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Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations
Open accessAbstract
Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.
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“Previous studies have found that population diversity may improve fine-mapping resolution38,39”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [39]
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“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).”
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Bridging genomics’ greatest challenge: The diversity gap
Corpas, M., Pius, M., Vincent, C., Guio, H., Dwek, M. V., Nagaraj, S. H., López-Correa, C., Popejoy, A. B., & Fatumo, S. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100724 · Cited by 88
Self-citationIdentifying causal variants by fine mapping across multiple studies
LaPierre, N., Taraszka, K., Huang, H. J., He, R., Hormozdiari, F., & Eskin, E. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009733 · Cited by 71
“It has been shown that many causal SNPs are shared across populations [12, 17, 18].”
BackgroundSelf-citationAbu‐El‐Haija, A., Reddi, H. V., Wand, H., Rose, N. C., Mori, M., Qian, E., & Murray, M. F. (2023) · Genetics in Medicine · doi:10.1016/j.gim.2023.100803 · Cited by 101
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Self-citationHaematological setpoints are a stable and patient-specific deep phenotype
Foy, B. H., Petherbridge, R., Roth, M., Zhang, C., de Souza, D. C., Mow, C., Patel, H. R., Patel, C. H., Ho, S. N., Lam, E., et al. (2024) · Nature · doi:10.1038/s41586-024-08264-5 · Cited by 88
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Barriers to clinical adoption of pharmacogenomic testing in psychiatry: a critical analysis
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The epidemiology of psychiatric disorders in Africa: a scoping review
Greene, M. C., Yangchen, T., Lehner, T., Sullivan, P. F., Pato, C. N., McIntosh, A. M., Walters, J., Gouveia, L., Msefula, C. L., Fumo, W., et al. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(21)00009-2 · Cited by 99
“The last few years have seen several calls to broaden the scope of ancestral and geographic diversity in large scale genetic studies.(1,2) The Psychiatric Genomics Consortium (PGC), the largest international consortium to study the genetic architecture of eleven psychiatric disorders, was formed in 2007 to launch a gl…”
BackgroundInfluentialSelf-citationeQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLsPreprint
Kerimov, N., Hayhurst, J., Peikova, K., Manning, J., Walter, P., Kolberg, L., Samoviča, M., Sakthivel, M. P., Kuzmin, I., Trevanion, S., et al. (2020) · bioRxiv · doi:10.1101/2020.01.29.924266 · Cited by 65
“However, stratified analyses ( 41 ) or approaches taking into account local ancestry ( 42 , 43 ) might be more appropriate in this specific setting.”
BackgroundOpen problems in human trait genetics
Brandes, N., Weissbrod, O., & Linial, M. (2022) · Genome biology · doi:10.1186/s13059-022-02697-9 · Cited by 92
“Additionally, it is a common practice to split cohorts by self-reported ethnic identities [14].”
BackgroundInfluentialMeta-Analyses of Genome-Wide Association Studies for Postpartum Depression
Guintivano, J., Byrne, E. M., Kiewa, J., Yao, S., Bauer, A. E., Åberg, K. A., Adams, M. J., Campbell, A., Campbell, M. L., Choi, K. W., et al. (2023) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20230053 · Cited by 80
Self-citationChen, C.-Y., Chen, T.-T., Feng, Y.-C. A., Yu, M.-R., Lin, S.-C., Longchamps, R. J., Wang, S.-H., Hsu, Y.-H., Yang, H. I., Kuo, P.-H., et al. (2021) · medRxiv · doi:10.1101/2021.04.12.21255236 · Cited by 63
“QC was performed using a combination of bash script, R, python and PLINK 4,27 , with scripts adapted from a recent biobank genotype QC project: https://github.com/Annefeng/PBK-QC-pipeline.”
MethodSelf-citationA variant-centric perspective on geographic patterns of human allele frequency variation
Biddanda, A., Rice, D. P., & Novembre, J. (2020) · eLife · doi:10.7554/elife.60107 · Cited by 67
Don’t ignore genetic data from minority populations
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Emerging applications of machine learning in genomic medicine and healthcare
Chafai, N., Bonizzi, L., Botti, S., & Badaoui, B. (2023) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2023.2259466 · Cited by 84
Gao, B., & Zhou, X. (2024) · Nature Genetics · doi:10.1038/s41588-023-01604-7 · Cited by 69
“We first provide an illustrative simulation multi-ancestry fine-mapping can often improve both the resolution and the statistical power of fine-mapping 17–19 .”
Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective
Wendt, F. R., Pathak, G. A., Tylee, D. S., Goswami, A., & Polimanti, R. A. (2020) · Chronic Stress · doi:10.1177/2470547020924844 · Cited by 65
Schizophrenia genomics: genetic complexity and functional insights
Sullivan, P. F., Yao, S., & Hjerling‐Leffler, J. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00837-7 · Cited by 64
“In addition, owing to different patterns of linkage disequilibrium, non-European data can help to narrow the set of loci identified in schizophrenia GWAS 30,145,146 .”
