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
Cited by
Sorted by citations
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
Self-citation23andMe Research Team, Lee, J. J., COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0147-3 · Cited by 2,470
Self-citationTutorial: a guide to performing polygenic risk score analyses
Choi, S. W., Mak, T. S. H., & O’Reilly, P. F. (2020) · Nature Protocols · doi:10.1038/s41596-020-0353-1 · Cited by 2,058
Genome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849
Self-citationThe personal and clinical utility of polygenic risk scores
Torkamani, A., Wineinger, N. E., & Topol, E. J. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0018-x · Cited by 1,655
The Missing Diversity in Human Genetic Studies
Sirugo, G., Williams, S. M., & Tishkoff, S. A. (2019) · Cell · doi:10.1016/j.cell.2019.02.048 · Cited by 1,516
PRSice-2: Polygenic Risk Score software for biobank-scale data
Choi, S. W., & O’Reilly, P. F. (2019) · GigaScience · doi:10.1093/gigascience/giz082 · Cited by 1,538
Polygenic risk scores: from research tools to clinical instruments
Lewis, C. M., & Vassos, E. (2020) · Genome Medicine · doi:10.1186/s13073-020-00742-5 · Cited by 1,360
Integrative omics for health and disease
Karczewski, K. J., & Snyder, M. P. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2018.4 · Cited by 1,158
Analysis of polygenic risk score usage and performance in diverse human populations
Duncan, L., Shen, H., Gelaye, B., Meijsen, J., Ressler, K., Feldman, M., Peterson, R., & Domingue, B. (2019) · Nature Communications · doi:10.1038/s41467-019-11112-0 · Cited by 1,038
Genetic analyses of diverse populations improves discovery for complex traits
Wojcik, G. L., Graff, M., Nishimura, K. K., Tao, R., Haessler, J., Gignoux, C. R., Highland, H. M., Patel, Y. M., Sorokin, E. P., Avery, C. L., et al. (2019) · Nature · doi:10.1038/s41586-019-1310-4 · Cited by 1,033
Self-citationMendelian Randomization: Concepts and Scope
Richmond, R. C., & Davey Smith, G. (2022) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a040501 · Cited by 841
Mahajan, A., Spracklen, C. N., Zhang, W., Ng, M. C. Y., Petty, L. E., Kitajima, H., Yu, G. Z., Rüeger, S., Speidel, L., Kim, Y. J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01058-3 · Cited by 704
A roadmap to increase diversity in genomic studies
Fatumo, S., Chikowore, T., Choudhury, A., Ayub, M., Martin, A. R., & Kuchenbaecker, K. (2022) · Nature Medicine · doi:10.1038/s41591-021-01672-4 · Cited by 707
Self-citationImproving polygenic prediction in ancestrally diverse populations
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., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01054-7 · Cited by 707
Self-citationHaplotype-resolved diverse human genomes and integrated analysis of structural variation
Ebert, P., Audano, P. A., Zhu, Q., Rodriguez-Martin, B., Porubsky, D., Bonder, M. J., Sulovari, A., Ebler, J., Zhou, W., Serra Mari, R., et al. (2021) · Science · doi:10.1126/science.abf7117 · Cited by 702
Comparative genetic architectures of schizophrenia in East Asian and European populations
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Lam, M., Indonesia Schizophrenia Consortium, Genetic REsearch on schizophreniA neTwork-China and the Netherlands (GREAT-CN), Chen, C.-Y., Li, Z., Martin, A. R., Bryois, J., Ma, X., Gaspar, H., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0512-x · Cited by 655
Self-citationNievergelt, C. M., Maihofer, A. X., Klengel, T., Atkinson, E. G., Chen, C.-Y., Choi, K. W., Coleman, J. R. I., Dalvie, S., Duncan, L. E., Gelernter, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12576-w · Cited by 635
Self-citationCano-Gamez, E., & Trynka, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00424 · Cited by 613
Towards clinical utility of polygenic risk scores
