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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]
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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Self-citationA cross-population atlas of genetic associations for 220 human phenotypes
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Barr, P. B., Neale, Z. E., Chatzinakos, C., Schulman, J., Mullins, N., Zhang, J., Chorlian, D. B., Kamarajan, C., Kinreich, S., Pandey, A. K., et al. (2025) · Complex Psychiatry · doi:10.1159/000543222 · Cited by 10
“COGA includes participants of both African-like and European-like groupings; thus, we used PRS-CSx [63], a method that integrates GWAS summary statistics from well-powered GWAS (typically of European-like individuals) with those from other populations to improve the predictive power of PGS in the…”
Chen, C., Li, Y., Gu, Y., Zhai, Q., Guo, S., Xiang, J., Xie, Y., An, M., Li, C., Qin, N., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56725-w · Cited by 4
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Self-citationAntonatos, C., Koskeridis, F., Ralliou, C. M., Evangelou, E., Grafanaki, K., Georgiou, S., Tsilidis, K. K., Tzoulaki, I., & Vasilopoulos, Y. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1451679 · Cited by 5
“…the germline genetic variants submitted to PRS models, thereby providing the framework for similar investigations in diseases with a complex genetic architecture, as well as application in cross-ancestry frameworks with available summary statistics and appropriate methodologies (Ruan et al., 2022).”
Polygenic Scoring for Detection of Ascending Thoracic Aortic Dilation
DePaolo, J. S., Biagetti, G. M., Judy, R., Wang, G. J., Kelly, J. J., Iyengar, A., Goel, N. J., Desai, N. D., Szeto, W. Y., Bavaria, J. E., et al. (2024) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.123.004512 · Cited by 5
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“WederivedPRSforfourpsychiatricandfourgastrointestinaldisorders using PRS-CSx (if large-scale GWAS summary was available in both Eur-opeanandEastAsianpopulations) 56 ,orPRS-CS 57 .”
Self-citationCha, J., Lee, E., van Dijk, M. V., Kim, B., Kim, G., Murphy, E., Talati, A., Joo, Y. Y., & Weissman, M. (2024) · Research Square · doi:10.21203/rs.3.rs-4264742/v1 · Cited by 4
“The posterior effect sizes of single nucleotide polymorphisms (SNPs) were estimated using PRS-CSx [49], a Bayesian approach that enables the merging of multiple GWAS summary statistics from diverse populations.”
MethodStep, K., Sima, C. A. A. N., Mata, I. F., & Bardien, S. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1380860 · Cited by 8
Leveraging haplotype information in heritability estimation and polygenic prediction
Meisner, J., Benros, M. E., & Rasmussen, S. (2025) · Nature Communications · doi:10.1038/s41467-024-55477-3 · Cited by 6
“…rely on population-speci fi c LD maps, heavily restricting the SNP set used, which could also deteriorate performance in diverse populations even further, or would require the need for acquiring summary statistics from multiple large-scale GWAS, covering a diverse range of populations 47 .”
Huang, Y.-J., Kurniansyah, N., Goodman, M., Spitzer, B., Wang, J., Stilp, A., Laurie, C., de Vries, P. D., Chen, H., Min, Y.-I., et al. (2024) · medRxiv · doi:10.1101/2024.03.05.24303738 · Cited by 4
Estimation of a genetic Gaussian network using GWAS summary data
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“…methods based on GWAS summary data treat the effects of individual genetic variants analogously to individual subjects in traditional studies, allowing investigating multiple phenotypes without individual-level data (Bulik-Sullivan et al., 2015b,a; Ruan et al., 2022; Lorincz-Comi et al., 2023).”
