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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]
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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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Self-citationA cross-population atlas of genetic associations for 220 human phenotypes
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Populations, Traits, and Their Spatial Structure in Humans
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“13(12) doi:10.1093/gbe/evab272 Advance Access publication 11 December 2021 17 a role in pushing it forward through the joint development of eugenics, genetics, and other forms of racial sciences (Yudell 2014; Saini 2019).”
BackgroundInfluentialDeciphering state-dependent immune features from multi-layer omics data at single-cell resolution
Edahiro, R., Sato, G. R., Naito, T., Shirai, Y., Saiki, R., Sonehara, K., Tomofuji, Y., Yamamoto, K., Namba, S., Sasa, N., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02266-3 · Cited by 15
“We constructed multipopulation PRSs of hospitalized COVID-19 using PRS-CSx 45 , combining the summary statistics of hospitalized COVID-19 (B2, n case = 32,519) in Europeans from COVID-19 HGI (round 7) 46 and those of hospitalized COVID-19 ( n case = 2,948) in Japanese individuals from the JCTF…”
Evolution, genetic diversity, and health
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“Recently developed PS models, such as PRS-CSx 126 , have improved prediction accuracy in diverse groups by combining GWAS data from multiple ancestries and including group-specific patterns of linkage disequilibrium (the non-random linkage of variants due to proximity and coinheritance, or evolutionary forces).”
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InfluentialThe breadth and impact of the Global Lipids Genetics Consortium
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“…with Pruning and Thresholding (PT) [11], double weight (DW) [12]; LD-related methods with LDpred [13] or LDpred2 [14], lassosum [15], SBayesS [16], BayesR or SBayesR [17], MegaPRS [18], PRS-CSx [19] and functionally informed methods with Poly-Fun [20], PolyPred and PolyPred + [21] (Figure 1A).”
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“Additionally, methods like PRS-CSx 23 can integrate data from multiple sets of summary statistics across different ancestries 24 .”
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An ensemble penalized regression method for multi-ancestry polygenic risk prediction
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“Existing multi-ancestry methods include PRS-CSx 34 and CT-SLEB 21 .”
Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populations
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“…been widely used for simulating large-scale genetic datasets that mimic the LD structures of reference populations such as European, African, American, East Asian, and South Asian using data from the 1000 Genomes Project (Su et al. 2011, Ruan et al. 2022, Zhang et al. 2022, Miao et al. 2023).”
MethodSelf-citationHassanin, E., Lee, K.-H., Hsieh, T.-C., Aldisi, R., Lee, Y.-L., Bobbili, D., Krawitz, P., May, P., Chen, C.-Y., & Maj, C. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1286561 · Cited by 8
“In the last few years, the availability of large population-based cohorts and cross-ancestry GWAS also enabled the development of novel computational algorithms to improve the generalizability of PRS (Ruan et al., 2022; Hoggart et al., 2023).”
BackgroundMethodInfluentialSelf-citationCommon- and rare-variant genetic architecture of heart failure across the allele frequency spectrum
Lee, D. S. M., Cardone, K. M., Zhang, D. Y., Tsao, N. L., Abramowitz, S., Sharma, P., DePaolo, J., Conery, M., Aragam, K., Biddinger, K., et al. (2023) · medRxiv · doi:10.1101/2023.07.16.23292724 · Cited by 8
“The multi-population PRS was constructed using the “auto” setting of PRS-CSx 33 , using population-specific LD reference panels from the UK Biobank.”
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Confronting ethical and social issues related to the genetics of musicality
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“To maximize prediction in the African ancestry subset of ABCD, we used PRS-CSx’s (Ruan et al.”
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“Numerous studies have documented the role of parenting behaviors, such as parental monitoring and acceptance in influencing adolescent alcohol use outcomes (Ryan et al., 2010; Yap et al., 2017).”
