The Department of Biomedical Informatics and Data Science (DBIDS) invites you to join us for our PowerTalks Seminar Series featuring Hongyu Zhao, Ph.D., Professor of Biostatistics, Genetics and Statistics and Data Science at Yale University.

Dr. Zhao's presentation focusing on genetic risk, will be entitled "Statistical Methods for Cross-Population Genetic Risk Prediction of Complex Traits."

Dr. Zhao is the Ira V. Hiscock Professor of Biostatistics at Yale University. He received his B.S. in Probability and Statistics from Peking University in 1990 and Ph.D. in Statistics from UC Berkeley in 1995. His research interests are the developments and applications of statistical methods in molecular biology, genetics, drug developments, and precision medicine. He is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. He is the recipient of multiple honors, including the Mortimer Spiegelman Award for a top statistician in health statistics by the American Public Health Association, and Pao-Lu Hsu Prize by the International Chinese Statistical Association.

Abstract: The polygenic risk score (PRS) has demonstrated great utility in biomedical research through identifying high-risk individuals for different diseases based on genotypes. However, the broader application of PRS to the general population is hindered by the limited transferability of PRS developed in Europeans to non-European populations. To improve PRS prediction accuracy in non-European populations, we have developed Bayesian methods that can effectively integrate genome wide association study (GWAS) summary statistics from different populations. Our methods automatically adjust for linkage disequilibrium differences between populations, and characterize the joint distribution of the effect sizes of a variant in different populations to be null, population specific, or shared with correlation. Through simulations and applications to real traits, we have shown that our methods improve prediction performance over existing methods in non-European populations