Analysis of polygenic risk score usage and performance in diverse human populations
Open accessPublished version of a preprint
Abstract
A historical tendency to use European ancestry samples hinders medical genetics research, including the use of polygenic scores, which are individual-level metrics of genetic risk. We analyze the first decade of polygenic scoring studies (2008–2017, inclusive), and find that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were among cohorts of African, Hispanic, or Indigenous peoples. We find that predictive performance of European ancestry-derived polygenic scores is lower in non-European ancestry samples (e.g. African ancestry samples: t = −5.97, df = 24, p = 3.7 × 10 −6 ), and we demonstrate the effects of methodological choices in polygenic score distributions for worldwide populations. These findings highlight the need for improved treatment of linkage disequilibrium and variant frequencies when applying polygenic scoring to cohorts of non-European ancestry, and bolster the rationale for large-scale GWAS in diverse human populations.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
PGIs constructed from GWAS in European genetic ancestries generally have much lower predictive power in samples with other genetic ancestries; on average across phenotypes, relative accuracy in African-ancestry to European-ancestry samples has been 22% and 36%.
“PGIs like ours that are constructed from GWAS in samples of European genetic ancestries are generally found to have much lower predictive power in samples with other genetic ancestries; for example, on average across phenotypes, estimates of relative accuracy (ratio of R2) in African-genetic-ancestry to European-genetic-ancestry samples have been 22%23 and 36%24.”
From Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals (2022) · cites this paper as [24]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredEach original variable used in the models was put in linear regressions with the 20 first principal components of the SNP genotype data to correct for population stratification, as well as with age and site to correct for differences.
“Then, each original variable used in the models was put in linear regressions with the 20 first principal components of the SNP genotype data to correct for population stratification (associations of environmental effects with genetic structure due to geographic separation, population mixtures, and migrations that occurred throughout our evolutionary history)91,92, as well as with the age of the”
From The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background (2022) · cites this paper as [91]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
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
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709 · see citing works
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876 · see citing works
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877 · 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
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 · cited 1× · see citing works
“2 (see schizophrenia and Purcell 2009 in the citation information for this point).”
BackgroundeQTLGen, Wray, N. R., 23andMe, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., Adams, M. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0090-3 · Cited by 3,005 · 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
Blundell, J. E., Baker, J. L., Boyland, E. J., Blaak, E. E., Charzewska, J., Henauw, S. D., Frühbeck, G., González‐Gross, M., Hebebrand, J., Holm, L., et al. (2017) · Obesity Facts · doi:10.1159/000455952 · Cited by 1,980 · 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
The Type 1 Diabetes Genetics Consortium, Barrett, J. C., Clayton, D. G., Concannon, P., Akolkar, B., Cooper, J. D., Erlich, H. A., Julier, C., Morahan, G., Nerup, J., et al. (2009) · Nature Genetics · doi:10.1038/ng.381 · Cited by 1,746 · see citing works
The 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
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
Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
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. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.03.004 · Cited by 1,391 · see citing works
Modeling 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
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 · cited 1× · see citing works
“(6) Random error in the estimation of GWAS betas”
BackgroundSparse whole-genome sequencing identifies two loci for major depressive disorder
CONVERGE consortium, Cai, N., Bigdeli, T. B., Kretzschmar, W., Li, Y., Liang, J., Song, L., Hu, J., Li, Q., Jin, W., et al. (2015) · Nature · doi:10.1038/nature14659 · Cited by 864 · see citing works
Self-citationPrediction 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
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 673 · see citing works
Bustamante, C. D., Vega, F. M. D. L., & Burchard, E. G. (2011) · Nature · doi:10.1038/475163a · Cited by 594 · see citing works
A population genetic signal of polygenic adaptation
