The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
Open accessPublished version of a preprint
Abstract
The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.
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“Admixture, or the genetic merging of two or more parental lineages, has been demonstrated as an important contributor to modern human genetic variation (Rudan 2006; Hellenthal et al. 2014; Martin et al. 2017).”
From The Impact of Modern Admixture on Archaic Human Ancestry in Human Populations (2023) · cites this paper as Martin et al. 2017
In the citing paper
“These issues are insurmountable through statistical methods alone13, but rather motivate substantial investments in more diverse populations to produce similar-sized GWAS of biomedical phenotypes in other populations.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“The scientific basis of this imbalance has been demonstrated theoretically, in simulations, and empirically across many traits and diseases11–22.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“However, the major ethical and scientific challenge surrounding clinical implementation is that they are many-fold more accurate in European ancestry individuals than others.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“This dependence means that the most significant associations tend to be more common in the populations in which they are discovered than elsewhere13,30.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“population genetics theory predicts that genetic risk prediction accuracy will decay with increasing genetic divergence between the original GWAS sample and target of prediction, a function of population history13,14.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“Eurocentric GWAS biases mean that variants associated with risk are disproportionately common and discovered in European populations, accounting for a larger fraction of the phenotypic variance there13.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“In contrast, PRS are uniformly less useful in understudied populations due to differences in genomic variation and population history13,14.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [13]
In the citing paper
“Because the discovery sample used to construct the score consisted of individuals of European ancestry, we would not expect the predictive power of our score to be as high in other ancestry groups7,26,27.”
From Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals (2018) · cites this paper as [26]
In the citing paper
“A third limit to transferability of effect estimates arises from population differences in features of correlations between nearby sites—linkage disequilibrium patterns—that influence aggregations of signals across loci [32].”
From Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences (2019) · cites this paper as [32]
In the citing paper
“As just one example, incorrect inference can readily occur if the GRS training and target datasets are from different ancestries (Martin et al., 2017).”
From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Martin et al., 2017
In the citing paper
“Because allele frequencies, linkage disequilibrium patterns, and effect sizes of common polymorphisms vary with ancestry, the specific GPS here will not have optimal predictive power for other ethnic groups.29”
From Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations (2018) · cites this paper as [29]
In the citing paper
“As expected from previous work (Martin et al., 2017), our results indicate that genetic risk prediction is strongly affected by population structure.”
From An Unexpectedly Complex Architecture for Skin Pigmentation in Africans (2017) · cites this paper as Martin et al., 2017
Citations
Cites
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Self-citationInference of Population Structure Using Multilocus Genotype Data
Pritchard, J. K., Stephens, M., & Donnelly, P. (2000) · Genetics · doi:10.1093/genetics/155.2.945 · Cited by 33,871
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
genalex 6: genetic analysis in Excel. Population genetic software for teaching and research
Peakall, R., & Smouse, P. E. (2005) · Molecular Ecology Notes · doi:10.1111/j.1471-8286.2005.01155.x · Cited by 16,596
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
Fast model-based estimation of ancestry in unrelated individuals
Alexander, D. H., Novembre, J., & Lange, K. (2009) · Genome Research · doi:10.1101/gr.094052.109 · Cited by 9,898
Analysis of protein-coding genetic variation in 60,706 humans
Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587
