| Title: | SDPRX: A statistical method for cross-population prediction of complex traits |
| Journal: | American Journal of Human Genetics |
| Published: | 1 Dec 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36460009/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2022.11.007 |
Publication 8054
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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 develop a statistical method called SDPRX that can effectively integrate genome wide association study summary statistics from different populations. SDPRX automatically adjusts for linkage disequilibrium differences between populations and characterizes the joint distribution of the effect sizes of a variant in two populations to be both null, population specific, or shared with correlation. Through simulations and applications to real traits, we show that SDPRX improves the prediction performance over existing methods in non-European populations.3 Authors
- Geyu Zhou
- Tianqi Chen
- Hongyu Zhao
1 Application
| Application ID | Title |
|---|---|
| 29900 | Defining physical activity phenotypes based on objective measures and dissecting the genetic and phenotypic relationships of physical activity with diseases and health outcomes |
Enabling scientific discoveries that improve human health