Multi-ancestry polygenic risk scores for venous thromboembolism
Jee, Y., Thibord, F., Dominguez, A., Sept, C., Boulier, K., Venkateswaran, V., Ding, Y., Cherlin, T., Verma, S., Faro, V. L., et al. (2024) · medRxiv · doi:10.1101/2024.01.09.24300914 · Cited by 4
Transferability of polygenic risk score among diverse ancestries
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Lennon, N., Kottyan, L. C., Kachulis, C., Abul-Husn, N. S., Arias, J., Belbin, G., Below, J., Berndt, S. I., Chung, W. K., Cimino, J. J., et al. (2023) · medRxiv · doi:10.1101/2023.05.25.23290535 · Cited by 4
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Self-citationKamarajan, C., Pandey, A. K., Chorlian, D. B., Meyers, J. L., Kinreich, S., Pandey, G., de Viteri, S. S., Zhang, J., Kuang, W., Barr, P. B., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.30.522330 · Cited by 2
The clinical utility of polygenic risk scores for combined hyperlipidemia
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Niarchou, M., Sealock, J., Straub, P., Sanchez‐Roige, S., Sutcliffe, J. S., & Davis, L. K. (2022) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32911 · Cited by 4
Understanding Anhedonia from a Genomic Perspective
Bondy, E., & Bogdan, R. (2022) · Current topics in behavioral neurosciences · doi:10.1007/7854_2021_293 · Cited by 9
Wei, M., & Peng, Q. (2026) · arXiv.org · doi:10.48550/arxiv.2604.07368 · Cited by 3
Polygenic risk scores for glaucoma: Impact on diagnosis and disease course
Kolovos, A., Aung, T., Khawaja, A. P., Wiggs, J. L., & Craig, J. E. (2026) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2026.101469 · Cited by 3
Yang, Y., & Zhu, X. (2026) · Genome biology · doi:10.1186/s13059-026-04051-9 · Cited by 2
“Information obtained in European (EUR) ancestry can be borrowed for studies in non-EUR ancestry, as demonstrated in constructing polygenic risk scores [20] and fine-mapping of causal variants [21].”
Evaluating the impact of family history and polygenic risk scores on cardiometabolic disease risk
Onyenobi, E., Oyibo, K., Zhong, M., & Adebamowo, S. N. (2026) · BMC Medicine · doi:10.1186/s12916-026-04666-6 · Cited by 4
“…previously published multi-ancestry PRS that have been validated in diverse populations, including T2D (PGS002308) [21] derived using PRS-CSx [31] and incorporating EUR, EAS, and AFR ancestries; BMI (PGS004994) [32] developed using PRS-CSx and incorporating EUR, AFR, and AMR ancestries;…”
Abramowitz, S. A., Hoffman-Andrews, L., Zhang, D. Y., Judy, R., Cappola, T. P., Day, S. M., Reza, N., Owens, A. T., Damrauer, S., & Levin, M. G. (2025) · JAMA cardiology · doi:10.1001/jamacardio.2025.4739 · Cited by 3
Colbert, S. M. C., Koromina, M., Hatoum, A. S., Stephenson, M., Edwards, A. C., Johnson, E. C., Qin, X., Shabalin, A. A., Ito, L. T., O’Connell, K. S., et al. (2025) · medRxiv · doi:10.1101/2025.10.22.25338076 · Cited by 5
“We used the “--auto” option to automatically infer model parameters, and then combined population-specific posterior effect size estimates using the“--meta” option 26 .”
InfluentialXu, C., Ganesh, S. K., & Zhou, X. (2025) · Nature Genetics · doi:10.1038/s41588-025-02360-6 · Cited by 7
“Specifically, as in Gao & Zhou 62 , Zhou et al. 63 and Ruan et al. 64 , we obtained publicly available European ancestry-based summary statistics for five quantitative traits and one binary trait, for which external summary statistics from at least 300,000 samples are available outside the UKB.”
STREAM-PRS: a multi-tool pipeline for streamlining polygenic risk score computation
Becelaere, S., Abakkouy, Y., Jans, D. S., David, M., Vermeire, S., & Cleynen, I. (2025) · Genome Medicine · doi:10.1186/s13073-025-01539-0 · Cited by 3
“For now, we refer to already existing tools for these purposes [38, 42, 43].”
Molecular Signatures of Schizophrenia and Insights into Potential Biological Convergence
Saada, M., & Stern, S. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26199830 · Cited by 3
“Novel multi-ancestry methods like PRS-CSx offer hope to enhance cross-population portability [76].”
Sánchez-Baizán, N., Herlin, M., Millán, A., Martínez, P., Belluga, M. D. L., & Piferrer, F. (2025) · Environmental epigenetics · doi:10.1093/eep/dvaf027 · Cited by 3
“Despite we validated the method using fish with and without GS, additional testing is required in other populations and species, in line with recommendations to validate genomic prediction methods [ 60 ].”