Self-citationModeste, E. S., Ping, L., Watson, C. M., Duong, D. M., Dammer, E. B., Johnson, E. C. B., Roberts, B. R., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00638-z · Cited by 66
“There was no genetics a priori performed on these study participants to confirm enrichment of African vis a vis European ancestry (56) as we stratified race solely by self-identification.”
BackgroundTaking an antiracist posture in scientific publications in human genetics and genomics
Brothers, K. B., Bennett, R. L., & Cho, M. K. (2021) · Genetics in Medicine · doi:10.1038/s41436-021-01109-w · Cited by 62
sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human
Yin, M., Feng, C., Yu, Z., Zhang, Y., Li, Y., Wang, X., Song, C., Guo, M., & Li, C. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1008 · Cited by 51
Dahl, A., Thompson, M., An, U., Krebs, M. D., Appadurai, V., Border, R., Bacanu, S., Werge, T. M., Flint, J., Schork, A. J., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01559-9 · Cited by 63
“Consistent with previous studies 51–53 , we found that the PRSs we derived from GWASs on European-ancestry cohorts generally had”
ResultCross-ancestry genome-wide association studies of brain imaging phenotypes
Fu, J., Zhang, Q., Wang, J., Wang, M., Zhang, B., Zhu, W., Qiu, S., Geng, Z., Cui, G., Yu, Y., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01766-y · Cited by 43
“Despite the fact that nongenetic factors may contribute to these interancestry differences 13 , they may also result from the distinction in genetic architectures such as linkage disequilibrium (LD) and allele frequency 14 .”
Genetic Architecture of Dilated Cardiomyopathy in Individuals of African and European Ancestry
Jordan, E., Kinnamon, D. D., Haas, G. J., Hofmeyer, M. R., Kransdorf, E. P., Ewald, G. A., Morris, A. A., OWENS, A. T., Lowes, B. D., Stoller, D. A., et al. (2023) · JAMA · doi:10.1001/jama.2023.11970 · Cited by 46
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Limitations of principal components in quantitative genetic association models for human studies
Yao, Y., & Ochoa, A. (2023) · eLife · doi:10.7554/elife.79238 · Cited by 58
An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction
Zhang, J.-N., Zhan, J., Jin, J., Ma, C., Zhao, R., O'Connell, J., Jiang, Y.-X., Koelsch, B., Zhang, H.-Y., & Chatterjee, N. (2023) · bioRxiv · doi:10.1101/2023.03.15.532652 · Cited by 46
Companion Diagnostics: State of the Art and New Regulations
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Chen, C., Chen, T., Feng, Y. A., Yu, M., Lin, S., Longchamps, R. J., Wang, S., Hsu, Y., Yang, H., Kuo, P., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100436 · Cited by 82
“9,43 QC was performed using a combination of bash script, R, python and PLINK, 9,43 with scripts adapted from a recent biobank genotype QC project: https://github.com/Annefeng/PBK-QC-pipeline.”
MethodSelf-citationWhole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations
Feofanova, E. V., Brown, M. R., Alkis, T., Manuel, A. M., Li, X., Tahir, U. A., Li, Z., Mendez, K. M., Kelly, R. S., Qi, Q., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38800-2 · Cited by 49
A Systematic Review and Analysis of the Use of Polygenic Scores in Pharmacogenomics
Johnson, D., Wilke, M. A. P., Lyle, S. M., Kowalec, K., Jorgensen, A., Wright, G. E. B., & Drögemöller, B. I. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2520 · Cited by 47
Johnson, R., Ding, Y., Venkateswaran, V., Bhattacharya, A., Boulier, K. M., Chiu, A. M., Knyazev, S., Schwarz, T., Freund, M. K., Zhan, L., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01106-x · Cited by 50
“[26] for a more comprehensive description of terms for GWAS in ancestrally diverse populations.”
BackgroundFan, C. C., Loughnan, R. J., Wilson, S., Hewitt, J. K., ABCD Genetic Working Group, Arpana Agrawal, A., Dowling, G. J., Garavan, H., LeBlanc, K. H., Neale, M. C., et al. (2023) · Behavior Genetics · doi:10.1007/s10519-023-10143-0 · Cited by 53
Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies
Nowbandegani, P. S., Wohns, A. W., Ballard, J. L., Lander, E. S., Bloemendal, A., Neale, B. M., & O’Connor, L. J. (2023) · Nature Genetics · doi:10.1038/s41588-023-01487-8 · Cited by 41
Missing heritability in Parkinson’s disease: the emerging role of non-coding genetic variation
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“37 Similarly, we were not able to externally validate this model as most large genetic biobanks lack detailed pregnancy information.”
BackgroundPolygenic Scores in Psychiatry: On the Road From Discovery to Implementation
Lewis, C. M., & Vassos, E. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20220795 · Cited by 48
Self-citationIncluding diverse and admixed populations in genetic epidemiology research
Caliebe, A., Ayele, F. T., Darst, B. F., Wang, X., Song, Y. E., Gui, J., Sebro, R., Balding, D. J., Saad, M., & Dubé, M. (2022) · Genetic Epidemiology · doi:10.1002/gepi.22492 · Cited by 46
“This is an area of active development, improving upon existing genome‐wide analytic approaches, such as mixed models, meta‐analyses, and admixture mapping Box 1 (Peterson et al., 2019).”
