The DOI matches its Crossref record; 63 of the 66 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Sorted by citations
Items 101–200 of 609
Rajabli, F., Benchek, P. H., Tosto, G., Kushch, N. A., Sha, J., Bazemore, K., Zhu, C., Lee, W., Haut, J., Hamilton‐Nelson, K. L., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03564-z · Cited by 44
“…and loci specific to non-European ancestral backgrounds [7–9], determine shared genetic risk and protective factors across diverse populations [10], and to capitalize on differential linkage disequilibrium (LD) structures across varied ancestral genetic backgrounds to improve fine-mapping of…”
Cao, C., Zhang, S., Wang, J., Tian, M., Ji, X., Huang, D., Yang, S., & Gu, N. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1029 · Cited by 24
“…strategies such as a linear combination of PGSs from different ancestries and / or different meth-ods (i.e. PRS-CSx, PolyPred and PGSagg) ( 5 , 31 , 38 ); transferring learning to fine-tune existing models on new an-cestries ( 10 ); and re-estimation of effect sizes based on cross-ancestry…”
BackgroundOptimizing and benchmarking polygenic risk scores with GWAS summary statistics
Zhao, Z., Gruenloh, T., Yan, M., Wu, Y., Sun, Z., Miao, J., Wu, Y., Song, J., & Lu, Q. (2024) · Genome biology · doi:10.1186/s13059-024-03400-w · Cited by 28
“Third, the ensemble learning approach which combines multiple predictive models through a second-level regression has been viewed as a highly effective but data-demanding approach [28, 29, 33].”
BackgroundClinical applications of polygenic risk score for coronary artery disease through the life course
Fahed, A. C., & Natarajan, P. (2023) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2023.117356 · Cited by 24
Clarifying the causes of consistent and inconsistent findings in genetics
Dattani, S., Howard, D. M., Lewis, C. M., & Sham, P. C. (2022) · Genetic Epidemiology · doi:10.1002/gepi.22459 · Cited by 29
Gyawali, P., Guen, Y. L., Liu, X., Belloy, M. E., Tang, H., Zou, J., & He, Z. (2023) · Communications Biology · doi:10.1038/s42003-023-05352-6 · Cited by 25
Quantitative Human Paleogenetics: What can Ancient DNA Tell us About Complex Trait Evolution?
Irving-Pease, E. K., Muktupavela, R., Dannemann, M., & Racimo, F. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.703541 · Cited by 27
“Another promising approach is to jointly model PRS using GWAS summary statistics from multiple populations (Márquez-Luna et al., 2017; Ruan et al., 2021; Turley et al., 2021).”
MethodA method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data
Uddin, M. M., Shin, J., Lee, S., Truong, B. M. T., Benyamin, B., & Lee, S. (2023) · Nature Communications · doi:10.1038/s41467-023-36281-x · Cited by 20
Self-citationAssociations of polygenic risks, depression, and obesity-related traits in Taiwan Biobank
Liao, S.-F., Su, C.-Y., Su, M., Chen, C., Chen, C., Lin, K., Pan, Y., Hsiao, P., Chen, P., Huang, Y., et al. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.09.149 · Cited by 24
Self-citationYuan, S., Chen, J., Ruan, X., Li, Y., Abramowitz, S. A., Wang, L., Jiang, F., Xiong, Y., Levin, M. G., Voight, B. F., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61720-2 · Cited by 25
Cao, X., Jiang, M., Guan, Y., Li, S., Duan, C., Gong, Y. Q., Kong, Y., Shao, Z., Hong-ji, W., Yao, X., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58782-7 · Cited by 22
Pharmacogenomics polygenic risk score: Ready or not for prime time?
Singh, S., Stocco, G., Theken, K. N., Dickson, A. L., Feng, Q., Karnes, J. H., Mosley, J. D., & Rouby, N. M. E. (2024) · Clinical and Translational Science · doi:10.1111/cts.13893 · Cited by 25
“61 While SNP selection approaches to mitigate this problem are under development, 62 the underlying issue is likely best addressed by expanded inclusion of other ancestral populations in genetic studies.”