An efficient LASSO framework for admixture-aware polygenic scores
Ockerman, F. P., Chen, B. D., Sun, Q., Kharitonova, E. V., Chen, C., Zhou, L. Y., Loos, R. J. F., Kooperberg, C. L., Peters, U., Haessler, J., et al. (2026) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2026.100628 · Cited by 4
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“18,19 In this study, we used GWAS summary statistics from a meta-analysis of continental African individuals to develop single-ancestry PRSs for SBP, DBP, PP, and hypertension using various PRS methods.”
The Eating Disorders Genetics Initiative 2 (EDGI2): study protocol
Berthold, N., MacDermod, C. M., Thornton, L. M., Parker, R., Morales, S. A. C., Hog, L., Kennedy, H., Guintivano, J., Sullivan, P. F., Crowley, J. J., et al. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-06777-5 · Cited by 5
“We will compare two methods, the classical P -value thresholding method and SBayesRC [57], and implement appropriate cross-population approaches, for example PRS-CSx [58].”
Using Genomics to Develop Personalized Cardiovascular Treatments
Sanghvi, M. M., Young, W. J., Naderi, H., Burns, R., Ramírez, J., Bell, C. G., & Munroe, P. B. (2025) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.125.319221 · Cited by 5
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“17 In contrast to these, the TL-PRS method represents a distinct multi-source approach: it starts with an existing “baseline” PGS model (a set of SNP weights tuned in one population) and further tunes it using GWAS data from the target population’s ancestry.”
InfluentialRecurrent Stroke Prediction by Applying a Stroke Polygenic Risk Score in the Japanese Population
Kojima, N., Koido, M., He, Y., Shimmori, Y., Hachiya, T., Japan, B., Debette, S., & Kamatani, Y. (2025) · Stroke · doi:10.1161/strokeaha.124.047786 · Cited by 5
Genetic aspects of pediatric nephrotic syndrome and anti-nephrin antibodies
Horinouchi, T., Nozu, K., & Iijima, K. (2025) · Clinical and Experimental Nephrology · doi:10.1007/s10157-025-02645-4 · Cited by 4
“However, this study employed PRS-CSx [51], an analytical method that accounts for these factors.”
Su, J., Jamil, B., Elam, K. K., Trevino, A. D., Lemery‐Chalfant, K., Seaton, E. K., Cruz, R. A., & Grimm, K. J. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1505035 · Cited by 4
Influence and role of polygenic risk score in the development of 32 complex diseases
Liu, Y., Hou, W., Gao, T., Yan, Y., Wang, T., Zheng, C., & Zeng, P. (2025) · Journal of Global Health · doi:10.7189/jogh.15.04071 · Cited by 3
“A crucial future direction is to explore the association between multi-ancestry PRS and disease risk [ 78 ].”
Jin, J., Li, B., Wang, X., Yang, X., Li, Y., Wang, R., Ye, C., Shu, J., Fan, Z., Xue, F., et al. (2025) · medRxiv · doi:10.1101/2025.02.07.25321875 · Cited by 5
Self-citationMetabolic Polygenic Risk Scores for Prediction of Obesity, Type 2 Diabetes, and Related MorbiditiesPreprint
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Genetic Risk Stratification of Primary Open-Angle Glaucoma in Japanese Individuals.
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“Two NEER patients were removed from the analysis because their common variant pro fi les diverged from thoseofEuropean origin, which mostpublished PRS pro fi les have been computed, although this is likely to change soon 20,21 .”
Dannhauser, F., Taylor, L. C., Tung, J., & Usher-Smith, J. (2024) · Journal of Community Genetics · doi:10.1007/s12687-024-00709-8 · Cited by 3
“The lack of diversity in genetic studies is well recognised (Fatumo et al. 2022) and different methods are being developed to construct PGS from distinct ancestry groups to improve prediction and accuracy (Weissbrod et al. 2022; Ruan et al. 2022; Cavazos and Witte 2021).”