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“To create polygenic scores, we used PRS-CSx (Ruan et al., 2022), a Python based command line tool that integrates GWAS summary statistics and external linkage disequilibrium (LD) reference panels from multiple populations to improve cross-population polygenic prediction.”
InfluentialStephenson, M. E., Alıev, F., Kuo, S. I., Edwards, A. C., Pandey, G., Su, J., Kamarajan, C., Dick, D. M., & Salvatore, J. E. (2022) · Development and Psychopathology · doi:10.1017/s0954579422000785 · Cited by 8
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“PRS-CSx is a Bayesian polygenic modelling framework 31 and in this study produced a weight file with >1.2 million SNPs.”
InfluentialZhao, X., Sun, F., Li, J., Zhang, D., Zhang, Q., Liu, Z., Tan, C., Ma, H., & Wang, K. (2025) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2025.06.007 · Cited by 3
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East and South Asian-specific blood pressure trajectories and cardiovascular disease
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“The BP genetic score was developed using Bayesian regression models with continuous shrinkage priors 22 leveraging ancestry-specific genome-wide association summary statistics from unrelated Million Veteran Program participants (92% male sex, 69% non-Hispanic White).”
Self-citationZhu, M., Zhu, X., Han, Y., Ma, Z., Chen, J., Wang, T., Yan, C., Song, C., Yu, C., Sun, D., et al. (2025) · PLoS Medicine · doi:10.1371/journal.pmed.1004534 · Cited by 6
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Whole‐genome sequencing analysis of Japanese autism spectrum disorder trios
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“Next, we calculated the PRS using PRS-csx 63 to perform cross-population polygenic predictions, as the base data were generated from the European population.”
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“To optimize the PRS prediction, we used PRS-CSx (https://github.com/getian107/PRScsx; https://doi. org/10.1038/s41588-022-01054-7) [33], which considers continuous shrinkage prior to variant effect sizes by utilizing a Bayesian regression framework and jointly coupled GWAS summary statistics of…”
InfluentialSelf-citationDu, S., Chen, J., Li, J., Qian, W., Wu, S., Peng, Q., Liu, Y., Pan, T., Li, Y., Hadi, S. S., et al. (2024) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2024.07.005 · Cited by 7
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“The PRS may need to be recalibrated or adapted for specific populations [76, 77].”
Self-citationPaul, S. E., Baranger, D. A. A., Johnson, E. C., Jackson, J. J., Gorelik, A. J., Miller, A. P., Hatoum, A. S., Thompson, W. K., Strube, M., Dick, D. M., et al. (2024) · Psychological Medicine · doi:10.1017/s003329172400076x · Cited by 6
“PGS were computed in the COGA-P European ancestry (EA) subsample ( n = 1935) using PRS-CS (Ge, Chen, Ni, Feng, & Smoller, 2019), and in the African-American ancestry (AFR) subsample ( n = 918) using PRS-CSx (Ruan et al., 2022; Supplemental Methods).”
Comorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome
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Canadian COVID-19 host genetics cohort replicates known severity associations
Garg, E., Arguello-Pascualli, P., Vishnyakova, O., Halevy, A. R., Yoo, S., Brooks, J. D., Bull, S. B., Gagnon, F., Greenwood, C. M. T., Hung, R. J., et al. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011192 · Cited by 6
Elam, K. K., Su, J., Kutzner, J., & Trevino, A. D. (2023) · Behavior Genetics · doi:10.1007/s10519-023-10167-6 · Cited by 7
“Given that no GWAS on depression exist in Latinx samples, we used Dep-PGS meta-analyzed across EA and AA GWAS generated by PRS-CSx, which have shown enhanced portability across populations (Ruan et al. 2021).”
