Statistical methods for cross-population prediction of complex traits
Conference
64th ISI World Statistics Congress - Ottawa, Canada
Format: IPS Abstract
Wednesday 19 July 2 p.m. - 3:40 p.m. (Canada/Eastern)
Abstract
Polygenic risk score (PRS) has demonstrated its great utility in biomedical research through identifying high risk individuals for different diseases from their 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 a statistical method called SDPRX that can effectively integrate genome wide association study summary statistics from different populations. SDPRX characterizes the joint distribution of the effect sizes of a variant in two populations to be both null, population specific or shared with correlation. It automatically adjusts for linkage disequilibrium differences between populations. Through simulations and applications to seven traits, we compared the prediction performance of SDPRX with other methods. We show that SDPRX outperforms other cross population prediction methods in the prediction accuracy in non-European populations. This is joint work with Geyu Zhou and Tianqi Chen.