Correlates of Risk for Disinhibited Behaviors in the Million Veteran Program Cohort
Barr, P. B., Bigdeli, T. B., Meyers, J. L., Peterson, R. E., Sanchez‐Roige, S., Mallard, T. T., Dick, D. M., Harden, K. P., Wilkinson, A. V., Graham, D. P., et al. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.4141 · Cited by 8
Su, J., Kuo, S. I., Alıev, F., Rabinowitz, J. A., Jamil, B., Chan, G., Edenberg, H. J., Francis, M. W., HESSELBROCK, V. M., Kamarajan, C., et al. (2023) · Development and Psychopathology · doi:10.1017/s0954579423001141 · Cited by 3
“…alc-PRS for our AA subsample using PRS-CSx, an extension of the PRS-CS method that integrates GWAS summary statistics from multiple populations and leveraging linkage disequilibrium (LD) diversity across discovery samples to improve polygenic prediction in non-EA samples (Ruan et al., 2022).”
Miller, A. P., Kuo, S., Johnson, E., Tillman, R., Brislin, S. J., Dick, D. M., Kamarajan, C., Kinreich, S., Kramer, J., McCutcheon, V., et al. (2023) · medRxiv · doi:10.1101/2023.06.12.23291164 · Cited by 3
Investigation of heteroscedasticity in polygenic risk scores across 15 quantitative traits
Jung, H., Jung, H., Baek, E. J., Chung, J. Y., Kwon, S. Y., Kang, J., Lim, J. E., & Oh, B. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1150889 · Cited by 5
“Previous studies showed the possibility of PRS to stratify individuals and predict complex traits in large populations (Khera et al., 2019; Ruan et al., 2022; Tanigawa et al., 2022).”
BackgroundMethodResultInfluentialBarr, P. B., Neale, Z. E., Chatzinakos, C., Schulman, J., Mullins, N., Zhang, J., Chorlian, D., Kamarajan, C., Kinreich, S., Pandey, A., et al. (2023) · medRxiv · doi:10.1101/2023.04.28.23289173 · Cited by 3
“Because COGA includes participants of both African and European ancestries, we used PRS-CSx [47], a method that integrates GWAS summary statistics from well-powered GWAS (typically of European ancestries) with those from other populations to improve the predictive power of PGS in the participants…”
Correlates of Risk for Disinhibited Behaviors in the Million Veteran Program Cohort
Barr, P. B., Bigdeli, T., Meyers, J., Peterson, R. E., Sanchez-Roige, S., Mallard, T., Dick, D., Harden, K., Wilkinson, A. V., Graham, D. P., et al. (2023) · medRxiv · doi:10.1101/2023.03.22.23286865 · Cited by 3
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Self-citationKuo, S. I., Thomas, N., Alıev, F., Bucholz, K. K., Dick, D. M., McCutcheon, V. V., Meyers, J. L., Chan, G., Kamarajan, C., Kramer, J., et al. (2023) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.15042 · Cited by 7
“Although we attempted to mitigate this issue with the PRS-CSx (Ruan et al., 2022) method, which integrates GWAS summary statistics of European ancestry and African ancestry populations to improve the performance of PRS, the predictive power of PRS in the AA sample was still attenuated.”
InfluentialAraujo, D. S., Nguyen, C., Hu, X.-W., Mikhaylova, A., Gignoux, C., Ardlie, K., Taylor, K., Durda, P., Liu, Y.-M., Papanicolaou, G., et al. (2023) · bioRxiv · doi:10.1101/2023.02.09.527747 · Cited by 3
“Indeed, other methods like PRS-CSx jointly model complex traits effects across populations in order to improve PRS 41 .”
MethodKeat, K., Hui, D., Xiao, B., Bradford, Y., Cindi, Z., Daar, E., Gulick, R., Riddler, S., Sinxadi, P., Haas, D., et al. (2022) · Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · doi:10.1142/9789811270611_0022 · Cited by 3
“18”
The Construction of Multi-ethnic Polygenic Risk Score using Transfer Learning
Zhao, Z., Fritsche, L. G., Smith, J. A., Mukherjee, B., & Lee, S. (2022) · medRxiv · doi:10.1101/2022.03.08.22272114 · Cited by 3
Polygenic risk scores in clinical applications – opportunities and challenges
Schumacher, J., Nöthen, M., & Heilmann-Heimbach, S. (2026) · Medizinische Genetik · doi:10.1515/medgen-2026-3016 · Cited by 2
“Despite significant methodological advances [18, 19], existing performance disparities cannot be fully resolved through computational approaches alone [27].”
Preimplantation genetic testing for polygenic diseases: A novel paradigm in embryo selection
Jing, T., & Du, Y. (2026) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2026.102532
“A prominent example is PRS-CSx, an extension of the PRS-CS framework [128].”