Berg, J. J., & Coop, G. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004412 · Cited by 487 · see citing works
Cultural evolutionary theory: How culture evolves and why it matters
Creanza, N., Kolodny, O., & Feldman, M. W. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1620732114 · Cited by 482 · 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
Duncan, L. E., Ratanatharathorn, A., Aiello, A. E., Almli, L. M., Amstadter, A. B., Ashley-Koch, A. E., Baker, D. G., Beckham, J. C., Bierut, L. J., Bisson, J., et al. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.77 · Cited by 425 · see citing works
Self-citationEhret, G., Ferreira, T. C., Chasman, D. I., Jackson, A., Schmidt, E. M., Johnson, T., Þorleifsson, G., Luan, J., Donnelly, L. A., Kanoni, S., et al. (2016) · Nature Genetics · doi:10.1038/ng.3667 · Cited by 411 · see citing works
Detection of human adaptation during the past 2000 years
Field, Y., Boyle, E. A., Telis, N., Gao, Z., Gaulton, K. J., Golan, D., Yengo, L., Rocheleau, G., Froguel, P., McCarthy, M. I., et al. (2016) · Science · doi:10.1126/science.aag0776 · Cited by 383 · 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
Reduced 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
Morris, D. L., Sheng, Y., Zhang, Y., Wang, Y., Zhu, Z., Tombleson, P., Chen, L., Graham, D. S. C., Bentham, J., Roberts, A. L., et al. (2016) · Nature Genetics · doi:10.1038/ng.3603 · Cited by 312 · see citing works
Hoffmann, T. J., Ehret, G., Nandakumar, P., Ranatunga, D. K., Schaefer, C. A., Kwok, P., Iribarren, C., Chakravarti, A., & RISCH, N. J. (2016) · Nature Genetics · doi:10.1038/ng.3715 · Cited by 310 · see citing works
Models for cultural inheritance I. Group mean and within group variation
Cavalli-Sforza, L. L., & Feldman, M. W. (1973) · Theoretical Population Biology · doi:10.1016/0040-5809(73)90005-1 · Cited by 299 · see citing works
Self-citationCognitive Costs of Exposure to Racial Prejudice
Salvatore, J., & Shelton, J. N. (2007) · Psychological Science · doi:10.1111/j.1467-9280.2007.01984.x · Cited by 298 · see citing works
Petrovski, S., & Goldstein, D. B. (2016) · Genome Biology · doi:10.1186/s13059-016-1016-y · Cited by 255 · see citing works
Population genetic differentiation of height and body mass index across Europe
Robinson, M. R., Hemani, G., Medina‐Gómez, C., Mezzavilla, M., Esko, T., Shakhbazov, K., Powell, J. E., Vinkhuyzen, A. A. E., Berndt, S. I., Gustafsson, S., et al. (2015) · Nature Genetics · doi:10.1038/ng.3401 · Cited by 225 · see citing works
Cardiovascular disease: The rise of the genetic risk score
Knowles, J. W., & Ashley, E. A. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002546 · Cited by 182 · see citing works
Regularized Machine Learning in the Genetic Prediction of Complex Traits
Okser, S., Pahikkala, T., Airola, A., Salakoski, T., Ripatti, S., & Aittokallio, T. A. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004754 · Cited by 156 · see citing works
Duncan, L. E., Pollastri, A. R., & Smoller, J. W. (2014) · American Psychologist · doi:10.1037/a0036320 · Cited by 137 · cited 1× · see citing works
“(1) True differences due to drift (2) True differences due to selection (3) True differences in genetic effects due to environmental differences (gene-environment interactions) (4) Bias due to uncorrected population stratification in discovery and/or training samples (5) Bias due to discovery/training population data…”
BackgroundSelf-citationUsing 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
Curtis, D. (2018) · Psychiatric Genetics · doi:10.1097/ypg.0000000000000206 · Cited by 132 · see citing works
Detecting Polygenic Adaptation in Admixture Graphs
Racimo, F., Berg, J. J., & Pickrell, J. K. (2018) · Genetics · doi:10.1534/genetics.117.300489 · Cited by 115 · see citing works
He, M., Xu, M., Zhang, B., Liang, J., Chen, P., Lee, J.-Y., Johnson, T. A., Li, H., Yang, X., Dai, J., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddu583 · Cited by 106 · see citing works
Self-citationGeneralizing polygenic risk scores from Europeans to Hispanics/Latinos
Grinde, K. E., Qi, Q., Thornton, T. A., Liu, S., Shadyab, A. H., Chan, K. H. K., Reiner, A. P., & Sofer, T. (2018) · Genetic Epidemiology · doi:10.1002/gepi.22166 · Cited by 92 · see citing works
Global genetic differentiation of complex traits shaped by natural selection in humans
Guo, J., Wu, Y., Zhu, Z., Zheng, Z., Trzaskowski, M., Zeng, J., Robinson, M. R., Visscher, P. M., & Yang, J. (2018) · Nature Communications · doi:10.1038/s41467-018-04191-y · Cited by 87 · see citing works
A machine-learning heuristic to improve gene score prediction of polygenic traits
Paré, G., Mao, S., & Deng, W. Q. (2017) · Scientific Reports · doi:10.1038/s41598-017-13056-1 · Cited by 85 · see citing works
Qi, Q., Stilp, A. M., Sofer, T., Moon, J., Hidalgo, B., Szpiro, A. A., Wang, T., Ng, M. C. Y., Guo, X., Chen, Y. I., et al. (2017) · Diabetes · doi:10.2337/db16-1150 · Cited by 82 · see citing works
Vargas, J. D., Manichaikul, A. W., Wang, X., Rich, S. S., Rotter, J. I., Post, W. S., Polak, J. F., Budoff, M. J., & Bluemke, D. A. (2015) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2015.11.034 · Cited by 65 · see citing works
