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Improving polygenic prediction in ancestrally diverse populations
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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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A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
Self-citationInference from iterative simulation using multiple sequences
Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709
Self-citationBiological insights from 108 schizophrenia-associated genetic loci
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) · Nature · doi:10.1038/nature13595 · Cited by 7,577 · cited 2×
“Specifically, we used GWAS summary statistics derived from Psychiatric Genomics Consortium (PGC) wave 2 EUR samples (33,640 cases and 43,456 controls) 28 and 10 PGC EAS cohorts 15 (7,856 cases and 11,562 controls) as the discovery dataset.”
Self-citationLD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821
Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P. T., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0183-z · Cited by 2,953
Clinical use of current polygenic risk scores may exacerbate health disparities
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019) · Nature Genetics · doi:10.1038/s41588-019-0379-x · Cited by 2,821 · cited 3×
“…of factors including differential genetic architectures (i.e., causal variants and their effect sizes), allele frequency and linkage disequilibrium (LD) patterns, phenotype definition and cohort ascertainment, and non-genetic factors and gene-by-environment interactions across populations 7-10 .”
Self-citationA cross-population atlas of genetic associations for 220 human phenotypes
Sakaue, S., Kanai, M., Tanigawa, Y., Karjalainen, J. M., Kurki, M., Koshiba, S., Narita, A., Konuma, T., Yamamoto, K., Akiyama, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00931-x · Cited by 2,335
Cited by
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Items 401–500 of 609
Clinical utility of polygenic risk scores in cardiovascular disorders
Schunkert, H., Li, L., & Trenkwalder, T. (2026) · Medizinische Genetik · doi:10.1515/medgen-2026-3008 · Cited by 1
“New approaches, like machine learning and tools integrating multi-ancestry panels (e.g., PRS-CSx), further enhance portability and predictive power [24].”
Polygenic scores in psychiatric research and clinical practice
Frach, L., David, F. S., Nöthen, M. M., & Forstner, A. J. (2026) · Medizinische Genetik · doi:10.1515/medgen-2026-3012
“Notably, studies have shown that when GWAS from different ancestries are used in their development, PGS performance in independent cohorts improves [104, 105].”
Kamal, R., & Narayanan, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.26.625410
“PRS-CSx: Polygenic Risk Scores - Continuous Shrinkage was proposed by Ruan et al. in 2022 [23] as an extension to their previously published PRS-CS [65] single-ancestry PRS computation method.”
InfluentialPolygenic risk score translation across diverse populations
Krieger, J. E. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1870807 · Cited by 1
Liu, W., Ye, Y., Huang, Z., Zhu, J., Wang, S., Li, P., Zhuang, H., Zhang, S., Cai, F., Song, Z., et al. (2026) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2026.100770 · Cited by 1
Kumar, S., & Cui, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.18.658431 · Cited by 1
“Cross-ancestry generalizability in polygenic models has been enhanced through approaches that recalibrate genetic effect sizes or adjust model sparsity and shrinkage parameters across ancestry groups [18, 23-29].”
Nazaretyan, L., & Kircher, M. (2026) · Trends in Genetics · doi:10.1016/j.tig.2026.02.007
Hernandez, L. M., Loughnan, R. J., Cheng, W., Zhang, L., Fan, C. C., & Arpana Agrawal, A. (2026) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2026.101723 · Cited by 1
Huerta‐Chagoya, A., Kim, J., Mandla, R., Lu, Y., Ken, S., Petty, L. E., Ng, H. K., Choi, J., Lee, S., Rout, M., et al. (2026) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(25)00405-x · Cited by 13
Self-citationAlemu, R. G., Young, A. S., Benjamin, D. J., Turley, P., & Okbay, A. (2025) · Research Square · doi:10.1101/2025.09.11.675704 · Cited by 1
“Genome-wide association studies (GWASs) have primarily been conducted on individuals of European (EUR) genetic ancestries, resulting in a lack of genetic diversity in GWAS datasets 1–3 .”
Muneeb, M., & Ascher, D. (2026) · bioRxiv · doi:10.64898/2026.03.22.713457 · Cited by 1
Polygenic risk score and phenome-wide association study of the Epstein-Barr virus antibody response
Namjou, B., Lape, M., Weirauch, M., Kaufman, K. M., & Kottyan, L. C. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1786510 · Cited by 1
“A polygenic risk score was developed and validated using established computational tools (PLINK and PRS-CSx) that jointly model the GWAS summary statistics from different ancestries and couple genetic effects across populations using a shared continuous shrinkage prior (Ruan et al., 2022).”
Biobank-Scale Polygenic Prediction in Admixed Populations Using Local Ancestry via the Group Lasso
Bonet, D., Yang, J., Hastie, T. J., & Ioannidis, A. (2026) · bioRxiv · doi:10.64898/2026.03.14.711808 · Cited by 1
“Complementary to individual-level sparse regression, multi-ancestry summary-statistic meth-ods such as PRS-CSx improve transfer across single-ancestry cohorts by using genetic ancestry-matched LD reference panels [28].”
