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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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Items 501–600 of 609
Cindi, Z., Cardone, K. M., Bradford, Y., Hall, M., Kim, H., Daar, E., Gulick, R. M., Riddler, S., Vujković, M., Chang, K., et al. (2026) · Pharmacogenomics (London) · doi:10.1080/14622416.2026.2698921
Development and validation of a multiancestry and multitrait polygenic risk score for lung cancer
Zhang, Y.-X., Dai, J.-L., Gu, P., Zhao, Y., Christiani, D., Chen, F., & Shen, S. (2026) · Nature Communications · doi:10.1038/s41467-026-74964-3
“We constructed PRSs for lung cancer based on multiancestry GWAS meta-analysis results using PRS-CSx 53,54 , a shrinkage method that improves cross-population polygenic prediction.”
Polygenic Risk Scores for Incident Dementia in the Multi‐Ethnic Study of Atherosclerosis
Xue, D., Blue, E., Sofer, T., Hughes, T. M., Rotter, J. I., Post, W. S., & Fohner, A. E. (2026) · Genetic Epidemiology · doi:10.1002/gepi.70046 · Cited by 5
“In addition to the C+T models with varying p ‐value stringency, two Bayesian models were compared: PRS‐CS and PRS‐CSx (Ge et al. 2025; Ruan et al. 2022).”
Lee, E. Y. P., Dinh, B. L., Tang, J., Streicher, S. A., Wang, X., Biswas, S., Tian, H., Yu, X., Tappara, K., Naseri, T., et al. (2026) · Genes & Nutrition · doi:10.1186/s12263-026-00811-1 · Cited by 2
Self-citationGenetic Susceptibility to Incisional Hernia Evaluation of Hernia Polygenic Risk Scores
Pregnall, A., Hornick, M. M., Broach, R., Judy, R., DePaolo, J., Yuan, S., Levin, M., Fischer, J. P., Damrauer, S., & Wachtel, H. (2026) · medRxiv · doi:10.64898/2026.06.10.26355374
“Allele weights for each score were estimated using PRS-CSx, a Bayesian polygenic prediction method that employs a continuous shrinkage on SNP effect sizes and jointly models summary statistics from multiple ancestry groups to improve cross-ancestry prediction.26”
Puchiele, A. A., Mio, M., Dimick, M. K., Zai, C., Boulos, M., Rabin, J. S., & Goldstein, B. I. (2026) · Journal of child and adolescent psychopharmacology · doi:10.1177/10445463261456656
“Therefore, the present insomnia-PRS may not be fully generalizable to youth with diverse ancestral origins (Ruan et al., 2022).”
MIXPRS enables multi-population and multi-method polygenic risk scores using summary statistics
Xu, L., Dong, Y., Zeng, X., Bian, Z., Zhou, G., Guan, L., & Zhao, H. (2026) · Nature Genetics · doi:10.1038/s41588-026-02637-4 · Cited by 2
“PRS-CSx 17 , an extension of PRS-CS 49 , integrates GWAS summary statistics from multiple populations using a shared Bayesian shrinkage prior.”
Smith, J. L., Norland, K., Hamed, M. E., Yu, Y., Na, J., Dikilitas, O., Schaid, D. J., & Kullo, I. J. (2026) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.125.005382
Barr, P. B., Neale, Z. E., Bigdeli, T. B., Chatzinakos, C., Harvey, P. D., Peterson, R. E., & Meyers, J. L. (2026) · Psychological Medicine · doi:10.1017/s003329172610470x · Cited by 1
“WecreatedPGSsusingPRS-CSx(Ruanetal.,2022),a Bayesianregressionandcontinuousshrinkagemethodthatestimates theposterioreffectsizesforeachSNPinagivensetofGWAS summarystatistics.”
Choi, M. J., Kim, Y., Lee, H., Joo, E.-J., Ahn, Y. M., Myung, W., Kim, S. H., Lee, B. D., Ha, T. H., Kim, S. J., et al. (2026) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2026.105034
Self-citationScoping Review of Polygenic Risk Scores in Kidney-Related Traits
Sugawara, Y., Ito, K., Iwagami, M., Inokuchi, R., Nihei, Y., Nangaku, M., & Hirakawa, Y. (2026) · Kidney International Reports · doi:10.1016/j.ekir.2026.106646
“Several strategies, including increasing GWAS sample sizes in underrepresented populations, developing ancestry-specific and cross-ancestry PRS methods, 45 and prioritizing external validation across diverse populations, may help address this gap.”