Self-citationAn integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
Biological 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
Self-citationWhiteford, H. A., Degenhardt, L., Rehm, J., Baxter, A. J., Ferrari, A. J., Erskine, H. E., Charlson, F. J., Norman, R. E., Flaxman, A. D., Johns, N., et al. (2013) · The Lancet · doi:10.1016/s0140-6736(13)61611-6 · Cited by 6,006
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Scheinfeldt, L., Brangan, A., Kusic, D., Kumar, S., & Gharani, N. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11020131 · Cited by 14
Changes in the fine-scale genetic structure of Finland through the 20th century
Kerminen, S., Cerioli, N., Pacauskas, D., Havulinna, A. S., Perola, M., Jousilahti, P., Salomaa, V. V., Daly, M. J., Vyas, R., Ripatti, S., et al. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009347 · Cited by 14
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Saunders, G., Liu, M., Vrieze, S. I., McGue, M. K., & Iacono, W. G. (2021) · Developmental Psychology · doi:10.1037/dev0001028 · Cited by 15
Amin, V., Fletcher, J. M., Sun, Z., & Lu, Q. (2021) · SSM - Population Health · doi:10.1016/j.ssmph.2021.101018 · Cited by 14
Genetic European Ancestry and Incident Diabetes in Black Individuals: Insights From the SPRINT Trial
Parcha, V., Heindl, B. F., Kalra, R., Bress, A. P., Rao, S., Pandey, A., Gower, B., Irvin, M. R., McDonald, M.-L. N., Li, P., et al. (2022) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.121.003468 · Cited by 14
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Niedbalski, S. D., & Long, J. C. (2022) · Scientific Reports · doi:10.1038/s41598-022-08212-1 · Cited by 13
Spendlove, S. J., Bondhus, L., Lluri, G., Sul, J. H., & Arboleda, V. A. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100112 · Cited by 14
Lucas-Sánchez, M., Fadhlaoui‐Zid, K., & Comas, D. (2022) · Human Genetics · doi:10.1007/s00439-022-02503-3 · Cited by 14
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Michalek, D. A., Önengüt-Gümüşcü, S., Repaske, D. R., & Rich, S. S. (2023) · Journal of the Indian Institute of Science · doi:10.1007/s41745-023-00356-x · Cited by 14
Su, J., Kuo, S. I., Meyers, J. L., Guy, M. C., & Dick, D. M. (2018) · Development and Psychopathology · doi:10.1017/s0954579418000962 · Cited by 14
Smith, J. A., Zhao, W., Yu, M., Rumfelt, K. E., Moorjani, P., Ganna, A., Dey, A. B., Lee, J., & Kardia, S. L. R. (2020) · Journal of the American Geriatrics Society · doi:10.1111/jgs.16735 · Cited by 14
Suzuki, T., Koike, Y., Ashikawa, K., Otomo, N., Takahashi, A., Aoi, T., Kamatani, N., Nakamura, Y., Kubo, M., Kamatani, Y., et al. (2021) · Epilepsia · doi:10.1111/epi.16911 · Cited by 14
Qiao, J., Wu, Y., Zhang, S., Xu, Y., Zhang, J., Zeng, P., & Wang, T. (2023) · BMC Genomics · doi:10.1186/s12864-023-09425-y · Cited by 13
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Wang, Y.-Z., Zhao, W., Moorjani, P., Gross, A. L., Zhou, X., Dey, A. B., Lee, J., Smith, J. A., & Kardia, S. L. R. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14052 · Cited by 14
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Goldman, D. A. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.09.002 · Cited by 13
Zhao, M., Ji, S., & Wei, Z. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238443 · Cited by 13
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Self-citationBorinstein, S. C., Agamasu, D., Schildcrout, J. S., Bastarache, L. A., Bagheri, M., Davis, L. K., Roden, D. M., Stein, C., Driest, S. L. V., & Mosley, J. D. (2022) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.17346 · Cited by 13
Geneviève, L. D., Elger, B. S., & Wangmo, T. (2022) · Cambridge Prisms Precision Medicine · doi:10.1017/pcm.2022.4 · Cited by 13
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Smith, R. L., Salvatore, J. E., Alıev, F., Neale, Z. E., Barr, P. B., Spit for Science Working Group, & Dick, D. M. (2019) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.14037 · Cited by 13
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Lam, M., Chen, C.-Y., Li, Z., Martin, A. R., Bryois, J., Ma, X., Gaspar, H., Ikeda, M., Benyamin, B., Brown, B. C., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/445874 · Cited by 14
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Saitou, M., Dahl, A. W., Wang, Q., & Liu, X. (2022) · medRxiv · doi:10.1101/2022.10.21.22281371 · Cited by 13
Warne, R. T. (2021) · The American Journal of Psychology · doi:10.5406/amerjpsyc.134.4.0479 · Cited by 12
Saitou, M., Dahl, A. W., Wang, Q., & Liu, X. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.10.009 · Cited by 13