Development and validation of a trans-ancestry polygenic risk score for type 1 diabetes
Jumentier, B., Qu, H.-Q., Lu, T.-Y., Liu, K.-W., Kleinbrink, E., Klein, K., Belbellaj, W., Gamache, I., Ferrat, L., Butler-Laporte, G., et al. (2026) · Diabetologia · doi:10.1007/s00125-026-06706-5 · Cited by 2
“For the trans-ancestry optimisation, we modified GRS2x’s non-HLA part using the published methods PRS-CSx [18] and JointPRS [17]; for comparison purposes, we also used LDpred2 to optimise for Europeans [19].”
Importance of genetic ancestry in pharmacogenomics for precision medicine
Venkatesh, R., Keat, K., Salvatore, M., Cindi, Z., Hall, M. A., & Ritchie, M. D. (2025) · Pharmacogenomics · doi:10.1080/14622416.2026.2620360 · Cited by 3
“Recent methodological innovations like PRS-CSx and CT-SLEB aim to address these disparities by integrating genetic data from diverse populations [71,72].”
Chen, Y., Yuan, Q. E., Dimitrov, L. V., Risk, B. B., Ku, B. S., & Hüls, A. (2026) · Nature Mental Health · doi:10.1038/s44220-025-00563-8 · Cited by 3
“The slight difference in the estimate and conclusion in statistical significance could be due to differences in the algorithm between SBayesRC and PRScsx.”
InfluentialWang, N., Zhang, Y.-X., Schroeder, P. H., Huerta-Chagoya, A., Mandla, R., Meigs, J., Manning, A. K., Liu, C.-T., Dupuis, J. E., & Mercader, J. (2025) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2025.0557 · Cited by 2
Verhulst, B. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1698381 · Cited by 2
“For example, methods such as PRS-CSx (Ruan et al., 2022), MegaPRS (Zhang et al., 2021), LDPred2 (Privé et al., 2021), and SBayesR (Lloyd-Jones et al., 2019) extend PRS analyses to diverse ancestry groups, re-weight the fi nding by including functional genomic information, and utilize powerful Bayesian methods.”
Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children
Joo, Y. Y., Kim, B., Kim, G., Lee, E., Seo, J., & Cha, J. (2025) · Nature Communications · doi:10.1038/s41467-025-63312-6 · Cited by 10
“For better cross-population polygenic prediction, PRS-CSx (June 4, 2021 release) 79 , a recently developed Bayesian polygenic modeling technique, was used to construct the PGS for our multi-ancestry study participants.”
Maternal stress, cord blood zinc and attention deficit hyperactivity disorder
Takahashi, N., Nishimura, T., Okumura, A., Iwabuchi, T., Harada, T., Rahman, M. S. F. U., Nomura, Y., Newcorn, J., & Tsuchiya, K. J. (2025) · npj Mental Health Research · doi:10.1038/s44184-025-00149-3 · Cited by 2
“Finally,thePRSconstructionwas basedonGWASsummarystatisticsfromdifferentethnicbackgrounds (EuropeanandChinese),andwhileweusedtrans-ethnicmethods(PRS-CSx),thegeneralizabilitytoJapanesepopulationsmaystillbelimited.”
Pozuelo, J. R., Lahiri, A., Singh, R. S. P., Kushwah, A., Khanduri, M., Shukla, A., Khan, A., Sruthi, G., Shende, V., Parashar, Y., et al. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-07030-9 · Cited by 4
“As methods for optimizing cross-ancestry PRS performance evolve, we will incorporate new approaches as appropriate [88].”
Evaluation of diverse polygenic risk score models for lung cancer in a small-scale Chinese cohort
Gao, M., Zheng, Q., Jiang, Y., Chang, X., & Zheng, X. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1646997 · Cited by 2
Almost Free Enhancement of Multi-Population PRS: From Data-Fission to Pseudo-GWAS SubsamplingPreprint
Xu, L., Dong, Y., Zeng, X., Bian, Z., Zhou, G., Guan, L., & Zhao, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.16.659952 · Cited by 1
“We conducted comprehensive simulations to evaluate the performance of MIXPRS compared to seven existing multi-population PRS methods: JointPRS [14], XPASS [15], SDPRX [18], PRS-115 CSx [17], MUSSEL [20], PROSPER [21], and BridgePRS [22].”