Polygenic Risk Scores for Breast Cancer Among African American Women With High Risk
Sun, Y., Simmons, T. C., Li, J., Jamal, A., Manirakiza, A. V. C., Pruss, D., Ratzel, S., Ol, O., Gutin, A., Hughes, E., et al. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.19285
Qiao, M.-X., Wei, W., Zhou, M., Li, M., Zhang, Y., Li, X.-J., Deng, W., Guo, W.-J., Wang, Q., Yu, H., et al. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-08216-5
“Scores were calculated using PRS-CSx [23] based on genome-wide association studies of European and East Asian individuals with bipolar disorder [24] and reference linkage disequilibrium panels from phase 3 of the 1000 Genomes Project ( h t t p : / / g i t h u b . c o m / g e t i a n 1 0 7 / P R S c…”
Familial coaggregation and shared genetic loading of mental disorders and cardiovascular diseases
Chen, P.-C., Pan, Y., Lin, M.-C., Su, M.-H., Fan, C.-C., Wu, C.-S., & Wang, S.-H. (2026) · Psychological Medicine · doi:10.1017/s0033291725101724
“PRS for schizophrenia, bipolar disorder, and MDD were derived using PRS-CSx (if large-scale genome-wide association study (GWAS) summary was available in both European and East Asian populations) (Ruan et al., 2022), or PRS-CS (Ge et al., 2019).”
Polygenic risk scores in diabetes: are the goalposts shifting?
Sridhar, G., Gumpeny, L., & Narasimhadevara, S. (2026) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-026-01684-1
Zhu, M., Zhang, C., Han, Y., Chen, J. M., Miao, S., Zhang, J., Gong, L., Mou, Y., Chen, J. M., Fu, Y., et al. (2026) · Genome Medicine · doi:10.1186/s13073-026-01666-2
“In contrast, in the CKB cohort, PRS-CSx consistently outper-formed PRS-CS across all sensitivity analyses, consistent with prior evidence that the advantage of PRS-CSx is greater in non-EUR populations and limited in EUR populations [12].”
InfluentialCharacterizing the Uncertainty, Misclassification and Inconsistency of Polygenic Prediction
Zhang, Y.-Z., Zhang, R., & Ge, T. (2026) · medRxiv · doi:10.64898/2026.05.11.26352897
“Although we focused on PRS-CS 12 , the framework extends to other Bayesian PRS construction methods 10,11,13,31,53 that provide reliable posterior variance estimates.”
Self-citationHanson, R. L., Kobes, S., WEDEKIND, L. E., Arreola, E. V., Ramirez-Luzuriaga, M. J., & Hsueh, W. (2026) · BMC Medical Genomics · doi:10.1186/s12920-026-02386-7
“PRSCx is a Bayesian method that estimates effect sizes across different ancestry groups, accounting for linkage disequilibrium in each group, and these ancestry-specific estimates are then summarized to produce an overall PRS [39].”
Hindley, G., Koch, E. M., Parker, N., Shadrin, A. A., Smeland, O. B., Frei, O., O'Connell, K. S., & Andreassen, O. A. (2026) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.70067 · Cited by 1
Tran, P., & Do, A. (2026) · bioRxiv · doi:10.64898/2026.04.24.720752
“Statistical methods such as LDpred2 [Privé et al., 2021] and PRS-CSx [Ruan et al., 2022] model LD differences across populations but operate on summary statistics rather than individual-level representations.”
Empirical evaluation of analytic validity of polygenic scores
Lin, T., Zeng, J., Gordon, S. D., Wallace, L. M., Ziser, L., Shah, S. H., Pain, O., Nolte, I. M., Snieder, H., Study, L. C., et al. (2026) · Genome Medicine · doi:10.1186/s13073-026-01654-6 · Cited by 1
“The S step is the most researched; methods to optimise SNP weights is an active area of research [15–18].”
Peloso, G., Adhikari, N., Young, M. M., Cho, K., & Kinlay, S. (2026) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.125.005442
Multi ancestry genome wide association meta analysis of urinary aMT6s levels
Żebrowska, M., Zhang, Z., Chuang, G.-T., Evans, D. S., Valliere, J., Maher, M., Hu, J., Richmond, R., Turman, C., Hart, J. E., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-49491-2
Cho, S., Lee, H., Ha, J., Park, Y., Moon, J., Jang, H. C., Park, K., Cho, N. H., Bergman, M., & Kwak, S. (2026) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2025.0362
“…risk score estimated using Bayesian regression with continuous shrink-age priors (PRS-CSx), a Bayesian regression method that improves cross-population polygenic prediction by integrating GWAS summary statistics from multiple ancestry groups, leading to more accurate effect size estimation [28].”