BackgroundLessons and Applications of Omics Research in Diabetes Epidemiology
Yu, G., Tam, H. C. H., Huang, C., Shi, M., Lim, C. K. P., Chan, J. C. N., & Ronald, C. W. (2024) · Current Diabetes Reports · doi:10.1007/s11892-024-01533-7 · Cited by 23
“For instance, based on a Bayesian polygenic modeling method, PRS-CSx [62], Ge and colleagues [61] constructed a trans-ancestry T2D PRS by integrating T2D GWAS from European, African and East Asian populations and demonstrated that the top 2% of the trans-ancestry PRS distribution can identify…”
MethodClinical utility of polygenic scores for cardiometabolic disease in Arabs
Shim, I., Kuwahara, H., Chen, N., Hashem, M. O., AlAbdi, L., Abouelhoda, M. I., Won, H., Natarajan, P., Ellinor, P. T., Khera, A. V., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41985-1 · Cited by 21
“…from the largest and most diverse GWAS for the trait of interest are obtained, and multiple scores are derived using fi ve scoring methods — a baseline method (PRSice-2), as well as methods that factor in the genetic architecture (LDpred2,lassosum2,andPRS-CS)andancestry(PRS-CSx) 6,31 – 34 .”
InfluentialSelf-citationMulti-ancestry study of the genetics of problematic alcohol use in >1 million individualsPreprint
Zhou, H., Kember, R., Deak, J., Xu, H., Toikumo, S., Yuan, K., Lind, P., Farajzadeh, L., Wang, L., Hatoum, A., et al. (2023) · medRxiv · doi:10.1101/2023.01.24.23284960 · Cited by 21
Self-citationGenetic nurture effects for alcohol use disorder
Thomas, N., Salvatore, J. E., Kuo, S. I., Alıev, F., McCutcheon, V. V., Meyers, J. L., Bucholz, K. K., Brislin, S. J., Chan, G., Edenberg, H. J., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01816-z · Cited by 24
“Imputed genotypes for the COGA parent-offspring trios, in combination with summary statistics from independent GWAS discovery samples, were used to construct the genome-wide polygenic risk scores using PRS-CSx [41].”
BackgroundMethodYang, S., Ye, X., Ji, X., Li, Z., Tian, M., Huang, P., & Cao, C. (2025) · Genome Medicine · doi:10.1186/s13073-025-01505-w · Cited by 23
Ojima, T., Namba, S., Ken, S., Yamamoto, K., Sonehara, K., Narita, A., The Biobank Japan Project, Kamatani, Y., Tamiya, G., Yamamoto, M. P., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01782-y · Cited by 18
“The designs of the figures were inspired by and followed after the referenced studies 3,13,31 .”
InfluentialIshikawa, T., Masuda, T., Hachiya, T., Dina, C., Simonet, F., Nagata, Y., Tanck, M. W. T., Sonehara, K., Glinge, C., Tadros, R., et al. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae251 · Cited by 24
Social-Science Genomics: Progress, Challenges, and Future Directions
Benjamin, D., Cesarini, D., Turley, P., & Young, A. S. (2024) · doi:10.3386/w32404 · Cited by 20
Singh, S., Choudhury, A., Hazelhurst, S., Crowther, N. J., Boua, P. R., Sorgho, H., Agongo, G., Nonterah, E. A., Micklesfield, L. K., Norris, S. A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-44079-0 · Cited by 35
“This stressestheimportanceoffurther research tooptimizePRSprediction in non-European populations, speci fi cally African populations, with increased sample sizes to enhance PRS prediction 58,60 .”
He, Y., Qian, D. C., Diao, J. A., Cho, M. H., Silverman, E. K., Gusev, A., Manrai, A. K., Martin, A. R., & Patel, C. J. (2023) · Nature Communications · doi:10.1038/s41467-023-44047-8 · Cited by 36
“They reported that in addition to genetic factors, socioeconomic and environmental factors beyond smoking can predict and stratify COPD risk for both non-and smoking individuals.”
Self-citationDarst, B. F., Shen, J., Madduri, R., Rodriguez, A., Xiao, Y., Sheng, X., Saunders, E. J., Dadaev, T., Brook, M. N., Hoffmann, T. J., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.05.010 · Cited by 29
Gustavson, D. E., Coleman, P. L., Wang, Y., Nitin, R., Petty, L. E., Bush, C. T., Mosing, M. A., Wesseldijk, L. W., Ullén, F., Below, J. E., et al. (2023) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14964 · Cited by 22
Nürnberger, J. I., Wang, Y., Zang, Y., Lai, D., Wetherill, L. F., Edenberg, H. J., Alıev, F., Plawecki, M. H., Chorlian, D. B., Chan, G., et al. (2021) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2021.10.007 · Cited by 20
“A similar histogram for AA subjects is shown in Figure S2 (no significant shift by thresholded analysis or by PRS-CSx).”