Lambert, S. A., Abraham, G., & Inouye, M. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz187 · Cited by 586
15 years of GWAS discovery: Realizing the promise
Abdellaoui, A., Yengo, L., Verweij, K. J. H., & Visscher, P. M. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.12.011 · Cited by 570
FinnGen, Mars, N., Koskela, J. T., Ripatti, P., Kiiskinen, T., Havulinna, A. S., Lindbohm, J. V., Ahola‐Olli, A., Kurki, M., Karjalainen, J. M., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0800-0 · Cited by 498
Self-citationGenetic diversity fuels gene discovery for tobacco and alcohol use
Saunders, G., Wang, X., Chen, F., Jang, S.-K., Liu, M., Wang, C., Gao, S., Jiang, Y., Khunsriraksakul, C., Otto, J. M., et al. (2022) · Nature · doi:10.1038/s41586-022-05477-4 · Cited by 481
Variable prediction accuracy of polygenic scores within an ancestry group
Mostafavi, H., Harpak, A., Agarwal, I., Conley, D., Pritchard, J. K., & Przeworski, M. (2020) · eLife · doi:10.7554/elife.48376 · Cited by 457
Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders
Sullivan, P. F., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.01.015 · Cited by 452
Efficient ancestry and mutation simulation with msprime 1.0
Baumdicker, F., Bisschop, G., Goldstein, D., Gower, G., Ragsdale, A. P., Tsambos, G., Zhu, S. J., Eldon, B., Ellerman, E. C., Galloway, J., et al. (2021) · Genetics · doi:10.1093/genetics/iyab229 · Cited by 488
Self-citationA scientometric review of genome-wide association studies
Mills, M. C., & Rahal, C. (2018) · Communications Biology · doi:10.1038/s42003-018-0261-x · Cited by 446
O’Sullivan, J. W., Raghavan, S., Márquez‐Luna, C., Luzum, J. A., Damrauer, S. M., Ashley, E. A., O’Donnell, C. J., Willer, C. J., & Natarajan, P. (2022) · Circulation · doi:10.1161/cir.0000000000001077 · Cited by 443
Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps
Polygenic Risk Score Task Force of the International Common Disease Alliance, Adeyemo, A., Balaconis, M. K., Darnes, D. R., Fatumo, S., Granados Moreno, P., Hodonsky, C. J., Inouye, M., Kanai, M., Kato, K., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01549-6 · Cited by 413
Self-citationA major role for common genetic variation in anxiety disorders
Purves, K. L., Coleman, J. R. I., Meier, S. M., Rayner, C., Davis, K. A. S., Cheesman, R., Bækvad‐Hansen, M., Børglum, A. D., Cho, S. W., Deckert, J., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0559-1 · Cited by 436
Underspecification Presents Challenges for Credibility in Modern Machine Learning
D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv · doi:10.48550/arxiv.2011.03395 · Cited by 398
Sohail, M., Maier, R. M., Ganna, A., Bloemendal, A., Martin, A. R., Turchin, M. C., Chiang, C. W., Hirschhorn, J., Daly, M. J., Patterson, N., et al. (2019) · eLife · doi:10.7554/elife.39702 · Cited by 359
Self-citationPersonalized Medicine and the Power of Electronic Health Records
Abul-Husn, N. S., & Kenny, E. E. (2019) · Cell · doi:10.1016/j.cell.2019.02.039 · Cited by 355
Self-citationThe GWAS Diversity Monitor tracks diversity by disease in real time
Mills, M. C., & Rahal, C. (2020) · Nature Genetics · doi:10.1038/s41588-020-0580-y · Cited by 342
Principles and methods for transferring polygenic risk scores across global populations
Kachuri, L., Chatterjee, N., Hirbo, J. B., Schaid, D. J., Martin, I. K., Kullo, I. J., Kenny, E. E., Paşaniuc, B., Auer, P. L., Conomos, M. P., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00637-2 · Cited by 366
Self-citationPrivé, F., Aschard, H., Carmi, S., Folkersen, L., Hoggart, C., O’Reilly, P. F., & Vilhjálmsson, B. J. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.11.008 · Cited by 375
Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer
