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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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“4a), which is consistent with previous studies 14–19 , 41 .”
InfluentialPrivé, F., Aschard, H., Carmi, S., Folkersen, L., Hoggart, C., O’Reilly, P. F., & Vilhjálmsson, B. J. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.11.008 · Cited by 375
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Polygenic scores in biomedical research
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Self-citationClinical utility of polygenic risk scores for coronary artery disease
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Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals
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Self-citationCommon variants contribute to intrinsic human brain functional networks
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“PRS-CSx was evaluated with the fully Bayesian approach to identify the optimal global shrinkage parameter φ , as recommended for large GWAS training data.”
MethodInfluentialGe, T., Irvin, M. R., Patki, A., Srinivasasainagendra, V., Lin, K., Tiwari, H. K., Armstrong, N. D., Benoit, B., Chen, C., Choi, K. W., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01074-2 · Cited by 178
“We used PRS-CSx, a recently developed Bayesian polygenic modeling method, to construct the trans-ancestry PRS [18].”
MethodSelf-citationGenetic prediction of complex traits with polygenic scores: a statistical review
Ma, Y., & Zhou, X. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.06.004 · Cited by 136
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Self-citationGlobal Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts
Wang, Y., Namba, S., Lopera-Maya, E. A., Kerminen, S., Tsuo, K., Läll, K., Kanai, M., Zhou, W., Wu, K.-H., Favé, M.-J., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2022.100241 · Cited by 114
“For example, PRS methods that model GWAS summary statistics alongside LD information from multiple ancestries have shown promising accuracy improvements for some traits.(16,48) But statistical methods are insufficient for equitable accuracy without simultaneous progress in generating largescale diverse data, as early…”
BackgroundMethodSelf-citationA new method for multiancestry polygenic prediction improves performance across diverse populations
Zhang, H., Zhan, J., Jin, J., Zhang, J., Lu, W., Zhao, R., Ahearn, T. U., Yu, Z., O’Connell, J., Jiang, Y., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01501-z · Cited by 116
“Other studies proposed Bayesian methods using multi-variate priors for effect-size distribution to borrow information across populations 30,33,34 .”
BackgroundMethodInfluentialKhan, A., Gogarten, S. M., McHugh, C., Stilp, A. M., Sofer, T., Bowers, M., Wong, Q., Cupples, L. A., Hidalgo, B., Johnson, A. D., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100155 · Cited by 110
Recent advances in polygenic scores: translation, equitability, methods and FAIR tools
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“One such approach is PRS-CSx [127], an extension of the population PRS-CS continuous shrinkage (CS) models that can be shared across ancestries.”
BackgroundIntegrative polygenic risk score improves the prediction accuracy of complex traits and diseases
Truong, B., Hull, L. E., Ruan, Y., Huang, Q.-Q., Hornsby, W., Martin, H., van Heel, D. V., Wang, Y., Martin, A. R., Lee, H.-S., et al. (2023) · medRxiv · doi:10.1101/2023.02.21.23286110 · Cited by 87
“However, optimizing PRS performance requires methodological approaches to adjust GWAS estimate effect sizes that take into account correlated SNPs (i.e., linkage disequilibrium) and refine PRS for the target population 4,5,7,9–12 .”
Self-citationJohansson, Å., Andreassen, O. A., Brunak, S., Franks, P. W., Hedman, H., Loos, R. J. F., Meder, B., Melén, E., Wheelock, C. E., & Jacobsson, B. (2023) · Journal of Internal Medicine · doi:10.1111/joim.13640 · Cited by 102
“First, most GWAS have been performed in cohorts of European descent, and PRS computed to date are much less informative in other ethnicities [24].”
Genome-wide risk prediction of common diseases across ancestries in one million people
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Self-citationInferring disease architecture and predictive ability with LDpred2-auto
Privé, F., Albiñana, C., Arbel, J., Pasaniuc, B., & Vilhjálmsson, B. (2023) · bioRxiv · doi:10.1101/2022.10.10.511629 · Cited by 73
“, 2022), multi-ancestry prediction and inference (Brown et al., 2016; Shi et al., 2020; Ruan et al., 2022; Lu et al., 2022), as well as extending it to learn from functional annotations (Zhang et al.”