Self-citationQinye, L., Shen, H., Dang, X., He, M. Z., Yizhou, W., Shi, Y., Xiaoyu, W., Chen, F., Han, D., Chengyong, L., et al. (2026) · Frontiers in Chemistry · doi:10.3389/fchem.2026.1778732
“To address this, we applied PRS-CSx (Ruan et al., 2022), combining GWAS from European and East Asian populations (denoted as EUR + EAS), and generated posterior effect estimates for each single nucleotide polymorphism using a Bayesian regression model.”
InfluentialRamirez-Luzuriaga, M. J., Safabakhsh, S., Salehi, R., Kobes, S., Aflague, T., Sarmiento, J. D., Curran, J. E., Nelson, R. G., Curtis, J. M., Moffett, C. D., et al. (2026) · Journal of Human Genetics · doi:10.1038/s10038-026-01474-x
“Previous studies found that PRSs derived from multi-ancestry data often outperform those of single ancestries, particularly in diverse populations [1, 29].”
HADDAD, Y. L. (2026) · Biology & Philosophy · doi:10.1007/s10539-026-10017-9 · Cited by 1
“In scientific research, overrepresentation of individuals with European ancestry reduces the clinical relevance of findings for individuals of non-European descent (Ruan et al. 2022).”
Genetic evidence for the association between schizophrenia and obesity
Su, M., Yeh, T.-C., Cheng, C., Kao, W.-Y., Lin, M., Lin, Y.-F., Chen, C.-Y., Wu, C., & Wang, S. (2026) · International Journal of Obesity · doi:10.1038/s41366-026-02059-6
“Because large-scale GWAS summary for SCZ was available in both European and East Asian populations, PRS for SCZ (PRS SCZ ) was derived using PRS-CSx [18], a state-of-the-art Bayesian polygenic modeling method jointly modeling GWAS summary statistics of different discovery samples, to integrate GWAS…”
Self-citationLannoy, S., Barr, P. B., Gillespie, N. A., Stephenson, M. E., & Edwards, A. C. (2026) · Archives of Suicide Research · doi:10.1080/13811118.2026.2648604
“We used PRS-CSx, a Bayesian polygenic method (Ruan et al., 2022), and PLINK 2.0 (Chang et al., 2015) to compute PGS. PRS-CSx allows the joint modeling of the GWAS sum-mary statistics from EA and AA and combines genetic effects across populations using a shared continuous shrinkage prior on the…”
InfluentialCooke, M. E., Kuo, S., Lumpe, E., Aliev, F., Brislin, S., Bucholz, K., Chan, G., Dick, D., Edenberg, H., Kamarajan, C., et al. (2026) · Psychological Medicine · doi:10.1017/s0033291726103237
“For individuals most genetically similar to reference panels from Africa (AFR-like), we used PRS-CSx (Ruan et al., 2022) which boosts power from the results of the smaller AFR-like GWAS by meta-analyzing these with the results of the larger GWAS of EUR-like samples.”
Skvortsova, L., Yergali, К., Zhaxylykova, A., Begmanova, M., & Mansharipova, A. T. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1927147
“This is especially relevant because GWAS of coronary artery disease (CAD) remains dominated by European ancestry cohorts, while ancestry-specific CAD loci have been reported in available multi-ancestry studies (Silva et al., 2023; Ruan et al., 2022).”
Wang, T., Li, J., Liu, Y., Fang, C., Wang, X., Song, Z., Guo, M., Yao, Y., & Zheng, X. (2026) · Brain Conflux · doi:10.71321/ptbkr794
“This decline is driven by divergent LD patterns, differences in allele frequencies, and heterogeneous gene-environment interactions [53] .”
Stinson, E. A., Lellapalli, S., Hawn, S. E., Merrill, N. A., Rappaport, L. M., Sheerin, C. M., Michopoulos, V., Bacanu, S., Peterson, R. E., Powers, A. D., et al. (2026) · European Journal of Psychotraumatology · doi:10.1080/20008066.2026.2635214
Pathway-specific polygenic risk scores for blood pressure traits in a West African cohort
Bormes, G., Robbin, V., Chikowore, T., Zhang, Y., Choudhury, A., Hazelhurst, S., Hanchard, N. A., Adebamowo, S. N., Adeyemo, A., & Tayo, B. O. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1707809
“However, we understand that other methods, including PRSice, PRSice-2, PRS-CSx, LDpred2, and SbayesR, may yield different results and perhaps offer greater utility in studies where a predefined list of pathways was not already predetermined (13, 55–58).”
