Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
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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 has been an important contributor to modern human genetic variation.
“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
The issues of Eurocentric GWAS biases and imputation panel biases are insurmountable through statistical methods alone, but motivate substantial investments in more diverse populations to produce similar-sized GWAS of biomedical phenotypes in other populations.
“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]
The scientific basis of the imbalance in PRS accuracy has been demonstrated theoretically, in simulations, and empirically across many traits and diseases.
“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]
PRS are many-fold more accurate in European ancestry individuals than in others, and this disparity is the major ethical and scientific challenge surrounding clinical implementation.
“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]
The most significant associations tend to be more common in the populations in which they are discovered than elsewhere.
“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]
Population genetics theory predicts that genetic risk prediction accuracy will decay with increasing genetic divergence between the original GWAS sample and the target of prediction, as a function of population history.
“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]
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 there.
“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]
PRS are uniformly less useful in understudied populations due to differences in genomic variation and population history.
“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]
Because the discovery sample consisted of individuals of European ancestry, the predictive power of the score is not expected to be as high in other ancestry groups.
“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]
A third limit to transferability of effect estimates arises from population differences in linkage disequilibrium patterns that influence aggregations of signals across loci.
“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]
Incorrect inference from GRS can readily occur if the training and target datasets are from different ancestries.
“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
Because allele frequencies, linkage disequilibrium patterns, and effect sizes vary with ancestry, the specific GPS will not have optimal predictive power for other ethnic groups.
“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]
Genetic risk prediction is strongly affected by population structure.
“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
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Common SNPs explain a large proportion of the heritability for human height
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446 · see citing works
Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S., & Manolio, T. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903103106 · Cited by 4,013 · see citing works
A Large-Scale, Consortium-Based Genomewide Association Study of Asthma
Moffatt, M. F., Gut, I. G., Démenais, F., Strachan, D. P., Bouzigon, E., Heath, S. C., von Mutius, E., Farrall, M., Lathrop, M. G., & Cookson, W. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa0906312 · Cited by 2,003 · see citing works
Hundreds of variants clustered in genomic loci and biological pathways affect human height
Lango Allen, H., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., Willer, C. J., Jackson, A. U., Vedantam, S., Raychaudhuri, S., et al. (2010) · Nature · doi:10.1038/nature09410 · Cited by 1,945 · see citing works
Genomics is failing on diversity
Popejoy, A. B., & Fullerton, S. M. (2016) · Nature · doi:10.1038/538161a · Cited by 1,924 · see citing works
The Electronic Medical Records and Genomics (eMERGE) Consortium, Wood, A. R., The MIGen Consortium, The PAGE Consortium, Study, T. L. C., Esko, T., Yang, J., Vedantam, S., Pers, T. H., Gustafsson, S., et al. (2014) · Nature Genetics · doi:10.1038/ng.3097 · Cited by 1,853 · see citing works
Evolution and functional impact of rare coding variation from deep sequencing of human exomes
Tennessen, J. A., Bigham, A. W., O’Connor, T. D., Fu, W., Kenny, E. E., Gravel, S., McGee, S., Do, R., Liu, X., Jun, G., et al. (2012) · Science · doi:10.1126/science.1219240 · Cited by 1,613 · see citing works