Chen, Y.-E., Onthoni, D. D., Chuang, S.-Y., Li, G.-H., Zhuang, Y.-S., Chiou, H.-Y., Sheu, W. H.-H., & Chung, R.-H. (2025) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2024.0319 · Cited by 2
“In addition, we incorporated the PRS for T2DM, calculated using the PRS-CSx algorithm [21].”
Hyat, M., Zhu, J., Boltz, T. A., Conomos, M. P., Hughes, D., Fohner, A. E., Foster, K. T., Bigdeli, T. B., & Forsyth, J. K. (2025) · Psychological Medicine · doi:10.1017/s0033291725102304 · Cited by 2
“This method was chosen due to its demonstrated accuracy in improving cross-ancestry PRS accuracy compared to other methods (Ruan et al., 2022).”
InfluentialYuan, Q. E., Chen, Y., Ying, X., Dimitrov, L. V., Risk, B. B., Walker, E. F., Huels, A., & Ku, B. S. (2025) · Schizophrenia Research · doi:10.1016/j.schres.2025.03.034 · Cited by 3
Jiang, X., Zai, C. C. H., Merranko, J. A., Young, L. T., Birmaher, B. B., & Goldstein, B. I. (2025) · Journal of Child and Adolescent Psychopharmacology · doi:10.1089/cap.2024.0130 · Cited by 1
“…other calls for well-powered GWAS in globally diverse populations, which would be essential to improve the potential clinical utility of PRS and ancestry-related health care disparities (Polygenic Risk Score Task Force of the International Common Disease Alliance et al., 2021; Ruan et al., 2022).”
Diversifying Psychiatric Genomics
Giusti‐Rodríguez, P., Okewole, N., Jain, S., Montalvo‐Ortiz, J. L., & Peterson, R. E. (2025) · Psychiatric Clinics of North America · doi:10.1016/j.psc.2025.01.003 · Cited by 4
Trans-ancestry Genome-Wide Analyses in UK Biobank Yield Novel Risk Loci for Major DepressionPreprint
Singh, M., Chatzinakos, C., Barr, P. B., Gentry, A. E., Bigdeli, T. B., Webb, B. T., & Peterson, R. E. (2025) · medRxiv · doi:10.1101/2025.02.22.25322721 · Cited by 4
“The non-EUR-like ancestry groups were considerably smaller and had less power for ancestry-specific GWAS, post-GWAS analyses, or for computing cross-ancestry PGSs by meta-analyzing ancestry-specific results with PRS-CSx 68 .”
Huang, J. V., Che, J., Kee, M. Z. L., Tan, A. P., Law, E. C. N., Silveira, P. P., Pokhvisneva, I., Patel, S. H., Godfrey, K. M., Daniel, L. M., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105579 · Cited by 2
“15 In brief, a shared continuous shrinkage (CS) prior was used to infer posterior SNP effect sizes using the GWAS summary statistics of different ancestries and the ancestry-matched LD reference panels.”
Approaches to enable equitable psychiatric genetic research in Africa
Kalungi, A., Stein, D. J., Okewole, A., & Fatumo, S. (2024) · Communications Medicine · doi:10.1038/s43856-024-00639-z · Cited by 2
Xu, Y., Qiao, M., Gunasekaran, T. I., Gu, Y., Reyes-Dumeyer, D., Piriz, A., Sanchez, D., Soriano, B., Franco, Y., Coronado, Z. D., et al. (2024) · medRxiv · doi:10.1101/2024.10.21.24315877 · Cited by 2
“PROSPER, integrating European, African, and Hispanic GWASs, provided the most predictive PRS on average for incident AD (pooled HR: 1.26 [1.21-1.32], mean R² = 0.038 [0.026-0.050]) and cognition (pooled beta: -0.40 [-0.43 to -0.37], mean R² = 0.017 [0.015-0.020]) (Figure 1B-1C).”
BackgroundMethodInfluentialHobson, L. M., Martin, R. M., Smith-Byrne, K., Smith, G. D., Hemani, G., Gilbody, J. H., Yarmolinsky, J., Bailey, S. E. R., Goudswaard, L. J., & Haycock, P. (2025) · PLoS ONE · doi:10.1371/journal.pone.0312970 · Cited by 2
“PRS-CSx applies the same methodology as PRS-CS to multi-ancestry GWAS summary statistics, improving generalisability of results to more ancestry groups within the global majority by allowing the use of different ancestry GWAS summary statistics and LD panels for those populations [27].”