Step, K., Sima, C. A. N. N., Grant, S., Kim, J., Waldo, E., Bardien, S., & Mata, I. (2026) · PLoS Genetics · doi:10.1371/journal.pgen.1012064
“As LD resources tailored to AFR and admixed genomes continue to improve, evaluating LD-aware methods such as PRS-CS or PRS-CSx will be critical to determine whether these approaches can further enhance PRS performance in this setting [22,23].”
Edwards, A. C., Passero, K., Eglovitch, M., Polak, K., Parlier‐Ahmad, A. B., Ngjelina, E., Stephenson, M. E., Lannoy, S., Svikis, D. S., & Kendler, K. S. (2026) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.70276
“Where appropriate, we attempted to mitigate these issues through the use of summary statistics derived from multiple ancestry groups (Peterson et al., 2019; Ruan et al., 2022 ), although we were limited to majority-EUR summary statistics for PAU.”
Xu, B., Dijkzeul, A., Zhang, Y., Schuurmans, I. K., Cecil, C. A. M., Lee, P. H., Muetzel, R. L., & Tiemeier, H. W. (2026) · Biological Psychiatry · doi:10.1016/j.biopsych.2026.03.989
Nakamura, S., Inoue, R., & Narimatsu, H. (2026) · PNAS Nexus · doi:10.1093/pnasnexus/pgag062 · Cited by 2
“Approximately one-third of the population is covered by national health insurance (19).”
Wells, J. L., Rabinowitz, J. A., Maher, B. S., Gentry, A. E., Burch, J. B., & Prom‐Wormley, E. C. (2026) · Behavior Genetics · doi:10.1007/s10519-026-10263-3
“This was expected as the new PGS used larger sample sizes and were generated using stronger and more sensitive methods (Ruan et al. 2022; Zhou et al. 2023; Meng et al. 2024).”
Statistical uncertainty explains the poor agreement in polygenic scoring for type 2 diabetesPreprint
Mandla, R., Li, X., Shi, Z., Abramowitz, S. A., Lapinska, S., Biobank, P. M., Levin, M. G., Damrauer, S. M., & Paşaniuc, B. (2026) · medRxiv · doi:10.64898/2026.02.25.26347015
“Next we evaluated the impact of choice of PGS on our results using a new multi-ancestry PGS for T2D from five single population GWAS meta-analyses 24,28 (T2DGGI-MA PGS; Methods ).”
Mapping modifiable risk factors for incident epilepsy: a large prospective cohort study
Cui, X.-H., Zhang, Y., He, X., Cheng, W., & Yu, J.-T. (2026) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2026.110935
Du, J., Horimoto, A. R. V. R., Best, L. G., Zhang, Y., Cole, S. A., Umans, J. G., Franceschini, N., & Sun, Q. (2026) · medRxiv · doi:10.64898/2026.02.08.26345859
“…and efforts to include diverse populations in genomic research to mitigate health disparities 12–15 , with a wide range of PGS methods developed specifically for non-49 European populations 8,16–19 , yet many populations remain underrepresented in genome-wide association studies (GWAS) 13,20 .”
Genomic insights into substance use and disinhibitory disordersPreprint
Williams, C. M., Poore, H. E., Londoño-Correa, D., Ning, Y., Courchesne‐Krak, N. S., Choi, M., Tubbs, J. D., Rosenblatt, M., Tanksley, P. T., Alıev, F., et al. (2026) · medRxiv · doi:10.64898/2026.02.09.26344198 · Cited by 3
“Briefly, we used PRS-CSx to apply a continuous shrinkage prior to ancestry-specific SNP effect estimates and infer posterior SNP weights for common HapMap3 SNPs (MAF ≥ 0.01), using the 1000 Genomes Phase 3 v5 reference dataset to model ancestry-specific LD patterns.”
InfluentialSelf-citationWang, C., Yin, G., Zhou, Y., Song, Y.-C., Tian, M.-L., Wang, X.-Y., Wang, J., Li, Q., Zang, R., Yang, Z.-L., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-38189-0
Penna, L. S., Amemiya, R. B., Núñez, I. A., Grefenhagen, Á. I. D. S., Flores, V. S., Oliveira, M. V. L., Almodobar, L., Mauer, J., Neto, F. A. S., Filho, R. D. L., et al. (2026) · Human Genetics · doi:10.1007/s00439-025-02811-4
“To construct the PRS, we used PRS-CSx, a Bayesian approach that combines GWAS summary statistics from diverse populations to enhance the accuracy of PRS predictions (Ruan et al. 2022).”