Martschenko, D. O., Trejo, S., & Domingue, B. W. (2019) · AERA Open · doi:10.1177/2332858418810516 · Cited by 64 · see citing works
Self-citationPopulation differentiation in allele frequencies of obesity-associated SNPs
Mao, L., Fang, Y., Campbell, M. C., & Southerland, W. M. (2017) · BMC Genomics · doi:10.1186/s12864-017-4262-9 · Cited by 57 · see citing works
Focused Classroom Coaching and Widespread Racial Equity in School Discipline
Gregory, A., Ruzek, E. A., DeCoster, J., Mikami, A. Y., & Allen, J. P. (2019) · AERA Open · doi:10.1177/2332858419897274 · Cited by 50 · see citing works
Large Scale Genetic Research on Neuropsychiatric Disorders in African Populations is Needed
Dalvie, S., Koen, N., Duncan, L. E., Abbo, C., Akena, D. H., Atwoli, L., Chiliza, B., Donald, K. A., Kinyanda, E., Löchner, C., et al. (2015) · EBioMedicine · doi:10.1016/j.ebiom.2015.10.002 · Cited by 38 · see citing works
Self-citation
Flagged first, then by citations
1 of the 53 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Tutorial: 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 1,914 · 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
Mullins, N., Forstner, A. J., O’Connell, K. S., Coombes, B., Coleman, J. R. I., Qiao, Z., Als, T. D., Bigdeli, T. B., Børte, S., Bryois, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00857-4 · Cited by 1,517 · see citing works
The power of genetic diversity in genome-wide association studies of lipids
Graham, S. E., Clarke, S. L., Wu, K.-H. H., Kanoni, S., Zajac, G. J. M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T. W., et al. (2021) · Nature · doi:10.1038/s41586-021-04064-3 · Cited by 972 · see citing works
Mendelian 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
A saturated map of common genetic variants associated with human height
Yengo, L., Vedantam, S., Marouli, E., Sidorenko, J., Bartell, E., Sakaue, S., Graff, M., Eliasen, A. U., Jiang, Y., Raghavan, S., et al. (2022) · Nature · doi:10.1038/s41586-022-05275-y · Cited by 825 · see citing works
The trans-ancestral genomic architecture of glycemic traits
Chen, J., Spracklen, C. N., Marenne, G., Varshney, A., Corbin, L. J., Luan, J., Willems, S. M., Wu, Y., Zhang, X., Horikoshi, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00852-9 · Cited by 798 · see citing works
“In agreement with results for other traits 36,77,78 , we confirm that European ancestry-derived PGS perform much worse when the test dataset is from a different ancestry.”
Peterson, R. E., Kuchenbaecker, K., Walters, R. K., Chen, C.-Y., Popejoy, A. B., Periyasamy, S., Lam, M., Iyegbe, C., Strawbridge, R. J., Brick, L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.08.051 · Cited by 797 · see citing works
“Genetic risk prediction attenuates with increasing divergence between the discovery and target populations, indicating that polygenic risk scores (PRSs) based on Eurocentric GWASs are not equally predictive when applied to non-European populations (Duncan et al., 2019; Martin et al., 2019).”
Self-citationAlgorithmic fairness in artificial intelligence for medicine and healthcare
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01056-8 · Cited by 750 · see citing works
“Additionally, cross-ancestry association studies that include populations from divergent ancestries have uncovered new diseased loci 174 .”
BackgroundAragam, K. G., Jiang, T., Goel, A., Kanoni, S., Wolford, B. N., Atri, D. S., Weeks, E. M., Wang, M., Hindy, G., Zhou, W., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01233-6 · Cited by 721 · see citing works
The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation
Lambert, S. A., Gil, L., Jupp, S., Ritchie, S. C., Xu, Y., Buniello, A., McMahon, A., Abraham, G., Chapman, M., Parkinson, H., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00783-5 · Cited by 690 · see citing works
Improving 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
Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., Sidorenko, J., Kweon, H., Goldman, G., Gjorgjieva, T., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01016-z · Cited by 654 · see citing works
Nievergelt, 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-citationConti, D. V., Darst, B. F., Moss, L. C., Saunders, E. J., Sheng, X., Chou, A., Schumacher, F. R., Olama, A. A. A., Benlloch, S., Dadaev, T., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00748-0 · Cited by 467 · 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
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
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
“, 2018), but face difficulties when applied to new patient populations (Martin et al., 2017; Duncan et al., 2019; Berg et al., 2019).”
BackgroundThe 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
Privé, 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 337 · 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
“PRSs have lower accuracy in cross-population prediction when the target sample is genetically distant from the discovery GWAS sample, regardless of the PRS construction method 3,11–16 (Fig.”
Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification
Dapas, M., & Dunaif, A. (2022) · Endocrine Reviews · doi:10.1210/endrev/bnac001 · Cited by 300 · 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
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
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
Epidemiology and genomics of prostate cancer in Asian men
Zhu, Y., Mo, M., Yu, W., Wu, J., Pan, J. G., Freedland, S. J., Zheng, Y., & Ye, D. (2021) · Nature Reviews Urology · doi:10.1038/s41585-021-00442-8 · Cited by 286 · see citing works
Prospective study design and data analysis in UK Biobank
Allen, N. E., Lacey, B., Lawlor, D. A., Pell, J. P., Gallacher, J., Smeeth, L. L., Elliott, P., Matthews, P. M., Lyons, R. A., Whetton, A. D., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adf4428 · Cited by 311 · see citing works
“As such, other large population cohorts with biological samples are needed around the world to increase the heterogeneity of genetic and nongenetic risk factors for disease ( 42 ) (Table 2).”
BackgroundThompson, D. J., Wells, D., Selzam, S., Peneva, I., Moore, R., Sharp, K., Tarran, W. A., Beard, E. J., Riveros-Mckay, F., Giner-Delgado, C., et al. (2022) · doi:10.1101/2022.06.16.22276246 · Cited by 280 · 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
Weissbrod, O., Kanai, M., Shi, H., Gazal, S., Peyrot, W. J., Khera, A. V., Okada, Y., The Biobank Japan Project, Matsuda, K., Yamanashi, Y., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01036-9 · Cited by 252 · see citing works
Common genetic variation influencing human white matter microstructure
Zhao, B., Li, T., Yang, Y., Wang, X., Luo, T., Shan, Y., Zhu, Z., Xiong, D., Hauberg, M. E., Bendl, J. M., et al. (2021) · Science · doi:10.1126/science.abf3736 · Cited by 217 · see citing works
“For example, glioma risk genes from five more regions were associated with whitematter microstructure,including RAVER2 (1p31.”
BackgroundZheutlin, A. B., Dennis, J. K., Linnér, R. K., Moscati, A., RESTREPO, N. A., Straub, P., Ruderfer, D. M., Castro, V. M., Chen, C., Ge, T., et al. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.18091085 · Cited by 215 · see citing works
“It is well known that PRSs calculated from genome-wide association studies (GWASs) performed primarily in one ancestry demonstrate poorer performance in other ancestries as a function of differing linkage disequilibrium (LD) structures with causal variants and lack of diversity on genotyping platforms (23, 24).”
BackgroundGenomics yields biological and phenotypic insights into bipolar disorder
O’Connell, K. S., Koromina, M., van der Veen, T., Boltz, T. A., David, F. S., Yang, J. M. K., Lin, K., Wang, X., Coleman, J. R. I., Mitchell, B. L., et al. (2025) · Nature · doi:10.1038/s41586-024-08468-9 · Cited by 253 · see citing works
“These results highlight the benefits of multi-ancestry representation in the PRS training data, in line with findings from other diseases 51 .”
Zheng, Z., Liu, S., Sidorenko, J., Wang, Y., Lin, T., Yengo, L., Turley, P., Ani, A., Wang, R., Nolte, I. M., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01704-y · Cited by 223 · see citing works
“4a ), in line with previous studies 14-19 , 48 .”
ResultLennon, 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
“In particular, the vast majority of GWASs have focused on individuals of European ancestry, and the predictive accuracy of PRSs declines with increasing genetic distance from the discovery population 5,30,35 the burden of a range of conditions 22 .”
Genetics of Atrial Fibrillation in 2020
Roselli, C., Rienstra, M., & Ellinor, P. T. (2020) · Circulation Research · doi:10.1161/circresaha.120.316575 · Cited by 208 · see citing works
Wells, P. M., Adebayo, A. S., Bowyer, R. C. E., Freidin, M. B., Finckh, A., Strowig, T., Lesker, T. R., Alpizar-Rodriguez, D., Gilbert, B., Kirkham, B., et al. (2020) · The Lancet Rheumatology · doi:10.1016/s2665-9913(20)30064-3 · Cited by 192 · see citing works
Calculating Polygenic Risk Scores (PRS) in UK Biobank: A Practical Guide for Epidemiologists
Collister, J. A., Liu, X., & Clifton, L. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.818574 · Cited by 194 · see citing works
“PRS will perform best in populations of the same ancestry as those in which they were derived (Duncan et al., 2019).”
MethodAfrican 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
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
“Similarly, the median effect size for PRS for a variety of traits in African ancestry samples was only 42% of that in matched European ancestry samples in an analysis of published PRS studies.(40) In contrast, risk scores based on data derived in African Americans have been demonstrated to perform significantly better…”
BackgroundAmariuta, 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
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
Lu, 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
“Polygenic scoring studies have primarily been conducted in European ancestry populations, but its predictive utility decreased largely in other ethnic groups.(16,17) Due to the lack of comprehensively characterized and genotyped cohorts, few studies have evaluated the performance of PRS in a prospective setting among…”
BackgroundSelf-citationThompson, D. J., Wells, D., Selzam, S., Peneva, I., Moore, R., Sharp, K., Tarran, W. A., Beard, E. J., Riveros-Mckay, F., Giner-Delgado, C., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307270 · Cited by 427 · see citing works
“One critical aspect is PRS performance in different ancestry groups [21, 22].”
BackgroundResultPolygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses
Sun, L., Pennells, L. A. M., Kaptoge, S. K., Nelson, C. P., Abraham, G., Arnold, M., Bell, S., Bolton, T., Burgess, S., Dudbridge, F., et al. (2021) · PLoS Medicine · doi:10.1371/journal.pmed.1003498 · Cited by 161 · see citing works
A global view of the genetic basis of Alzheimer disease
Reitz, C., Pericak‐Vance, M. A., Foroud, T., & Mayeux, R. P. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00789-z · Cited by 171 · see citing works
Opportunistic genomic screening. Recommendations of the European Society of Human Genetics
On behalf of the European Society of Human Genetics, de Wert, G. M. W. R., Dondorp, W., Clarke, A. J., Dequeker, E. M. C., Cordier, C., Deans, Z. C., van El, C. G., Fellmann, F., Hastings, R. J., et al. (2020) · European Journal of Human Genetics · doi:10.1038/s41431-020-00758-w · Cited by 156 · see citing works
Dementia in Latin America: Paving the way toward a regional action plan
Parra, M. A., Báez, S., Sedeño, L., Campo, C. G., Santamaría‐García, H. A., Aprahamian, I., Bertolucci, P. H. F., Bustin, J., Bicalho, M. A. C., Cano, C. A., et al. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.12202 · Cited by 148 · see citing works
Márquez‐Luna, C., Gazal, S., Loh, P., Kim, S. S., Furlotte, N. A., Auton, A., Agee, M., Alipanahi, B., Bell, R. K., Bryc, K., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25171-9 · Cited by 151 · 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
“It is vital that an individual’s PRS is compared to population specific distribution, often done using Principal Component Analysis, to ensure that the right adjustments can be made depending on which ethnicity the individual has [68], as in most studies, ethnicity is self-declared.”
MethodGenetic 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
“With methodological advances and increased availability of GWASs in under-represented populations [112,114], PGS accuracy in diverse populations will be further improved.”
BackgroundBigdeli, T. B., Genovese, G., Georgakopoulos, P., Meyers, J. L., Peterson, R. E., Iyegbe, C. O., Medeiros, H., Valderrama, J., Achtyes, E. D., Kotov, R., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0517-y · Cited by 121 · see citing works
Self-citationPerkins, D. O., Loohuis, L. M. O., Barbee, J., Ford, J. R., Jeffries, C. D., Addington, J., Bearden, C. E., Cadenhead, K. S., Cannon, T. D., Cornblatt, B. A., et al. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.18060721 · Cited by 124 · see citing works
Polygenic Scores for Height in Admixed Populations
Bitarello, B. D., & Mathieson, I. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.120.401658 · Cited by 118 · see citing works
Anderson, K. M., Collins, M. A., Chin, R., Ge, T., Rosenberg, M. D., & Holmes, A. J. (2020) · Nature Communications · doi:10.1038/s41467-020-16710-x · Cited by 119 · see citing works
“Reports of My Death Were Greatly Exaggerated”: Behavior Genetics in the Postgenomic Era
Harden, K. P. (2020) · Annual Review of Psychology · doi:10.1146/annurev-psych-052220-103822 · Cited by 120 · see citing works
Polygenic scores: prediction versus explanation
Plomin, R. J., & von Stumm, S. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01348-y · Cited by 121 · see citing works
FinnGen, Sakaue, S., Kanai, M., Karjalainen, J. M., Akiyama, M., Kurki, M., Matoba, N., Takahashi, A., Hirata, M., Kubo, M., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0785-8 · Cited by 119 · see citing works
Lees, J. A., Mai, T., Galardini, M., Wheeler, N. E., Horsfield, S., Parkhill, J., & Corander, J. (2020) · mBio · doi:10.1128/mbio.01344-20 · Cited by 120 · see citing works
Cavazos, T. B., & Witte, J. S. (2020) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2020.100017 · Cited by 123 · see citing works
“5-fold higher than their percentage of the world population; conversely, there is underrepresentation of diverse populations such as individuals of African ancestry in GWASs, which is one-fifth their percentage.(3) Potential explanations for the limited portability of European-derived PRSs across populations includes…”
BackgroundCollin, C. B., Gebhardt, T., Golebiewski, M., Karaderi, T., Hillemanns, M., Khan, F. M., Salehzadeh‐Yazdi, A., Kirschner, M. W., Krobitsch, S., & Kuepfer, L. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020166 · Cited by 122 · see citing works
Shi, H., Gazal, S., Kanai, M., Koch, E. M., Schoech, A. P., Siewert, K. M., Kim, S. S., Luo, Y., Amariuta, T., Huang, H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21286-1 · Cited by 119 · see citing works
Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts
Wang, Y., Namba, S., Lopera-Maya, E. A., Kerminen, S., Tsuo, K., Läll, K., Kanai, M., Zhou, W., Wu, K.-H., Favé, M.-J., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2022.100241 · Cited by 112 · see citing works
Khan, S. S., Post, W. S., Guo, X., Tan, J., Zhu, F., Bos, D., Sedaghati-Khayat, B., van Rooij, J., Aday, A., Allen, N. B., et al. (2023) · JAMA · doi:10.1001/jama.2023.7575 · Cited by 117 · see citing works
Genetic contributions to bipolar disorder: current status and future directions
O’Connell, K. S., & Coombes, B. J. (2021) · Psychological Medicine · doi:10.1017/s0033291721001252 · Cited by 115 · see citing works
“, 2018) by limiting the therapeutic advances gained by pharmacogenomics and improved genetic risk predictions to those of European descent (Duncan et al., 2019; Martin et al., 2019; Sirugo et al., 2019).”