BackgroundGenetically adjusted PSA levels for prostate cancer screening
Kachuri, L., Hoffmann, T. J., Jiang, Y., Berndt, S. I., Shelley, J. P., Schaffer, K. R., Machiela, M. J., Freedman, N. D., Huang, W., Li, S. A., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02277-9 · Cited by 83
A scalable variational inference approach for increased mixed-model association power
Loya, H., Kalantzis, G., Cooper, F., & Palamara, P. F. (2025) · Nature Genetics · doi:10.1038/s41588-024-02044-7 · Cited by 92
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Self-citationHuang, Q. Q., Sallah, N., Dunca, D., Trivedi, B., Hunt, K. A., Hodgson, S., Lambert, S. A., Arciero, E., Wright, J., Griffiths, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32095-5 · Cited by 66
The construction of cross-population polygenic risk scores using transfer learning
Zhao, Z., Fritsche, L. G., Smith, J. A., Mukherjee, B., & Lee, S. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.09.010 · Cited by 85
“7, and 1.(10) We generated 20 datasets in each scenario to evaluate the predictive performance of different PRS construction methods.”
MethodPark, S., Kim, S., Kim, B., Kim, D. S., Kim, J., Ahn, Y., Kim, H., Song, M., Shim, I., Jung, S., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01933-1 · Cited by 82
“This may be attributed to the fact that the performance of PRS depends on the GWAS sample size 59 , and the sample size of the FG GWAS was comparatively smaller than that of the other components.”
Self-citationClinical use of polygenic risk scores: current status, barriers and future directions
Kullo, I. J. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00900-8 · Cited by 73
Chen, C.-Y., Chen, T.-T., Feng, Y.-C. A., Yu, M.-R., Lin, S.-C., Longchamps, R. J., Wang, S.-H., Hsu, Y.-H., Yang, H. I., Kuo, P.-H., et al. (2021) · medRxiv · doi:10.1101/2021.04.12.21255236 · Cited by 63
“Population-specific PRS for each biomarker was calculated using PRS-CSx 25 , a Bayesian polygenic prediction method that jointly models GWAS summary statistics from multiple populations to improve polygenic prediction.”
MethodInfluentialSelf-citationShi, Y., Zhou, M., Chang, C., Jiang, P., Wei, K.-S., Zhao, J.-N., Yu, S., Zheng, Y.-X., Zhao, F., Lv, X., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1409555 · Cited by 59
“…reality, PRS exhibits limited transferability among populations, and its clinical utility in RA remains undetermined, necessitating substantial investment in extensive data collection across diverse ethnic groups and methodological research to enhance genetic prediction in admixed individuals (75).”
BackgroundKlarin, D. M., Devineni, P., Sendamarai, A. K., Angueira, A. R., Graham, S. E., Shen, Y. H., Levin, M. G., Pirruccello, J. P., Surakka, I. L., Karnam, P., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01420-z · Cited by 80
Miao, J., Guo, H., Song, G., Zhao, Z., Hou, L., & Lu, Q. (2023) · Nature Communications · doi:10.1038/s41467-023-36544-7 · Cited by 61
Polygenic risk score portability for common diseases across genetically diverse populations
Moreno–Grau, S., Vernekar, M., Pineda, A. L., Montserrat, D. M., Barrabés, M., Quinto-Cortés, C. D., Moatamed, B., Lee, M. T. M., Yu, Z., Numakura, K., et al. (2024) · Human Genomics · doi:10.1186/s40246-024-00664-y · Cited by 54
“With that objective, we first selected the most optimal PRS model for Europeans from a set of 166 PRS available from PGS Catalog [12] or de novo constructed using three different PRS methods (LDpred2 [13, 14], PRS-CSx [15] and SNPnet [16]) in the UKBB data.”
