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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“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
Items 801–900 of 1,001
Polygenic risk scores: effect estimation and model optimization
Zhao, Z., Song, J., Wang, T., & Lu, Q. (2021) · Quantitative Biology · doi:10.15302/j-qb-021-0238 · Cited by 7
Phenotypic and genetic markers of psychopathology in a population-based sample of older adults
Gard, A. M., Ware, E. B., Hyde, L. W., Schmitz, L., Faul, J. D., & Mitchell, C. M. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01354-2 · Cited by 7
Wilson, M. A. (2020) · Science · doi:10.1126/science.abd8340 · Cited by 7
Capacities and Limitations of Using Polygenic Risk Scores for Reproductive Decision Making
Barlevy, D., Lencz, T., Carmi, S., Kostick, K. M., Mukherjee, M., Pereira, S., & Lázaro‐Muñoz, G. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2021.2013983 · Cited by 7
Zhao, Z., Dorn, S., Wu, Y., Yang, X., Beckerman, A., Jin, J., & Lu, Q. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.27.625748 · Cited by 8
Performance Under Fire: Older Adult Cognitive Risks and Protections Under Heat Strain
Shin, S. H., Walker, S. L., Ji, H., & Lee, H. Y. (2024) · The Gerontologist · doi:10.1093/geront/gnae116 · Cited by 6
Ancestral diversity in complex disease genetics: from discovery to translation
Kuchenbaecker, K. B., & Navoly, G. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-025-00921-3 · Cited by 6
Anwar, M. Y., Raffield, L. M., Lange, L. A., Correa, A., & Taylor, K. C. (2021) · PLoS ONE · doi:10.1371/journal.pone.0255609 · Cited by 6
Kuo, S. I., Thomas, N., Alıev, F., Bucholz, K. K., Dick, D. M., McCutcheon, V. V., Meyers, J. L., Chan, G., Kamarajan, C., Kramer, J., et al. (2023) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.15042 · Cited by 7
Ang, K. C., Canfield, V. A., Foster, T. C., Harbaugh, T. D., Early, K. A., Harter, R. L., Reid, K. P., Leong, S. L., Kawasawa, Y. I., Liu, D. J., et al. (2023) · eLife · doi:10.7554/elife.77514 · Cited by 6
Liu, T., Li, C., Zhang, R., Millender, E., Miao, H., Ormsbee, M. J., Guo, J., Westbrook, A. L., Pan, Y., Wang, J., et al. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01343-1 · Cited by 6
Housini, M., Zhou, Z., Gutierrez, J., Rao, S. R., Jomaa, R., Subasinghe, K., Reid, D. M., Silzer, T. K., Phillips, N., O’Bryant, S. E., et al. (2023) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12518 · Cited by 6
Acland, E. L., Pocuca, N., Paquin, S., Boivin, M., Ouellet-Morin, I., Andlauer, T. F. M., Gouin, J. P., Côté, S. M., Tremblay, R. E., Geoffroy, M., et al. (2025) · Development and Psychopathology · doi:10.1017/s095457942400004x · Cited by 6
Chen, P., Li, Y., Zadrozny, S., Seifer, R., & Belger, A. (2024) · Preventive Medicine · doi:10.1016/j.ypmed.2024.108048 · Cited by 6
Fetse, J., Olawode, E. O., & Deb, S. (2024) · European Journal of Drug Metabolism and Pharmacokinetics · doi:10.1007/s13318-024-00912-5 · Cited by 6
Signore, I. A., Donoso, G., Bocchieri, P., Tobar-Calfucoy, E. A., Yañez, C. M., Carvajal-Silva, L., Silva, A. X., Carola Otth, L., Cappelli, C., Jorquera, H. V., et al. (2024) · Genes · doi:10.3390/genes15111352 · Cited by 6
Leveraging haplotype information in heritability estimation and polygenic prediction
