Population Stratification: Reliable Inference and Association Mapping
Halls department, Hall 2
Wednesday, 27 December 2017
12:00 - 13:00
Population stratification referes to clustering people of a population based on their original subpopulations. In this talk, the statistical model underlying the population structure will be presented. Based on the model, the theoretical aspects of population stratification will be investigated. In particular, sample size complexity for inferring the subpopulation will be discussed. Moving to association mapping, we develop a framework which jointly infers the disease causal factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability distribution of the model parameters.
Seyed Abolfazl Motahari is an assistant professor at Computer Engineering Department of the Sharif University of Technology. He received his B.Sc. degree from Iran University of Science and Technology (IUST), Tehran, Iran, in 1999, the M.Sc. degree from Sharif University of Technology, Tehran, Iran, in 2001, and the Ph.D. degree from University of Waterloo, Waterloo, Canada, in 2009, all in Electrical Engineering. From August 2000 to August 2001, he was a Research Scientist with the Advanced Communication Science Research Laboratory, Iran Telecommunication Research Center (ITRC), Tehran. From October 2009 to September 2010, he was a Postdoctoral Fellow at the University of Waterloo, Waterloo. From September 2010 to July 2013, he was a Postdoctoral Fellow with the Department of Electrical Engineering and Computer Sciences, University of California at Berkeley. His research interests include Information Theory and Bioinformatics. He received several awards including Natural Science and Engineering Research Council of Canada (NSERC) Post-Doctoral Fellowship.