MethodPolygenic prediction of body mass index and obesity through the life course and across ancestries
Smit, R. A. J., Wade, K. H., Hui, Q., Arias, J. D., Yin, X., Christiansen, M. R., Yengo, L., Preuss, M. H., Nakabuye, M., Rocheleau, G., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03827-z · Cited by 66
“We leveraged PRS-CSx’s unique ability to integrate GWAS summary statistics from all five broad ancestry population groups from the meta-analyses from GIANT and 23andMe 17 .”
InfluentialSelf-citationZheng, S. L., Jurgens, S. J., McGurk, K. A., Xu, X. Y., Grace, C., Theotokis, P. I., Buchan, R. J., Francis, C., de Marvao, A., Curran, L., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02094-5 · Cited by 66
“This lends hope that performing modestly sized ancestry-specific GWAS could be sufficient to generate PGS with comparable performance to European ancestry populations 16 .”
InfluentialToikumo, 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. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01851-6 · Cited by 62
“We used the default setting in PRS-CS to estimate the shrink-age parameters and fixed the random seed to 1 for reproducibility.”
Self-citationSun, Q., Rowland, B., Chen, J. C., Mikhaylova, A. V., Avery, C. L., Peters, U., Lundin, J. I., Matise, T. C., Buyske, S. G., Tao, R., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45135-z · Cited by 60
“PRS-CSx is a recently developed method that integrates GWAS summary statistics from multiple populations while accounting for LD from external reference panels to improve cross-population PRS prediction 18 .”
Hoggart, C. J., Choi, S. W., García‐González, J., Souaiaia, T., Preuss, M. H., & O’Reilly, P. F. (2023) · Nature Genetics · doi:10.1038/s41588-023-01583-9 · Cited by 67
“Throughout, we followed the implementation described in Ruan et al. 5 ; thus, values of ϕ = (10 −6 , 10 −4 , 10 −2 and 1) were considered.”
MethodBarry, A., McNulty, M. T., Jia, X., Gupta, Y., Dêbiec, H., Luo, Y., Nagano, C., Horinouchi, T., Jung, S., Colucci, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37985-w · Cited by 57
Treatment biomarkers for ADHD: Taking stock and moving forward
Michelini, G., Norman, L. J., Shaw, P., & Loo, S. K. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02207-2 · Cited by 65
Choudhury, A., Brandenburg, J., Chikowore, T., Sengupta, D., Boua, P. R., Crowther, N. J., Agongo, G., Asiki, G., Gómez-Olivé, F. X., Kisiangani, I., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30098-w · Cited by 55
An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction
Zhang, J.-N., Zhan, J., Jin, J., Ma, C., Zhao, R., O'Connell, J., Jiang, Y.-X., Koelsch, B., Zhang, H.-Y., & Chatterjee, N. (2023) · bioRxiv · doi:10.1101/2023.03.15.532652 · Cited by 46
“Bayesian methods have also been proposed that generate improved PRS for each population by jointly modeling the effect-size distribution across populations 34, 35 .”
Chen, C., Chen, T., Feng, Y. A., Yu, M., Lin, S., Longchamps, R. J., Wang, S., Hsu, Y., Yang, H., Kuo, P., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100436 · Cited by 82
Self-citationFan, C. C., Loughnan, R. J., Wilson, S., Hewitt, J. K., ABCD Genetic Working Group, Arpana Agrawal, A., Dowling, G. J., Garavan, H., LeBlanc, K. H., Neale, M. C., et al. (2023) · Behavior Genetics · doi:10.1007/s10519-023-10143-0 · Cited by 53
Xin, J., Du, M., Gu, D., Jiang, K., Wang, M., Jin, M., Hu, Y., Chen, S., Chen, S., Shao, W., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01156-9 · Cited by 51
“Although the performance of our PRS in the EUR population (e.g., CORSA dataset) is substantially lower than previous EUR-ancestry PRSs (e.g., Thomas et al.’s genome-wide PRS) [10], our aim was to improve the clinical utility of PRS in multiple ethnic groups, especially for non-EUR (e.g., EAS) populations.”