Zhang, M., Durvasula, A., Chiang, C., Koch, E., Strober, B., Shi, H., Barton, A. R., Kim, S. S., Weissbrod, O., Loh, P.-R., et al. (2023) · medRxiv · doi:10.1101/2023.12.04.23299391 · Cited by 7
Self-citationGriffiths, K., Smart, S. E., Barker, G. J., Deakin, J. F. W., Lawrie, S. M., Lewis, S. W., Lythgoe, D. J., Pardiñas, A. F., Singh, K. D., Semple, S., et al. (2023) · Schizophrenia Research · doi:10.1016/j.schres.2023.08.020 · Cited by 11
“Novel polygenic scoring methods which integrate GWAS summary statistics from multiple populations can improve cross-population prediction (Ruan et al., 2022).”
Toikumo, S., Jennings, M. V., Pham, B. K., Lee, H., Mallard, T. T., Bianchi, S. B., Meredith, J. J., Vilar‐Ribó, L., Xu, H., Hatoum, A. S., et al. (2023) · medRxiv · doi:10.1101/2023.03.27.23287713 · Cited by 8
Self-citationStatistical learning for sparser fine‐mapped polygenic models: The prediction of LDL‐cholesterol
Maj, C., Staerk, C., Borisov, O., Klinkhammer, H., Yeung, M. W., Krawitz, P. M., & Mayr, A. J. (2022) · Genetic Epidemiology · doi:10.1002/gepi.22495 · Cited by 8
Robust inference and correlates from genetic associations with personality
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“We also generated PGI weights both among a broader set of 2.9 million SNPs and, using PRS-CSx 90 , we compared this prediction to prior estimates.”
InfluentialSelf-citationCROMER, S. J., Cobran, E. K., Iyer, H. S., Hysong, M. R., Vargas, L. B., Smith, J. L., Konigsberg, I. R., Bogumil, D. D., Glover, L. M., King, G. A., et al. (2026) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2026.02.007 · Cited by 7
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“33).”
Recommendations for responsible use of population descriptors in polygenic risk score development
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Self-citationDevelopment and Validation of a Type 1 Diabetes Multi-Ancestry Polygenic Score
Deutsch, A. J., Bell, A. S., Michalek, D. A., Burkholder, A., Nam, S., Kreienkamp, R., Sharp, S. A., Huerta-Chagoya, A., Mandla, R., Nanjala, R., et al. (2025) · medRxiv · doi:10.1101/2025.06.20.25329522 · Cited by 6
“As a secondary analysis, we implemented PRS-CSx 36 , which extends PRS-CS by integrating GWAS data across multiple ancestries.”
InfluentialPolygenic scores for obstructive sleep apnoea reveal pathways contributing to cardiovascular disease
Kurniansyah, N., Strausz, S. J., Chittoor, G., Gupta, S., Justice, A. E., Hrytsenko, Y., Keenan, B. T., Cade, B. E., Spitzer, B., Wang, H., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105790 · Cited by 9
“In brief, we used GWAS summary statistics from multiple published GWAS of OSA using three modern methods, LDPred2, 25 PRS-CS, 26 and PRS-CSx, 27 that have been shown to be very successful for polygenic, complex, traits.”
InfluentialKhan, Y., Davis, C. N., Jinwala, Z., Feuer, K. L., Toikumo, S., Hartwell, E. E., Sanchez‐Roige, S., Peterson, R. E., Hatoum, A. S., Kranzler, H. R., et al. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.04.021 · Cited by 14
Dissecting cross-population polygenic heterogeneity across respiratory and cardiometabolic diseases
Yamamoto, Y., Shirai, Y., Sonehara, K., Namba, S., Ojima, T., Yamamoto, K., Edahiro, R., Ken, S., Kanai, A., the BioBank Japan Project, et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58149-y · Cited by 6
“WithPRS-CSx,weconstructed Bayesian pathway PRS to improve the predictive performance.”