ResultLarge-scale imputation models for multi-ancestry proteome-wide association analysisPreprint
Wu, C., Zhang, Z., Yang, X., & Zhao, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.05.561120 · Cited by 21
AI-based multi-PRS models outperform classical single-PRS models
Klau, J. H., Maj, C., Klinkhammer, H., Krawitz, P. M., Mayr, A. J., Hillmer, A. M., Schumacher, J., & Heider, D. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1217860 · Cited by 23
“, 2021), PRSice-2 (Choi and O’Reilly, 2019), PRS-CSx (Ruan et al., 2022), or PRSMix (Truong et al.”
Latin American Trans‐ancestry INitiative for OCD genomics (LATINO): Study protocol
Crowley, J. J., Cappi, C., Ochoa-Panaifo, M. E., Frederick, R. M., Kook, M., Wiese, A. D., Rancourt, D., Atkinson, E. G., Giusti‐Rodríguez, P., Anderberg, J. L., et al. (2023) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32962 · Cited by 21
“CSx(Ruan et al., 2022) couples genetic effects across populations via a shared continuous shrinkage (CS) prior, enabling more accurate effect size estimation by sharing”
BackgroundSelf-citationUse of Polygenic Risk Scores for Coronary Heart Disease in Ancestrally Diverse Populations
Dikilitas, O., Schaid, D. J., Tcheandjieu, C., Clarke, S. L., Assimes, T. L., & Kullo, I. J. (2022) · Current Cardiology Reports · doi:10.1007/s11886-022-01734-0 · Cited by 15
Schaid, D. J., Sinnwell, J. P., Batzler, A. J., & McDonnell, S. K. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.03.008 · Cited by 22
Wang, Y.-C., Wu, Y., Choi, J. Y., Allington, G., Zhao, S., Khanfar, M., Yang, K., Fu, P.-Y., Wrubel, M., Yu, X., et al. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020175 · Cited by 23
Global biobank analyses provide lessons for computing polygenic risk scores across diverse cohorts
Wang, Y., Namba, S., Lopera, E., Kerminen, S., Tsuo, K., Läll, K., Kanai, M., Zhou, W., Wu, K.-H. H., Favé, M., et al. (2021) · medRxiv · doi:10.1101/2021.11.18.21266545 · Cited by 19
Self-citationOptimal strategies for learning multi-ancestry polygenic scores vary across traits
Lehmann, B., Mackintosh, M. E., McVean, G., & Holmes, C. C. (2023) · Nature Communications · doi:10.1038/s41467-023-38930-7 · Cited by 20
“A number of methods to construct PGS by 303 combining GWAS results from distinct ancestry groups have recently been developed, demonstrating 304 improved prediction accuracy on the minority-ancestry group [20, 22, 21].”
MethodCross-ancestry genomic research: time to close the gap
Atkinson, E. G., Bianchi, S. B., Ye, G. Y., Martínez-Magaña, J. J., Tietz, G. E., Montalvo-Ortiz, J. L., Giusti-Rodriguez, P., Palmer, A. A., & Sanchez-Roige, S. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01365-7 · Cited by 19
Shared genetic architectures of educational attainment in East Asian and European populations
Chen, T.-T., Kim, J., Lam, M., Chuang, Y.-F., Chiu, Y.-L., Lin, S.-C., Jung, S.-H., Kim, B., Kim, S., Cho, C., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01781-9 · Cited by 18
“Furthermore, our PGS analyses also indicated the advantages of ancestral diversity over a single population in PGS construction 36 .”
Self-citationIntegrating common and rare variants improves polygenic risk prediction across diverse populations
Williams, J., Chen, T., Hua, X., Wong, W., Yu, K., Kraft, P., Li, X.-H., & Zhang, H.-Y. (2024) · Nature Communications · doi:10.1038/s41467-026-72185-2 · Cited by 18
Optimization of multi-ancestry polygenic risk score disease prediction models
Lerga-Jaso, J., Terpolovsky, A., Novković, B., Osama, A., Manson, C., Bohn, S., Marino, A. D., Kunitomi, M., & Yazdi, P. G. (2025) · Scientific Reports · doi:10.1038/s41598-025-02903-1 · Cited by 21
Multi-trait GWAS for diverse ancestries: mapping the knowledge gap
Troubat, L., Fettahoglu, D., Henches, L., Aschard, H., & Julienne, H. (2024) · BMC Genomics · doi:10.1186/s12864-024-10293-3 · Cited by 19
“…do not only address a public health gap, but also offer opportunities and several recent publications illustrate the benefit of methodological development for the analysis of diverse populations in various applications such as: polygenic risk scores [13], association testing [14], and TWAS [15].”