Self-citationNew genetic loci link adipose and insulin biology to body fat distribution
The ADIPOGen Consortium, Shungin, D., The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The GEFOS Consortium, The GENIE Consortium, The, G. L. G. C., The, I. C. B. P., The International Endogene Consortium, Study, T. L. C., et al. (2015) · Nature · doi:10.1038/nature14132 · Cited by 1,580 · see citing works
The Genetic Structure and History of Africans and African Americans
Tishkoff, S. A., Reed, F. A., Friedlaender, F. R., Ehret, C., Ranciaro, A., Froment, A., Hirbo, J. B., Awomoyi, A. A., Bodo, J.-M., Doumbo, O., et al. (2009) · Science · doi:10.1126/science.1172257 · Cited by 1,573 · see citing works
Power and predictive accuracy of polygenic risk scores
Dudbridge, F. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003348 · Cited by 1,509 · see citing works
Genes mirror geography within Europe
Novembre, J., Johnson, T., Bryc, K., Kutalik, Z., Boyko, A. R., Auton, A., Indap, A., King, K. S., Bergmann, S., Nelson, M. R., et al. (2008) · Nature · doi:10.1038/nature07331 · Cited by 1,479 · see citing works
Self-citationModeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
Vilhjálmsson, B. J., Yang, J., Finucane, H. K., Gusev, A., Lindström, S., Ripke, S., Genovese, G., Loh, P.-R., Bhatia, G., Do, R., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.09.001 · Cited by 1,303 · see citing works
Self-citationGenome-wide association study identifies 74 loci associated with educational attainment
Okbay, A., Beauchamp, J. P., Fontana, M. A., Lee, J. J., Pers, T. H., Rietveld, C. A., Turley, P., Chen, G.-B., Emilsson, V., Meddens, S. F. W., et al. (2016) · Nature · doi:10.1038/nature17671 · Cited by 1,297 · see citing works
A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes
MacArthur, D. G., Balasubramanian, S., Frankish, A., Huang, N., Morris, J., Walter, K., Jostins, L., Habegger, L., Pickrell, J. K., Montgomery, S. B., et al. (2012) · Science · doi:10.1126/science.1215040 · Cited by 1,272 · see citing works
New approaches to population stratification in genome-wide association studies
Price, A. L., Zaitlen, N., Reich, D., & Patterson, N. J. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2813 · Cited by 1,181 · see citing works
PRSice: Polygenic Risk Score software
Euesden, J., Lewis, C. M., & O’Reilly, P. F. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu848 · Cited by 1,149 · see citing works
The UK10K project identifies rare variants in health and disease
The UK10K Consortium, Writing group, Walter, K., Min, J. L., Huang, J., Crooks, L., Memari, Y., McCarthy, S., Perry, J. R. B., Xu, C., et al. (2015) · Nature · doi:10.1038/nature14962 · Cited by 1,040 · see citing works
DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Mahajan, A., Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes (SAT2D) Consortium, Mexican American Type 2 Diabetes (MAT2D) Consortium, Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Consortium, Go, M. J., Zhang, W., Below, J. E., Gaulton, K. J., et al. (2014) · Nature Genetics · doi:10.1038/ng.2897 · Cited by 1,034 · see citing works
RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference
Maples, B. K., Gravel, S., Kenny, E. E., & Bustamante, C. D. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.06.020 · Cited by 900 · see citing works
Self-citationReconstructing Native American population history
Reich, D., Patterson, N. J., Campbell, D., Tandon, A., Mazières, S., Ray, N., Parra, M. V., Rojas, W., Duque, C., Mesa, N., et al. (2012) · Nature · doi:10.1038/nature11258 · Cited by 849 · see citing works
(MCAAS), M. C. C. A. S., Ober, C., study, C. H. S. (. A. H., Genetics of Asthma in Latino Americans (GALA) Study, T. S. O. G.-E. A. A. I. L. A. (. A. T. S. O. A. A., (CAMP), C. A. M. P., (SAPPHIRE), S. O. A. P. A. P. I. B. R.-E., & Nicolae, D. L. (2011) · Nature Genetics · doi:10.1038/ng.888 · Cited by 833 · see citing works
Genetic Misdiagnoses and the Potential for Health Disparities
Manrai, A. K., Funke, B. H., Rehm, H. L., Olesen, M. S., Maron, B. A., Szolovits, P., Margulies, D. M., Loscalzo, J., & Kohane, I. S. (2016) · New England Journal of Medicine · doi:10.1056/nejmsa1507092 · Cited by 808 · see citing works
Walsh, R., Thomson, K. L., Ware, J. S., Funke, B., Woodley, J., McGuire, K., Mazzarotto, F., Blair, E. M., Seller, A., Taylor, J. C., et al. (2016) · Genetics in Medicine · doi:10.1038/gim.2016.90 · Cited by 733 · see citing works