Chen, X., Wang, F., Zhao, H., Hao, J., Yunga, A., Yang, X., Xu, T., Zhou, Y., Chen, Q., Zhang, R., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68599-7 · Cited by 4
“PRS-CSx 33 extends the PRS-CS framework by coupling genetic effects across populations via a shared continuous shrinkage prior, enabling more accurate effect size estimation by sharing information between summary statistics and leveraging LD diversity acrosspopulations.”
Keyes, K. M., Gimbrone, C., Rutherford, C. G., Zhang, Y., Choi, K. W., Smith, L. H., Greenland, P., Smoller, J. W., & Argos, M. (2026) · Psychological Medicine · doi:10.1017/s0033291725102420 · Cited by 2
“The PRS weights were generated using PRS-CSx (Ruan et al., 2022), a Bayesian method that integrates genetic effects across populations through a shared continuous shrinkage (CS) prior, resulting in more precise effect size estimates.”
He, Y., Lu, W., Jee, Y. H., Shih, M.-Y., Wang, Y., Tsuo, K., Qian, D. C., Diao, J. A., Huang, H., Patel, C. J., et al. (2026) · Nature Genetics · doi:10.1038/s41588-025-02470-1 · Cited by 6
“Given that smoking is the leading risk factor for COPD and lung cancer, we further investigated distinct and shared variants for smoking The number of case and control samples in each population for each trait can be found in Supplementary Tables 16–18.”
InfluentialSelf-citationCross‐Ancestry Polygenic Prediction: Comparing Methods and Assessing Transferability Across Traits
Momin, M. M., Zhou, X., Ahmed, M., Hyppönen, E., Benyamin, B., & Lee, S. H. (2026) · Genetic Epidemiology · doi:10.1002/gepi.70029 · Cited by 2
“When considering African ancestry, there was a lack of power to identify the best method although GBLUP and PRS‐CSx were always in the best group (asterisk) whether using BMI or standing height (Figure 1).”
InfluentialSelf-citationHoward, A. K., Gustavson, D. E., & Friedman, N. P. (2026) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2026.100690 · Cited by 2
“Furthermore, methods such as PRS-CSx have been shown to perform worse in the ABCD sample than European-derived PGSs applied to different ancestral groups (80).”
Wang, B., Morris, S., Fry, H., Iona, A., Clarke, J., Lin, K., Pupko, I., Kartsonaki, C., Bennett, D. A., Chen, Y., et al. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100681 · Cited by 1
“…summary statistics (24,25) (Table S1): one from GWASs conducted in EAS populations (excluding CKB data), including population-and setting-specific genetic associations, and the other from both GWASs in EAS populations and GWASs in EUR populations, which may improve overall predictive power (23).”
Sorlí, J. V., Ortega‐Azorín, C., Coltell, Ó., Fernández-Carrión, R., Asensio, E. M., Portolés, O., Fidalgo, J. A. P., Sabio, J. B. R., Saiz, J. G., Costa, J. A., et al. (2025) · Nutrients · doi:10.3390/nu17233791 · Cited by 1
“…new studies with multidisciplinary teams are needed to extend these findings and better understand the relationship between personality traits, adherence to the Mediterranean diet, and their genetic and environmental modulations, thereby advancing the emerging field of precision nutrition.”
Taylor, K., Huerta-Chagoya, A., Wang, X.-Y., Vora, M., Li, J., Turk, L., Heng, T., Kim, J., Moreno-Estrada, A., Tusié-Luna, T., et al. (2025) · medRxiv · doi:10.1101/2025.11.24.25340878 · Cited by 1
“Despite their importance, rare and low-frequency variants (MAF 0.01) are historically excluded from PRS due to their omission from GWAS summary statistics and the sparsity of corresponding variants in LD reference used by PRS methods such as PRS-CS and PRS-110 CSx 11,14 .”
InfluentialSelf-citationNeylan, C. J., Ueland, T. E., Abramowitz, S. A., DePaolo, J. S., Tsao, N. L., Roberson, J. L., Hawkins, A. T., Carey, D. J., Smelser, D. T., Levin, M. G., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-26455-6 · Cited by 1
“PLINK was then used to calculate CARMA PRSs in the PMBB. PRS-CSx PRS PRS-CSx is a Bayesian statistical method that calculates a weight for every SNP included in the genotyping array 43 .”
Elam, K. K., Shaw, D., Westling, E., Brown-Iannuzzi, J. L., & Lemery-Chalfant, K. (2025) · Development and Psychopathology · doi:10.1017/s0954579425100709 · Cited by 1
“PRS-CSx uses Bayesian regression and continuous shrinkage to include single nucleotide polymorphisms (SNPs) across the genome (Ruan et al., 2022).”