BackgroundHu, S.-L., Ferreira, L. A. F., Shi, S., Hellenthal, G., Marchini, J., Lawson, D., & Myers, S. (2023) · bioRxiv · doi:10.1101/2023.08.08.552281 · Cited by 18
Gene-based polygenic risk scores analysis of alcohol use disorder in African Americans
Lai, D., Schwantes-An, T.-H. L., Abreu, M., Chan, G., HESSELBROCK, V. M., Kamarajan, C., Liu, Y., Meyers, J. L., Nürnberger, J. I., Plawecki, M. H., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02029-2 · Cited by 18
“The authors of PRS-CSx recommend using estimated AA- and EA-only posterior effect sizes, then testing different linear combinations of them with different weights in a validating dataset, and choosing the one with the best performance for testing in independent datasets [23].”
MethodInfluentialBellou, E., Kim, W., Leonenko, G., Tao, F., Simmonds, E., Wu, Y., Mattsson, N., Hansson, O., Nagle, M. W., Escott‐Price, V., et al. (2025) · Alzheimer s Research & Therapy · doi:10.1186/s13195-024-01664-9 · Cited by 20
Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities
Zhai, S., Mehrotra, D. V., & Shen, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad470 · Cited by 23
“[112], we further assumed v follows a multivariate-normal distribution with a well-defined variance–covariance matrix (cid:13) (ρ E , ρ P , ρ C ) , where ρ E measures the correlation between prognostic and predictive effects, ρ P measures the effect correlation between European and non-European…”
InfluentialTsuo, K., Shi, Z., Ge, T., Mandla, R., Hou, K., Ding, Y., Paşaniuc, B., Wang, Y., & Martin, A. R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.06.606846 · Cited by 20
“We focused on the results from PRS-CS in the following sections as PRS derived from PRS-CS outperformed or performed comparably to P+T ( Supplementary Tables 9 and 10 ), consistent with previous findings 25 .”
Self-citationTuning parameters for polygenic risk score methods using GWAS summary statistics from training data
Jiang, W., Chen, L., Girgenti, M. J., & Zhao, H. (2024) · Nature Communications · doi:10.1038/s41467-023-44009-0 · Cited by 25
Power of inclusion: Enhancing polygenic prediction with admixed individuals
Tanigawa, Y., & Kellis, M. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.09.013 · Cited by 16
Polygenic risk score prediction of multiple sclerosis in individuals of South Asian ancestry
Breedon, J. R., Marshall, C. R., Giovannoni, G., van Heel, D. A., Akhtar, S., Anwar, M., Arciero, E., Asgar, O., Ashraf, S., Breen, G. D., et al. (2023) · Brain Communications · doi:10.1093/braincomms/fcad041 · Cited by 14
Huerta‐Chagoya, A., Schroeder, P. H., Mandla, R., Deutsch, A. J., Zhu, W., Petty, L. E., Yi, X., Cole, J. B., Udler, M. S., Dornbos, P., et al. (2023) · Diabetologia · doi:10.1007/s00125-023-05912-9 · Cited by 19
“Yet, recent studies of type 2 diabetes in Normalised PRS-CSx 0-4 -2 2 4 8 6 4 2 1.6 4.4 5.1 6.6 7.6 GWAS ancestry LAT EAS EUR EUR+EAS EUR+EAS+LAT 0 PRS-CS PRS-CSx a b 1.00 =0.008 0.750.500.250 1 - specificity 1.00 0.75 0.50 0.25 0 Baseline=0.698 PRS-CS EUR=0.729 PRS-CSx=0.748 AUC p c 0.5 0.4 0.3 0.2 0.1 0 All (OR/SD)…”
MethodInfluentialSelf-citationPolygenic risk, population structure and ongoing difficulties with race in human genetics
Kaplan, J. M., & Fullerton, S. M. (2022) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0427 · Cited by 20
“And while many, creative, solutions to the problem are currently being actively explored [63,69,70], it is not clear that even the best designed approach to PRS development and validation will be able to transcend more fundamental difficulties inherent to the distribution of health and disease among human groups.”