Prediction of individual genetic risk to disease from genome-wide association studiesPreprint
Wray, N. R., Goddard, M. E., & Visscher, P. M. (2007) · Genome Research · doi:10.1101/gr.6665407 · Cited by 714 · see citing works
Developing and evaluating polygenic risk prediction models for stratified disease prevention
Chatterjee, N., Shi, J., & García‐Closas, M. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.27 · Cited by 698 · see citing works
Pitfalls of predicting complex traits from SNPs
Wray, N. R., Yang, J., Hayes, B. J., Price, A. L., Goddard, M. E., & Visscher, P. M. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3457 · Cited by 672 · see citing works
Estimation of individual admixture: Analytical and study design considerations
Tang, H., Peng, J., Wang, P., & RISCH, N. J. (2005) · Genetic Epidemiology · doi:10.1002/gepi.20064 · Cited by 672 · see citing works
Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes
Kelleher, J., Etheridge, A. M., & McVean, G. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004842 · Cited by 660 · see citing works
Research Review: Polygenic methods and their application to psychiatric traits
Wray, N. R., Lee, S., Mehta, D., Vinkhuyzen, A. A. E., Dudbridge, F., & Middeldorp, C. M. (2014) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12295 · Cited by 657 · see citing works
Demographic history and rare allele sharing among human populations
Gravel, S., Henn, B. M., Gutenkunst, R. N., Indap, A. R., Marth, G., Clark, A. G., Yu, F., Gibbs, R. A., The 1000 Genomes Project, Bustamante, C. D., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1019276108 · Cited by 625 · see citing works
Self-citationPolygenes, Risk Prediction, and Targeted Prevention of Breast Cancer
Pharoah, P. D. P., Antoniou, A. C., Easton, D. F., & Ponder, B. A. J. (2008) · New England Journal of Medicine · doi:10.1056/nejmsa0708739 · Cited by 616 · see citing works
Admixture mapping identifies 8q24 as a prostate cancer risk locus in African-American men
Freedman, M. L., Haiman, C. A., Patterson, N., McDonald, G. J., Tandon, A., Waliszewska, A., Penney, K., Steen, R. G., Ardlie, K., John, E. M., et al. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605832103 · Cited by 612 · see citing works
Bustamante, C. D., Vega, F. M. D. L., & Burchard, E. G. (2011) · Nature · doi:10.1038/475163a · Cited by 601 · see citing works
Self-citationA General Approach for Haplotype Phasing across the Full Spectrum of Relatedness
O'Connell, J., Gurdasani, D., Delaneau, O., Pirastu, N., Ulivi, S., Cocca, M., Traglia, M., Huang, J., Huffman, J. E., Rudan, I., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004234 · Cited by 593 · see citing works
Genome-wide association studies in diverse populations
Rosenberg, N. A., Huang, L., Jewett, E. M., Szpiech, Z. A., Jankovic, I., & Boehnke, M. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2760 · Cited by 566 · see citing works
Genomic inflation factors under polygenic inheritance
Yang, J., Weedon, M. N., Purcell, S., Lettre, G., Estrada, K., Willer, C. J., Smith, A. V., Ingelsson, E., O’Connell, J. R., Mangino, M., et al. (2011) · European Journal of Human Genetics · doi:10.1038/ejhg.2011.39 · Cited by 560 · see citing works
Sensitive detection of chromosomal segments of distinct ancestry in admixed populations
Price, A. L., Tandon, A., Patterson, N., Barnes, K. C., Rafaels, N., Ruczinski, I., Beaty, T. H., Mathias, R., Reich, D., & Myers, S. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000519 · Cited by 539 · see citing works
A Composite of Multiple Signals Distinguishes Causal Variants in Regions of Positive Selection
Grossman, S. R., Shylakhter, I., Karlsson, E. K., Byrne, E. H., Morales, S., Frieden, G., Hostetter, E., Angelino, E., Garber, M., Zuk, O., et al. (2010) · Science · doi:10.1126/science.1183863 · Cited by 512 · see citing works
The genetics of Mexico recapitulates Native American substructure and affects biomedical traits
Moreno-Estrada, A., Gignoux, C. R., Fernández-López, J. C., Zakharia, F., Sikora, M., Contreras, A. V., Acuña-Alonzo, V., Sandoval, K., Eng, C., Romero-Hidalgo, S., et al. (2014) · Science · doi:10.1126/science.1251688 · Cited by 478 · see citing works
Self-citationGenome-wide patterns of population structure and admixture among Hispanic/Latino populations