Meisner, J., Benros, M. E., & Rasmussen, S. (2025) · Nature Communications · doi:10.1038/s41467-024-55477-3 · Cited by 6
Genetic underpinnnings of type 2 diabetes
Shah, A., Bush, C. O., & Perry, R. J. (2025) · Advances in genetics · doi:10.1016/bs.adgen.2024.12.001 · Cited by 6
A multi-ancestry genome-wide association study and evaluation of polygenic scores of LDL-C levels
Umlai, U. I., Toor, S. M., Al-Sarraj, Y. A., Mohammed, S., Hail, M. S. H. A., Ullah, E., Kunji, K. B., El-Menyar, A. A., Gomaa, M., Jayyousi, A., et al. (2025) · Journal of Lipid Research · doi:10.1016/j.jlr.2025.100752 · Cited by 6
Wang, S., Feng, Y. A., Lin, M., Cheng, C., Su, M., Chen, C., Wu, C., & Fan, C. C. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.02.888 · Cited by 6
Gupta, H., Chandratre, K. R., Sinha, S., Huang, T., Wu, X., Cui, J., Zhang, M. Q., & Wang, S. M. (2020) · BMC Genomics · doi:10.1186/s12864-020-07222-5 · Cited by 6
Analytical protocol to identify local ancestry-associated molecular features in cancer
Carrot‐Zhang, J., Han, S., Zhou, W., Damrauer, J. S., Kemal, A., Berger, A. C., Meyerson, M. M., Hoadley, K. A., Felau, I., Johnson, R., et al. (2021) · STAR Protocols · doi:10.1016/j.xpro.2021.100766 · Cited by 6
Wickrama, K. A. S., O’Neal, C. W., Lee, T. K., & Lee, S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0256967 · Cited by 6
Mills, M. C. (2022) · Edward Elgar Publishing eBooks · doi:10.4337/9781789909432.00022 · Cited by 8
Toward a More Just System of Care in Molecular Pathology
Lilley, C. M., DELILLE, M., Mirza, K., & Parilla, M. (2022) · Milbank Quarterly · doi:10.1111/1468-0009.12587 · Cited by 7
Su, Y., Sakoda, L. C., Jeon, J., Thomas, M., Lin, Y., Schneider, J. L., Udaltsova, N. V., Lee, J. K., Lansdorp‐Vogelaar, I., Peterse, E. F. P., et al. (2023) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-22-0817 · Cited by 7
Smith, J. L., Schaid, D. J., & Kullo, I. J. (2023) · Current Atherosclerosis Reports · doi:10.1007/s11883-023-01104-3 · Cited by 6
Cross-ancestry genetic architecture and prediction for cholesterol traits
Momin, M. M., Zhou, X., Hyppönen, E., Benyamin, B., & Lee, S. (2024) · Human Genetics · doi:10.1007/s00439-024-02660-7 · Cited by 6
The Year In Genetic Anthropology: New Lands, New Technologies, New Questions
Gökçümen, Ö. (2018) · American Anthropologist · doi:10.1111/aman.13032 · Cited by 6
Passchier, R. V., Stein, D. J., Uhlmann, A., van der Merwe, C., & Dalvie, S. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.01018 · Cited by 6
Sidun, N. M., & Flores, Y. G. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108348607.019 · Cited by 6
Advances in clinical genetics and genomics
Zhao, S., Cheng, X., Wen, W., Qiu, G., Zhang, J., Wu, Z., & Wu, N. (2021) · Intelligent Medicine · doi:10.1016/j.imed.2021.03.005 · Cited by 8
Demographic history differences between Hispanics and Brazilians imprint haplotype features
da Cruz, P. R. S., Ananina, G., Secolin, R., Gil‐da‐Silva‐Lopes, V. L., Lima, C. S. P., de França, P. H. C., Donatti, A., Lourenço, G. J., de Araujo, T. K., Simioni, M., et al. (2022) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkac111 · Cited by 6
Genotype imputation and polygenic score estimation in northwestern Russian population