BackgroundRatman, D., Tshiaba, P., Levin, M. G., Sun, J., Tunstall, T. S., Maier, R., Shah, P., Rabinowitz, M., Rader, D. J., Kumar, A., et al. (2025) · npj Cardiovascular Health · doi:10.1038/s44325-025-00049-7 · Cited by 16
“PRS models were independentlyconstructedforCADandT2DusingPRS-CSx 57 witharange of values for the global shrinkage parameter(see Supplementary Table 6 for the list GWAS studies and corresponding parameter values used in development).”
Hoffmann, T. J., Graff, R. E., Madduri, R., Rodriguez, A. A., Cario, C. L., Feng, K.-J., Jiang, Y., Wang, A., Klein, R. J., Pierce, B. L., et al. (2025) · Nature Genetics · doi:10.1038/s41588-024-02068-z · Cited by 16
Self-citationGWAS highlights the neuronal contribution to multiple sclerosis susceptibilityPreprint
Zeng, L., Khan, A., Lama, T. D., Chitnis, T., Weiner, H. L., Wang, G., Fujita, M., Zipp, F., Taga, M. F., Kiryluk, K., et al. (2024) · medRxiv · doi:10.1101/2024.12.04.24318500 · Cited by 11
“Genome-wide polygenic score (GPS) design and optimization We used PRS-CSx, a Bayesian polygenic modeling framework, to develop genomic prediction scores (GPS) across diverse ancestries ( 23 ).”
Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases
Truong, B. M. T., Hull, L. E., Ruan, Y., Huang, Q., Hornsby, W., Martin, H. C., van Heel, D. A., Wang, Y., Martin, A. R., Lee, S., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100523 · Cited by 104
Self-citationIncreasing diversity of functional genetics studies to advance biological discovery and human health
George, S. H. L., Medina-Rivera, A., Idaghdour, Y., Lappalainen, T., & Romero, I. G. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.10.012 · Cited by 18
Pragmatic Approach to Applying Polygenic Risk Scores to Diverse Populations
Patel, A. P., & Fahed, A. C. (2023) · Current Protocols · doi:10.1002/cpz1.911 · Cited by 15
“This problem is now considered a top priority area in genomics, with ongoing efforts to increase representation in genomic studies and improve methods of computing polygenic scores (Adeyemo et al., 2021; Pa-tel et al., 2023; Ruan et al., 2022; Wand et al., 2021).”
BackgroundMethodAdmix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations
Hou, K., Gogarten, S., Kim, J., Hua, X., Dias, J. A., Sun, Q., Wang, Y., Tan, T.-T., Atkinson, E. G., Martin, A., et al. (2023) · bioRxiv · doi:10.1101/2023.09.30.560263 · Cited by 15
Self-citationAhern, J., Thompson, W. K., Fan, C. C., & Loughnan, R. J. (2023) · Behavior Genetics · doi:10.1007/s10519-023-10139-w · Cited by 19
Vassy, J. L., Kerman, B. J., Harris, E. J., Lemke, A. A., Clayman, M. L., Antwi, A. A., MacIsaac, K., Yi, T., & Brunette, C. A. (2023) · European Journal of Human Genetics · doi:10.1038/s41431-023-01318-8 · Cited by 20
Kuo, S. I., Poore, H. E., Barr, P. B., Chirico, I. S., Alıev, F., Bucholz, K. K., Chan, G., Kamarajan, C., Kramer, J., McCutcheon, V. V., et al. (2022) · Development and Psychopathology · doi:10.1017/s0954579422000700 · Cited by 23
Construction and Application of Polygenic Risk Scores in Autoimmune Diseases
Khunsriraksakul, C., Markus, H., Olsen, N. J., Carrel, L., Jiang, B., & Liu, D. J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.889296 · Cited by 31
Utility of a Systolic Blood Pressure Polygenic Risk Score With Chlorthalidone Response
Armstrong, N. D., Srinivasasainagendra, V., Patki, A., Jones, A. C., Parcha, V., Pampana, A., Broeckel, U., Lange, L. A., Arora, P. K., Limdi, N. A., et al. (2024) · JAMA Cardiology · doi:10.1001/jamacardio.2024.3649 · Cited by 14
Tian, J., Zhang, M., Zhang, F., Gao, K., Lu, Z., Cai, Y., Chen, C., Ning, C., Li, Y. X., Qian, S., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01355-y · Cited by 12
“…many studies have proposed methodological improvements to address the limitations of traditional PRS models, including Poly-Pred [35], PRS-CSx [15], and XPASS [16], which might improve the trans-ancestry polygenic prediction through reducing heterogeneity or sharing information between…”
BackgroundMethodInfluentialSelf-citationEpigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate
Mack, T., Raddatz, M. A., Pershad, Y., Nachun, D. C., Taylor, K. D., Guo, X., Shuldiner, A. R., O’Connell, J. R., Kenny, E. E., Loos, R. J. F., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00647-7 · Cited by 16
“Scores were calculated using PRScsx 52 (https://github.com/getian107/PRScsx), a tool for cross-ethnic polygenic score prediction, with both the European and the African reference panels used, and PLINK 53 (http://pngu. mgh.harvard.edu/purcell/plink/).”