Lowe, C. L., Gomez‐Stafford, J., & Martschenko, D. O. (2024) · Journal of Genetic Counseling · doi:10.1002/jgc4.1976 · Cited by 2
Wang, Z., Wallace, D. A., Spitzer, B., Huang, T.-Y., Taylor, K., Rotter, J., Rich, S. S., Liu, P. Y., Daviglus, M., Hou, L.-F., et al. (2024) · medRxiv · doi:10.1101/2024.09.04.24313008 · Cited by 2
Multiancestry transferability of a polygenic risk score for diverticulitis
Ueland, T. E., Mosley, J. D., Neylan, C., Shelley, J. P., Robinson, J. R., Gamazon, E. R., Maguire, L., Peek, R. M., & Hawkins, A. (2024) · BMJ Open Gastroenterology · doi:10.1136/bmjgast-2024-001474 · Cited by 2
“In addition, we did not include rare variants or gene by environment interactions though these are critical areas for future study.”
He, Y., Lu, W., Jee, Y. H., Wang, Y., Tsuo, K., Qian, D. C., Diao, J. A., Huang, H., Patel, C. J., Byun, J., et al. (2024) · medRxiv · doi:10.1101/2024.08.25.24312558 · Cited by 5
“We then used PRS-CSx to jointly model trait-specific MTAG results across ancestry groups to derive candidate scores 35,36 to include in the final PRSxtra.”
MethodSelf-citationA genome-wide association study identifies novel loci of vertigo in an Asian population-based cohort
Chen, S., Hsu, C., Chen, T., Pan, L. H., Wang, Y.-F., Ling, Y., Chang, H.-C., Chen, Y., Fann, C. S. J., & Wang, S. (2024) · Communications Biology · doi:10.1038/s42003-024-06603-w · Cited by 4
Real-time dynamic polygenic prediction for streaming data
Tubbs, J., Chen, Y., Duan, R., Huang, H.-L., & Ge, T. (2024) · medRxiv · doi:10.1101/2024.07.12.24310357 · Cited by 2
“A straightforward extension could involve combining the SGD algorithm with the PRS-CSx framework 3,25 , thereby allowing for the modeling of samples from multiple genetic ancestries.”
BackgroundMethodSelf-citationBlechter, B., Wang, X., Shi, J., Shiraishi, K., Choi, J., Matsuo, K., Chen, T.-Y.-T., Dai, J., Hung, R., Chen, K., et al. (2024) · medRxiv · doi:10.1101/2024.06.26.24309127 · Cited by 2
“Notably, the development of multi-ancestry PRS methods marks a considerable advancement, leveraging data from various ancestral backgrounds to enrich the predictive accuracy and robustness of PRSs 20–26 .”
Vince, R., Sun, H., Singhal, U., Schumacher, F. R., Trapl, E., Rose, J., Cullen, J., Zaorsky, N. G., Shoag, J., Hartman, H., et al. (2024) · European Urology Oncology · doi:10.1016/j.euo.2024.04.017 · Cited by 2
Pharmacogenomic scores in psychiatry: Systematic review of existing evidencePreprint
Sharew, N. T., Clark, S. R., Schubert, K. O., & Amare, A. T. (2024) · medRxiv · doi:10.1101/2024.04.05.24305376 · Cited by 2
Lee, Y., Zhang, Y.-Z., Kennedy, C. J., Mallard, T., Liu, Z., Vu, P., Feng, Y.-C. A., Ge, T., Petukhova, M., Kessler, R., et al. (2024) · JMIR Bioinformatics and Biotechnology · doi:10.2196/58357 · Cited by 2
Self-citationA Stacking Framework for Polygenic Risk Prediction in Admixed IndividualsPreprint
Liao, K., & Zöllner, S. K. (2024) · medRxiv · doi:10.1101/2024.01.31.24302103 · Cited by 2
“The extent in how LD differs across populations varies along the genome 19 , prompting work in the transferability of PRS across diverse populations to often consider a local approach in combining genetic evidence 20,21 .”