ResultInfluentialExtremely sparse models of linkage disequilibrium in ancestrally diverse association studies
Nowbandegani, P. S., Wohns, A. W., Ballard, J. L., Lander, E. S., Bloemendal, A., Neale, B. M., & O’Connor, L. J. (2023) · Nature Genetics · doi:10.1038/s41588-023-01487-8 · Cited by 41
Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups
Kurniansyah, N., Goodman, M. O., Khan, A., Wang, J., Feofanova, E. V., Bis, J. C., Wiggins, K. L., Huffman, J. E., Kelly, T. N., Elfassy, T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38990-9 · Cited by 45
“In keeping with this general approach, PRS-CSx 33 has been developed to compute ancestry-speci fi c PRSs based on GWAS performed on populations of distinct genetic ancestries, followed by summations of the ancestry-speci fi c PRSs. Environment and lifestyle, as well as gene-by-environment…”
Addressing the challenges of polygenic scores in human genetic research
Novembre, J., Stein, C. M., Asgari, S., Gonzaga-Jauregui, C. G., Landstrom, A. P., Lemke, A. A., Li, J. Z., Mighton, C. E., Taylor, M. R., & Tishkoff, S. A. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.10.012 · Cited by 49
Polygenic Scores in Psychiatry: On the Road From Discovery to Implementation
Lewis, C. M., & Vassos, E. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20220795 · Cited by 48
Monti, R., Eick, L., Hudjashov, G., Läll, K., Kanoni, S., Wolford, B. N., Wingfield, B., Pain, O., Wharrie, S., Jermy, B. S., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.06.003 · Cited by 52
The Taiwan Precision Medicine Initiative provides a cohort for large-scale studies
Yang, H., Kwok, P., Li, L.-H., Liu, Y.-M., Jong, Y., Lee, K., Wang, D.-W., Tsai, M.-F., Yang, J.-H., Chen, C.-H., et al. (2025) · Nature · doi:10.1038/s41586-025-09680-x · Cited by 56
“- PRS-CSx weights for T2D across multiple populations from the PGS Catalog are available at https://www.pgscatalog.org/score/ PGS002308/.”
InfluentialSelf-citationThe PRIMED Consortium: Reducing disparities in polygenic risk assessment
Kullo, I. J., Conomos, M. P., Nelson, S. C., Adebamowo, S. N., Choudhury, A., Conti, D., Gogarten, S. M., Gogarten, S. M., Hornsby, W., Kenny, E. E., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.10.010 · Cited by 39
Multiethnic polygenic risk prediction in diverse populations through transfer learning
Tian, P., Chan, T. H., Wang, Y., Yang, W., Yin, G., & Zhang, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.906965 · Cited by 40
Stability of polygenic scores across discovery genome-wide association studies
Schultz, L. M., Merikangas, A. K., Ruparel, K., Jacquemont, S., Glahn, D. C., Gur, R. E., Barzilay, R., & Almasy, L. A. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100091 · Cited by 42
Imamura, M., & Maeda, S. (2024) · Journal of Diabetes Investigation · doi:10.1111/jdi.14149 · Cited by 40
“In addition, ongoing methodological developments in cross-population polygenic prediction by jointly modeling GWAS summary statistics from multiple populations might help considerably 76 – 78 .”
