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Analysis of polygenic risk score usage and performance in diverse human populations

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Duncan, L., Shen, H., Gelaye, B., Meijsen, J., Ressler, K., Feldman, M., Peterson, R., Domingue, B.·2019·Nature Communications

DOI 10.1038/s41467-019-11112-0PMC PMC6658471

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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.

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Fig. 1 Ancestry representation in the first decade of polygenic scoring studies (2008–2017; N = 733 studies). a Cumulative numbers of studies by year are denoted by color. The stacked bar graph below the cumulative distribution plot shows proportional ancestry by year. b Stacked bar charts depict world ancestry representation (left) and polygenic scoring study representation (right). c The percentage representation for each ancestry group is given, such that 100% would indicate equal representation in the world and in polygenic scoring studies. For example, European ancestry samples are over-represented (460%) whereas African ancestry samples are under-represented (17%)CC BY · PubMed Central

Fig. 2 Forest plot of performance shows variation in polygenic score performance by ancestry (26 studies). Each row in the forest plot (left) represents one pair of polygenic analyses (i.e., in a non-European ancestry sample and a matched European ancestry sample from the same study. Phenotypes, citation information, and available effect sizes are given for each comparison. The vertical black line at 100% corresponds to equal performance in the non-European ancestry and the European ancestry samples. Vertical colored lines denote median standardized effect sizes, for each of the major ancestry groups. On the top right, median values for standardized effect sizes, for each major ancestry group, are given. Standard errors are not provided because many studies lacked sufficient information; however, statistical significance of each non-European ancestry analyses is denoted by point size. HDL-C high density lipoprotein cholesterol, VLDL very low-density lipoprotein, GERA Genetic Epidemiology Research on Aging, OR odds ratio, UKB UK Biobank, IgAN immunoglobulin A nephropathy, AUC area under the curve, BP blood pressure, BMI body mass indexCC BY · PubMed Central

Fig. 3 Polygenic score distributions vary by ancestry and methodical choices. For polygenic score construction, clumping is often used, and investigator-driven choices can produce large differences in score distributions for global populations. Polygenic score distributions for the five major 1000Genomes populations are plotted, showing how investigator-driven choices impact score distributions. For all plots, weights were derived from the UK biobank height GWAS. Both r2 values used in clumping (r2 = .2, .05, .01; see columns) and 1000Genomes populations used for clumping were varied (ALL, EUR, AFR, AMR, EAS, SAS; see rows). a, b correspond to the p-value threshold (pT) applied to the height summary statistics. a pT = genome-wide significant variants (p < 5 × 10−8); b pT = full genome variants (p < 1). PRS=polygenic risk score. ALL union of five 1000Genomes populationsCC BY · PubMed Central

Fig. 4 Scatterplots of height polygenic scores (x-axis) and phenotypic height (y-axis). Plots demonstrate that correlations between polygenic scores for height and height are not consistent across discovery GWAS. The y-values for height are the same for each plot and reflect average height of individuals in the country of origin for each population included. Average heights (y-axis) are from a different height GWAS used to construct polygenic scores (x-axis). Three different GWAS of height were used (i.e., three rows) with three different p-value thresholds (i.e., three columns) for the construction of polygenic scores. a GIANT-based polygenic scores for height. b UK Biobank-based polygenic scores for height. c East Asian based polygenic scores for height. The last two plots are missing because only genome-wide significant variants were available for the East Asian GWAS of height. p and r values for each plot are for correlation tests between polygenic scores for height (x-axis) and height (y-axis). GWAS=genome-wide association study, GIANT=Genetic Investigation of ANthropometric Trait, PRS=polygenic risk score, population abbreviations within scatterplots are those used by the 1000Genomes Consortium and are available in Supplementary Table 3CC BY · PubMed Central

