The DOI matches its Crossref record; 63 of the 66 references it cites were found in a registry or an index.
Improving polygenic prediction in ancestrally diverse populations
Open accessPublished version of a preprint
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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Cites
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Self-citationBiological insights from 108 schizophrenia-associated genetic loci
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“Specifically, we used GWAS summary statistics derived from Psychiatric Genomics Consortium (PGC) wave 2 EUR samples (33,640 cases and 43,456 controls) 28 and 10 PGC EAS cohorts 15 (7,856 cases and 11,562 controls) as the discovery dataset.”
Self-citationLD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559
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Clinical use of current polygenic risk scores may exacerbate health disparities
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Self-citationA cross-population atlas of genetic associations for 220 human phenotypes
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Cited by
Sorted by citations
Items 601–609 of 609
What's UPDOG? A novel tool for trans-ancestral polygenic score prediction
Howard, D. M., Pain, O., Gillett, A., Vassos, E., & Lewis, C. (2023) · medRxiv · doi:10.1101/2023.05.15.23289985
Chen, S., Tan, A. L., Perry, C., Churchill, S., Vella, M., Mao, J.-F., Viswanadham, V., Kobren, S., & Kohane, I. (2023) · medRxiv · doi:10.1101/2023.02.22.23285773
“Earlier PRS models have been shown to be brittle in their performance when applied to populations differing from those they were developed on[15–19] even though recent methods and data sources are resulting in greater robustness[20,21].”
BackgroundMethodAljumaan, M. A., Chu, H., Al-Sulaiman, A., Camp, S. Y., Han, S., Gillani, R., Marzooq, Y. A., Almulhim, F., Vatte, C., Nemer, A. M. A., et al. (2022) · medRxiv · doi:10.1101/2022.12.07.22282785
“Moreover, while method developments are taking place in ensuring PRS has comparable risk prediction performance across ancestry [39], few have looked at the interactions between PRS with other clinical variables, especially in the context of understudied populations, which could be a missed opportunity to understand h…”
Li, Q., Patrick, M., Zhang, H.-H., Khunsriraksakul, C., Stuart, P., Gudjonsson, J., Nair, R., Elder, J. T., Liu, D.-J. J., Kang, J., et al. (2022) · doi:10.48550/arxiv.2210.06025
“Simulation We compare the prediction and variable selection performance of the BRIGHT estimation procedure with three competing methods: a trans-ethnic PRS method, PRS-CSx (Ruan et al., 2021); a single-ethnic prediction model, LASSOsum; and a null Principal Component (PC) model.”
BackgroundMethodInfluentialClinical genomics and precision medicine
Pena, S. D., & Tarazona-Santos, E. (2022) · Genetics and Molecular Biology · doi:10.1590/1678-4685-gmb-2022-0150
Assessing the risk stratification of breast cancer polygenic risk scores in two Brazilian samplesPreprint
Barreiro, R., Almeida, T. F., Gomes, C., Monfardini, F., Farias, A. A., Tunes, G., Souza, G. M., Duim, E., Correia, J., Coelho, A., et al. (2022) · medRxiv · doi:10.1101/2022.09.09.22279721
Barr, P. B., Driver, M. N., Kuo, S., Stephenson, M., Aliev, F., Linnér, R. K., Anokhin, A., Bucholz, K., Chan, G., Edenberg, H., et al. (2022) · medRxiv · doi:10.1101/2022.01.27.22269750
“PRS-CSx integrates GWAS summary statistics across multiple input populations and employs a Bayesian approach to correct GWAS summary statistics for the non-independence of SNPs in linkage disequilibrium (LD) with one another 47 .”
MethodRoberts, M., Thompson, S., Anderson, J., & Smith, R. (2026) · International journal of pharmaceutical research and allied sciences · doi:10.51847/08gmscs3fz
Traoré, M., Coulibaly, A., Dembele, S., & Diallo, B. (2025) · Pharmacophore · doi:10.51847/3i8xro74vf
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
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