Carrot‐Zhang, J., Chambwe, N., Damrauer, J. S., Knijnenburg, T., Robertson, A. G., Yau, C. S. F., Zhou, W., Berger, A. C., Huang, K., Newberg, J. Y., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.04.012 · Cited by 331
The Missing Diversity in Human Genetic Studies
Sirugo, G., Williams, S. M., & Tishkoff, S. A. (2019) · Cell · doi:10.1016/j.cell.2019.04.032 · Cited by 317
Khera, A. V., Chaffin, M., Zekavat, S. M., Collins, R. L., Roselli, C., Natarajan, P., Lichtman, J. H., D’Onofrio, G., Mattera, J., Dreyer, R., et al. (2019) · Circulation · doi:10.1161/circulationaha.118.035658 · Cited by 315
Polygenic scoring accuracy varies across the genetic ancestry continuum
Ding, Y., Hou, K., Xu, Z., Pimplaskar, A., Petter, E., Boulier, K. M., Privé, F., Vilhjálmsson, B. J., Loohuis, L. M. O., & Paşaniuc, B. (2023) · Nature · doi:10.1038/s41586-023-06079-4 · Cited by 330
Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine
Udler, M. S., McCarthy, M. I., Florez, J. C., & Mahajan, A. (2019) · Endocrine Reviews · doi:10.1210/er.2019-00088 · Cited by 302
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Gurdasani, D., Barroso, I., Zeggini, E., & Sandhu, M. S. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0144-0 · Cited by 312
Predicting Polygenic Risk of Psychiatric Disorders
Martin, A. R., Daly, M. J., Robinson, E., Hyman, S. E., & Neale, B. M. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.12.015 · Cited by 301
Self-citationThe influence of evolutionary history on human health and disease
Benton, M. L., Abraham, A., LaBella, A. L., Abbot, P., Rokas, A., & Capra, J. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00305-9 · Cited by 291
Deconstructing the sources of genotype-phenotype associations in humans
Young, A. I., Benonisdottir, S., Przeworski, M., & Kong, A. (2019) · Science · doi:10.1126/science.aax3710 · Cited by 288
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Wang, Y., Guo, J., Ni, G., Yang, J., Visscher, P. M., & Yengo, L. (2020) · Nature Communications · doi:10.1038/s41467-020-17719-y · Cited by 271
Karczewski, K. J., Gupta, R., Kanai, M., Lu, W., Tsuo, K., Wang, Y., Walters, R. K., Turley, P., Callier, S., Shah, N. N., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02335-7 · Cited by 274
Self-citationAndreassen, O. A., Hindley, G., Frei, O., & Smeland, O. B. (2023) · World Psychiatry · doi:10.1002/wps.21034 · Cited by 284
Keaton, J. M., Kamali, Z., Xie, T., Vaez, A., Williams, A., Goleva, S. B., Ani, A., Εvangelou, Ε., Hellwege, J. N., Yengo, L., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01714-w · Cited by 241
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Zeberg, H., & Pääbo, S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2026309118 · Cited by 239
Atkinson, E. G., Maihofer, A. X., Kanai, M., Martin, A. R., Karczewski, K. J., Santoro, M. L., Ulirsch, J. C., Kamatani, Y., Okada, Y., Finucane, H. K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00766-y · Cited by 239
Self-citationStein, M. B., Levey, D. F., Cheng, Z., Wendt, F. R., Harrington, K. M., Pathak, G. A., Cho, K., Quaden, R. M., Radhakrishnan, K., Girgenti, M. J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00767-x · Cited by 239
Nievergelt, C. M., Maihofer, A. X., Atkinson, E. G., Chen, C., Choi, K. W., Coleman, J. R. I., Daskalakis, N. P., Duncan, L. E., Polimanti, R. A., Aaronson, C. J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01707-9 · Cited by 234
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Rakocevic, G., Semenyuk, V., Lee, W., Spencer, J., Browning, J., Johnson, I. J., Arsenijevic, V., Nadj, J., Ghose, K., Suciu, M., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0316-4 · Cited by 223
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van der Wijst, M. G. P., de Vries, D., Groot, H. E., Trynka, G., Hon, C.-C., Bonder, M. J., Stegle, O., Nawijn, M. C., Idaghdour, Y., van der Harst, P., et al. (2020) · eLife · doi:10.7554/elife.52155 · Cited by 218
