Advisory Committee Chair
Nianjun Liu
Advisory Committee Members
Nita A Limdi
Jianming Tang
Hemant K Tiwari
Document Type
Dissertation
Date of Award
2014
Degree Name by School
Doctor of Philosophy (PhD) School of Public Health
Abstract
This dissertation research focuses on developing statistical methods for set-based association tests at both pathway and gene levels in genetic studies. Set based analysis considers the biological hierarchical structure, while traditional genome-wide association studies usually focus on single-marker analysis which only access marginal effect. Therefore, pathway analysis may potentially complement single-marker analysis and provide additional insights for the genetic architecture of complex diseases. In the first study, we propose a novel way for pathway analysis that assesses the effects of genes using the sequence kernel association test (SKAT) and the effects of pathways via an extended adaptive-rank-truncated product statistic. For sequencing data, SKAT as a set-based method at gene level has been shown to be able to complement traditional single marker association test. However, the standard SKAT methodology only considers the phenotype measurement at one time point. Therefore, in the second study, we introduce a framework for association test, which uses multiple phenotype measurements for each subject. The proposed method is based on kernel machine regression and can be viewed as an extension of SKAT (L-SKAT). Furthermore, in the third study, we propose an analytical method that can be viewed as an extension of SKAT to be applicable for familial data with non-continuous phenotype (F-SKAT) based on generalized linear mixed model.
Recommended Citation
Yan, Qi, "Statistical Methods for Set-Based Association Tests in Genetic Studies" (2014). All ETDs from UAB. 3396.
https://digitalcommons.library.uab.edu/etd-collection/3396