Meyers, J. L., McCutcheon, V. V., Horne‐Osipenko, K. A., Waters, L. R., Barr, P. B., Chan, G., Chorlian, D. B., Johnson, E. C., Kuo, S. I., Kramer, J., et al. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02577-1 · Cited by 14
“We constructed PRS using PRS-CSx, which applies a Bayesian regression with a continuous shrinkage parameter to GWAS summary statistics from multiple populations simultaneously [46, 47].”
MethodBridgePRS: A powerful trans-ancestry Polygenic Risk Score methodPreprint
Hoggart, C., Choi, S., García-González, J., Souaiaia, T., Preuss, M. H., & O'Reilly, P. F. (2023) · bioRxiv · doi:10.1101/2023.02.17.528938 · Cited by 14
“This is the approach of PRS-CSx [4], developed to tackle the PRS portability problem, which makes cross-population inference on the inclusion of each SNP across the genome (or more precisely, the degree of shrinkage of variant effect sizes to zero).”
MethodComparison of Methods Utilizing Sex-Specific PRSs Derived From GWAS Summary Statistics
Zhang, C., Ye, Y., & Zhao, H. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.892950 · Cited by 14
Ishigaki, K., Sakaue, S., Terao, C. C., Luo, Y., Sonehara, K., Yamaguchi, K., Amariuta, T., Too, C. L., Laufer, V. A., Scott, I. C., et al. (2021) · medRxiv · doi:10.1101/2021.12.01.21267132 · Cited by 13
Self-citationSmith, S. P., Shahamatdar, S., Cheng, W., Zhang, S., Paik, J., Graff, M., Haiman, C. A., Matise, T. C., North, K. E., Peters, U., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.03.005 · Cited by 17
Polygenic Risk Score in African populations: progress and challenges
Adam, Y. A. I., Sadeeq, S., Kumuthini, J., Ajayi, O., Wells, G. C., Solomon, R., Ogunlana, O., Adetiba, E., Iweala, E., Brors, B., et al. (2021) · F1000Research · doi:10.12688/f1000research.76218.2 · Cited by 14
Polygenic risk score for type 2 diabetes shows context-dependent effects across populations
Guo, B., Cai, Y., Kim, D., Smit, R. A. J., Wang, Z., Iyer, K. R., Hilliard, A. T., Haessler, J., Tao, R., Broadaway, K. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63546-4 · Cited by 20
“PRS-CSx estimates population-speci fi c and multi-ancestry PRS by jointly modeling discovery GWAS summary statistics and pre-calculated linkage disequilibrium (LD) reference panels based on the 1000 Genomes Project (1KG) Phase 3 participants from AFR, Admixed American (AMR), EAS, EUR, and SAS population samples 12 .”
InfluentialPerkins, D. O., Jeffries, C. D., Clark, S. R., Upthegrove, R., Wannan, C., Wray, N. R., Li, Q. S., Kim, Q., Walker, E. F., Amminger, G. P., et al. (2025) · Schizophrenia · doi:10.1038/s41537-025-00610-4 · Cited by 15
“Method development to improve translation of PGS across ancestries is an active area of research 67,68 .”
Self-citationInstability of high polygenic risk classification and mitigation by integrative scoring
Misra, A., Truong, B. M. T., Urbut, S., Sui, Y., Fahed, A. C., Smoller, J. W., Patel, A. P., & Natarajan, P. (2025) · Nature Communications · doi:10.1038/s41467-025-56945-0 · Cited by 25
“…all CAD Best(Pub n ) with CAD, 1.30-2.58 for all T2DM Best(Pub n ) with T2DM, and 0.99 – 1.47 for all MDD Best(Pub n ) with MDD.(Supplementary Data 4 – 6) While each Best(Pub n ) demonstrated consistent strengths of association, the classi fi cation of individuals into the top 10% of each PRS…”
InfluentialPromoting equity in polygenic risk assessment through global collaboration
Kullo, I. (2024) · Nature Genetics · doi:10.1038/s41588-024-01843-2 · Cited by 13
“Lack of diversity in reference genomes and imputation panels can lead to biased variant calls and missing information, exacerbating disparities in genomics 41 .”