Kolosov, N., Rezapova, V., Rotar, O. P., Loboda, A., Freylikhman, O., Melnik, O., Sergushichev, A., Stevens, C. R., Voortman, T., Kostareva, A. A., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269434 · Cited by 6
Self-citationLiu, N., Sadlon, T. J., Wong, Y. Y., Pederson, S., Breen, J., & Barry, S. C. (2022) · Epigenetics & Chromatin · doi:10.1186/s13072-022-00456-5 · Cited by 6
Reyes, D. M., Bose, A., Karavani, E., & Parida, L. (2022) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811270611_0019 · Cited by 6
Bocher, O., Gilly, A., Park, Y., Zeggini, E., & Morris, A. P. (2023) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2023.100214 · Cited by 7
Analytic and Translational Genetics
Karczewski, K. J., & Martin, A. R. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-072018-021148 · Cited by 6
Self-citationGenetic Risks, Adolescent Health and Schooling Attainment
Amin, V., Behrman, J. R., Fletcher, J. M., Flores, C. A., Flores‐Lagunes, A., & Kohler, H. (2020) · Health Economics · doi:10.1002/hec.4421 · Cited by 6
Hanson, R. L., Hout, C. V. V., Hsueh, W., Shuldiner, A. R., Kobes, S., Sinha, M., Baier, L. J., & Knowler, W. C. (2020) · Diabetologia · doi:10.1007/s00125-020-05272-8 · Cited by 7
Human Rights and Reconciliation
Vélez, G., Twose, G. H. J., & López‐López, W. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108348607.037 · Cited by 6
Assessment of polygenic risk of hypertension
Limonova, A. S., Ershova, A. I., Kiseleva, A. V., Ramensky, V. E., Vyatkin, Y. V., Kutsenko, V. A., Meshkov, A. N., & Drapkina, O. M. (2023) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2022-3464 · Cited by 6
Domingue, B. W., Belsky, D. W., Fletcher, J. M., Conley, D., Boardman, J. D., & Harris, K. M. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/107045 · Cited by 6
Lebovitch, D. S., Johnson, J., Duenas, H. R., & Huckins, L. M. (2021) · medRxiv · doi:10.1101/2021.01.25.21249615 · Cited by 6
Beauchamp, J. P., Schmitz, L., McGue, M., & Lee, J. J. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/d93ax · Cited by 6
Francis, M., Gorman, B. R., Bigdeli, T. B., Genovese, G., Voloudakis, G., Bendl, J. M., Zeng, B., Venkatesh, S., Chatzinakos, C., Hoffman, G. E., et al. (2024) · medRxiv · doi:10.1101/2024.04.24.24306301 · Cited by 6
Campbell, C., Lewis-Smith, D., Leu, C., Martins, H., Wolking, S., Krause, R., O’Brien, T., Sill, G., Zara, F., Koeleman, B., et al. (2023) · medRxiv · doi:10.1101/2023.07.04.23292071 · Cited by 6
The Geneticization of Education and Its Bioethical Implications
Matthews, L. J. (2024) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s096318012400046x · Cited by 5
Power and Limitations of Inferring Genetic Ancestry
Bird, N., King, T., & Hellenthal, G. (2025) · Annals of Human Genetics · doi:10.1111/ahg.70007 · Cited by 5
Fu, B., Pazokitoroudi, A., Shi, Z., Kar, A., Xue, A., Anand, A., Anand, P., Liu, Z., Border, R., Pajukanta, P., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02411-y · Cited by 5
Shin, S. H., Wright, C., & Johnston, S. S. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.772841 · Cited by 5
Identifying signatures of positive selection in human populations from North Africa
Caro-Consuegra, R., Lucas-Sánchez, M., Comas, D., & Bosch, E. (2023) · Scientific Reports · doi:10.1038/s41598-023-35312-3 · Cited by 5