Risk score roulette: A cautionary tale of polygenic risk score reliability
Xue, D., Blue, E. E., Wood, A. C., Rotter, J. I., & Fohner, A. E. (2025) · Journal of Alzheimer's Disease · doi:10.1177/13872877251375098 · Cited by 1
“16 For the C + T models, we used an LD threshold of r 2 = 0.01 and compared two p-value thresholds: 5e-08 (typical threshold for genome-wide signi fi cance) and 1e-05 (typical threshold for genome-wide suggestive association).”
InfluentialWilding, H. E., Dyer, W. G., Sutara, B., Lee, S., Linden-Carmichael, A., Lanza, S. T., & Lee, H. H. (2025) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-025-02987-0 · Cited by 1
Self-citationA Multivariate Genomic Investigation of the Externalizing Spectrum and Suicide RiskPreprint
Barr, P. B., Stephenson, M., Poore, H., Chatzinakos, C., Qin, X.-J., Ashley-Koch, A., Hauser, E. R., Meyers, J., Peterson, R. E., Sanchez-Roige, S., et al. (2025) · medRxiv · doi:10.1101/2025.08.19.25332327 · Cited by 1
“PRS-CSx employs a Bayesian approach to correct GWAS summary statistics for the non-independence of SNPs in the genome.”
A multi-ancestry polygenic risk score for body mass index predicts longitudinal weight change
Lu, T., Stalter, L. N., Lauer, K. V., Hanlon, B. M., Zhang, W., & Funk, L. M. (2026) · Genome Medicine · doi:10.1186/s13073-026-01741-8 · Cited by 1
“…previously developed PRS for BMI based on European ancestry GWAS, using direct LD clumping and thresholding (PGS000717 [19]), LDpred [37] (PGS000027 [12]), and penalized regression (PGS001228 [20]), and (3) a recently developed cross-ancestry PRS using PRS-CSx [38] (PGS005199 [39]), respectively.”
Velasco-Parra, H. M., Cardona, D. S., Buitrago, C. A., Vanegas, M. N., Hincapié, S. G., Jaramillo, C. J., Cock-Rada, A., Duque, C. B., Piedrahita, C., Bustamante, C., et al. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1556907 · Cited by 1
Liu, A., Liu, J., & Wu, Q. F. (2025) · medRxiv · doi:10.1101/2025.06.06.25329139 · Cited by 1
“PRS-CS employs continuous shrinkage prior on SNP effect sizes, explicitly incorporating linkage disequilibrium (LD) structure from a 1000 Genomes European reference panel, robustly capturing fine-scale LD patterns and improving predictive accuracy[29].”
Self-citationUnraveling genetic risk contributions to nonverbal status in autism spectrum disorder probands
Liu, H., Wang, S., Cao, B., Zhu, J., Huang, Z., Pan, L., Zhang, S., Liu, X., Yu, J., Huang, Z., et al. (2025) · Behavioral and Brain Functions · doi:10.1186/s12993-025-00278-x · Cited by 1
“Considering the ethnic differences between discovery and target dataset, Polygenic Risk Score-Continuous Shrinkage (PRS-CS) was applied to eliminate this discrepancy and improve cross-population polygenic prediction [42].”
Self-citationGenetic risk and polygenic risk score assessment of prediabetes and progression to type 2 diabetes
Aliyu, U., Umlai, U. I., Al‐Thani, N. M., Toor, S. M., Abou‐Samra, A., & Albagha, O. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16490 · Cited by 1
“We observed that PGS derived from multi-ancestry or well-powered GWAS, when combined with advanced computational methods such as PRS-CS(x), 33 generally outperformed those based on single-ancestry GWAS or conventional approaches like pruning and thresholding (P + T).”
Kuo, S., McCutcheon, V., Bucholz, K., Dick, D., Aliev, F., Meyers, J., Brislin, S. J., Chan, G., Edenberg, H., Kamarajan, C., et al. (2025) · JAACAP Open · doi:10.1016/j.jaacop.2025.06.001 · Cited by 1
“Because PRS-CSx improves predictive power for non-European ancestry samples with smaller GWAS, 35 we included both the European and African ancestry – derived polygenic scores in the AA sample in COGA, but only the European ancestry – derived polygenic scores in the EA sample in COGA.”
Mediating Role of Trauma Connecting Psychiatric Family History and Adolescent Mental Health
Chaiyachati, B. H., Catalano, J. L., Schultz, L. M., Almasy, L. A., Visoki, E., Seidlitz, J., Moore, T. M., Taylor, J. H., Calkins, M. E., Gur, R. E., et al. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100525 · Cited by 3
“Alternative approaches to PGS generation such as PRS-CSx may be considered in future analysis (77).”
