Home > Events > 2011 Seminars & Colloquia > Xiaoquan Wen - University of Chicago

Xiaoquan Wen - University of Chicago

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Bayesian Analysis of Genetic Association Data, Accounting For Heterogeneity
In genetic association analysis it is often desired to analyze data from multiple potentially-heterogeneous subgroups. For example, 1. To detect modest genetic association signals that are too weak to be detected in smaller individual studies, meta-analysis of multiple studies are often required. These studies are typically carried out by different investigators, at different centers, which might be expected to exhibit heterogeneity of genetic effects. 2. In analysis of a single study, genuine biological/environmental interactions may cause some genetic variants to have different effects on individuals in different subgroups. In this talk, I will describe Bayesian methods of analysis developed to deal with these situations. In particular, we develop some computationally-efficient approaches to computing Bayes factors in these settings. One of these approaches yields a Bayes Factor with a simple and intuitive analytic form. Various interesting properties of meta-analysis Bayes Factors will be discussed and demonstrated through real data examples. Finally, I will discuss our ongoing work on mapping tissue/population specific expression Quantitative Traits Loci (eQTL) using a novel hierarchical mixture model built on the groundwork laid by this Bayesian meta-analysis approach.