Description:

Department of Statistics Spring 2019 Seminar Series

Talk Title: Bayesian Variable Selection for Integrative Genome-Wide Association Analysis

Speaker: Min Zhang, Professor of Statistics, Department of Statistics, Purdue University

Time: 11:00am

Abstract: A variable selection framework is proposed to integrate pathway information for genome-wide association analysis. Unlike other methods that rely on computation-intensive Markov chain Monte Carlo algorithms, we proposed an iterated conditional modes/medians algorithm to implement an empirical Bayes variable selection. Iterated conditional modes are first utilized to optimize values of the hyper-parameters and to implement the empirical Bayes method, and then iterated conditional medians are used to estimate the model parameters and therefore implement the variable selection function. In addition to the advantages of Bayesian inference, the proposed method enjoys efficient computation, increased statistical power of the analysis, and improved estimation of the model parameters. Simulation studies showed the superior performance of the proposed approach, and the method has been applied to real data from genome-wide association studies.

Location: Department of Statistics, Room B02, 2006 Sheridan Rd, Evanston 60208