Pellenz, F., de Oliveira, M. D., Duarte, G. C., Lemos, N. E., Dieter, C., Canani, L., Assmann, T., & Crispim, D. (2025) · Genetics and Molecular Biology · doi:10.1590/1678-4685-gmb-2023-0308 · Cited by 1
“However, since GWASs have primarily focused on European populations, the transferability of genetic association data to non-European populations is limited (Ruan et al., 2022).”
The independent role of fine particulate matter and genetic liability on cognition in older adults
Liao, S.-F., Chan, T., Su, M., Lin, M., Wu, C., Fan, C.-C., & Wang, S. (2025) · Annals of General Psychiatry · doi:10.1186/s12991-025-00559-9 · Cited by 1
“To optimize PRS prediction, we derived SCZ PRS using PRS-CSx [31] to integrate GWAS in 22,778 cases and 35,362 controls of EAS descent [32] and GWAS in 53,386 cases and 77,258 controls of European descent [33].”
Polygenic risk scores for incident dementia in the Multi-Ethnic Study of Atherosclerosis
Xue, D., Blue, E. E., Sofer, T., Hughes, T. M., Rotter, J. I., & Fohner, A. E. (2025) · medRxiv · doi:10.1101/2025.03.05.25323412 · Cited by 1
“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., 2019; Ruan et al., 2022).”
张亚宁, Y. Z., 郑启文, Q. Z., Qian, Q., Yuan, N., Liu, T., Gao, X., Fan, X., Bi, Y., Ji, G., Jia, P., et al. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf048 · Cited by 1
“Other methods, such as PRS-CSx [40], CT-SLEB [41], PolyPred-P þ [42], JointPRS [43], and Polygenic Risk scOres based on an enSemble PEnalized Regression (PROSPER) [44], are specifically designed for deployment across multiple ancestries, enhancing generalizability across diverse populations.”
Yao, Y.-H., Li, X.-Y., Wu, L., Zhang, J., Gui, Y., Yu, X.-Y., Zhou, Y., Li, X.-F., Liu, X.-Y., Xing, S., et al. (2025) · Eye and Vision · doi:10.1186/s40662-024-00421-1 · Cited by 1
Nostaeva, A., Shimansky, V., Apalko, S., Kuznetsov, I., Sushentseva, N., Popov, O., Asinovskaya, A., Mosenko, S., Karssen, L., Sarana, A., et al. (2024) · Epidemiology and Infection · doi:10.1017/s0950268824001778 · Cited by 1
“…population have shown relative improvement in predictive accuracy compared to methods that rely solely on GWAS data from a single ancestry source [46], incorporating more genetic data from non-Western European populations into these models is expected to enhance their predictive accuracy,…”
Elam, K. K., Su, J., Qin, W. A., & Lemery‐Chalfant, K. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1499395 · Cited by 1
Song, Y., Xu, J.-L., Geng, W., Yin, L., Wang, J., & Zhao, J.-H. (2024) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-024-03316-5 · Cited by 1
“In the field of forecasting, the PRS model has also been studied by using machine algorithms, and good prediction efficiency has been achieved [29, 30].”
MethodEfficient blockLASSO for polygenic scores with applications to All of Us and UK Biobank
Raben, T. G., Lello, L., Widén, E., & Hsu, S. (2025) · BMC Genomics · doi:10.1186/s12864-025-11505-0 · Cited by 1
“Examples of this approach include PRS-cs [7, 8] and LDpred [9–11] which uses as inputs the results of a GWAS and the LD information, and then re-weights the SNPs using continuous shrinkage and sparse priors respectively.”
Tang, L., You, C., Kong, X.-J., Napolioni, V., & Jie, H. (2024) · medRxiv · doi:10.1101/2024.05.16.24307490 · Cited by 1
“Building on top of PRS-CSX, 13 we present PGS-GRID, where “GRID” stands for “ G enetic R eference based on I ndividual D istance”.”
Method(2024) · European Heart Journal · doi:10.1093/eurheartj/ehae318 · Cited by 1
Delphi: A Deep-learning Framework for Polygenic Risk PredictionPreprint
Georgantas, C., Kutalik, Z., & Richiardi, J. (2024) · medRxiv · doi:10.1101/2024.04.19.24306079 · Cited by 1
“Multiple approaches have been proposed to increase the generalizability of PRS, for instance, by aggregating results from multiple GWAS studies (14; 15) or prioritizing functional variants (16).”
MethodBenchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort
Shah, Y., Kulm, S., Nauseef, J., Chen, Z., Elemento, O., Kensler, K. H., & Sharaf, R. N. (2024) · PLoS Comput. Biol. · doi:10.1371/journal.pcbi.1011990 · Cited by 1
Bountress, K. E., Bustamante, D., Ahangari, M. M., Alıev, F., Aggen, S. H., Lancaster, E. E., The Spit for Science Working Group, Peterson, R. E., Vassileva, J., Dick, D. M., et al. (2024) · Journal of American College Health · doi:10.1080/07448481.2024.2308255 · Cited by 1
“34 The AUD PRS was created utilizing the latest published results from the Million Veterans Program (MVP; EA case N = 34,658; AFR case N = 17,267).”
Sheerin, C. M., O’Hara-Payne, R. K., Lancaster, E. E., Suarez-Rivas, H., Spit for Science Working Group, Chatzinakos, C., Prom‐Wormley, E. C., & Peterson, R. E. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2023.1274381 · Cited by 4
“Here, PGS were developed using PRS-CSx, a method that has been shown to perform well in large, diverse training samples by utilizing a Bayesian regression framework to adjust SNP effect sizes for local linkage disequilibrium patterns across populations (Ruan et al., 2022).”
Cardone, K. M., Dudek, S., Keat, K., Bradford, Y., Cindi, Z., Daar, E., Gulick, R. M., Riddler, S., Lennox, J. L., Sinxadi, P., et al. (2023) · Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · doi:10.1142/9789811286421_0045 · Cited by 1
“Additionally, we plan to use PRScsx, a method that more effectively predicts polygenic risk in global populations 39 .”
MethodCara, M. C., Montserrat, D., & Ioannidis, A. (2023) · bioRxiv · doi:10.1101/2023.10.10.561715 · Cited by 1
Enhancing polygenic risk prediction in diverse populations: opportunities and challenges
Zhang, H.-Y., & Chatterjee, N. (2023) · Nature Genetics · doi:10.1038/s41588-023-01502-y · Cited by 1
Heterogeneity-aware integrative regression for ancestry-specific association studies
Molstad, A. J., Cai, Y., Reiner, A. P., Kooperberg, C. L., Sun, W., & Hsu, L.-I. (2024) · Biometrics · doi:10.1093/biomtc/ujae109 · Cited by 2
“For example, in the context of cross-population polygenic prediction, Ruan et al. (2022) assumes exactly the model (1) (where their response is an arbitrary phenotype) but in a Bayesian framework.”
BackgroundPolygenic prediction of bipolar disorder in a Latin American sample
Barboza, A. B. C., Prieto, M. L., Coombes, B. J., Gardea‐Resendez, M., Núñez, N. A., Winham, S. J., Romo‐Nava, F., González, S., McElroy, S. L., Frye, M. A., et al. (2023) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32936 · Cited by 1
“BD, bipolar disorder; EUR, European descent; LAT, Latin American; PRS, polygenic risk score aim to increase PRS transferability (such as PRS-CSx) (Ruan et al., 2022).”
BackgroundThomas, M., Su, Y.-R., Rosenthal, E., Sakoda, L., Schmit, S., Timofeeva, M. N., Chen, Z., Fernández-Rozadilla, C., Law, P., Murphy, N., et al. (2023) · medRxiv · doi:10.1101/2023.01.19.23284737 · Cited by 1
Yogaprabhu, S., Bodhini, D., Sneha, J., Anjana, R., Mohan, V., & Radha, V. (2023) · Journal of the Indian Institute of Science · doi:10.1007/s41745-023-00401-9 · Cited by 1
Gao, Y., & Cui, Y. (2022) · bioRxiv · doi:10.1101/2022.09.22.509055 · Cited by 1
“Recent studies show that the polygenic prediction for non-European populations can be improved by calibrating parameters for genetic effect sizes or model sparsity (or shrinkage) patterns across ancestry groups in various regression models 30-35 .”
BackgroundPolygenic risk scores adaptation for Height in a Vietnamese population
Tran, T. T. H., Tran, M. H., Dương, N. T., Pham, T. M., Vu, G. M., Vo, N. S., Nguyen, N. N., & Vu, Q. T. (2022) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse56063.2022.9953620 · Cited by 2
“The author evidenced significant improvement in comparison to Lassosum and PT-Multi approaches [21].”
ResultVassy, J., Kerman, B., Harris, E., Lemke, A., Clayman, M., Antwi, A., MacIsaac, K., Yi, T., & Brunette, C. A. (2022) · medRxiv · doi:10.1101/2022.09.29.22280488 · Cited by 1
Variational Bayes for Variable Selection with Adaptive Sharing across Populations and Responses
Yang, Y.-H., Chen, P.-F., Yang, F., & You, C.-T. (2026) · doi:10.48550/arxiv.2610.39677
“For polygenic prediction, PRS-CSx shares continuous-shrinkage scales across populations while retaining population-specific effects (Ruan et al., 2022).”
