that fit haplotype-haplotype and haplotype-environment interactions have not yet been fully developed. Furthermore, statistical methods for detecting the association between rare haplotypes and disease have not kept pace with their counterpart of common haplotypes. For both population-based and family-based association analyses, we herein propose two efficient and robust methods to separately tackle these problems based on Bayesian hierarchical generalized linear models. Our models simultaneously fit environmental effects, main effects of numerous common and rare haplotypes, and haplotype-haplotype and haplotype-environment interactions. The key to the approaches is the use of a continuous prior distribution on coefficients that favors sparsity in the fitted model and facilitates computation. We develop a fast expectation-maximization (EM) algorithm to fit models by estimating posterior modes of coefficients. We incorporate our algorithm into the iteratively weighted least squares for classical generalized linear models as implemented in the R package glm. We evaluate the proposed methods and compare their statistical properties to existing approaches on extensive simulated data. The results show that the proposed methods perform well under all situations and are more powerful than the competitors.

Included in

Public Health Commons



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