Precision Approaches for Scalable Digital and Clinic-Based Interventions in Mental Health
Bohnert, A. S. B., Fritsche, L., & Sen, S. (2026) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2026.01.004
Antonyan, L., Shaheen, S., Burton, C. L., Gehring, W. J., Soreni, N., Szura, P., Bellamy, J., Rajan, U., Rosenberg, D. E., Hanna, G. L., et al. (2026) · Psychiatry Research · doi:10.1016/j.psychres.2026.116977
A statistical review of polygenic risk scores: from heuristics to model-based inference
Huang, X., & Jiang, W. (2026) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2026-0007
“For example, PT-meta applies clumping and thresholding to meta-analyzed GWAS summary statistics, whereas PT-mult applies P + T separately to each ancestry-specific discovery GWAS and then combines the resulting ancestry-specific PRS (Ge et al. 2022; Ruan et al. 2022).”
Seagle, H., Kim, J., Akerele, A. T., Hung, A. M., Hellwege, J., & Edwards, T. L. (2025) · Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · doi:10.1142/9789819824755_0048
Cannabis use and psychotic-like experiences in the All of Us Research ProgramPreprint
Johnson, E. C., Luo, Z., Villela, P. N. R., Arpana Agrawal, A., Hatoum, A. S., & Karcher, N. R. (2025) · medRxiv · doi:10.64898/2025.12.01.25341322
Polygenicity and APOE ε4 shape response to intervention in mild cognitive impairment
Hwang, J., Moon, S., Lee, H., Lee, J., Park, Y., Jeong, J., Hong, C. H., Jung, J.-W., Na, H., Cho, S. H., et al. (2025) · Alzheimer's Research & Therapy · doi:10.1186/s13195-025-01907-3
“PRS-CSx [28] method was also used to transfer the ethnic differences in genetic architectures, based on AD genome-wide association study (GWAS) summary statistics from DeRojas et al. (409,435 individuals) [29] for Europeans, and Shigemizu et al. (8,036 individuals) [30] for East Asians.”
Self-citationYouth Correlates of Genetic Liability to Substance Use DisordersPreprint
Paul, S., Gorelik, A. J., Karcher, N., Miller, A. P., Baranger, D., Johnson, E., LeBlanc, K., Dowling, G., Barch, D., Hatoum, A., et al. (2025) · medRxiv · doi:10.1101/2025.11.22.25340798
Effect of polygenic scores on the relationship between psychosis and cognition
Varney, L., Jedlovszky, K., Wang, B., Murtough, S., Cotic, M., Richards‐Belle, A., Khani, N. S., Lau, R., Abidoph, R., McQuillin, A., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03666-z
“All polygenic scores were generated using PRS-CSx [55].”
Non-coding genetic variants underlying higher prostate cancer risk in men of African ancestry
Li, S., Fatema, K., Sundarraj, N., Singh, A., Rajagopal, P. S., Notani, D., Takeda, D. Y., & Hannenhalli, S. S. (2025) · Nature Communications · doi:10.1038/s41467-025-64631-4 · Cited by 1
“In general, GWAS-derived risk variants do not generalize well to independent discovery cohorts 11 – 13 .”
Choi, E. S., Jung, S., Jung, Y. M., Park, C. W., Park, J. S., Jun, J. K., Kim, J., Kim, B., Cho, C., Shim, I., et al. (2025) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.70072
Self-citationTian, X., Fabiha, T., Li, W. F., Dey, K. K., Kellis, M., & Tanigawa, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.13.688144 · Cited by 3
“For instance, the multi-ancestry modeling component could be implemented with PRS-CSx [17] instead of iPGS [18] .”
Rukavishnikov, G. V., Skitchenko, R. K., Kibitov, A. O., & Pinakhina, D. V. (2025) · V M BEKHTEREV REVIEW OF PSYCHIATRY AND MEDICAL PSYCHOLOGY · doi:10.31363/2313-7053-2025-4-1160
Real-time Dynamic Polygenic Prediction for Streaming Data
Tubbs, J., Chen, Y., Duan, R., Huang, H.-L., & Ge, T. (2025) · Nature Genetics · doi:10.1038/s41588-025-02381-1
“A straightforward extension could involve combining the SGD algorithm with the PRS-CSx framework 3,26 , thereby allowing for the modeling of samples from multiple genetic ancestries.”