Bryc, K., Velez, C., Karafet, T., Moreno-Estrada, A., Reynolds, A., Auton, A., Hammer, M., Bustamante, C. D., & Ostrer, H. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914618107 · Cited by 442 · see citing works
Self-citationRuiz-Linares, A., Adhikari, K., Acuña-Alonzo, V., Quinto-Sanchez, M., Jaramillo, C., Arias, W., Fuentes, M., Pizarro, M., Everardo, P., de Avila, F., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004572 · Cited by 442 · see citing works
Quantifying prion disease penetrance using large population control cohorts
Minikel, E. V., Vallabh, S. M., Lek, M., Estrada, K., Samocha, K. E., Sathirapongsasuti, J. F., McLean, C. Y., Tung, J. Y., Yu, L. P. C., Gambetti, P., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aad5169 · Cited by 442 · see citing works
Self-citationNext generation disparities in human genomics: concerns and remedies
Need, A. C., & Goldstein, D. B. (2009) · Trends in Genetics · doi:10.1016/j.tig.2009.09.012 · Cited by 398 · see citing works
Model-free Estimation of Recent Genetic Relatedness
Conomos, M. P., Reiner, A. P., Weir, B. S., & Thornton, T. A. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.11.022 · Cited by 397 · see citing works
Gaulton, K. J., Ferreira, T. C., Lee, Y., Raimondo, A., Mägi, R., Reschen, M. E., Mahajan, A., Locke, A. E., Rayner, N. W., Robertson, N., et al. (2015) · Nature Genetics · doi:10.1038/ng.3437 · Cited by 390 · see citing works
Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis
Diabetes Genetics Replication and Meta-analysis Consortium, Stahl, E. A., Myocardial Infarction Genetics Consortium, Wegmann, D., Trynka, G., Gutierrez-Achury, J., Do, R., Voight, B. F., Kraft, P., Chen, R., et al. (2012) · Nature Genetics · doi:10.1038/ng.2232 · Cited by 388 · see citing works
Differential confounding of rare and common variants in spatially structured populations
Mathieson, I., & McVean, G. (2012) · Nature Genetics · doi:10.1038/ng.1074 · Cited by 385 · see citing works
Proportionally more deleterious genetic variation in European than in African populations
Lohmueller, K. E., Indap, A. R., Schmidt, S., Boyko, A. R., Hernandez, R. D., Hubisz, M. J., Sninsky, J. J., White, T. J., Sunyaev, S. R., Nielsen, R., et al. (2008) · Nature · doi:10.1038/nature06611 · Cited by 374 · see citing works
Self-citationReduced signal for polygenic adaptation of height in UK Biobank
Berg, J. J., Harpak, A., Sinnott-Armstrong, N., Joergensen, A. M., Mostafavi, H., Field, Y., Boyle, E. A., Zhang, X., Racimo, F., Pritchard, J. K., et al. (2019) · eLife · doi:10.7554/elife.39725 · Cited by 339 · see citing works
Reconstructing the population genetic history of the Caribbean
Moreno-Estrada, A., Gravel, S., Zakharia, F., McCauley, J. L., Byrnes, J. K., Gignoux, C. R., Ortiz-Tello, P. A., Martínez, R. J., Hedges, D. J., Morris, R. W., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003925 · Cited by 327 · see citing works
Self-citationA Genomewide Association Study of Skin Pigmentation in a South Asian Population
Stokowski, R. P., Pant, P. V. K., Dadd, T., Fereday, A., Hinds, D. A., Jarman, C., Filsell, W., Ginger, R. S., Green, M. R., van der Ouderaa, F. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/522235 · Cited by 315 · see citing works
Population Genetics Models of Local Ancestry
Gravel, S. (2012) · Genetics · doi:10.1534/genetics.112.139808 · Cited by 311 · see citing works
Self-citationEvans, D. M., Visscher, P. M., & Wray, N. R. (2009) · Human Molecular Genetics · doi:10.1093/hmg/ddp295 · Cited by 297 · see citing works
The deleterious mutation load is insensitive to recent population history.
Simons, Y. B., Turchin, M. C., Pritchard, J. K., & Sella, G. (2014) · Nature Genetics · doi:10.1038/ng.2896 · Cited by 279 · see citing works
Carlson, C. S., Matise, T. C., North, K. E., Haiman, C. A., Fesinmeyer, M. D., Buyske, S. G., Schumacher, F. R., Peters, U., Franceschini, N., Ritchie, M. D., et al. (2013) · PLoS Biology · doi:10.1371/journal.pbio.1001661 · Cited by 258 · see citing works
Distance from sub-Saharan Africa predicts mutational load in diverse human genomes
Henn, B. M., Botigué, L. R., Peischl, S., Dupanloup, I., Lipatov, M., Maples, B. K., Martin, A. R., Musharoff, S. A., Cann, H. M., Snyder, M. P., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510805112 · Cited by 257 · see citing works
Self-citationPetrovski, S., & Goldstein, D. B. (2016) · Genome Biology · doi:10.1186/s13059-016-1016-y · Cited by 255 · see citing works
A genomewide admixture mapping panel for Hispanic/Latino populations.