BackgroundMulti-Ancestry Meta-Analysis yields novel genetic discoveries and ancestry-specific associations
Turley, P., Martin, A. R., Goldman, G., Li, H., Kanai, M., Walters, R. K., Jala, J. B., Lin, K., Millwood, I. Y., Carey, C. E., et al. (2021) · doi:10.1101/2021.04.23.441003 · Cited by 35
Self-citationAI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes
Huang, Y., Chen, C., & Yang, H. (2024) · Nature Communications · doi:10.1038/s41467-024-48618-1 · Cited by 37
Self-citationCalibrated prediction intervals for polygenic scores across diverse contexts
Hou, K., Xu, Z.-Q., Ding, Y., Harpak, A., & Pasaniuc, B. (2023) · medRxiv · doi:10.1101/2023.07.24.23293056 · Cited by 35
Lee, D. S. M., Cardone, K. M., Zhang, D. Y., Tsao, N. L., Abramowitz, S. A., Sharma, P., DePaolo, J. S., Conery, M., Aragam, K. G., Biddinger, K. J., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02140-2 · Cited by 44
“To evaluate how common variants may clinically contribute to heart failure risk, we generated a multi-population PRS for heart failure using summary statistics from the GWAS meta-analysis (excluding PMBB participants), using PRS-CSx 46 (Supplementary Fig.”
Borda, V., Loesch, D. P., Guo, B., Laboulaye, R., Véliz-Otani, D., French, J. N., Leal, T. P., Gogarten, S. M., Ikpe, S., Gouveia, M. H., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100692 · Cited by 33
Busby, G., Kulm, S., Bolli, A., Kintzle, J., Domenico, P. D., & Bottà, G. (2023) · Nature Communications · doi:10.1038/s41467-023-42897-w · Cited by 41
“Here we describe the development and validation of multi-ancestry PRSs for CAD using an approach that utilizes multiple ancestry-speci fi c GWASs 17 and diverse PRS discovery, validation, and testing datasets.”
MethodInfluentialSDPRX: A statistical method for cross-population prediction of complex traits
Zhou, G., Chen, T., & Zhao, H. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.11.007 · Cited by 36
The Psychiatric Genomics Consortium: Discoveries and Directions
Arpana Agrawal, A., Bulik, C. M., Abebe, D., Andreassen, O. A., Atkinson, E. G., Choi, K. W., Corvin, A. P., Davies, H. L., Davis, L. K., Docherty, A. R., et al. (2025) · The Lancet Psychiatry · doi:10.1016/s2215-0366(25)00124-5 · Cited by 40
“19 These scores, when combined with multi-modal clinical data, and information on other types of genetic variation such as CNVs, could aid personalization of treatments and early interventions—an aspect of PGS research that we will pursue.”
InfluentialSelf-citationHuman genetics and epigenetics of alcohol use disorder
Zhou, H., & Gelernter, J. E. (2024) · Journal of Clinical Investigation · doi:10.1172/jci172885 · Cited by 48
“Phenome-wide PRS analysis in the independent biobank BioVU confirmed the genetic correlations between PAU and substance use and psychiatric disorders.”
InfluentialBarr, P. B., Driver, M. N., Kuo, S. I., Stephenson, M. E., Alıev, F., Linnér, R. K., Marks, J. A., Anokhin, A. P., Bucholz, K. K., Chan, G., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01801-6 · Cited by 35
Thomas, M., Su, Y., Rosenthal, E. A., Sakoda, L. C., Schmit, S. L., Timofeeva, M. N., Chen, Z., Fernández–Rozadilla, C., Law, P., Murphy, N., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41819-0 · Cited by 33
“Here we consider two different approaches to PRSdevelopment, (1) ancestry-speci fi c PRS using PRS-CSx 15 based on ancestry-speci fi c GWAS while leveraging cross-ancestry information and (2)singlecross-ancestry Asian-European PRS using LDPred2 16 based on combined meta-analysis summary statistics and LD matrices across…”
InfluentialSelf-citationSpence, J. P., Sinnott-Armstrong, N., Assimes, T. L., & Pritchard, J. K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.18.488696 · Cited by 34
Joo, Y. Y., Moon, S., Wang, H., Kim, H., Lee, E., Kim, J. H., Posner, J. E., Ahn, W., Choi, I., Kim, J.-W., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.48585 · Cited by 36
Human genetic admixture through the lens of population genomics
Gopalan, S., Smith, S. P., Korunes, K. L., Hamid, I., Ramachandran, S., & Goldberg, A. (2022) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0410 · Cited by 37
“extensions are needed for PRS frameworks to be applicable to individuals from admixed populations is an area of active research [39,150,151,155,156,187].”