Self-citationLin, S.-C., Fang, C., Hsu, C., Chung, A.-N., Chen, T., Hsu, K.-H., Yeh, C.-C., Zheng, J., Liu, C.-Y., Wu, C., et al. (2025) · Neuropsychopharmacology · doi:10.1038/s41386-025-02269-y
“We generated PGSs using PRS-CS [37] and PRS-CSx [38], the latter leveraging cross-population GWAS summary statistics to improve PGS accuracy in non-European samples.”
Self-citationGarrison‐Desany, H. M., Hinojosa, C. A., Tubbs, J. D., Meyers, J. L., Linnstaedt, S. D., House, S. L., Beaudoin, F. L., An, X., Stevens, J. S., Neylan, T. C., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03593-z
“For PRS-CSx, we used linkage disequilibrium (LD) reference panels from the 1000 Genome Project Phase 3 [45] for European and African ancestry.”
Hsu, Y., Lin, M., Cheng, C., Su, M., Pan, Y., Fan, C. C., Wu, C., Chen, L.-Y., & Wang, S. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.120464
Żebrowska, M., Zhang, Z.-W., Chuang, G.-T., Evans, D. S., Valliere, J., Maher, M., Hu, J., Richmond, R., Turman, C., Hart, J. E., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7723851/v1
Genetic Architecture of Cerebral White Matter Hyperintensities in Diverse Hispanic/Latino Adults
Fornage, M., Xia, R., Ordonez, A., Sofer, T., Isasi, C., Lipton, R., Stickel, A. M., Tarraf, W., González, H. M., & Decarli, C. (2025) · Neurology: Genetics · doi:10.1212/nxg.0000000000200305
“PRS-CSx 44 : We applied PRS-CSx, a Bayesian polygenic modeling method that leverages population-speci fi c LD and shared genetic information between populations through joint modeling of multiple GWAS summary data.”
Hrytsenko, Y., Spitzer, B., Wang, H., Bertisch, S. M., Taylor, K. D., Garcia‐Bedoya, O., Ramos, A. R., Daviglus, M. L., Gallo, L. C., Isasi, C. R., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01107-y
“Next, we used PRS-CSx 35 (global shrinkage parameter ϕ was learnt from the data, other parameters left at default) to develop ancestry-speci fi c PRS for EUR, AFR, EAS, SAS, and AMR groups (now using population descriptors provided by the Linkage Disequilibrium reference panels implemented by…”
InfluentialSarnowski, C., Zhang, Y., Ammous, F., Shade, L. M. P., DICORPO, D. A., Jian, X., Arnett, D. K., Austin, T. R., Beiser, A., Bis, J. C., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08674-9 · Cited by 2
“PRS-CSx, unlike other traditional methods based on clumping and thresholding, does not require a p -value threshold and thus uses all genetic variants.”
InfluentialStickley, S. A., Fang, Z.-Y., Ambalavanan, A., George, E., Petersen, C., Dai, D. L. Y., Moossavi, S., Miliku, K., Bode, L., Field, C. J., et al. (2025) · bioRxiv · doi:10.1101/2025.09.19.676891
“The PRS-CSx integrates GWAS summary statistics with linkage disequilibrium reference panels from different populations to improve cross-population PRS computation and utilized shared CS priors across populations to estimate posterior SNP effect sizes 94 .”
InfluentialPersonalized polygenic risk prediction and assessment with a Mixture-of-Experts frameworkPreprint
Zabad, S., Li, Y., & Gravel, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.15.676165
“To address the first concern, recent research focused on ensemble learning techniques that pool together multiple scores to optimize prediction accuracy on a particular validation cohort [19,25–27].”
Optimizing Polygenic Scores for Complex Morphological Traits: A Case Study in Nasal Shape PredictionPreprint
Yuan, M., Goovaerts, S., Claessens, N., Devine, J., Becelaere, S., Cleynen, I., & Claes, P. (2025) · bioRxiv · doi:10.1101/2025.09.14.676081
“Although increased diversity introduces statistical challenges, methods like PRS-CSx [37] can effectively couple genetic effects across populations through a shared prior.”