Mao, X., Bigham, A. W., Mei, R., Gutiérrez, G., Weiss, K., Brutsaert, T., Lèon-Velarde, F., Moore, L. G., Vargas, E., McKeigue, P., et al. (2007) · American Journal of Human Genetics · doi:10.1086/518564 · Cited by 254 · see citing works
Fast and accurate inference of local ancestry in Latino populations
Baran, Y., Paşaniuc, B., Sankararaman, S., Torgerson, D. G., Gignoux, C. R., Eng, C. S., Rodríguez-Cintrón, W., Chapela, R., Ford, J. G., Avila, P. C., et al. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts144 · Cited by 243 · see citing works
Self-citationMaier, R., Moser, G., Chen, G., Ripke, S., Absher, D., Agartz, I., Akil, H., D, F. A. M., Andreassen, O. A., Anjorin, A., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.12.006 · Cited by 227 · see citing works
Self-citationLi, Y. R., & Keating, B. J. (2014) · Genome Medicine · doi:10.1186/s13073-014-0091-5 · Cited by 214 · see citing works
Human size evolution: no evolutionary allometric relationship between male and female stature
Gustafsson, A., & Lindenfors, P. (2004) · Journal of Human Evolution · doi:10.1016/j.jhevol.2004.07.004 · Cited by 203 · see citing works
Adhikari, K., Fontanil, T., Cal, S., Mendoza‐Revilla, J., Fuentes‐Guajardo, M., Chacón-Duque, J. C., Al-Saadi, F. A., Johansson, J. A., Quinto‐Sánchez, M., Acuña-Alonzo, V., et al. (2016) · Nature Communications · doi:10.1038/ncomms10815 · Cited by 195 · see citing works
Waters, K. M., Stram, D. O., Hassanein, M. T., Le Marchand, L., Wilkens, L. R., Maskarinec, G., Monroe, K. R., Kolonel, L. N., Altshuler, D., Henderson, B. E., et al. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001078 · Cited by 191 · see citing works
The Great Migration and African-American Genomic Diversity
Baharian, S., Barakatt, M., Gignoux, C., Shringarpure, S., Errington, J., Blot, W., Bustamante, C., Kenny, E., Williams, S. M., Aldrich, M., et al. (2015) · bioRxiv · doi:10.1371/journal.pgen.1006059 · Cited by 191 · see citing works
Self-citationCharacterizing the admixed African ancestry of African Americans
Zakharia, F., Basu, A., Absher, D., Assimes, T. L., Go, A. S., Hlatky, M. A., Iribarren, C., Knowles, J. W., Li, J., Narasimhan, B., et al. (2009) · Genome Biology · doi:10.1186/gb-2009-10-12-r141 · Cited by 185 · see citing works
Reconstructing Native American Migrations from Whole-Genome and Whole-Exome Data
Gravel, S., Zakharia, F., Moreno‐Estrada, A., Byrnes, J., Muzzio, M., Rodríguez-Flores, J. L., Kenny, E. E., Gignoux, C. R., Maples, B. K., Guiblet, W. M., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1004023 · Cited by 179 · see citing works
Self-citationEvidence for Polygenic Susceptibility to Multiple Sclerosis—The Shape of Things to Come
The International Multiple Sclerosis Genetics Consortium (IMSGC) (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.02.027 · Cited by 166 · see citing works
A continuum of admixture in the Western Hemisphere revealed by the African Diaspora genome
Mathias, R. A., Taub, M. A., Gignoux, C. R., Fu, W., Musharoff, S. A., O’Connor, T. D., Vergara, C., Torgerson, D. G., Pino-Yanes, M. D. M., Shringarpure, S., et al. (2016) · Nature Communications · doi:10.1038/ncomms12522 · Cited by 165 · see citing works
Self-citationDumitrescu, L. C., Carty, C. L., Taylor, K. C., Schumacher, F. R., Hindorff, L. A., Ambite, J. L., Anderson, G. L., Best, L. G., Brown-Gentry, K. D., Bůžková, P., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002138 · Cited by 158 · see citing works
Dudbridge, F. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21966 · Cited by 156 · see citing works
Demography and the Age of Rare Variants
Mathieson, I., & McVean, G. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004528 · Cited by 146 · see citing works
Leveraging Genetic Variability across Populations for the Identification of Causal Variants
Zaitlen, N., Paşaniuc, B., Gur, T., Ziv, E., & Halperin, E. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.11.016 · Cited by 146 · see citing works
Fejerman, L., Ahmadiyeh, N., Hu, D., Huntsman, S., Beckman, K. B., Caswell-Jin, J. L., Tsung, K., John, E. M., Torres-Mejı́a, G., Carvajal‐Carmona, L. G., et al. (2014) · Nature Communications · doi:10.1038/ncomms6260 · Cited by 144 · see citing works
Self-citationExome sequencing and complex disease: practical aspects of rare variant association studies
Do, R., Kathiresan, S., & Abecasis, G. R. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds387 · Cited by 138 · see citing works
Using Genetic Distance to Infer the Accuracy of Genomic Prediction
Scutari, M., Mackay, I., & Balding, D. (2016) · PLOS Genetics · doi:10.1371/journal.pgen.1006288 · Cited by 137 · see citing works