BackgroundCalibrated prediction intervals for polygenic scores across diverse contexts
Hou, K., Xu, Z., Ding, Y., Mandla, R., Shi, Z., Boulier, K. M., Harpak, A., & Paşaniuc, B. (2024) · Nature Genetics · doi:10.1038/s41588-024-01792-w · Cited by 63
Jin, J., Zhan, J., Zhang, J., Zhao, R., O’Connell, J., Jiang, Y., Aslibekyan, S. W., Auton, A., Babalola, E., Bell, R. K., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100539 · Cited by 37
“same single-ancestry methods applied to GWAS and LD reference data for EUR: (3) EUR C+T, (4) EUR LDpred2; and three existing multi-ancestry methods applied to ancestry-specific GWAS and LD reference data for all ancestry groups: (5) weighted C+T (weighted PRS using C+T as the base method), (6) weighted LDpred2 (weight…”
Cai, M., Wang, Z., Xiao, J., Hu, X., Chen, G., & Yang, C. (2023) · Nature Communications · doi:10.1038/s41467-023-42614-7 · Cited by 26
“Accumulating evidence 524 of a shared genetic basis across populations [28, 25, 26, 55, 56] implies that b1 and b2 tend to 525”
BackgroundWidén, E., Raben, T. G., Lello, L., & Hsu, S. (2021) · Genes · doi:10.3390/genes12070991 · Cited by 33
“Being trained on European ancestry only, the predictors suffer the now familiar [90,91] fall-off pattern according to genetic distance, with performance generally being successively worse for South-Asian, East-Asian, and African ancestries.”
BackgroundMethodologies underpinning polygenic risk scores estimation: a comprehensive overview
Sima, C. A. A. N., Step, K., Swart, Y., Schurz, H., Uren, C., & Möller, M. (2024) · Human Genetics · doi:10.1007/s00439-024-02710-0 · Cited by 34
“Even in a case where neither discovery datasets matched the target (e.g., African cohort), PRS-CSx improved accuracy by 45.1% and 16.9% over LDpred2 and PRS-CS (Ruan et al., 2022).”
BackgroundMethodResultInfluentialThe GenoPred pipeline: a comprehensive and scalable pipeline for polygenic scoring
Pain, O., Al‐Chalabi, A., & Lewis, C. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae551 · Cited by 36
“To demonstrate the simplicity, scalability, and performance of the GenoPred pipeline, we applied it to calculate polygenic scores in the UK Biobank sample (Bycroft et al. 2018).”
MethodMartschenko, D. O., Wand, H., Young, J. L., & Wojcik, G. L. (2023) · Nature Genetics · doi:10.1038/s41588-023-01394-y · Cited by 33
Advances and Applications of Polygenic Scores for Coronary Artery Disease
Patel, A. P., & Khera, A. V. (2022) · Annual Review of Medicine · doi:10.1146/annurev-med-042921-112629 · Cited by 35
“…these challenges, important ongoing efforts aim to gather GWAS data in individuals of non-European ancestry as well as to develop polygenic scoring methods that incorporate multiancestry data or adjust variant weights to leverage shared genetic variation between ancestries (50, 51, 101-107).”
Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference
Zabad, S., Gravel, S., & Li, Y. (2022) · bioRxiv · doi:10.1101/2022.05.10.491396 · Cited by 29
Towards a global view of multiple sclerosis genetics
Jacobs, B. M., Peter, M., Giovannoni, G., Noyce, A. J., Morris, H. R., & Dobson, R. (2022) · Nature Reviews Neurology · doi:10.1038/s41582-022-00704-y · Cited by 31
Jayasinghe, D., Eshetie, S., Beckmann, K., Benyamin, B., & Lee, S. (2024) · Human Genetics · doi:10.1007/s00439-024-02716-8 · Cited by 32
“However, challenges persist, particularly in the effective application of these methods to admixed populations, where the complexity of genetic backgrounds is not yet fully accounted for (Zhang et al. 2023; Ruan et al. 2022).”