InfluentialIncorporating multiple functional annotations to improve polygenic risk prediction accuracy
Shao, Z., Tang, W., Hong-ji, W., Kong, Y., & Hao, X. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100850 · Cited by 8
Breaking binary in cardiovascular disease risk prediction
Zhang, Y., & Fahed, A. C. (2025) · npj Cardiovascular Health · doi:10.1038/s44325-024-00041-7 · Cited by 6
“Among current genomic studies, there is also a signi fi cant under-representation of patients with non-European ancestries, resulting in limited generalizability in more racially and ethnically diverse populations 83 .”
Xia, C., Alliey-Rodriguez, N., Tamminga, C., Keshavan, M., Pearlson, G., Keedy, S., Clementz, B., McDowell, J., Parker, D., Lencer, R., et al. (2024) · medRxiv · doi:10.1101/2024.12.05.24318404 · Cited by 6
“It outperforms single-population discovery methods and improves polygenic risk prediction accuracy for disease in single ancestry samples 51 .”
Self-citationDNA and IQ: Big deal or much ado about nothing? – A meta-analysis
Oxley, F. A. R., Wilding, K., & von Stumm, S. (2024) · Intelligence · doi:10.1016/j.intell.2024.101871 · Cited by 7
Fast and scalable ensemble learning method for versatile polygenic risk prediction
Chen, T., Zhang, H., Mazumder, R., & Lin, X. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2403210121 · Cited by 7
Hrytsenko, Y., Shea, B., Elgart, M., Kurniansyah, N., Lyons, G. R., Morrison, A. C., Carson, A. P., Haring, B., Mitchell, B. D., Psaty, B. M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-62945-9 · Cited by 8
Genetic studies of type 2 diabetes, and microvascular complications of diabetes
Imamura, M., & Maeda, S. (2024) · Diabetology International · doi:10.1007/s13340-024-00727-4 · Cited by 9
“In addition, ongoing methodological developments in cross-population polygenic prediction by jointly modeling GWAS summary statistics from multiple populations may help considerably [71–73].”
Identification of germline population variants misclassified as cancer-associated somatic variants
Pollard, R. D., Wilkerson, M. D., & Rajagopal, P. S. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1361317 · Cited by 6
“These include inadequate training of clinical tools (as observed in the first generation of commercially available polygenic risk scores) (5, 6); less accurate prediction of treatment response for specific populations in clinic (7–9); inadvertent biases against offering available interventions or…”
BackgroundLi, F., Xie, S., Cui, J.-S., Li, Y., Li, T., Wang, Y., & Jia, J. (2024) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2024.29 · Cited by 7
“Furthermore, we utilized the PRS-CSx method to improve the cross-population polygenic prediction by integrating GWAS summary statistics from multiple populations to re-calculate PRSs in our validation dataset (30).”
Bocher, O., Gilly, A., Park, Y., Zeggini, E., & Morris, A. P. (2023) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2023.100214 · Cited by 7
“We chose to focus here on PGSmethods constructed from SNPs that meet a predefined significance threshold and therefore did not consider the most recent methods integrating genome-wide SNPs such as PRSCSx.(24) Indeed, the main purpose of this study was not to performa comprehensive quantitative comparisonof available P…”
BackgroundMethodInfluentialBorda, V., Loesch, D., Guo, B., Laboulaye, R., Véliz-Otani, D., French-Kwawu, J. N., Leal, T. P., Gogarten, S., Ikpe, S., Gouveia, M., et al. (2023) · bioRxiv · doi:10.1101/2023.01.07.522490 · Cited by 6
Zheng, B., Fletcher, J. M., Zheng, F., & Lu, Q. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.115249 · Cited by 10
Estimating prevalence of human traits among populations from polygenic risk scores
Graham, B. E., Plotkin, B., Muglia, L. J., Moore, J. H., & Williams, S. M. (2021) · Human Genomics · doi:10.1186/s40246-021-00370-z · Cited by 9
“One recent example is PRS-CSx that uses GWAS summary statistics from multiple populations to improve the cross-ethnic transferability of PRSs [51].”
Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scores
Warmerdam, R., Lanting, P., Study, L. C., Deelen, P., & Franke, L. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab783 · Cited by 6
Feng, Y., Jia, N., Huang, P., Hu, S., & Yang, S. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03541-3 · Cited by 7
Self-citationImproved polygenic risk prediction models for breast cancer subtypes in women of African ancestry
Li, J., Zhang, H., Wang, X., Jia, G., McClellan, J. C., Guo, W., Sun, Y., Fiorica, P. N., Ambs, S., Barnard, M. E., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02501-5 · Cited by 6
Improving polygenic score prediction for underrepresented groups through transfer learning
Wu, H., Pérez‐Rodríguez, P., Boehnke, M., Cui, Y., Liang, X., Vazquez, A. I., & Campos, G. D. L. (2026) · Nature Communications · doi:10.1038/s41467-026-68696-7 · Cited by 6
“However, to demonstrate the feasibility of applying TL methods using nearly1 million variants and toprovide a benchmarkof TL against PRS-CSx 15 and PROSPER 16 – two PGS methods for multi-ancestry data that use sparse LD reference panels – we present results from analyses done without applying p…”
InfluentialTai, M.-C., Dennis, J., Park, S. K., Kim, S., Lee, J. W., Hassan, N. T., Kwong, A., Hartman, M., Yoon, S., Ngeow, J., et al. (2025) · npj Breast Cancer · doi:10.1038/s41523-025-00820-0 · Cited by 5
“Additionally, Ho et al. 10 gen-eratedaPRSusingatrans-ancestryBayesianpolygenicpredictionapproach implementedinPRS-CSx 11 , resulting in a PRS that includes approximately one million SNVs (PRS GW ) and demonstrates the highest predictive accuracy compared to the other two PRS in predicting breast…”
Self-citationSmith, J. L., Wong, Q., Hornsby, W., Conomos, M. P., Heavner, B. D., Kullo, I. J., Psaty, B. M., Rich, S. S., Stilp, A. M., Tayo, B. O., et al. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.06.004 · Cited by 6
Abramowitz, S. A., Hoffman-Andrews, L., Zhang, D. Y., Judy, R., Cappola, T. P., Day, S., Reza, N., Owens, A. T., Damrauer, S., & Levin, M. G. (2025) · medRxiv · doi:10.1101/2025.06.20.25329138 · Cited by 5
Xiang, R., Ben-Eghan, C., Liu, Y., Roberts, D. J., Ritchie, S. C., Lambert, S. A., Xu, Y., Takeuchi, F., & Inouye, M. (2025) · Nature Communications · doi:10.1038/s41467-025-59525-4 · Cited by 9
“Such research should initially focus on multi-ancestry vQTL mapping, combining single-ancestry vQTL mapping results using sophisticated meta-analysis methods, and extend to the latest polygenic score approaches that prioritise ancestry transferability, such as PRS-CSx 63 .”
Zhang, C., Zhou, G.-Y., Chen, T., & Zhao, H.-Y. (2025) · bioRxiv · doi:10.1371/journal.pgen.1012026 · Cited by 5
“We consider observations of a complex trait Y 1 with N 1 samples and a complex trait Y 2 with N 2 samples, and assume that there are N s samples overlapped between the two traits.”
InfluentialStephenson, M. E., Salvatore, J. E., Lannoy, S., & Edwards, A. C. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.05.030 · Cited by 7
Xu, L., Zhou, G., Jiang, W., Zhang, H., Dong, Y., Guan, L., & Zhao, H. (2025) · Nature Communications · doi:10.1038/s41467-025-59243-x · Cited by 21
Wang, S., Feng, Y. A., Lin, M., Cheng, C., Su, M., Chen, C., Wu, C., & Fan, C. C. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.02.888 · Cited by 6
Self-citationAssessing the risk stratification of breast cancer polygenic risk scores in a Brazilian cohort.