Shi, J., Park, J., Duan, J., Berndt, S. I., Moy, W., Yu, K. F., Song, L., Wheeler, W., Hua, X., Silverman, D. T., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006493 · Cited by 128 · see citing works
Murakami, T., Yang, S., Xie, L., Kawano, T., Fu, D., Mukai, A., Böhm, C., Chen, F., Robertson, J., Suzuki, H., et al. (2011) · Human Molecular Genetics · doi:10.1093/hmg/ddr417 · Cited by 125 · see citing works
Paşaniuc, B., Zaitlen, N., Lettre, G., Chen, G. K., Tandon, A., Kao, W. H. L., Ruczinski, I., Fornage, M., Siscovick, D. S., Zhu, X., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001371 · Cited by 122 · see citing works
Genome-wide ancestry of 17th-century enslaved Africans from the Caribbean
Schroeder, H., Ávila‐Arcos, M. C., Malaspinas, A., Poznik, G. D., Sandoval‐Velasco, M., Carpenter, M. L., Moreno-Mayar, J. V., Sikora, M., Johnson, P. L. F., Allentoft, M. E., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421784112 · Cited by 114 · see citing works
Self-citationMuñoz, M., Pong‐Wong, R., Canela‐Xandri, O., Rawlik, K., Haley, C., & Tenesa, A. (2016) · Nature Genetics · doi:10.1038/ng.3618 · Cited by 108 · see citing works
Geographic Variation and Bias in the Polygenic Scores of Complex Diseases and Traits in Finland
Kerminen, S., Martin, A. R., Koskela, J. T., Ruotsalainen, S., Havulinna, A. S., Surakka, I. L., Palotie, A. V., Perola, M., Salomaa, V. V., Daly, M. J., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.05.001 · Cited by 108 · see citing works
Self-citationInterpreting polygenic scores, polygenic adaptation, and human phenotypic differences
Rosenberg, N. A., Edge, M. D., Pritchard, J. K., & Feldman, M. W. (2019) · Evolution, Medicine, and Public Health · doi:10.1093/emph/eoy036 · Cited by 105 · see citing works
N’Diaye, A., Chen, G. K., Palmer, C. D., Ge, B., Tayo, B. O., Mathias, R. A., Ding, J., Nalls, M. A., Adeyemo, A., Adoue, V., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002298 · Cited by 98 · see citing works
Tread Lightly Interpreting Polygenic Tests of Selection
Novembre, J., & Barton, N. H. (2018) · Genetics · doi:10.1534/genetics.118.300786 · Cited by 97 · see citing works
Teruel, B. M., Parra, E. J., Fuentes-Smith, E., Salas, A., Buttenschøn, H. N., Demontis, D., Torres, M. L., Padrón, L. C. M., Gómez-Cabezas, E. J., Álvarez‐Iglesias, V., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004488 · Cited by 93 · see citing works
Bhatia, G., Patterson, N. J., Paşaniuc, B., Zaitlen, N., Genovese, G., Pollack, S. J., Mallick, S., Myers, S., Tandon, A., Spencer, C. C. A., et al. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.07.025 · Cited by 85 · see citing works
Fu, W., Gittelman, R. M., Bamshad, M. J., & Akey, J. M. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.09.006 · Cited by 85 · see citing works
Kessler, M. D., Yerges-Armstrong, L. M., Taub, M. A., Shetty, A. C., Maloney, K. A., Jeng, L. J. B., Ruczinski, I., Levin, A. M., Williams, L. K., Beaty, T. H., et al. (2016) · Nature Communications · doi:10.1038/ncomms12521 · Cited by 77 · see citing works
Self-citationde Candia, T. R., Lee, S., Yang, J., Browning, B. L., Gejman, P. V., Levinson, D. F., Mowry, B. J., Hewitt, J. K., Goddard, M. E., O’Donovan, M. C., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.07.007 · Cited by 72 · see citing works
Admixture mapping identifies a locus on 6q25 associated with breast cancer risk in US Latinas
Fejerman, L., Chen, G. K., Eng, C. S., Huntsman, S., Hu, D., Williams, A. L., Paşaniuc, B., John, E. M., Vía, M., Gignoux, C. R., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/ddr617 · Cited by 71 · see citing works
Self-citationA hidden Markov model for investigating recent positive selection through haplotype structure
Chen, H., Hey, J., & Slatkin, M. (2014) · Theoretical Population Biology · doi:10.1016/j.tpb.2014.11.001 · Cited by 56 · see citing works
Rare Variation Facilitates Inferences of Fine-Scale Population Structure in Humans
O’Connor, T. D., Fu, W., Mychaleckyj, J. C., Logsdon, B. A., Auer, P. L., Carlson, C. S., Leal, S. M., Smith, J. D., Rieder, M. J., Bamshad, M. J., et al. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu326 · Cited by 50 · see citing works
Efficient analysis of large datasets and sex bias with ADMIXTURE
Shringarpure, S., Bustamante, C. D., Lange, K. L., & Alexander, D. H. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1082-x · Cited by 50 · see citing works
Self-citationSo, H., & Sham, P. C. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw745 · Cited by 48 · see citing works
Mapping the human reference genome's missing sequence by three-way admixture in Latino genomes.