Polygenic Liability for Major Psychiatric Disorders and Clinical Features of Schizophrenia
Serretti, A. (2026) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.70039
Psychiatric and substance use disorder polygenic scores are associated with the exposome in youth
Hung, I., Thomas, N., Sosnowski, D. W., Felton, J. W., Barzilay, R., Moore, T. M., Visoki, E., Gillespie, N. A., Dick, D. M., Maher, B. S., et al. (2026) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-026-03201-5
Genetic evidence for the psychosis continuum: polygenic score analysis
Haussler, S. M., Prata, D. P., Kempton, M. J., Bhavsar, V., Arathimos, R., Coleman, J. R. I., Valmaggia, L., Tognin, S., Modinos, G., Fusar‐Poli, P., et al. (2026) · The British Journal of Psychiatry · doi:10.1192/bjp.2026.10795
Ma, Z.-H., Sheng, Z., Wen, L.-P., Cai, D.-F., Zhang, S.-F., Zhou, T.-H., Pu, C., Ma, H., Wang, Z.-J., Liu, K.-Z., et al. (2026) · Schizophrenia Research · doi:10.1016/j.schres.2026.10.003
Neuropsychiatry genetics in Africa
Iyegbe, C. O., Chaouch, M., Isewon, I., Majara, L., Ayinde, O. O., O’Hare, M. A., Mufford, M. S., Okoh, C. O. A., Larnaout, A., Grobler, A.-L., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-78057-z
“Recent studies of major depression demonstrate this 63 , while highlighting the value of ancestry-aligned discovery alongside trans-ancestry methods 64,65 .”
Instrumental Variable Analysis in Underrepresented Subpopulations Powered by Knowledge Transfer
Wang, R.-Y., Xu, Z.-J., Guo, Z., Lin, X.-H., & Liu, M.-L. (2026) · doi:10.48550/arxiv.2610.05680
Self-citationMohsen-Pour, N., Esmaeili, S., Feyzi, A., Sharafi, A., Maleki, M., & Kalayinia, S. (2026) · Clinical Cardiology · doi:10.1002/clc.70482
“Community partnerships and benefit‐sharing arrangements are critical to avoid extractive research practices [162].”
Polygenic Risk Scores in Alzheimer’s Disease: A Cross-Scale View from Genes to Brain and Behavior
Angelopoulou, E., Stamelos, I. G., Sampatakakis, S. N., Papageorgiou, S. G., Scarmeas, N., Hatzimanolis, A., Proukakis, C., & Villa, C. (2026) · Biology · doi:10.3390/biology15201805
“Multi-ancestry methods such as PRS-CSx integrate GWAS summary statistics from multiple populations using population-specific LD reference panels and a coupled continuous shrinkage prior that shares information across populations while allowing SNP effect estimates to differ [70].”
Al‐Saud, S. A. (2026) · Genes · doi:10.3390/genes17101312
“Ancestry-specific validation is essential because a well-calibrated European score can still be systematically biased in Arab or other underrepresented populations [7,35–39].”
Torres-Vásquez, C., Rodríguez-Violante, M., Cervantes-Arriaga, A., Moronta-Franco, M., Cesarini, M., & Gatto, E. M. (2026) · Equity Neuroscience · doi:10.1016/j.neuros.2026.100078
Das, A., & Cui, Y. (2026) · bioRxiv · doi:10.64898/2026.09.25.754520
“Our plan for releasing the synthetic task generators, task manifests, and the tuned model checkpoint is described in Appendix F. Off-the-shelf TabICL Instruction tuned on HGP + PRS-CSx tasks Off-the-shelf TabICL Instruction tuned on HGP + PRS-CSx tasks”
InfluentialClinical Uses of Common Genetic Variants Associated with Common Diseases.
Hunter, D. J., & Inouye, M. (2026) · New England Journal of Medicine · doi:10.1056/nejmra2413201
de Hoyos, L., Schlag, F., Jahagirdar, S., Corfield, E. C., Allegrini, A. G., Admiraal, D., de Zeeuw, E. L., Nolte, I. M., Llonga, N., Neumann, A., et al. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02551-z
Tanigawa, Y., & Kellis, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.14.751452
“…of PGS. Existing approaches include prioritizing variants present in diverse populations[58] or overlapping with bio-sample-specific regulatory elements[59], integrating statistical fine-mapping results [56] , and incorporating genetic data from ancestry-diverse individuals [7,9,56,57] .”
Gillespie, N. A., Lannoy, S., Stephenson, M. E., Kirkpatrick, R., Ceja, Z., Rentería, M. E., & Edwards, A. C. (2026) · Research Square · doi:10.21203/rs.3.rs-10021308/v1
All of Us diversity and scale yield context-dependent improvements in polygenic prediction
Tsuo, K., Shi, Z., Ge, T., Mandla, R., Hou, K., Ding, Y., Paşaniuc, B., Wang, Y., & Martin, A. R. (2026) · Nature Genetics · doi:10.1038/s41588-026-02734-4 · Cited by 24
“We used the auto model, which automatically estimates the global shrinkage parameter, implemented in PRS-CS and PRS-CSx.”