Self-citationThe Polygenic Score Catalog: new functionality and tools to enable FAIR researchPreprint
Lambert, S. A., Wingfield, B., Gibson, J. T., Gil, L., Ramachandran, S., Yvon, F., Saverimuttu, S. C. C., Tinsley, E., Lewis, E., Ritchie, S., et al. (2024) · medRxiv · doi:10.1101/2024.05.29.24307783 · Cited by 28
Miller, A. P., Kuo, S. I., Johnson, E. C., Tillman, R., Brislin, S. J., Dick, D. M., Kamarajan, C., Kinreich, S., Kramer, J., McCutcheon, V. V., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.37192 · Cited by 29
Population-specific polygenic risk scores for people of Han Chinese ancestry
Chen, H., Chen, C.-H., Hou, M., Fu, Y., Li, L., Chou, C.-Y., Yeh, E., Tsai, M.-F., Chen, C.-H., Yang, H., et al. (2025) · Nature · doi:10.1038/s41586-025-09350-y · Cited by 29
“The cross-population PRS models were based on both TPMI and UKB European GWAS (https:// pheweb.org/UKB-TOPMed/), and PRS-CSx was applied 80 .”
Self-citationNicolas, A., Sherva, R., Grenier‐Boley, B., Kim, Y. T., Kikuchi, M., Timsina, J., de Rojas, I., Dalmasso, M. C., Zhou, X., Guen, Y. L., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02227-w · Cited by 33
Diabetes mellitus polygenic risk scores: heterogeneity and clinical translation
Ortega, H. I., Udler, M. S., Gloyn, A. L., & Sharp, S. A. (2025) · Nature Reviews Endocrinology · doi:10.1038/s41574-025-01132-w · Cited by 26
“Further small gains have been made with methodological refinement 85 and the inclusion of populations with diverse ancestry (AUC-ROC of 0.75) 86 (Fig.”
InfluentialLoesch, D. P., Garg, M., Matelska, D., Vitsios, D., Jiang, X., Ritchie, S. C., Sun, B. B., Runz, H., Whelan, C. D., Holman, R. R., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56695-z · Cited by 34
“We trained genome-wide T2D, CAD, BMI, and CKD PGS using PRS-CS 74 (when only one GWAS per trait was available) or PRS-CSx 75 with these GWAS summary statistics.”
Inflammatory Conditions During Pregnancy and Risk of Autism and Other Neurodevelopmental Disorders
Croen, L., Ames, J., Qian, Y., Alexeeff, S., Ashwood, P., Gunderson, E. P., Wu, Y. W., Boghossian, A. S., Yolken, R. H., de Water, J. V., et al. (2023) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2023.09.008 · Cited by 29
“Genome-wide association study (GWAS) summary statistics for asthma (31) and BMI (32) were used to generate polygenic risk scores (PRSs) for individuals in the IMPaCT dataset using PRS-CSx (33) with 1000 Genomes linkage disequilibrium reference panels.”
MethodXPXP: improving polygenic prediction by cross-population and cross-phenotype analysis
Xiao, J., Cai, M., Hu, X., Wan, X., Chen, G., & Yang, C. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac029 · Cited by 24
Pharmacogenomic scores in psychiatry: systematic review of current evidence
Sharew, N. T., Clark, S. R., Schubert, K. O., & Amare, A. T. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02998-6 · Cited by 36
Beyond GWAS: from simple associations to functional insights
Ishigaki, K. (2021) · Seminars in Immunopathology · doi:10.1007/s00281-021-00894-5 · Cited by 33
Leveraging fine-mapping and non-European training data to improve trans-ethnic polygenic risk scoresPreprint
Weissbrod, O., Kanai, M., Shi, H., Gazal, S., Peyrot, W., Khera, A., Okada, Y., Martin, A. R., Finucane, H., & Price, A. L. (2021) · medRxiv · doi:10.1101/2021.01.19.21249483 · Cited by 25
Self-citation
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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