Task-Agnostic Machine-Learning-Assisted Inference
Miao, J.-C., & Lu, Q.-S. (2024) · Neural Information Processing Systems · doi:10.48550/arxiv.2405.20039 · Cited by 13
“Summary statistics-based methods have been developed for tasks such as variance component inference and genetic risk prediction [11, 12, 34, 39, 53].”
Cross-ancestry analyses identify new genetic loci associated with 25-hydroxyvitamin D
Wang, X., Hivert, V., Groot, S., Wang, Y., Yengo, L., McGrath, J. J., Kemper, K. E., Visscher, P. M., Wray, N. R., & Revez, J. A. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1011033 · Cited by 15
Zheng, S.-L., Jurgens, S., McGurk, K. A., Xu, X., Grace, C., Theotokis, P., Buchan, R., Francis, C., de Marvao, A., Curran, L., et al. (2023) · medRxiv · doi:10.1101/2023.03.14.23286621 · Cited by 13
Sun, Q., Rowland, B., Chen, J. C., Mikhaylova, A. V., Avery, C. L., Peters, U., Lundin, J. I., Matise, T. C., Buyske, S. G., Tao, R., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.06.511219 · Cited by 8
Genetics of Chronic Kidney Disease in Low-Resource Settings
Ilori, T. O., Watanabe, A., Ng, K. H., Solarin, A. U., Sinha, A. P., & Gbadegesin, R. (2022) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2023.151314 · Cited by 14
Huerta-Chagoya, A., Kim, J., Mandla, R., Lu, Y.-C., Suzuki, K., Petty, L. E., Ng, H., Choi, J., Lee, S., Rout, M., et al. (2025) · medRxiv · doi:10.1101/2025.07.21.25331778 · Cited by 12
Self-citationAssessment of polygenic risk score performance in East Asian populations for ten common diseases
Jung, H., Jung, H., Baek, E. J., Kang, J., Kwon, S. Y., You, J., Lim, J. E., & Oh, B. (2025) · Communications Biology · doi:10.1038/s42003-025-07767-9 · Cited by 13
“Thecross-populationmethodofPRS-CSx 16 .”
InfluentialCocoş, R., & Popescu, B. O. (2024) · Human Genomics · doi:10.1186/s40246-024-00704-7 · Cited by 17
“Integrating GWAS data from diverse ancestries could substantially enhance predictive accuracy, addressing the limited transferability across populations with distinct ethnic backgrounds [95].”
Predicting Thoracic Aortic Dissection in a Diverse Biobank Using a Polygenic Risk Score
DePaolo, J. S., Zamirpour, S., Abramowitz, S. A., Biagetti, G. M., Judy, R., Beeche, C., Duda, J., Gee, J. C., Witschey, W. R. T., Chirinos, J. A., et al. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.101743 · Cited by 14
Hafeman, D., Uher, R., Merranko, J. A., Zwicker, A., Goldstein, B. I., Goldstein, T. R., Axelson, D., Monk, K., Sakolsky, D. J., Iyengar, S., et al. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.09.107 · Cited by 12
“Ongoing efforts to scale up GWAS in non-European populations (O’Connell, 2021) and/or methodological advances (Ruan et al., 2022) are critical to the development of improved BD-PRS in diverse samples.”