Transferability of genetic risk scores in African populations
Kamiza, A. B., Touré, S. M., Vujković, M., Machipisa, T., Soremekun, O. S., Kintu, C., Corpas, M., Pirie, F. J., Young, E. H., Gill, D., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01835-x · Cited by 113 · see citing works
Elgart, M., Lyons, G. R., Brufau, S. R., Kurniansyah, N., Brody, J. A., Guo, X., Lin, H. J., Raffield, L. M., Gao, Y. L., Chen, H., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03812-z · Cited by 113 · see citing works
Implementation and implications for polygenic risk scores in healthcare
Slunecka, J. L., van der Zee, M. D., Beck, J. J., Johnson, B. N., Finnicum, C. T., Pool, R., Hottenga, J., de Geus, E. J. C., & Ehli, E. A. (2021) · Human Genomics · doi:10.1186/s40246-021-00339-y · Cited by 114 · see citing works
Is population structure in the genetic biobank era irrelevant, a challenge, or an opportunity?
Lawson, D. J., Davies, N. M., Haworth, S. J., Ashraf, B. H., Howe, L. J., Crawford, A. A., Hemani, G., Smith, G. D., & Timpson, N. J. (2019) · Human Genetics · doi:10.1007/s00439-019-02014-8 · Cited by 106 · see citing works
Amplification is the primary mode of gene-by-sex interaction in complex human traits
Zhu, C., Ming, M. J., Cole, J. M., Edge, M. D., Kirkpatrick, M., & Harpak, A. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100297 · Cited by 96 · see citing works
Validation of a Genome-Wide Polygenic Score for Coronary Artery Disease in South Asians
Wang, M., Menon, R., Mishra, S., Patel, A. P., Chaffin, M. D., Tanneeru, D., Deshmukh, M., Mathew, O., Apte, S., Devanboo, C. S., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.06.024 · Cited by 103 · see citing works
Characterizing the Genetic Architecture of Parkinson's Disease in Latinos
Loesch, D. P., Horimoto, A. R. V. R., Heilbron, K., Sarihan, E. I., Inca‐Martinez, M., Mason, E., Cornejo‐Olivas, M., Torres, L., Mazzetti, P., Cosentino, C., et al. (2021) · Annals of Neurology · doi:10.1002/ana.26153 · Cited by 96 · see citing works
A new method for multiancestry polygenic prediction improves performance across diverse populations
Zhang, H., Zhan, J., Jin, J., Zhang, J., Lu, W., Zhao, R., Ahearn, T. U., Yu, Z., O’Connell, J., Jiang, Y., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01501-z · Cited by 101 · see citing works
Matoba, N., Liang, D., Sun, H., Aygün, N., McAfee, J. C., Davis, J. E., Raffield, L. M., Qian, H., Piven, J., Li, Y., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00953-9 · Cited by 95 · see citing works
Recent advances in polygenic scores: translation, equitability, methods and FAIR tools
Xiang, R., Kelemen, M., Xu, Y., Harris, L. W., Parkinson, H., Inouye, M., & Lambert, S. A. (2024) · Genome Medicine · doi:10.1186/s13073-024-01304-9 · Cited by 96 · see citing works
Zhao, H., Rasheed, H., Nøst, T. H., Cho, Y., Liu, Y., Bhatta, L., Bhattacharya, A., Initiative, G. B. M.-A., Hemani, G., Smith, G. D., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100195 · Cited by 89 · see citing works
Cultural evolution of genetic heritability
Uchiyama, R., Spicer, R., & Muthukrishna, M. (2021) · Behavioral and Brain Sciences · doi:10.1017/s0140525x21000893 · Cited by 97 · see citing works
Sun, C., Kovacs, P. L., & Guiu‐Jurado, E. (2021) · Genes · doi:10.3390/genes12060841 · Cited by 96 · see citing works
Genetic associations with mathematics tracking and persistence in secondary school
Harden, K. P., Domingue, B. W., Belsky, D. W., Boardman, J. D., Crosnoe, R., Malanchini, M., Nivard, M., Tucker-Drob, E. M., & Harris, K. M. (2020) · npj Science of Learning · doi:10.1038/s41539-020-0060-2 · Cited by 92 · see citing works
“Of those in the genetic sample, we focused on unrelated respondents of European ancestry, due to the problem of population stratification in diverse samples.(33,57) Transcripts (N= 12,032) and genetic samples (N= 5,045 of European origin) were collected for partially overlapping subsets of the Wave 1 respondents.”