Alamad, B., Elliott, K., & Knight, J. C. (2024) · Cambridge Prisms Precision Medicine · doi:10.1017/pcm.2023.25 · Cited by 12
Norland, K., Schaid, D., Naderian, M., Na, J., & Kullo, I. (2024) · medRxiv · doi:10.1101/2024.01.10.24301105 · Cited by 2
“PRS CHD was developed using PRS-CSx (22) with 1.3 million variants.”
MethodInfluentialBrīvība, M., Atava, I., Pečulis, R., Elbere, I., Ansone, L., Rozenberga, M., Silamiķelis, I., & Kloviņš, J. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25021151 · Cited by 6
Editorial on polygenic risk scores: Colloquium held at the Centre for Personalised Medicine, Oxford
Dixon, P., Briggs, S., & Lucassen, A. (2023) · Cambridge Prisms: Precision Medicine · doi:10.1017/pcm.2023.22 · Cited by 2
Toward the Next Generation of Precision Medicine
Verhulst, B., & Benstock, S. E. (2023) · Policy Insights from the Behavioral and Brain Sciences · doi:10.1177/23727322231197765 · Cited by 2
“Addressing this limitation requires collecting genomic data in diverse populations and developing more sophisticated tools to calculate the PRS (e.g., Ruan et al., 2022).”
MethodEvaluating and Improving Health Equity and Fairness of Polygenic Scores
Zhang, T., Klei, L., Liu, P., Chouldechova, A., Roeder, K., G’Sell, M., & Devlin, B. (2023) · bioRxiv · doi:10.1101/2023.09.22.559051 · Cited by 2
Hassanin, E., Lee, K.-H., Hsieh, T.-C., Aldisi, R., Lee, Y.-L., Bobbili, D., Krawitz, P., May, P., Chen, C.-Y., & Maj, C. (2023) · medRxiv · doi:10.1101/2023.08.03.23293320 · Cited by 2
Self-citationRole of genetics in capturing racial disparities in cardiovascular diseasePreprint
Bose, A., Platt, D. E., Kartoun, U., Ng, K., & Parida, L. (2023) · medRxiv · doi:10.1101/2023.02.10.23285769 · Cited by 2
“Recent advances in applications of machine learning also help establish more generalizable PRS for clinical use [30,31].”
Barry, A., McNulty, M. T., Jia, X., Gupta, Y., Dêbiec, H., Luo, Y., Nagano, C., Horinouchi, T., Jung, S., Colucci, M., et al. (2022) · medRxiv · doi:10.1101/2022.09.13.22279644 · Cited by 1
Levin, M. G., & Rader, D. J. (2022) · Best Practice & Research Clinical Endocrinology & Metabolism · doi:10.1016/j.beem.2022.101702 · Cited by 3
Improving the computation efficiency of polygenic risk score modeling: faster in Julia
Faucon, A. B., Samaroo, J., Ge, T., Davis, L., Cox, N. J., Tao, R., & Shuey, M. M. (2021) · Life Science Alliance · doi:10.26508/lsa.202201382 · Cited by 2
“2019), a limitation that has been addressed by PRS-CSx (Huang et al. 2021).”
Self-citationZeng, L., Khan, A., Fitzgerald, K. C., Lama, T., Chen, J., Li, R., Harroud, A., Compston, A., Sawcer, S. J., Kermode, A. G., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02731-7 · Cited by 5
“Finally, we designed and tested a genome-wide polygenic score (GPS) 25 for MS that maximizes performance across diverse ancestries, conducted a phenome-wide association study (PheWAS) to identify diseases/traits associated with the GPS and evaluated GPS associations with brain magnetic resonance…”
Schizophrenia: advances in pathophysiology, management, and precision medicine
Sawa, A., Chiappelli, J., Hamrah, O., & Cascella, N. G. (2026) · BMJ · doi:10.1136/bmj-2026-635470 · Cited by 4
“38 In addition to hereditary”
Self-citationLocus-specific gene-context interactions improve polygenic prediction
Fonseca, R., Caggiano, C., Costantino, M., Dominguez, O., Kenny, E., & Dahl, A. W. (2026) · bioRxiv · doi:10.64898/2026.08.26.746823 · Cited by 1
“Finally, we used standard clumping and thresholding (C+T) as the underlying tuning method in PGS, PGSC, and ampPGS, which provides a fair like-with-like evaluation, but more sophisticated models 10,52–54 should further improve accuracy for all approaches.”
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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