Lennon, N. J., Kottyan, L. C., Kachulis, C., Abul‐Husn, N. S., Arias, J. D., Belbin, G. M., Below, J. E., Berndt, S. I., Chung, W. K., Cimino, J. J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02796-z · Cited by 221
Self-citationThe genetic basis of major depression
Kendall, K. M., Van Assche, E., Andlauer, T. F. M., Choi, K. W., Luykx, J. J., Schulte, E. C., & Lu, Y. (2021) · Psychological Medicine · doi:10.1017/s0033291721000441 · Cited by 213
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Kim, M. S., Patel, K. P., Teng, A. K., Berens, A. J., & Lachance, J. (2018) · Genome Biology · doi:10.1186/s13059-018-1561-7 · Cited by 210
Coleman, J. R. I., Peyrot, W. J., Purves, K. L., Davis, K. A. S., Rayner, C., Choi, S. W., Hübel, C., Gaspar, H. A., Kan, C., der Auwera, S. V., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-019-0546-6 · Cited by 206
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Pereira, L., Mutesa, L., Tindana, P. O., & Ramsay, M. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00306-8 · Cited by 194
Abraham, G., Malik, R., Yonova‐Doing, E., Salim, A., Wang, T., Danesh, J., Butterworth, A. S., Howson, J. M. M., Inouye, M., & Dichgans, M. (2019) · Nature Communications · doi:10.1038/s41467-019-13848-1 · Cited by 191
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Kelleher, J., Thornton, K., Ashander, J., & Ralph, P. L. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006581 · Cited by 184
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McInnes, G., Lavertu, A., Sangkuhl, K., Klein, T. E., Whirl‐Carrillo, M., & Altman, R. B. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2122 · Cited by 184
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Evaluating the promise of inclusion of African ancestry populations in genomics
Bentley, A. R., Callier, S., & Rotimi, C. N. (2020) · npj Genomic Medicine · doi:10.1038/s41525-019-0111-x · Cited by 189
Amariuta, T., Ishigaki, K., Sugishita, H., Ohta, T., Koido, M., Dey, K. K., Matsuda, K., Murakami, Y., Price, A. L., Kawakami, E., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00740-8 · Cited by 184
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Natarajan, P., Peloso, G. M., Zekavat, S. M., Montasser, M. E., Ganna, A., Chaffin, M. D., Khera, A. V., Zhou, W., Bloom, J. M., Engreitz, J., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05747-8 · Cited by 183
Self-citationReplicability and Prediction: Lessons and Challenges from GWAS
Marigorta, U. M., Rodríguez, J. A., Gibson, G. C., & Navarro, A. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.03.005 · Cited by 186
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Kullo, I. J., Lewis, C. M., Inouye, M., Martin, A. R., Ripatti, S., & Chatterjee, N. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00470-z · Cited by 185
Self-citationResource profile and user guide of the Polygenic Index Repository
Becker, J., Burik, C. A. P., Goldman, G., Wang, N., Jayashankar, H., Bennett, M., Belsky, D. W., Linnér, R. K., Ahlskog, R., Kleinman, A., et al. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01119-3 · Cited by 179
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Wang, Y., Tsuo, K., Kanai, M., Neale, B. M., & Martin, A. R. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-111721-074830 · Cited by 173
Self-citationLu, X., Liu, Z., Cui, Q., Liu, F., Li, J., Niu, X., Shen, C., Hu, D., Huang, K., Chen, J., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac093 · Cited by 165
Docherty, A. R., Mullins, N., Ashley-Koch, A. E., Qin, X., Coleman, J. R. I., Shabalin, A., Kang, J., Murnyak, B., Wendt, F., Adams, M., et al. (2023) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21121266 · Cited by 160