The Inclusion of Underrepresented Populations in Cardiovascular Genetics and Epidemiology
Chappell, E., Arbour, L. T., & Laksman, Z. (2024) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd11020056 · Cited by 5
Huider, F., Milaneschi, Y., Hottenga, J., Bot, M., Rietman, M. L., Kok, A. A. L., Galesloot, T. E., Hart, L. M. ‘., Rutters, F., Blom, M. T., et al. (2024) · Twin Research and Human Genetics · doi:10.1017/thg.2024.4 · Cited by 5
Paul, S. E., Baranger, D. A. A., Johnson, E. C., Jackson, J. J., Gorelik, A. J., Miller, A. P., Hatoum, A. S., Thompson, W. K., Strube, M., Dick, D. M., et al. (2024) · Psychological Medicine · doi:10.1017/s003329172400076x · Cited by 6
Lin, L., Andersen, M. K., Stæger, F. F., Li, Z., Hanghøj, K., Linneberg, A. R., Grarup, N., Jørgensen, M. E., Hansen, T. F., Moltke, I., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01326-3 · Cited by 5
Añorve-Garibay, V., Huerta‐Sánchez, E., Sohail, M., & Vecchyo, D. O.-D. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.05.009 · Cited by 6
Nierenberg, J. L., Adamson, A. W., Hu, D., Huntsman, S., Patrick, C. S., Li, M., Steele, L., Tao, S., Ding, Y. C., Tong, B., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60564-0 · Cited by 5
Okorie, M. C., Jonson, C., Oddi, A. P., Castruita, P. A., Fulton‐Howard, B., Yaffe, K. S., Yokoyama, J. S., Udeh‐Momoh, C. T., Andrews, S. J., & for the Alzheimer's Disease Sequencing Project and the Healthy Aging Brain Study—Health Disparities (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71529 · Cited by 6
Boahen, C. K., Joosten, L. A. B., Netea, M. G., & Kumar, V. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06755 · Cited by 5
Shin, S. H. (2021) · The Journal of the Economics of Ageing · doi:10.1016/j.jeoa.2021.100327 · Cited by 5
Revisiting the effect of retirement on Cognition: Heterogeneity and endowment
Jung, D., Lee, J., & Meijer, E. (2021) · The Journal of the Economics of Ageing · doi:10.1016/j.jeoa.2021.100361 · Cited by 5
Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem
Dyke, S. O. M., Connor, K., Nembaware, V., Munung, N. S., Reinold, K., Kerry, G., Mbiyavanga, M., Zass, L. J., Moldes, M., Das, S., et al. (2022) · Neuroinformatics · doi:10.1007/s12021-022-09577-4 · Cited by 5
Global diversity in individualized cortical network topography
Yang, G., Božek, J., Noble, S., Han, M., Xin, W., Xue, M., Kang, J., Jia, T., Fu, J., Ge, J., et al. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad002 · Cited by 5
FGFR2 genetic variants in women with breast cancer
Dix‐Peek, T., Dickens, C., Augustine, T. N., Phakathi, B., van den Berg, E. J., Joffe, M., Ayeni, O., Cubasch, H., Nietz, S., Mathew, C. G., et al. (2023) · Molecular Medicine Reports · doi:10.3892/mmr.2023.13113 · Cited by 5
Lai, R., Su, M., Lin, K., Chen, C., Pan, Y., Hsiao, P., Chen, P., Huang, Y., Wu, C., & Wang, S. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.10.141 · Cited by 5
Personalized prevention: possibilities and limitations of polygenic risk assessment
Drapkina, O. M., Limonova, A. S., Garbuzova, E. V., Meshkov, A. N., & Ershova, A. I. (2023) · Russian Journal for Personalized Medicine · doi:10.18705/2782-3806-2023-3-5-14-21 · Cited by 5
Genome‐wide analysis of spina bifida risk variants in a case–control study from Bangladesh
