Set-based Tests for Genetic Association and Gene-Environment Interaction.
Year: 2016
Type: article
Abstract: In the first project, we propose a new statistical model based on the random field theory, referred to as a genetic random field model (GenRF), for gene/region based association analysis in a cross-sectional study. Using a pseudo-likelihood approach, a GenRF test for the joint association of multiple genetic variants is developed, which has the following advantages: 1. accommodating complex interactions for improved performance; 2. natural dimension reduction; 3. boosting power in the presence of LD; 4. computationally efficient. Simulation studies are conducted under various scenarios. Compared with the sequence kernel association test (SKAT), as well as other more standard methods, GenRF shows overall comparable performance and better performance in the presence of complex interactions. In the second project, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set of genetic variants. Generalized score type tests are developed, which we show are robust to misspecification of within-subject correlation. In addition, a joint test incorporating gene-time interaction is further proposed. Computational advancement is made for scalable implementation of the proposed methods in large-scale genome-wide association studies (GWAS). In the third project, we propose a generalized score type test for set-based inference for gene-environment interaction with longitudinally measured quantitative traits. The test is robust to misspecification of within subject correlation structure and has enhanced power compared to existing alternatives. Unlike tests for marginal genetic association, set-based tests for gene-environment interaction face the challenges of a potentially misspecified and high-dimensional main effect model under the null hypothesis. We show that our proposed test is robust to main effect misspecification of environmental exposure and genetic factors under the gene-environment independence condition. When genetic and environmental factors are dependent, the method of sieves is further proposed to eliminate potential bias due to a misspecified main effect of a continuous environmental exposure. A weighted principal component analysis approach is developed to perform dimension reduction when the number of genetic variants in the set is large relative to the sample size.
Author: Zihuai He
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funders: University of Michigan, Drexel University
OpenAlex ID: W2561429041