Pham, D., Truong, B. M. T., Tran, K., Ni, G., Nguyen, D., Tran, T. T. H., Tran, M. H., Thuy, D. N., Vo, N. S., & Nguyen, Q. H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac459 · Cited by 12
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Ligthart, S., Hasbani, N. R., Ahmadizar, F., van Herpt, T. T. W., Leening, M. J. G., Uitterlinden, A. G., Sijbrands, E. J. G., Morrison, A. C., Boerwinkle, E., Pankow, J. S., et al. (2021) · Diabetic Medicine · doi:10.1111/dme.14639 · Cited by 13
Rea‐Sandin, G., Oro, V., Strouse, E., Clifford, S., Wilson, M. N., Shaw, D. S., & Lemery‐Chalfant, K. (2021) · Genes Brain & Behavior · doi:10.1111/gbb.12762 · Cited by 14
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Gao, Y., & Cui, Y. (2024) · Genome Medicine · doi:10.1186/s13073-024-01345-0 · Cited by 13
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Raffield, L. M., Ellis, J., Olson, N. C., Duan, Q., Li, J., Durda, P., Pankratz, N., Keating, B. J., Wassel, C. L., Cushman, M., et al. (2018) · Journal of Human Genetics · doi:10.1038/s10038-017-0384-9 · Cited by 12
Kim, R., Lippert, A. M., Wedow, R., Jimenez, M. P., & Subramanian, S. V. (2020) · American Journal of Epidemiology · doi:10.1093/aje/kwaa058 · Cited by 12
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Lin, C., Xia, M., Dai, Y., Huang, Q., Sun, Z., Zhang, G., Luo, R., Peng, Q., Li, J., Wang, X., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100810 · Cited by 11
Dallaire, X., Normandeau, É., Brazier, T., Harris, L. N., Hansen, M. M., Mérot, C., & Moore, J. (2025) · Molecular Ecology · doi:10.1111/mec.17772 · Cited by 12
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Schmitz, L., Gard, A. M., & Ware, E. B. (2019) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32748 · Cited by 11
Chande, A. T., Rishishwar, L., Ban, D., Nagar, S. D., Conley, A. B., Rowell, J. L., Valderrama-Aguirre, A., Medina-Rivas, M. A., & Jordan, I. K. (2020) · Genome Biology and Evolution · doi:10.1093/gbe/evaa154 · Cited by 11
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Naslavsky, M. S., Scliar, M. O., Nunes, K., Wang, J. Y. T., Yamamoto, G. L., Guio, H., Tarazona‐Santos, E., Duarte, Y. A. O., Passos‐Bueno, M. R., Meyer, D., et al. (2021) · American Journal of Medical Genetics Part C Seminars in Medical Genetics · doi:10.1002/ajmg.c.31931 · Cited by 12
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Elam, K. K., Ha, T., Neale, Z. E., Alıev, F., Dick, D. M., & Lemery‐Chalfant, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-01923-x · Cited by 11
Li, X., Qiu, Y., Zhou, J. H., & Xie, Z. (2021) · Current Genomics · doi:10.2174/1389202923666211216163049 · Cited by 11
Hämmerle, M., Forer, L., Schönherr, S., Peters, A., Grallert, H., Kronenberg, F., Gieger, C., & Lamina, C. (2022) · Stroke · doi:10.1161/strokeaha.121.036551 · Cited by 11
Tonini, E., Watkeys, O. J., Quidé, Y., Whitford, T. J., Cairns, M. J., & Green, M. J. (2022) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2022.110612 · Cited by 11
Arpawong, T. E., Klopack, E. T., Kim, J. K., & Crimmins, E. M. (2023) · Clinical Epigenetics · doi:10.1186/s13148-023-01484-y · Cited by 14
Corte, L., Liou, L., O’Reilly, P. F., & García‐González, J. (2023) · Gigabyte · doi:10.46471/gigabyte.89 · Cited by 11
Wang, S., Lai, R., Lee, Y., Su, M., Chen, C., Hsiao, P., Yang, A. C., Liu, Y., Tsai, S., & Kuo, P. (2020) · Genes Brain & Behavior · doi:10.1111/gbb.12639 · Cited by 11
Yeung, E. W., Spychala, K. M., Miller, A. P., Otto, J. M., Deak, J. D., Kim, H., Gilder, D. A., Ehlers, C. L., Wilhelmsen, K. C., & Gizer, I. R. (2021) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2021.109117 · Cited by 11
Zhang, D., Li, Q., Povysil, G., Newton, C. A., Maher, T. M., Molyneaux, P. L., Noth, I., Martínez, F. J., Raghu, G., Todd, J. L., et al. (2022) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202203-0622le · Cited by 13
Meta-Analysis of SNP-Environment Interaction with Heterogeneity
Jin, Q., & Shi, G. (2019) · Human Heredity · doi:10.1159/000504170 · Cited by 11
Hamad, R., Glymour, M. M., Calmasini, C., Nguyen, T. T. X., Walter, S., & Rehkopf, D. H. (2022) · PubMed · doi:10.1097/ede.0000000000001425 · Cited by 10
Hu, J., Jiang, G., Qin, J., Luo, S., Fan, B., Xie, Z., Wan, R., Li, X., Tam, C. H. T., Wang, Z., et al. (2025) · Diabetologia · doi:10.1007/s00125-025-06455-x · Cited by 10
Morozova, A. Y., Ushakova, V. M., Pavlova, O., Bairamova, S., Andriushchenko, N., Ochneva, A., Abramova, O. V., Zorkina, Y., Spektor, V. A., Гадисов, Т. Г., et al. (2024) · Genes · doi:10.3390/genes15020240 · Cited by 10
The breadth and impact of the Global Lipids Genetics Consortium