Hwang, L.-D. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.06.020
Ostner, S. G., Clifford, S., Cruz, R. A., Tein, J., Westling, E., Shaw, D. S., Brown-Iannuzzi, J. L., Wilson, M. N., & Lemery-Chalfant, K. (2025) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-025-01354-6
“To form the CUD PRS scores for our sample, we used PRS-CSx (Ruan et al., 2022), a Bayesian method designed to increase the accuracy and power of PRS across multiple populations by integrating smaller ancestrally-matched GWAS, and large well-powered GWAS derived from Euro-pean-ancestry samples (for more information, se…”
Influentialde Hoyos, L., Schlag, F., Jahagirdar, S., Corfield, E., Allegrini, A., Admiraal, D., de Zeeuw, E. D., Nolte, I., Llonga, N., Neumann, A., et al. (2025) · bioRxiv · doi:10.1101/2025.08.16.667148
Antonyan, L., Shaheen, S., Burton, C. L., Gehring, W. J., Soreni, N., Szura, P., Bellamy, J., Rajan, U., Rosenberg, D. R., Hanna, G. L., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7115885/v1
Zhang, J.-F., Liu, X., Zhang, B., & Chen, X.-Y. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1627629
Sosnowski, D., Rabinowitz, J. A., Feder, K. A., Strickland, J., Hancock, D. B., Uhl, G., Ialongo, N. S., & Maher, B. S. (2025) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.23-00397
Cheng, Y., Zhou, G., Li, H., Zhang, X., Justice, A. C., Martinez, C. A., Aouizerat, B. E., Xu, K., & Zhao, H. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf325
“Thus, the genetic diversity inherent to admixed populations makes it inappropriate to treat them as one homogeneous group [17–20].”
McLaurin, D. M., Turk, P. J., Affuso, O., Raffield, L. M., & Heitman, E. (2025) · Open Heart · doi:10.1136/openhrt-2025-003501
PTL-PRS: an R package for transfer learning of polygenic risk scores with pseudovalidation
Cho, B., & Lee, S. (2025) · medRxiv · doi:10.1093/bioinformatics/btaf540
“While recent “auto” models like PRS-CSx-auto (Ruan et al. 2022), JointPRS-auto (Xu et al. 2025) bypass validation sets through implicit, in-model (empirical-Bayes) estimation, PTL-PRS enables explicit, summary statistics-based hyper-parameter tuning and testing without a separate individual-level…”
Stephenson, M. E., Lannoy, S., & Edwards, A. C. (2025) · JCPP Advances · doi:10.1002/jcv2.70019 · Cited by 1
“PGS for SA (PGS SA ) were constructed using PRS - CSx (Ruan et al., 2022).”
Polygenic risk scores in kidney transplantation
Jelencsics, K., & Oberbauer, R. (2025) · Current Opinion in Organ Transplantation · doi:10.1097/mot.0000000000001212
Barr, P. B., Bountress, K. E., Chatzinakos, C., Hart, J. W., Neale, Z. E., Sheerin, C. M., Johnson, E. C., Atkinson, E. G., Nievergelt, C. M., Maihofer, A. X., et al. (2026) · Molecular Psychiatry · doi:10.1101/2025.03.24.25324454 · Cited by 3
Predicting suicidality in people living with HIV in Uganda: a machine learning approach
Mutema, A. B., Linda, L., Jjingo, D., Fatumo, S., Kinyanda, E., & Kalungi, A. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1584335 · Cited by 1
“Given GWAS summary statistics of the discovery cohort, PRS-CSx models population-speci fi c allele frequencies and LD patterns by relying on Bayesian regression and continuous shrinkage priors on a reference population (61).”
Interaction between Neighborhood Exposome and Genetic Risk in Child Psychotic-like Experiences
Chen, Y.-X., Yuan, Q., Dimitrov, L., Risk, B., Ku, B., & Huels, A. (2025) · Research Square · doi:10.21203/rs.3.rs-5830171/v1
“To evaluate the robustness of SBayesRC, we also applied another multi-ancestral PRS approach, PRScsx, 35 to calculate the SCZ-PRS.”
Yaraş, T., Oktay, Y., & Karakülah, G. (2025) · PeerJ · doi:10.7717/peerj.18973
Chen, T.-H., Lee, C.-J., Chen, S., Wu, S.-J., & Fann, C. S. (2025) · Bioinform. · doi:10.1093/bioinformatics/btaf071
“These AUCs are comparable to or exceed the best performances from the marginal analysis of the three methods (C þ T, LDpred, PRS-CS) using TWB data alone.”
InfluentialIdentifying genetic variants for brain connectivity using Ball Covariance Ranking and Aggregation
Dai, W., & Zhang, H.-P. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2450837
“Future research should include more diverse populations to broaden these findings (Ruan et al. 2022).”
