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Improving polygenic prediction in ancestrally diverse populations
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Abstract
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.
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“The cross-population PGS from PRS-CSx explaining up to 4.0% of the phenotypic variance in EduYears in the EAS cohorts.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [36]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
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“Furthermore, our PGS analyses also indicated the advantages of ancestral diversity over a single population in PGS construction36.”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [36]
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Metadata
- Authors
- Ruan, Y., Lin, Y.-F., Feng, Y.-C. A., Chen, C.-Y., Lam, M., Guo, Z., Initiatives, S. G. A., Ahn, Y. M., Akiyama, K., Arai, M., Baek, J. H., Chen, W. J., Chung, Y.-C., Feng, G., Fujii, K., Glatt, S. J., Ha, K., Hattori, K., Higuchi, T., Hishimoto, A., Hong, K. S., Horiuchi, Y., Hwu, H.-G., Ikeda, M., Ishiwata, S., Itokawa, M., Iwata, N., Joo, E.-J., Kahn, R. S., Kim, S.-W., Kim, S. J., Kim, S. H., Kinoshita, M., Kunugi, H., Kusumawardhani, A., Lee, J., Lee, B. D., Lee, H.-J., Liu, J., Liu, R., Ma, X., Myung, W., Numata, S., Ohmori, T., Otsuka, I., Ozeki, Y., Schwab, S. G., Shi, W., Shimoda, K., Sim, K., Sora, I., Tang, J., Toyota, T., Tsuang, M., Wildenauer, D. B., Won, H.-H., Yoshikawa, T., Zheng, A., Zhu, F., He, L., Sawa, A., Martin, A. R., Qin, S., Huang, H., & Ge, T.
- Affiliations
- Broad Institute (US)
- Biogen (United States) (US)
- Seoul National University (KR)
- Dokkyo University (JP)
- Samsung Medical Center (KR)
- National Taiwan University (TW)
- Digital China Health (China) (CN)
- Shiga University of Medical Science (JP)
- National Center of Neurology and Psychiatry (JP)
- Fujita Health University (JP)
- Tokyo Metropolitan Institute of Medical Science (JP)
- Eulji University (KR)
- Chonnam National University (KR)
- University of Indonesia (ID)
- Nanyang Technological University (SG)
- Korea University (KR)
- Agency for Science, Technology and Research (SG)
- Seoul National University Bundang Hospital (KR)
- Tokushima University (JP)
- Kobe University (JP)
- University of Wollongong (AU)
- Institute of Mental Health (SG)
- Sir Run Run Shaw Hospital (CN)
- Central South University (CN)
- RIKEN Center for Brain Science (JP)
- The University of Western Australia (AU)
- Shanghai Jiao Tong University (CN)
- Johns Hopkins University (US)
- Harvard University (US)
- Corresponding author
- Qin, Shengying
- Huang, Hailiang
- Ge, Tian
- ORCID
- Ruan, Yunfeng
- Lin, Yen-Feng
- Feng, Yen-Chen Anne
- Chen, Chia-Yen
- Lam, Max
- Ahn, Yong Min
- Akiyama, Kazufumi
- Baek, Ji Hyun
- Chen, Wei J.
- Chung, Young-Chul
- Fujii, Kumiko
- Glatt, Stephen J.
- Ha, Kyooseob
- Hattori, Kotaro
- Higuchi, Teruhiko
- Hishimoto, Akitoyo
- Hong, Kyung Sue
- Horiuchi, Yasue
- Hwu, Hai-Gwo
- Ikeda, Masashi
- Itokawa, Masanari
- Iwata, Nakao
- Kim, Sung-Wan
- Kim, Se Joo
- Kim, Se Hyun
- Kinoshita, Makoto
- Kunugi, Hiroshi
- Lee, Jimmy
- Lee, Byung Dae
- Lee, Heon-Jeong
- Liu, Jianjun
- Ma, Xiancang
- Myung, Woojae
- Numata, Shusuke
- Ohmori, Tetsuro
- Otsuka, Ikuo
- Ozeki, Yuji
- Schwab, Sibylle G.
- Shimoda, Kazutaka
- Sim, Kang
- Sora, Ichiro
- Tang, Jinsong
- Toyota, Tomoko
- Tsuang, Ming
- Wildenauer, Dieter B.
- Won, Hong-Hee
- Yoshikawa, Takeo
- Zhu, Feng
- He, Lin
- Sawa, Akira
- Martin, Alicia R.
- Qin, Shengying
- Huang, Hailiang
- Ge, Tian
- Venue
- Nature Genetics
- Published
- 2022-05
- Volume
- 54
- Issue
- 5
- Pages
- 573-580
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC9117455
- OpenAlex
- W4229035861
- Cited by
- 707
- References
- 66
- Citations by year
- 2026: 153 · 2025: 175 · 2024: 160 · 2023: 133 · 2022: 74
- FWCI
- 84.16
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-07-04)
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Genetics (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- UWA Profiles and Research Repository (UWA) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Figshare (submittedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion)
- Funders
- Brain and Behavior Research Foundation
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (U01MH109539, R00MH117229)
- U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (K01DK114379)
- the Zhengxu and Ying He Foundation and the Stanley Center for Psychiatric Research
- Science and Technology Commission of Shanghai Municipality (2017SHZDZX01)
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (R00AG054573)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Social Sciences › Anthropology › Race, Genetics, and Society
- Keywords
- polygenic risk score, effect size estimation, schizophrenia risk, quantitative traits, non-European populations, health care disparities
- MeSH terms
- Genetics, Population, Humans, Risk Factors, Linkage Disequilibrium, Genetic Predisposition to Disease, Multifactorial Inheritance, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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