BackgroundSelf-citationWeale, M. E., Riveros-Mckay, F., Selzam, S., Seth, P., Moore, R., Tarran, W. A., Gradovich, E., Giner-Delgado, C., Palmer, D. S., Wells, D., et al. (2021) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2021.02.032 · Cited by 86 · see citing works
Twin studies to GWAS: there and back again
Friedman, N. P., Banich, M. T., & Keller, M. C. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.06.007 · Cited by 83 · see citing works
False discovery rate control in genome-wide association studies with population structure
Sesia, M., Bates, S., Candès, E. J., Marchini, J., & Sabatti, C. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105841118 · Cited by 81 · see citing works
Panofsky, A. L., Dasgupta, K., & Iturriaga, N. (2020) · American Journal of Physical Anthropology · doi:10.1002/ajpa.24150 · Cited by 95 · see citing works
“Geneticists have argued such comparisons are invalid, likely reflecting population stratification and different environments than different genetic endowments for education (Duncan et al., 2019; Novembre & Barton, 2018).”
BackgroundMcInnes, G., Sharo, A. G., Koleske, M. L., Brown, J. E. H., Norstad, M., Adhikari, A. N., Wang, S., Brenner, S. E., Halpern, J., Koenig, B. A., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.03.003 · Cited by 86 · see citing works
“For individuals seeking healthcare, polygenic risk scores are more accurate for patients of European ancestry because the data from which algorithms are trained are derived largely from individuals of European ancestry.(56,57) Similarly, variant impact predictors tend to be derived from cataloged variants in databases…”
BackgroundFrom genetics to epigenetics to unravel the etiology of adolescent idiopathic scoliosis
Pérez-Machado, G., Berenguer-Pascual, E., Bovea-Marco, M., Rubio-Belmar, P. A., García-López, E., Garzón, M. J., Mena, S., Pallardó, F. V., Bas, T., Vinã, J. R., et al. (2020) · Bone · doi:10.1016/j.bone.2020.115563 · Cited by 87 · see citing works
Gettler, K., Levantovsky, R. M., Moscati, A., Giri, M., Wu, Y., Hsu, N.-Y., Chuang, L.-S. F., Sazonovs, A., Venkateswaran, S., Korie, U. M., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.12.034 · Cited by 84 · see citing works
The construction of cross-population polygenic risk scores using transfer learning
Zhao, Z., Fritsche, L. G., Smith, J. A., Mukherjee, B., & Lee, S. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.09.010 · Cited by 85 · see citing works
“Overall, these methods perform well and help to identify high-risk individuals within the same ancestry group.(2,4,7,8) However, due to insufficient GWAS data from non-Europeanancestry groups such as South Asian and African ancestry, PRSs for these cross-population groups often show only limited prediction performance.”
MethodGenome-wide risk prediction of common diseases across ancestries in one million people
Mars, N., Kerminen, S., Feng, Y. A., Kanai, M., Läll, K., Thomas, L. F., Skogholt, A. H., Parolo, P. D. B., Neale, B. M., Smoller, J. W., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100118 · Cited by 78 · see citing works
“line with the previous studies using a smaller number of variants, with further evidence from comparisons of prediction accuracy of anthropometric traits and lipid biomarkers.(5,19,22,24,26,34,38,39) The genome-wide PRSs were also compared to the PRSs containing a smaller number of variants.”
ResultColtell, Ó., Sorlí, J. V., Asensio, E. M., Barragán, R., González, J. I., Gimenez-Alba, I. M., Zanón‐Moreno, V., Estruch, R., Sabio, J. B. R., Pascual, E. C., et al. (2020) · Nutrients · doi:10.3390/nu12020310 · Cited by 82 · see citing works
“…results emphasize the need to carry out specific studies to know which genetic variants are most relevant in each population, because the results of the most significant associations in a population or in a meta-analysis are not always those that are actually observed in a di ff erent population [89].”