Tindula, G. N., Issac, B., Mukherjee, S. K., Ekramullah, S. M., Arman, D., Islam, J., Suchanda, H. S., Sun, L., Rockowitz, S., Christiani, D. C., et al. (2024) · Birth Defects Research · doi:10.1002/bdr2.2331 · Cited by 5
Azhuvalappil, S., Prasad, R., Sahadevan, P., Chatterjee, P. B., Pradhan, H., Rai, P., Gupta, A., Kommaddi, R. P., Issac, T. G., & Sundarakumar, J. S. (2024) · Metabolism Open · doi:10.1016/j.metop.2024.100301 · Cited by 5
The Quantitative Genetics of Human Disease: 2 Polygenic Risk Scores
Cutler, D. J., Jodeiry, K., Bass, A. J., & Epstein, M. P. (2024) · Human Population Genetics and Genomics · doi:10.47248/hpgg2404030008 · Cited by 5
Piffer, D. (2025) · Twin Research and Human Genetics · doi:10.1017/thg.2024.49 · Cited by 5
Breinholt, A., Ware, E. B., Fomby, P., Notterman, D. A., Schneper, L. M., & Mitchell, C. M. (2025) · Longitudinal and Life Course Studies · doi:10.1332/17579597y2025d000000042 · Cited by 5
Polimanti, R. A., Kayser, M., & Gelernter, J. E. (2018) · Genome Medicine · doi:10.1186/s13073-018-0532-7 · Cited by 6
Topological linkage disequilibrium calculated from coalescent genealogies
Wirtz, J., Rauscher, M., & Wiehe, T. (2018) · Theoretical Population Biology · doi:10.1016/j.tpb.2018.09.001 · Cited by 5
Poverty and the Human Rights of Children and Youth through the Lenses of Psychology and Sociology
Gilles, J. K.-J., Pells, K., Morrow, V., & Ruck, M. D. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108348607.007 · Cited by 5
Local ancestry inference provides insight into Tilapia breeding programmes
Avallone, A., Bartie, K. L., Selly, S.-L. C., Taslima, K., Mendoza, A. C., & Bekaert, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-75744-9 · Cited by 5
Allele frequency differentiation at height-associated SNPs among continental human populations
Chen, M., & Chiang, C. W. K. (2021) · European Journal of Human Genetics · doi:10.1038/s41431-021-00938-2 · Cited by 5
Underrepresented Populations at the Archaic Introgression Frontier
Villanea, F. A., & Witt, K. E. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.821170 · Cited by 5
Polygenic risk scores for prediction of breast cancer in Korean women
Jee, Y. H., Ho, W.-K., Park, S., Easton, D. F., Teo, S., Jung, K. J., & Kraft, P. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyac206 · Cited by 5
Genome-wide meta-analysis identifies new candidate genes for sickle cell disease nephropathy
Garrett, M. E., Soldano, K. L., Erwin, K. N., Zhang, Y., Gordeuk, V. R., Gladwin, M. T., Telen, M. J., & Ashley‐Koch, A. E. (2022) · Blood Advances · doi:10.1182/bloodadvances.2022007451 · Cited by 5
Reconstructing recent population history while mapping rare variants using haplotypes
Yunusbaev, U., Valeev, A., Yunusbaeva, M. M., Kwon, H. W., Mägi, R., Metspalu, M., & Yunusbayev, B. (2019) · Scientific Reports · doi:10.1038/s41598-019-42385-6 · Cited by 5
Nature vs. Nurture in Precision Education: Insights of Parents and the Public
Sabatello, M., Martin, B. E., Corbeil, T., Lee, S., Link, B. G., & Appelbaum, P. S. (2021) · AJOB Empirical Bioethics · doi:10.1080/23294515.2021.1983666 · Cited by 5
Tao, L., Tang, S., & Wen, B. (2023) · Public Administration Review · doi:10.1111/puar.13744 · Cited by 7
Assari, S., Javanbakht, A., Saqib, M., Helmi, H., Bazargan, M., & Smith, J. A. (2019) · Journal of Medical Research and Innovation · doi:10.32892/jmri.183 · Cited by 5