Genovese, G., Handsaker, R., Li, H., Kenny, E., & Mccarroll, S. (2013) · American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.07.002 · Cited by 45 · see citing works
Self-citationChan, Y., Lim, E. T., Sandholm, N., Wang, S. R., McKnight, A. J., Ripke, S., Daly, M. J., Neale, B. M., Salem, R. M., & Hirschhorn, J. N. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.02.006 · Cited by 43 · see citing works
Self-citationBabron, M., de Tayrac, M., Rutledge, D. N., Zeggini, E., & Génin, E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046519 · Cited by 23 · see citing works
Subtle stratification confounds estimates of heritability from rare variantsPreprint
Bhatia, G., Gusev, A., Loh, P., Finucane, H. K., Vilhjálmsson, B. J., Ripke, S., Purcell, S., Stahl, E. A., Daly, M. J., de Candia, T. R., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/048181 · Cited by 17 · see citing works
Self-citationJoint genotype‐ and ancestry‐based genome‐wide association studies in admixed populations
Szulc, P., Bogdan, M., Frommlet, F., & Tang, H. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22056 · Cited by 13 · see citing works
Joint Genotype- and Ancestry-based Genome-wide Association Studies in Admixed PopulationsPreprint
Szulc, P., Bogdan, M., Frommlet, F., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/062554 · Cited by 6 · see citing works
Efficient analysis of large datasets and sex bias with ADMIXTUREPreprint
Shringarpure, S., Bustamante, C. D., Lange, K. L., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/039347 · Cited by 3 · see citing works
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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 · see citing works
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 2,831 · see citing works
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 · see citing works
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 1,913 · see citing works
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 · see citing works
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 1,605 · see citing works
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 · see citing works
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 1,481 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationCano-Gamez, E., & Trynka, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00424 · Cited by 608 · see citing works
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 566 · see citing works
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 542 · see citing works
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 486 · see citing works
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 470 · see citing works
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 · see citing works
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 · see citing works
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 439 · see citing works
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 430 · see citing works
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 424 · see citing works
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 · see citing works
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 412 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 338 · see citing works
Self-citationPrivé, F., Aschard, H., Carmi, S., Folkersen, L., Hoggart, C. J., O’Reilly, P. F., & Vilhjálmsson, B. J. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.11.008 · Cited by 336 · see citing works
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 328 · see citing works
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 · see citing works
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 · see citing works
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 307 · see citing works
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 · see citing works
Genomics of disease risk in globally diverse populations
Gurdasani, D., Barroso, I., Zeggini, E., & Sandhu, M. S. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0144-0 · Cited by 299 · see citing works
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 292 · see citing works
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 · see citing works
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 · see citing works
Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction
Demange, P., Malanchini, M., Mallard, T. T., Biroli, P., Cox, S. R., Grotzinger, A. D., Tucker–Drob, E. M., Abdellaoui, A., Arseneault, L., van Bergen, E., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00754-2 · Cited by 288 · see citing works
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 · see citing works
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 266 · see citing works
Self-citationAndreassen, O. A., Hindley, G., Frei, O., & Smeland, O. B. (2023) · World Psychiatry · doi:10.1002/wps.21034 · Cited by 255 · see citing works
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 232 · see citing works
A genomic region associated with protection against severe COVID-19 is inherited from Neandertals
Zeberg, H., & Pääbo, S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2026309118 · Cited by 230 · see citing works
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 229 · see citing works
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 230 · see citing works
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 221 · see citing works
Fast and accurate genomic analyses using genome graphs
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 215 · see citing works
The single-cell eQTLGen consortium
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 215 · see citing works
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 215 · see citing works
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 · see citing works
Genetic disease risks can be misestimated across global populations
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 · see citing works
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 195 · see citing works
African genetic diversity and adaptation inform a precision medicine agenda