Dron, J. S., Natarajan, P., & Peloso, G. M. (2024) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000966 · Cited by 10
Fullerton, J. M., & Tesfaye, M. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.02.008 · Cited by 10
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Sherman, C. A., Nataneli, S., Claw, K. G., & Mooney, J. A. (2025) · Genetics · doi:10.1093/genetics/iyaf127 · Cited by 10
Nguyen, H. H. K., Le, H. M., Thang, L. Q., Ngan, D. T. Q., Thoi, N. T., Phuong, N., Hoang, D. M., & Huynh, C. D. (2025) · iScience · doi:10.1016/j.isci.2025.114375 · Cited by 10
Liu, H., Motz, R. T., Tanksley, P. T., Barnes, J. C., & Harris, K. M. (2021) · Journal of Developmental and Life-Course Criminology · doi:10.1007/s40865-021-00166-8 · Cited by 10
Multi-Trait Genomic Risk Stratification for Type 2 Diabetes
Rohde, P. D., Nyegaard, M., Kjølby, M., & Sørensen, P. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.711208 · Cited by 10
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Prakrithi, P., Lakra, P., Sundar, D., Kapoor, M., Mukerji, M., & Gupta, I. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.714185 · Cited by 10
A linear adjustment-based approach to posterior drift in transfer learning
Maity, S., Dutta, D., Terhorst, J., Sun, Y., & Banerjee, M. (2023) · Biometrika · doi:10.1093/biomet/asad029 · Cited by 10
Populations, Traits, and Their Spatial Structure in Humans
Sohail, M., Izarraras-Gomez, A., & Vecchyo, D. O. (2021) · Genome Biology and Evolution · doi:10.1093/gbe/evab272 · Cited by 10
Barnekow, E., Liu, W., Helgadottir, H. T., Michailidou, K., Dennis, J., Bryant, P., Thutkawkorapin, J., Wendt, C., Czene, K., Hall, P. N., et al. (2022) · Cancers · doi:10.3390/cancers14051206 · Cited by 11
Elam, K. K., Bountress, K. E., Ha, T., Shaw, D. S., Wilson, M. N., Alıev, F., Dick, D. M., & Lemery‐Chalfant, K. (2022) · Development and Psychopathology · doi:10.1017/s0954579422000980 · Cited by 11
Zheng, B., Fletcher, J. M., Zheng, F., & Lu, Q. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.115249 · Cited by 10
Application of Genomic Data in Translational Medicine During the Big Data Era
Zhang, Y., Yu, J. M., Xie, X., Jiang, F., & Wu, C. (2024) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2901007 · Cited by 10
de Farias, A. A., Nunes, K., Lemes, R. B., de Moura, R. R., Fernandes, G. R., Melo, U. S., Zatz, M., Kok, F., & Santos, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-35022-1 · Cited by 10
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Gladstein, A. L., & Hammer, M. F. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz047 · Cited by 11
Mas-Sandoval, À., Arauna, L. R., Gouveia, M. H., Barreto, M. L., Horta, B. L., Lima‐Costa, M. F., Pereira, A. C., Salzano, F. M., Hünemeier, T., Tarazona‐Santos, E., et al. (2019) · Genome Biology and Evolution · doi:10.1093/gbe/evz161 · Cited by 10
Metadata
- Authors
- Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., Gravel, S., Daly, M. J., Bustamante, C. D., & Kenny, E. E.
- Affiliations
- Broad Institute (US)
- Stanford University (US)
- McGill University and Génome Québec Innovation Centre (CA)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Kenny, Eimear E.
- ORCID
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 100
- Issue
- 4
- Pages
- 635-649
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC5384097
- OpenAlex
- W2598022332
- Cited by
- 1,391
- References
- 111
- Citations by year
- 2026: 77 · 2025: 131 · 2024: 162 · 2023: 149 · 2022: 195 · 2021: 208 · 2020: 206 · 2019: 156 · 2018: 84 · 2017: 21
- FWCI
- 84.74
- Type
- journal article
- Language
- en
- Update notices
- erratum (2020-10-01)
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Canadian Institutes of Health Research
- National Institutes of Health
- Canada Research Chairs (MOP-136855)
- National Institute of Mental Health (5U01MH094432-02)
- National Human Genome Research Institute (R01GM083606, T32HG000044, U01HG005208, U01HG007417, U01HG007419, U01HG009080)
- National Institute of General Medical Sciences (T32GM007790)
- Directorate for Mathematical and Physical Sciences (1201234)
- National Science Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Social Sciences › Anthropology › Race, Genetics, and Society
- Keywords
- polygenic risk score, underrepresented groups, demographic history, genetic drift, allele frequency divergence, coalescent simulation, polygenic traits, height, heritability, genetic risk prediction
- MeSH terms
- Americas, Genetics, Medical, Genetics, Population, Haplotypes, Humans, Human Genome Project, Genetic Predisposition to Disease, Multifactorial Inheritance, Racial Groups
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