InfluentialSelf-citationChen, T., Zhang, H., Mazumder, R., & Lin, X. (2026) · Nature Methods · doi:10.1038/s41592-026-03235-2 · Cited by 4
Huang, T.-Y., Zhu, Y.-W., Balasubramanian, R., Roberts, A. L., Clish, C., Ávila-Pacheco, J., Rotter, J., Guo, X.-Q., Yao, J., Chen, Y., et al. (2026) · medRxiv · doi:10.64898/2026.09.10.26362734
Armstrong, N. D., Srinivasasainagendra, V., Patki, A., Pilla, L., Aldridge, C., Jones, A. C., Broeckel, U., Lange, E. M., Lange, L. A., Arora, P., et al. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1875388
“Thus, we did not leverage available multi-ancestry methods such as PRS-CSx (Ruan et al., 2022), Genomic Prediction through Transfer Learning (GPTL) (Wu et al., 2026), BridgePRS (Hoggart, et al., 2024) (Hoggart et al., 2024), or PROSPER (polygenic risk scores based on ensemble of penalized…”
Multimodal deep learning enhances genomic risk prediction for cardiometabolic diseases
Zhu, T., Ghose, U., Climente-González, H., Howson, J. M. M., Hu, S., & Nevado‐Holgado, A. J. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag550
“Complementary analyses under natural (imbalanced) prevalence are also provided, with sample sizes reported in Supplementary Table 2 and the PRS-CSx [ 39] comparison reported in Supplementary Fig.”
InfluentialBal, H. S., Pampana, A., Nayak, A., Gaonkar, M. P., Patel, S., Yerabolu, K., Vekariya, N., Patel, N., Kalra, R., Li, P., et al. (2026) · Nature Cardiovascular Research · doi:10.1038/s44161-026-00866-8
“In addition, the study utilized PRS-CSx, a Bayesian continuous shrinkage framework that jointly models ancestry-specific summary statistics while accounting for differences in linkage disequilibrium (LD) structure across populations 21 .”
Abdullaev, A., Zakirova, D., Kosushkin, S. A., Xonboev, F. Z., Abdullaeva, G., Alieva, R., & Turdikulova, S. U. (2026) · Genes · doi:10.3390/genes17091117
“…the reference resources on which such analyses depend remain heavily skewed: individuals of European descent continue to dominate genomic databases [7,8], and PRS transfer poorly across ancestries, with predictive accuracy declining as genetic distance from the discovery cohort increases [9–11].”
Park, C., Kim, J., Myung, W., Jung, S.-H., Kim, B., Park, S., Kang, J.-I., Won, H.-H., & Kim, S. J. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-04395-7
“Several methodological improvements for understanding the genetic diversities based on ancestry of disease have emerged [7, 8].”
Self-citationGardner, H. R., Meyer, M. L., Wagner, J. K., Perreira, K. M., Zhou, L. Y., Daviglus, M. L., Cordero, C., Justice, A. E., & Fernandez-Rhodes, L. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1801152
“PRS-CSx is a python-based tool that compiles GWAS results with LD information from reference panels across various populations to infer cross-population weights for SNPs. LD reference files for each of the 1,000 Genomes global population groups–AFR, EUR, AMR, EAS, or SAS–and their SNP information file were used to cal…”
InfluentialKurniansyah, N., Tasaki, S., Rehman, H., Zhu, C., Farrell, J. J., Sherva, R., Hauger, R. L., Merritt, V. C., Panizzon, M. S., Zhang, R., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02722-8 · Cited by 1
Zhang, M. J., Price, A. L., Durvasula, A., Chiang, C., Koch, E., Schoech, A. P., Strober, B., Shi, H., Barton, A. R., Kim, S. S., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02712-w · Cited by 6
Self-citationDevelopment and application of type 1 diabetes polygenic scores across diverse populations
Manousaki, D., Michalek, D. A., Qu, H.-Q., Ferrat, L., & Deutsch, A. J. (2026) · Diabetologia · doi:10.1007/s00125-026-06831-1
Martínez-Serrano, I., Segura, A. G., Ortuño, M., Fortea, A., Borràs, R., Camprodon-Boadas, P., de la Serna, E., Moreno, D., García-Rizo, C., Torrent, C., et al. (2026) · European Child and Adolescent Psychiatry · doi:10.1007/s00787-026-03123-2
Ye, J., Huang, J., Zhou, C.-W., Yuan, Z., Guan, H.-P., Long, Z.-Y., Chen, H.-N., & Li, W.-F. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-04342-6
“Many genetic loci identi fi ed in European cohorts may be rare or absent in other populations, and vice versa [6, 7].”