Pereira, L., Mutesa, L., Tindana, P. O., & Ramsay, M. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00306-8 · Cited by 188 · see citing works
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 185 · see citing works
Efficient pedigree recording for fast population genetics simulation
Kelleher, J., Thornton, K., Ashander, J., & Ralph, P. L. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006581 · Cited by 184 · see citing works
Pharmacogenetics at Scale: An Analysis of the UK Biobank
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 · see citing works
Using genetics for social science
Harden, K. P., & Koellinger, P. D. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0862-5 · Cited by 180 · see citing works
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 180 · see citing works
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 180 · see citing works
Deep-coverage whole genome sequences and blood lipids among 16,324 individuals
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 178 · see citing works
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 178 · see citing works
Polygenic scores in biomedical research
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 175 · see citing works
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 171 · see citing works
Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores
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 167 · see citing works
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 164 · see citing works
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 · see citing works
Machine learning for genetic prediction of psychiatric disorders: a systematic review
Bracher‐Smith, M., Crawford, K. L., & Escott‐Price, V. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0825-2 · Cited by 159 · see citing works
Whole-genome sequencing of 1,171 elderly admixed individuals from Brazil
Naslavsky, M. S., Scliar, M. O., Yamamoto, G. L., Wang, J. Y. T., Zverinova, S., Karp, T., Nunes, K., Ceroni, J. R. M., de Carvalho, D. L., Simões, C. E. D. S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28648-3 · Cited by 157 · see citing works
An Unexpectedly Complex Architecture for Skin Pigmentation in Africans
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 · see citing works
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 156 · see citing works
Genetic Endowments and Wealth Inequality
Barth, D. J., Papageorge, N., & Thom, K. (2019) · Journal of Political Economy · doi:10.1086/705415 · Cited by 153 · see citing works
Sociology, Genetics, and the Coming of Age of Sociogenomics
Mills, M. C., & Tropf, F. C. (2020) · Annual Review of Sociology · doi:10.1146/annurev-soc-121919-054756 · Cited by 152 · see citing works
Igo, R. P., Kinzy, T. G., & Bailey, J. N. C. (2019) · Current Protocols in Human Genetics · doi:10.1002/cphg.95 · Cited by 149 · see citing works
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 148 · see citing works
Self-citationSignificant sparse polygenic risk scores across 813 traits in UK Biobank
Tanigawa, Y., Qian, J., Venkataraman, G. R., Justesen, J. M., Li, R., Tibshirani, R. J., Hastie, T. J., & Rivas, M. A. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010105 · Cited by 148 · see citing works
The selection landscape and genetic legacy of ancient Eurasians
Irving-Pease, E. K., Refoyo-Martínez, A., Barrie, W., Ingason, A., Pearson, A., Fischer, A., Sjögren, K.-G., Halgren, A. S., Macleod, R., Demeter, F., et al. (2024) · Nature · doi:10.1038/s41586-023-06705-1 · Cited by 147 · see citing works
Polygenic Scores to Assess Atherosclerotic Cardiovascular Disease Risk
Aragam, K. G., & Natarajan, P. (2020) · Circulation Research · doi:10.1161/circresaha.120.315928 · Cited by 146 · see citing works
Enhancing the Polygenic Score Catalog with tools for score calculation and ancestry normalization
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 146 · see citing works
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 145 · see citing works
Mendelian imputation of parental genotypes improves estimates of direct genetic effects
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 · see citing works
Standardized Biogeographic Grouping System for Annotating Populations in Pharmacogenetic Research
Huddart, R., Fohner, A. E., Whirl‐Carrillo, M., Wojcik, G. L., Gignoux, C. R., Popejoy, A. B., Bustamante, C. D., Altman, R. B., & Klein, T. E. (2018) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1322 · Cited by 142 · see citing works
Self-citationInternational 22q11.2 Brain and Behavior Consortium, Davies, R. W., Fiksinski, A. M., Breetvelt, E. J., Williams, N. M., Hooper, S. R., Monfeuga, T., Bassett, A. S., Owen, M. J., Gur, R. E., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1103-1 · Cited by 139 · see citing works
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 137 · see citing works
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 136 · see citing works
Genetics and educational attainment
Cesarini, D. A., & Visscher, P. M. (2017) · npj Science of Learning · doi:10.1038/s41539-017-0005-6 · Cited by 135 · see citing works
Polygenic risk scores and breast cancer risk prediction
Roberts, E., Howell, S. J., & Evans, D. G. (2023) · The Breast · doi:10.1016/j.breast.2023.01.003 · Cited by 135 · see citing works
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 133 · see citing works
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 132 · see citing works
Curtis, D. (2018) · Psychiatric Genetics · doi:10.1097/ypg.0000000000000206 · Cited by 132 · see citing works
Genes, Education, and Labor Market Outcomes: Evidence from the Health and Retirement Study
Papageorge, N., & Thom, K. (2016) · Journal of the European Economic Association · doi:10.1093/jeea/jvz072 · Cited by 131 · see citing works
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 130 · see citing works
- 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 · 2016: 1
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › 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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