Statistical methods for large-scale cross-population data analysis

The development of polygenic-risk scores (PRS) has proved useful to stratify the general population into different risk groups for the European population. However, PRS is less accurate in non-European populations due to genetic differences across different populations. To improve the prediction accuracy in non-European populations, we propose a cross-population analysis framework for PRS construction with both individual-level (XPA) and summary-level (XPASS) GWAS data. By leveraging trans-ethnic genetic correlation, our methods can borrow information from the Biobank-scale European population data to improve risk prediction in the non-European populations. With novel data structure and algorithm design, our methods provide a substantial saving in computational time and memory usage. Through comprehensive simulation studies, we show that our framework provides accurate, efficient, and robust PRS construction across a range of genetic architectures. In a Chinese dataset collected by us, our methods achieved 7.3%-198.0% accuracy gain for height and 19.5%-313.3% accuracy gain for body mass index (BMI) in terms of predictive R-squared compared to existing PRS approaches, respectively.

Reference

  • Mingxuan Cai, Jiashun Xiao, Shunkang Zhang, Xiang Wan, Hongyu Zhao, Gang Chen, Can Yang. A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits. The American Journal of Human Genetics. [AJHG link][XPA software][XPASS software][Preprint Version][Supplementary Note]. 108, 632-655, April 2021.

  • Jiashun Xiao, Mingxuan Cai, Xianghong Hu, Xiang Wan, Gang Chen, and Can Yang. XPXP: Improving polygenic prediction by cross-population and cross-phenotype analysis. [XPXP software]. [link]. Bioinformatics, 2022.

  • Jiashun Xiao, Mingxuan Cai, Xinyi Yu, Xianghong Hu, Gang Chen, Xiang Wan, Can Yang. Leveraging the local genetic structure for trans-ancestry association mapping. The American Journal of Human Genetics [AJHG link][BioRxiv version][software]. 2022.