Speaker: Dr. Minsun Song Venue: B105 Date and Time:
About the Speaker
- (Current) Associate Professor, Dept of Statistics, Sookmyung Women's University - Assistant Professor, Dept of Mathematics & Statistics, University of Nevada, Reno - Postdoctoral Associate, Lewis-Sigler Institute for Integrative Genomics, Princeton University - Postdoctoral Associate, National Cancer Institute, National Institute of Health - Ph.D. in Statistics, University of Chicago - M.S. & B.S. in Statistics, Seoul National University - Research interest: Statistical genetics and epidemiology, high dimensional data analysis, and dimension reduction
Abstract We present a new statistical test of association between a trait and genetic markers, which we theoretically and practically prove to be robust to arbitrarily complex population structure. The statistical test involves a set of parameters that can be directly estimated from large-scale genotyping data, such as those measured in genome-wide associations studies. We also derive a new set of methodologies, called a genotype-conditional association test, shown to provide accurate association tests in populations with complex structures, manifested in both the genetic and non-genetic contributions to the trait. Our proposed framework provides a substantially different approach to the problem from existing methods.