Alotaibi, R., Alsaggaf, S., Alaofi, S., Alanazi, R., Fallatah, S., Alawi, I. A., Alhejeili, L. M., Al-Hujairi, R. A., Almuzaini, F. A., Jayyar, F. W., et al. (2026) · Journal of Advanced Trends in Medical Research · doi:10.4103/atmr.atmr_15_26
The Influence of Spousal Genotype on Alcohol Problems in Marriage
Kuo, S. I., McCutcheon, V. V., Alıev, F., Bucholz, K. K., Dick, D. M., Edenberg, H. J., HESSELBROCK, V. M., Kramer, J. R., Lai, D., Plawecki, M. H., et al. (2026) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.70399
“We constructed polygenic scores using PRS-CS (Ruan et al. 2022).”
Genetic basis of Asian diabetes
Ojima, T., & Yamauchi, T. (2026) · Journal of Diabetes Investigation · doi:10.1111/jdi.70409
“13, 32 Although most individual variants identi fi ed by GWAS show modest effect sizes for T2D, the PRS approach, which aggregates the risk variant effects according to an individual ’ s genomic pro fi le, has emerged as an effective approach for risk prediction 14, 15 (Figure 2a).”
InfluentialOkorie, M. C., Jonson, C., Oddi, A. P., Castruita, P. A., Fulton‐Howard, B., Yaffe, K. S., Yokoyama, J. S., Udeh‐Momoh, C. T., Andrews, S. J., & for the Alzheimer's Disease Sequencing Project and the Healthy Aging Brain Study—Health Disparities (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71529 · Cited by 6
“22,23 The present study evaluates the predictive accuracy of AD-PRS computedusingsingle-,multi-,andcross-ancestryPRSmodelsinmulti-ancestral populations.”
Polygenicity shapes distinct brain patterns and clinical profiles of bipolar disorder
Feng, Y., Guo, X., Huang, P., Jia, N., Wang, L., Ji, X., Qin, Z., Li, M. Y., Lai, J., Qin, X. F., et al. (2026) · BMC Medicine · doi:10.1186/s12916-026-05096-0
“We used MTAG to estimate the meta effect for PGC-BD and 126 IDP traits, respectively, and constructed 126 BD-IDP PRS by PRS-CS [75].”
Self-citationUsing polygenic risk scores to evaluate definitions of self-reported sleep phenotypes across cohorts
Wyss, A., Brown, M. R., Li, X., Spitzer, B., Huang, Z.-J., Wang, H.-M., Saxena, R., Gallo, L., Qi, Q.-B., Tarraf, W., et al. (2026) · Sleep · doi:10.1093/sleep/zsag200
“Second, we used PRS-CSx [38] with the following settings: 1000 Genomes European, African, Asian, or Admixed American as reference panels corresponding to the MVP group available, same target data as before, and all other parameters at the default settings.”
Webb, E., Jajoo, A., Balakundi, V., Sendi, M. S. E., Koenen, K., Linnstaedt, S., House, S., An, X., Stevens, J., Neylan, T., et al. (2026) · medRxiv · doi:10.64898/2026.07.17.26358149
“For example, multi-ancestry methods such as PRS-CSx 17 , which integrate GWAS summary statistics from multiple populations, have demonstrated improved prediction for several continuous and highly heritable traits, including height, weight, and schizophrenia 19 .”
Calibration-aware genomic prediction of paratuberculosis serostatus in indigenous Turkish goats
Yaman, Y., Eser, A., Coşkun, D., Aymaz, R., Ki̇şi̇, Y. E., Keleş, M., Yağcı, S., Gülaydın, Ö., ŞENGÜL, S. S., İrak, K., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-62518-y
Involvement of adiponectin in the biology of attention deficit hyperactivity disorder
Takahashi, N., Iwabuchi, T., Nishimura, T., Okumura, A., Harada, T., Rahman, M. S., & Tsuchiya, K. J. (2026) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-026-03114-3
Evidence standards for multi-ancestry polygenic prediction
Oselu, B., Isewon, I., Oyelade, J. O., & Iyegbe, C. (2026) · Genome Biology · doi:10.1186/s13059-026-04187-8
“For instance, some Bayesian shrinkage methods use HapMap3 single nucleotide polymorphism (about one million common, well-imputed variants) by default to stabilise linkage disequilibrium estimation and to save computing time [20, 39].”
InfluentialPolygenic Prediction of Non-goal Response to Statin Therapy
Liou, L., García‐González, J., Wu, H. M., Namba, S., Vaura, F. C., Okada, Y., Kovacic, J. C., & O’Reilly, P. F. (2026) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.125.005666
He, J.-T., Shen, X., Wang, M.-X., Gu, D.-F., & Lu, X.-F. (2026) · JACC: Asia · doi:10.1016/j.jacasi.2026.04.038
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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