Xia, C., Alliey‐Rodriguez, N., Tamminga, C. A., Keshavan, M. S., Pearlson, G. D., Keedy, S., Clementz, B. A., McDowell, J. E., Parker, D., Lencer, R., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02876-z · Cited by 9
“It outperforms single-population discovery methods and improves polygenic risk prediction accuracy for disease in single ancestry samples [51].”
Self-citationSmith, J., Norland, K., Hamed, M., Yu, Y., Na, J., Dikilitas, O., Schaid, D., & Kullo, I. (2024) · medRxiv · doi:10.1101/2024.11.05.24316750
“Performance of PRS depend on genetic architecture of phenotypes, size, heterogeneity of development and training cohorts, as well as quality of summary statistics 5,6 .”
Cataldo‐Ramirez, C. C., Lin, M. H., Mcmahon, A., Gignoux, C. R., Weaver, T. D., & Henn, B. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.28.620716
Campbell‐Sills, L., Choi, K. W., Strizver, S. D., Kautz, J. D., Papini, S., Aliaga, P. A., Lester, P. B., Naifeh, J. A., Ray, C., Kessler, R. C., et al. (2024) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2024.112459 · Cited by 2
Population Performance and Individual Agreement of Coronary Artery Disease Polygenic Risk Scores
Abramowitz, S. A., Boulier, K., Keat, K., Cardone, K. M., Shivakumar, M., DePaolo, J., Judy, R., Kim, D., Rader, D., Ritchie, et al. (2024) · medRxiv · doi:10.1101/2024.07.25.24310931
“Two main trends emerged: correlation was highest among pairs of scores derived from the same studies, and also tended to be higher among scores that performed similarly overall and better compared to the rest of the rest of the scores (eFigure 22-23).”
ResultRivier, C., Payabvash, S., Zhao, H.-Y., Hafler, D., Falcone, G., & Longbrake, E. (2024) · medRxiv · doi:10.1101/2024.06.11.24308714
“Primarily, the limited representation of African populations in existing GWAS likely results in PRS that are less applicable to this group 12 .”
BackgroundTowards fair and clinically relevant polygenic predictions.
Vilhjálmsson, B. (2024) · Trends in Genetics · doi:10.1016/j.tig.2024.04.002
Chen, T., Pham, G., Fox, L. W., Zhang, J.-N., Byun, J., Han, Y., Saunders, G. R. B., Liu, D.-J. J., Bray, M. J., Ramsey, A. T., et al. (2024) · medRxiv · doi:10.1101/2024.03.19.24304556
“With the burgeoning emphasis on incorporating minority populations in GWAS analyses and the ongoing development of new PRS approaches 7,8,36–39 that focus on enhancing predictive power in diverse populations, we can iteratively refine the PRS implementation in our trial to synchronize with the most…”
BackgroundMulti-ancestry polygenic risk scores using phylogenetic regularizationPreprint
Layne, E., Zabad, S., Li, Y., & Blanchette, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.14.580313
Zhao, H., Xu, L., Zhou, G., Jiang, W., & Guan, L. (2023) · Research Square · doi:10.21203/rs.3.rs-3741763/v1 · Cited by 2
“Through extensive simulations and real data applications, JointPRS had an overall better performance than the alternative methods, including PRS-CSx [17], PROSPER [19], MUSSEL [18], SDPRX [20], and XPASS [21].”
MethodInfluentialZhang, 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) · Research Square · doi:10.21203/rs.3.rs-3707248/v1
Self-citationGenome-wide risk prediction of primary open-angle glaucoma across multiple ancestriesPreprint
Gharahkhani, P., He, W.-X., Han, X.-K., Ong, J.-S., Rentería, M. E., Wiggs, J., Khawaja, A., Trzaskowski, M., Mackey, D. A., Craig, J., et al. (2023) · medRxiv · doi:10.1101/2023.11.08.23298255
“Secondly, we systematically assessed the predictive . accuracy of the newly derived PGS in SAS, EAS and AFR ancestries 14 .”
BackgroundMethodInfluentialCarvalho, N. R. G., Harris, A. M., & Lachance, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.29.514295 · Cited by 3
Lillo, A. D., Wendt, F., Pathak, G., & Polimanti, R. (2023) · medRxiv · doi:10.1101/2023.05.25.23290542
Predicting Breast Cancer Risk for Women Veterans of African Ancestry in the Million Veteran Program
Luoh, S.-W., Minnier, J., Zhao, H.-Y., & Gao, L.-N. (2023) · Health Equity · doi:10.1089/heq.2023.0011
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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