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Martin, A. R., Lin, M., Granka, J. M., Myrick, J. W., Liu, X., Sockell, A., Atkinson, E. G., Werely, C. J., Möller, M., Sandhu, M. S., et al. (2017) · Cell · doi:10.1016/j.cell.2017.11.015 · Cited by 156
Self-citationGenetic architecture of gene expression traits across diverse populations
Mogil, L. S., Andaleon, A., Badalamenti, A., Dickinson, S. P., Guo, X., Rotter, J. I., Johnson, W. C., Im, H. K., Liu, Y., & Wheeler, H. E. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007586 · Cited by 162
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Mills, M. C., & Tropf, F. C. (2020) · Annual Review of Sociology · doi:10.1146/annurev-soc-121919-054756 · Cited by 162
Igo, R. P., Kinzy, T. G., & Bailey, J. N. C. (2019) · Current Protocols in Human Genetics · doi:10.1002/cphg.95 · Cited by 160
Polygenic burden in focal and generalized epilepsies
Leu, C., Stevelink, R., Smith, A. C. W., Goleva, S. B., Kanai, M., Ferguson, L., Campbell, C., Kamatani, Y., Okada, Y., Sisodiya, S. M., et al. (2019) · Brain · doi:10.1093/brain/awz292 · Cited by 152
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The selection landscape and genetic legacy of ancient Eurasians
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Lambert, S. A., Wingfield, B., Gibson, J. T., Gil, L., Ramachandran, S., Yvon, F., Saverimuttu, S. C. C., Tinsley, E., Lewis, E., Ritchie, S. C., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01937-x · Cited by 150
Dikilitas, O., Schaid, D. J., Kosel, M., Carroll, R. J., Chute, C. G., Denny, J. C., Fedotov, A., Feng, Q., Hakonarson, H. H., Jarvik, G. P., et al. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.04.002 · Cited by 148
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Young, A. I., Nehzati, S. M., Benonisdottir, S., Okbay, A., Jayashankar, H., Lee, C., Cesarini, D., Benjamin, D. J., Turley, P., & Kong, A. (2022) · Nature Genetics · doi:10.1038/s41588-022-01085-0 · Cited by 144
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Xiang, R., MacLeod, I. M., Daetwyler, H. D., de Jong, G., O’Connor, E. S., Schrooten, C., Chamberlain, A. J., & Goddard, M. E. (2021) · Nature Communications · doi:10.1038/s41467-021-21001-0 · Cited by 140
Ding, Y., Hou, K., Burch, K. S., Lapinska, S., Privé, F., Vilhjálmsson, B. J., Sankararaman, S., & Paşaniuc, B. (2021) · Nature Genetics · doi:10.1038/s41588-021-00961-5 · Cited by 142
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Cesarini, D. A., & Visscher, P. M. (2017) · npj Science of Learning · doi:10.1038/s41539-017-0005-6 · Cited by 142
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Roberts, E., Howell, S. J., & Evans, D. G. (2023) · The Breast · doi:10.1016/j.breast.2023.01.003 · Cited by 135
Toward a fine-scale population health monitoring system
Belbin, G. M., Cullina, S., Wenric, S., Soper, E. R., Glicksberg, B. S., Torre, D., Moscati, A., Wojcik, G. L., Shemirani, R., Beckmann, N. D., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.034 · Cited by 136
Self-citationNagpal, S., Meng, X., Epstein, M. P., Tsoi, L. C., Patrick, M. T., Gibson, G. C., Jager, P. L. D., Bennett, D. A., Wingo, A. P., Wingo, T. S., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.05.018 · Cited by 134
Curtis, D. (2018) · Psychiatric Genetics · doi:10.1097/ypg.0000000000000206 · Cited by 132
Genes, Education, and Labor Market Outcomes: Evidence from the Health and Retirement Study
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Genetic prediction of complex traits with polygenic scores: a statistical review
Ma, Y., & Zhou, X. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.06.004 · Cited by 136
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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