ZiHuai He

Post date: Oct 8, 2013 3:59:47 PM

Topic 1:

Modeling and Testing for Joint Association Using a Genetic Random Field Model

Joint work with Min Zhang, Xiaowei Zhan and Qing Lu

Substantial progress has been made in identifying single genetic variants predisposing to common complex diseases. Nonetheless, the genetic etiology of human diseases remains largely unknown. Human complex diseases are likely influenced by the joint effect of a large number of genetic variants instead of a single variant. The joint analysis of multiple genetic variants considering linkage disequilibrium (LD) and potential interactions can further enhance the discovery process, leading to the identification of new disease-susceptibility genetic variants. Motivated by development in spatial statistics, we propose a new statistical model based on the random field theory, referred to as a genetic random field model (GenRF), for joint association analysis with the consideration of possible gene-gene interactions and LD. 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 a commonly adopted kernel machine approach, SKAT, as well as other more standard methods, GenRF shows overall comparable performance and better performance in the presence of complex interactions. The method is further illustrated by an application to the Dallas Heart Study.

Topic 2 as an extension of Topic 1:

Multi-Marker Tests for Joint Association in Longitudinal Studies Using the Genetic Random Field Model

Joint work with Min Zhang, Jennifer Smith, Sharon Kardia, Ana Diez Roux, Seunggeum Lee, Xiuqing Guo, Walter Palmas and Bhramar Mukherjee

Longitudinal studies of common and chronic diseases provide a valuable chance to better explore how genetic variants affect these traits over time. Statistical power to detect disease susceptibility variants can be improved if we jointly utilize the entire set of longitudinal outcomes that better describe the time-course development of the trait of interest. Since disease phenotypes are likely influenced by the joint effect of multiple variants, a joint analysis of multiple genetic variants in a region considering linkage disequilibrium (LD) and potential interactions among variants may help to identify additional heritability. In this article, we propose a novel statistical approach: longitudinal genetic random field model (LGRF), to test the joint association between a set of genetic variants and a phenotype measured repeatedly during the course of an observational study. We consider potential time dependency and correlation in the outcomes measured on the same subject. Several essential methodological improvements necessary for handling longitudinal data are further proposed to enhance the robustness upon misspecification of within-subject correlation structure and to improve computational efficiency. The performance of the proposed methods were evaluated through simulation studies and illustrated using data from the Multi-Ethnic Study of Atherosclerosis (MESA). Our simulation results indicate conspicuous gain in power using LGRF when compared to the two commonly used existing alternatives: (i) single marker tests using longitudinal outcome and (ii) existing multi-marker association tests like the sequence kernel association tests (SKAT) using average outcome.