Barreiro, R., de Almeida, T. D., Gomes, C. D. S., Monfardini, F., de Farias, A. A., Tunes, G., Souza, G. M. D., Duim, E., Correia, J. D. S., Coelho, A. V. C., et al. (2024) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2024.06.002 · Cited by 5
Im, C., Neupane, A., Baedke, J. L., Lenny, B., Delaney, A., Dixon, S. B., Chow, E. J., Mostoufi‐Moab, S., Yang, T., Richard, M. A., et al. (2024) · Journal of Clinical Oncology · doi:10.1200/jco.23.02281 · Cited by 7
Stein, M. B., Jain, S., Papini, S., Campbell‐Sills, L., Choi, K. W., Martis, B., Sun, X., He, F., Ware, E. B., Naifeh, J. A., et al. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.01.254 · Cited by 7
Self-citationDeconstructing the genetic architecture of treatment-resistant schizophrenia in East Asian ancestry.
Lim, K., Yee, J.-Y., Yuen, M.-S., Ng, B., Zheng, S., Tang, C., Lencz, T., Lee, J., & Lam, M. (2023) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2023.103826 · Cited by 5
Self-citationInterplay of Mendelian and polygenic risk factors in Arab breast cancer patients
Aljumaan, 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. (2023) · Genome Medicine · doi:10.1186/s13073-023-01220-4 · Cited by 5
“Moreover, while many methods are being developed to ensure PRS has comparable risk prediction performance across ancestry [49], 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…”
MethodLillo, A. D., Wendt, F. R., Pathak, G. A., & Polimanti, R. A. (2023) · Human Genomics · doi:10.1186/s40246-023-00514-3 · Cited by 4
“While methods are being developed to perform effectively cross-population polygenic prediction across ancestry groups [38, 39], our findings highlight that modeling polygenicity may need to account for ancestry-specific differences across different health domains.”
MethodNiarchou, M., Miller-Fleming, T., Malow, B., & Davis, L. (2023) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-023-09485-x · Cited by 5
“The PGS per BioVU participant was computed using a continuous shrinkage prior (CS) to SNP effect sizes using the PRS_CS software [12] for participants of EU ancestries and the PRS_CSx [29] for participants of AF ancestries.”
MethodPolygenic scores for psychiatric disorders in a diverse postmortem brain tissue cohort
Duncan, L. E., Shen, H., Schulmann, A., Li, T., Kolachana, B. S., Mandal, A., Feng, N., Auluck, P. K., & Marenco, S. (2023) · Neuropsychopharmacology · doi:10.1038/s41386-022-01524-w · Cited by 6
Predicting diabetes risk in diverse populations: what next?
Mercader, J., Ng, M., Manning, A. K., & Rich, S. (2021) · The Lancet Diabetes and Endocrinology · doi:10.1016/s2213-8587(21)00287-4 · Cited by 5
Feibel, A., Pham, H., Glover, V. A. S., O’Connor, T. G., & O’Donnell, K. J. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.4892 · Cited by 4
Sharing approaches in predictive genomics across animals, plants and humans
Arirangan, S., de Oliveira, L. F., Hasan, M. N., Sherman, A. B., Tuinstra, M. R., Brito, L. F., Wedow, R., & Tegtmeyer, M. (2026) · Nature Genetics · doi:10.1038/s41588-025-02491-w · Cited by 4
Pomales-Matos, D. A., Lyerly, M., Rivera-Madera, A., Echevarría-Bonilla, O. L., Alvarez-Cortes, M., Henriquez-Quiñones, S. E., Reyes-Sosa, G. M., Villanueva-Nogueras, R. A., & Peña-Martínez, E. G. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2025.1647176 · Cited by 4
Ancestral diversity in complex disease genetics: from discovery to translation
Kuchenbaecker, K. B., & Navoly, G. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-025-00921-3 · Cited by 6
Jamil, B., Su, J.-N., Elam, K. K., Lemery-Chalfant, K., Cruz, R. A., Grimm, K. J., & Seaton, E. K. (2025) · Development and Psychopathology · doi:10.1017/s0954579425101028 · Cited by 4
“PRS-CSx produces ancestry-specific posterior SNP effect sizes whereas PRS-CSx Meta produces a single set of posterior effects through inverse-variance weighted meta-analysis (Ahern et al., 2023; Ruan et al., 2022).”