The Australian Psychological Society’s Apology to Aboriginal and Torres Strait Islander People
Dudgeon, P., Carey, T. A., Hammond, S., Hirvonen, T., Kyrios, M., Roufeil, L., & Smith, P. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108348607.038 · Cited by 5
He, Y., Lakhani, C. M., Manrai, A. K., & Patel, C. J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/833632 · Cited by 5
Genome studies must account for history—Response
Ganna, A., Verweij, K. J. H., Nivard, M. G., Maier, R., Wedow, R., Busch, A. S., Abdellaoui, A., Guo, S., Sathirapongsasuti, J. F., 23andMe Research Team, et al. (2019) · Science · doi:10.1126/science.aaz8941 · Cited by 5
Self-citationShin, S. H., Lillard, D. R., & Bhattacharya, J. (2019) · National Bureau of Economic Research · doi:10.3386/w25526 · Cited by 5
The heritability and molecular genetics of mental disorders
de Wit, M. M., & Polderman, T. J. C. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91497-0.00160-0 · Cited by 5
Li, X., Zhou, Z., Ma, Y., Ding, K., Xiao, H., Chen, D., & Liu, N. (2024) · Biomedicines · doi:10.3390/biomedicines12061243 · Cited by 4
Schoolhouse risk: Can we mitigate the polygenic Pygmalion effect?
Matthews, L. J., Zhang, Z., & Martschenko, D. O. (2024) · Acta Psychologica · doi:10.1016/j.actpsy.2024.104403 · Cited by 4
Zhong, M., Onyenobi, E., Duomatey, A., Chen, G., Perry, J. A., Ye, Z., Rotimi, C. N., Adebamowo, C., Adeyemo, A., & Adebamowo, S. N. (2024) · Obesity · doi:10.1002/oby.24123 · Cited by 4
Jouabadi, S. M., Peymani, P., Shahraki, M. N., van Rooij, J., Broer, L., Roks, A. J. M., Stricker, B. H. C., & Ahmadizar, F. (2024) · The Pharmacogenomics Journal · doi:10.1038/s41397-024-00352-z · Cited by 4
The Black and African American Connections to Parkinson’s Disease (BLAAC PD) study protocol
Chahine, L. M., Louie, N., Solle, J. C., Akçimen, F., Ameri, A., Augenbraun, S., Avripas, S. A., Breaux, S., Causey, C. D., Chandra, S., et al. (2024) · BMC Neurology · doi:10.1186/s12883-024-03914-7 · Cited by 4
Lin, T.-Y., Wang, C., Chen, L. R., & Huang, S. (2025) · Ophthalmic Genetics · doi:10.1080/13816810.2025.2464843 · Cited by 4
Advances in magnetic resonance imaging of the developing brain and its applications in pediatrics
Chen, R., Li, M., Zhao, Z., Xu, H., Ning, C.-L., Lu, J., Zhu, Q.-F., Huang, Y., Zhao, R., Zhang, L.-X., et al. (2025) · World Journal of Pediatrics · doi:10.1007/s12519-025-00905-7 · Cited by 5
Bogumil, D. D., Sheng, X., Wan, P. C., Xia, L. Y., Pooler, L., Cheng, I. C., Streicher, S. A., Huang, B. Z., Chen, F., Stram, D. O., et al. (2026) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-25-1458 · Cited by 4
Bracher‐Smith, M., & Escott‐Price, V. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03330-4 · Cited by 5
Pomales-Matos, D. A., Lyerly, M., Rivera-Madera, A., Echevarría-Bonilla, O. L., Alvarez-Cortes, M., Henriquez-Quiñones, S. E., Reyes-Sosa, G. M., Villanueva-Nogueras, R. A., & Peña-Martínez, E. G. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2025.1647176 · Cited by 4
Chen, T., Zhang, H., Mazumder, R., & Lin, X. (2026) · Nature Methods · doi:10.1038/s41592-026-03235-2 · Cited by 4
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
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