Capalbo, A., Poli, M., Riera‐Escamilla, A., Shukla, V., Høffding, M. K., Krausz, C. G., Hoffmann, E. R., & Simón, C. (2020) · Human Reproduction Update · doi:10.1093/humupd/dmaa044 · Cited by 82 · see citing works
Thareja, G., Al-Sarraj, Y. A., Belkadi, A., Al-Motawa, M., Qatar Genome Project Management, Ismail, S. I., Al‐Muftah, W., Badji, R. M., Mbarek, H., Darwish, D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21381-3 · Cited by 80 · see citing works
Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges
Domingue, B. W., Trejo, S., Armstrong‐Carter, E., & Tucker–Drob, E. M. (2020) · Sociological Science · doi:10.15195/v7.a19 · Cited by 82 · see citing works
“Of particular note is the massive overrepresentation of European-descent individuals in both GWAS (Mills and Rahal 2019) and PGS (Duncan et al. 2019) studies.”
BackgroundSelf-citationKurniansyah, N., Goodman, M. O., Kelly, T. N., Elfassy, T., Wiggins, K. L., Bis, J. C., Guo, X., Palmas, W., Taylor, K. D., Lin, H. J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31080-2 · Cited by 79 · see citing works
Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data
Srinivasu, P. N., Shafi, J., Krishna, T. B. M., Sujatha, C. N., Praveen, S. P., & Ijaz, M. F. (2022) · Diagnostics · doi:10.3390/diagnostics12123067 · Cited by 81 · see citing works
“[8] for risk analysis using the sum of the weight of each risk-associated locus of genomic sequence obtained from the corresponding evidence.”
MethodPolygenic risk scores: An overview from bench to bedside for personalised medicine
Cross, B., Turner, R., & Pirmohamed, M. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1000667 · Cited by 81 · see citing works
“highlighting the huge disparities in genetic research populations (Duncan et al., 2019).”
BackgroundMulti-PGS enhances polygenic prediction by combining 937 polygenic scores
Albiñana, C., Zhu, Z., Schork, A. J., Ingason, A., Aschard, H., Brikell, I., Bulik, C. M., Petersen, L. V., Agerbo, E., Grove, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40330-w · Cited by 79 · see citing works
Sauce, B., Liebherr, M., Judd, N., & Klingberg, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-11341-2 · Cited by 74 · see citing works
Prado, P., Medel, V., González-Gómez, R., Sainz‐Ballesteros, A., Vidal, V., Santamaría‐García, H. A., Moguilner, S., Mejía, J. A., Slachevsky, A., Behrens, M. I., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02806-8 · Cited by 80 · see citing works
“…seems more urgent given the recent evidence that the so-called non-stereotypic populations 15 (participants from underrepresented populations in admixtures, genetics, cultural backgrounds, and demographics) defy the generalization of brain-phenotype models from stereotypical populations 19–22 .”
BackgroundA reusable benchmark of brain-age prediction from M/EEG resting-state signals
Engemann, D. A., Mellot, A., Höchenberger, R., Banville, H. J., Sabbagh, D., Gemein, L. A. W., Tonio Ball, A., & Gramfort, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119521 · Cited by 74 · see citing works
The complete and fully-phased diploid genome of a male Han Chinese
Yang, C., Zhou, Y., Song, Y., Wu, D., Zeng, Y., Nie, L., Liu, P., Zhang, S., Chen, G., Xu, J., et al. (2023) · Cell Research · doi:10.1038/s41422-023-00849-5 · Cited by 81 · see citing works
“The lack of ethnic diversity in genomic studies has hampered a complete understanding of the genomic components of global human populations and limited the translation of genetic research to clinical medicine in underrepresented populations.(3,4) The bias in genomic studies is also reflected in wide use of the current…”
Background
- Authors
- Duncan, L., Shen, H., Gelaye, B., Meijsen, J., Ressler, K., Feldman, M., Peterson, R., & Domingue, B.
- Affiliations
- Stanford University (US)
- Harvard University (US)
- Virginia Commonwealth University (US)
- Corresponding author
- Duncan, L.
- Venue
- Nature Communications
- Published
- 2019-07-25
- Volume
- 10
- Issue
- 1
- Pages
- 3328-3328
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6658471
- OpenAlex
- W2963160524
- Semantic Scholar
- 712594d2beca4baa700cc51e92361b74500c6aa8
- Cited by
- 1,038
- References
- 53
- Citations by year
- 2026: 100 · 2025: 126 · 2024: 140 · 2023: 157 · 2022: 186 · 2021: 207 · 2020: 100 · 2019: 19 · 2018: 2
- FWCI
- 77.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- SUNY Digital Repository Support (State University of New York System) (submittedVersion, cc-by-nc-nd)
- Funders
- none.
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Keywords
- polygenic risk score, ancestry bias, genetic ancestry, non-European ancestry populations, predictive performance, linkage disequilibrium, variant frequency, genome-wide association study, underrepresented groups
- MeSH terms
- Human Genetics, Female, Hispanic or Latino, Humans, Male, Linkage Disequilibrium, Multifactorial Inheritance, White People, Asian People, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