InfluentialThe Utility of Genome-Wide Association Studies in Inherited Arrhythmias and Cardiomyopathies
Dababneh, S., Ardehali, A., Badesha, J. K., & Laksman, Z. (2025) · Genes · doi:10.3390/genes16121448 · Cited by 5
“…on mixed ancestry data, there have been several tools developed to develop more ancestrally appropriate PRSs from European GWAS datasets, including PRS-Genes CSx and CT-SLEB [47,48], although these also remain limited and cannot be automatically applied to all ancestries across all diseases [49].”
Genome-wide meta-analyses of cross substance use disorders in diverse populations
Lai, D., Zhang, M., Green, N., Abreu, M., Schwantes-An, T.-H. L., Parker, C. C., Zhang, S., Jin, F., Sun, A., Zhang, P., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03294-5 · Cited by 13
“Combined posterior effect sizes across different populations were estimated by using PRS-CSx (i.e., meta-analysis results from PRS-CSx) [47].”
Rivier, C., Xu, L.-Q., Clocchiatti-Tuozzo, S., Zhao, H.-Y., Ohno-Machado, L., Hafler, D., Falcone, G., & Longbrake, E. (2025) · Multiple Sclerosis · doi:10.1177/13524585251377607 · Cited by 4
Lee, E., van Dijk, M. T., Kim, B., Kim, G., Murphy, E. J., Talati, A., Joo, Y. Y., Weissman, M. M., & Cha, J. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03221-8 · Cited by 6
“Further details for the measures are in the Supplementary Methods.”
Lo, Y.-C., Tian, H., Chan, T. F., Jeon, S., Alatorre, K., Dinh, B. L., Maskarinec, G., Taparra, K., Nakatsuka, N. J., Yu, M., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08050-7 · Cited by 4
“Future evaluation should focus on methods (e.g., 38,42,43 ) thatcan incorporate GWAS summary statisticsorPGS models from multiple populations in building a PGS model for Native Hawaiians.”
Self-citationGenome-Wide Assessment of Pleiotropy Across >1000 Traits from Global BiobanksPreprint
Levin, M. G., Koyama, S., Woerner, J., Zhang, D. Y., Rodriguez, A., Nandi, T., Truong, B. M. T., Abramowitz, S. A., Gupta, H., Kamineni, H., et al. (2025) · medRxiv · doi:10.1101/2025.04.18.25326074 · Cited by 6
“38 SNP effects combined across populations using inverse-variance weighting by specifying the “ --meta" flag, and the global shrinkage parameter was learned by specifying the “ --auto" flag.”
Implications of gene × environment interactions in post-traumatic stress disorder risk and treatment
Seah, C., Sidamon‐Eristoff, A. E., Huckins, L. M., & Brennand, K. (2025) · Journal of Clinical Investigation · doi:10.1172/jci185102 · Cited by 7
“Approaches to calculate PRS are still evolving, with newer methods able to account elegantly for complex relationships between variants, and incorporate trans-ancestry prediction (40– 42); however, predictive potential still varies greatly by ancestry.”
Guo, B., Cai, Y., Kim, D., Smit, R., Wang, Z., Iyer, K., Hilliard, A. T., Haessler, J., Tao, R., Broadaway, K. A., et al. (2025) · medRxiv · doi:10.1101/2025.02.15.25322341 · Cited by 4
“12 The first three PRS methods used the PRS-CSx “auto” option, which automatically learns the global shrinkage parameter from the discovery summary statistics.”
Influential
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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