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“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.”
Method
Genetic prediction of complex traits with polygenic scores: a statistical review
Ma, Y., & Zhou, X. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.06.004 · Cited by 136
“With methodological advances and increased availability of GWASs in under-represented populations [112,114], PGS accuracy in diverse populations will be further improved.”
Background
Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry
Bigdeli, 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 122
Self-citation
Polygenic Risk Score Contribution to Psychosis Prediction in a Target Population of Persons at Clinical High Risk
Perkins, 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 125
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 124
Transcriptional and imaging-genetic association of cortical interneurons, brain function, and schizophrenia risk
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 123
“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 125
Polygenic scores: prediction versus explanation
Plomin, R. J., & von Stumm, S. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01348-y · Cited by 139
Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan
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 122
Improved Prediction of Bacterial Genotype-Phenotype Associations Using Interpretable Pangenome-Spanning Regressions
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
Inclusion of variants discovered from diverse populations improves polygenic risk score transferability
Cavazos, T. B., & Witte, J. S. (2020) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2020.100017 · Cited by 126
“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…”
Background
Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation
Collin, 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 128
Population-specific causal disease effect sizes in functionally important regions impacted by selection
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
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 122
Coronary Artery Calcium Score and Polygenic Risk Score for the Prediction of Coronary Heart Disease Events
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
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 122
“, 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 114
Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations
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
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 120
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
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 100
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 107
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
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 116
Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism
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 97
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 102
Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases
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 90
Cultural evolution of genetic heritability
Uchiyama, R., Spicer, R., & Muthukrishna, M. (2021) · Behavioral and Brain Sciences · doi:10.1017/s0140525x21000893 · Cited by 103
Genetics of Body Fat Distribution: Comparative Analyses in Populations with European, Asian and African Ancestries
Sun, C., Kovacs, P. L., & Guiu‐Jurado, E. (2021) · Genes · doi:10.3390/genes12060841 · Cited by 104
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
“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-citation
Validation of an Integrated Risk Tool, Including Polygenic Risk Score, for Atherosclerotic Cardiovascular Disease in Multiple Ethnicities and Ancestries
Weale, 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 90
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 88
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 82
How White nationalists mobilize genetics: From genetic ancestry and human biodiversity to counterscience and metapolitics
Panofsky, A. L., Dasgupta, K., & Iturriaga, N. (2020) · American Journal of Physical Anthropology · doi:10.1002/ajpa.24150 · Cited by 99
“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).”
Background
Opportunities and challenges for the computational interpretation of rare variation in clinically important genes
McInnes, 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
“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…”
Background
From 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
Common and Rare Variant Prediction and Penetrance of IBD in a Large, Multi-ethnic, Health System-based Biobank Cohort
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 88
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
“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.”
Method
Genome-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 84
“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.”
Result
Genome-Wide Association Study for Serum Omega-3 and Omega-6 Polyunsaturated Fatty Acids: Exploratory Analysis of the Sex-Specific Effects and Dietary Modulation in Mediterranean Subjects with Metabolic Syndrome
Coltell, 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 83
“…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].”
Preconception genome medicine: current state and future perspectives to improve infertility diagnosis and reproductive and health outcomes based on individual genomic data
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 85
Whole genome sequencing in the Middle Eastern Qatari population identifies genetic associations with 45 clinically relevant traits
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 83
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 86
“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-citation
A multi-ethnic polygenic risk score is associated with hypertension prevalence and progression throughout adulthood
Kurniansyah, 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 80
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 84
“[8] for risk analysis using the sum of the weight of each risk-associated locus of genomic sequence obtained from the corresponding evidence.”
Method
Polygenic 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 86
“highlighting the huge disparities in genetic research populations (Duncan et al., 2019).”
Background
Multi-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 85
The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background
Sauce, B., Liebherr, M., Judd, N., & Klingberg, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-11341-2 · Cited by 74
The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds
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 93
“…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 .”
Background
A 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., Ball, T., & Gramfort, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119521 · Cited by 89
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 87
“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.
ORCID: Duncan, L.
Shen, H.
Gelaye, B.
Meijsen, J.
Ressler, K.
Feldman, M.
Peterson, R.
Domingue, B.
Venue: Nature Communications
Publisher: Springer Science and Business Media LLC
Published: 2019-07-25
Volume: 10
Issue: 1
Pages: 3328-3328
ISSN: 2041-1723 (ISSN-L)
DOI: 10.1038/s41467-019-11112-0
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
FWCI: 77.29
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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
Social Sciences › Anthropology › 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

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Analysis of polygenic risk score usage and performance in diverse human populations

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