Polygenic scoring accuracy varies across the genetic ancestry continuum in all human populationsPreprint
Ding, Y., Hou, K., Xu, Z.-Q., Pimplaskar, A., Petter, E., Boulier, K., Privé, F., Vilhjálmsson, B., Loohuis, L. M., & Pasaniuc, B. (2022) · bioRxiv · doi:10.1101/2022.09.28.509988 · Cited by 12
Kulm, S., Kolin, D. A., Langhans, M. T., Kaidi, A. C., Elemento, O., Bostrom, M. P., & Shen, T. S. (2022) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.22.00364 · Cited by 18
The transformative role of Artificial Intelligence in genomics: Opportunities and challenges
Dara, M., Dianatpour, M., Azarpira, N., & Tanideh, N. (2025) · Gene Reports · doi:10.1016/j.genrep.2025.102314 · Cited by 12
Sexual Trauma, Polygenic Scores, and Mental Health Diagnoses and Outcomes
Lake, A. M., Zhou, Y., Wang, B., Actkins, K. V., Zhang, Y., Shelley, J. P., Rajamani, A., Steigman, M., Kennedy, C. J., Smoller, J. W., et al. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2024.3426 · Cited by 13
The Taiwan Precision Medicine Initiative: A Cohort for Large-Scale Studies
Yang, H.-C., Kwok, P.-Y., Li, L.-H., Liu, Y.-M., Jong, Y.-J., Lee, K.-Y., Wang, D., Tsai, M.-F., Yang, J.-H., Chen, C.-H., et al. (2024) · bioRxiv · doi:10.1101/2024.10.14.616932 · Cited by 11
Self-citationInstability of high polygenic risk classification and mitigation by integrative scoring
Misra, A., Truong, B., Urbut, S., Sui, Y., Fahed, A., Smoller, J., Patel, A. P., & Natarajan, P. (2024) · medRxiv · doi:10.1101/2024.07.24.24310897 · Cited by 11
Guertin, K. A., Repaske, D. R., Taylor, J. F., Williams, E. S., Önengüt-Gümüşcü, S., Chen, W., Boggs, S. R., Yu, L., Allen, L., Botteon, L., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01305-8 · Cited by 13
Self-citationSingh, S., Choudhury, A., Hazelhurst, S., Crowther, N., Boua, P., Sorgho, H., Agongo, G., Nonterah, E., Micklesfield, L., Norris, S., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2532794/v1 · Cited by 11
Reyes, D. M., Bose, A., Karavani, E., & Parida, L. (2022) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811270611_0019 · Cited by 6
Ramanan, V. K., Heckman, M. G., Przybelski, S. A., Lesnick, T. G., Lowe, V. J., Graff‐Radford, J., Mielke, M. M., Jack, C. R., Knopman, D. S., Petersen, R. C., et al. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220164 · Cited by 9
Xu, Y.-X., Qiao, M., Gunasekaran, T. I., Gu, Y., Reyes-Dumeyer, D., Piriz, A., Sanchez, D., Soriano, B., Franco, Y., Coronado, Z. D., et al. (2025) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2025.101198 · Cited by 10
Polygenic Scores of Cardiometabolic Risk Factors in American Indian Adults
Sun, Q., Du, J., Tang, Y., Best, L. G., Haack, K., Zhang, Y., Cole, S. A., & Franceschini, N. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.0535 · Cited by 12
Orcales, F., Kumar, S., Bui, A., Johnson, C. E., Liu, J., Huang, Z.-M., & Liao, W. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05591-z · Cited by 10
“Future methods that can be used to deal with this limitation include re-calculation of PRS on full summary statistics and using more robust meth-ods such as PRS-CSx and PTRS [38, 39].”
MethodSelf-citationDecoding polygenic diseases: advances in noncoding variant prioritization and validation
Chin, I. M., Gardell, Z. A., & Corces, M. R. (2024) · Trends in Cell Biology · doi:10.1016/j.tcb.2024.03.005 · Cited by 18
Characterizing the phenotypic and genetic structure of psychopathology in UK Biobank
Williams, C. M., Peyre, H., Wolfram, T., Lee, Y. A., Seidlitz, J., Ge, T., Smoller, J. W., Mallard, T. T., & Ramus, F. (2024) · Nature Mental Health · doi:10.1038/s44220-024-00272-8 · Cited by 15
“Extending these analyses to additional cohorts with more diverse data 52 and less healthy volunteer bias 53 will be critical to evaluating the robustness and generalizability of our findings.”
MethodSelf-citationComorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome
Hicks, E., Niarchou, M., Goleva, S., Kabir, D., Ciarcia, J., Smoller, J., Davis, L. K., Nievergelt, C., Koenen, K., Huckins, L., et al. (2023) · medRxiv · doi:10.1101/2023.08.25.23294572 · Cited by 10
Darst, B., Shen, J., Madduri, R., Rodriguez, A. A., Xiao, Y., Sheng, X., Saunders, E., Dadaev, T., Brook, M., Hoffmann, T., et al. (2023) · medRxiv · doi:10.1101/2023.05.12.23289860 · Cited by 10
Risk factors affecting polygenic score performance across diverse cohorts
Hui, D., Dudek, S., Kiryluk, K., Walunas, T. L., Kullo, I., Wei, W.-Q., Tiwari, H., Peterson, J. F., Chung, W. K., Davis, B. H., et al. (2023) · medRxiv · doi:10.1101/2023.05.10.23289777 · Cited by 10
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
