본문 바로가기 사이드메뉴 바로가기 대메뉴 바로가기

Applied Mathematics and Statistics

News & Events

[Seminar] Testing for Genetic Associations in Arbitrarily Structured Populations

AuthorApplied Mathematics & Statistics REG_DATE2023.03.17 Hits333

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.