Details

Autor(en) / Beteiligte

• Ruan, Yunfeng
• Lin, Yen-Feng
• Feng, Yen-Chen Anne
• Chen, Chia-Yen
• Lam, Max
• Guo, Zhenglin
• He, Lin
• Sawa, Akira
• Martin, Alicia R.
• Qin, Shengying
• Huang, Hailiang
• Ge, Tian

Titel

Improving polygenic prediction in ancestrally diverse populations

Ist Teil von

• Nature genetics, 2022-05, Vol.54 (5), p.573-580

Ort / Verlag

New York: Nature Publishing Group US

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• Polygenic risk scores (PRS) have attenuated cross-population predictive performance. As existing genome-wide association studies (GWAS) have been conducted predominantly in individuals of European descent, the limited transferability of PRS reduces their clinical value in non-European populations, and may exacerbate healthcare disparities. Recent efforts to level ancestry imbalance in genomic research have expanded the scale of non-European GWAS, although most remain underpowered. Here, we present a new PRS construction method, PRS-CSx, which improves cross-population polygenic prediction by integrating GWAS summary statistics from multiple populations. PRS-CSx couples genetic effects across populations via a shared continuous shrinkage (CS) prior, enabling more accurate effect size estimation by sharing information between summary statistics and leveraging linkage disequilibrium diversity across discovery samples, while inheriting computational efficiency and robustness from PRS-CS. We show that PRS-CSx outperforms alternative methods across traits with a wide range of genetic architectures, cross-population genetic overlaps and discovery GWAS sample sizes in simulations, and improves the prediction of quantitative traits and schizophrenia risk in non-European populations. PRS-CSx is a polygenic risk score construction method that improves cross-population polygenic prediction by integrating GWAS summary statistics from multiple populations.