BackgroundInfluentialTen challenges for clinical translation in psychiatric genetics
Derks, E. M., Thorp, J. G., & Gerring, Z. F. (2022) · Nature Genetics · doi:10.1038/s41588-022-01174-0 · Cited by 58
A framework for research into continental ancestry groups of the UK Biobank
Constantinescu, A., Mitchell, R. E., Zheng, J., Bull, C. J., Timpson, N. J., Amulic, B., Vincent, E. E., & Hughes, D. A. (2022) · Human Genomics · doi:10.1186/s40246-022-00380-5 · Cited by 49
“245 Throughout the paper, when we speak of ancestry, we are referring to “genetic 246 ancestry”, or individuals who share a demographic history [13,21,22].”
Genetics of obsessive-compulsive disorder
Mahjani, B., Bey, K., Boberg, J., & Burton, C. L. (2021) · Psychological Medicine · doi:10.1017/s0033291721001744 · Cited by 141
“The addition of non-European ancestry samples is critical for improving our understanding of the genetics and biology of OCD and improving prediction accuracy but will also help ensure equity in outcomes and possible clinical insights (Bentley, Callier, & Rotimi, 2017; Peterson et al., 2019).”
A multi-ancestry genome-wide association study in type 1 diabetes
Michalek, D. A., Tern, C., Zhou, W., Robertson, C. C., Farber, E., Campolieto, P., Chen, W., Önengüt-Gümüşcü, S., & Rich, S. S. (2024) · Human Molecular Genetics · doi:10.1093/hmg/ddae024 · Cited by 43
Multiethnic polygenic risk prediction in diverse populations through transfer learning
Tian, P., Chan, T. H., Wang, Y., Yang, W., Yin, G., & Zhang, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.906965 · Cited by 40
“Polygenic risk scores (PRS) are one of the approaches to reflect a mathematical aggregation of risk by variants such as single nucleotide polymorphisms (SNPs) (Peterson et al., 2019).”
BackgroundStability of polygenic scores across discovery genome-wide association studies
Schultz, L. M., Merikangas, A. K., Ruparel, K., Jacquemont, S., Glahn, D. C., Gur, R. E., Barzilay, R., & Almasy, L. A. (2022) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2022.100091 · Cited by 42
Genetic Architecture and Molecular Neuropathology of Human Cocaine Addiction
Huggett, S. B., & Stallings, M. C. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2879-19.2020 · Cited by 47
“Notably, while, genome-wide research has begun to disentangle the genetic architecture of human traits across ancestries (Peterson et al., 2019), we are aware of no transcriptome-wide studies characterizing potential similarities/differences across ancestries/ethnicities.”
BackgroundImproving the completeness of public metadata accompanying omics studies
Rajesh, A., Chang, Y., Abedalthagafi, M., Wong‐Beringer, A., Love, M. I., & Mangul, S. (2021) · Genome biology · doi:10.1186/s13059-021-02332-z · Cited by 34
““ Ethni-city ” refers to a group of people hailing from a similar cultural background while “ Ancestry ” can denote genetic or self-reported ancestry [6].”
BackgroundYang, X., Kar, S., Antoniou, A. C., & Pharoah, P. D. P. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00599-x · Cited by 45
Somineni, R. P., Nagpal, S., Venkateswaran, S., Cutler, D. J., Okou, D. T., Haritunians, T., Simpson, C. L., Begum, F., Datta, L. W., Quiros, A., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.02.001 · Cited by 44
“Although there may be systemic biases in the use of pseudo-controls (see subjects and methods), these results confirm and extend recent observations concerning the propensity for PRS distributions to differ between Europeans and Africans based on discovery ancestry group.(42,44,45) In order to further validate these c…”
ResultGenetic control of DNA methylation is largely shared across European and East Asian populations
Hatton, A. A., Cheng, F., Lin, T., Shen, R., Chen, J., Zheng, Z., Qu, J., Lyu, F., Harris, S. E., Cox, S. R., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47005-0 · Cited by 39
“The utility of cross-ancestry fi ne-mapping inreducing the number of putativecausalvariantshasbeenpreviously demonstratedformany complex traits 2,7,43 – 47 including schizophrenia 46 , type 2 diabetes 48,49 , BMI 50 , RA 51 and height 7 .”
Self-citationThe UCLA ATLAS Community Health Initiative: Promoting precision health research in a diverse biobank
Johnson, R., Ding, Y., Bhattacharya, A., Knyazev, S., Chiu, A. M., Lajonchere, C., Geschwind, D. H., & Paşaniuc, B. (2023) · Cell Genomics · doi:10.1016/j.xgen.2022.100243 · Cited by 35
“Genetic ancestry information is necessary for numerous types of genetic and epidemiological studies, such as genome-wide association studies and polygenic risk score estimation.(19) Although the EHR contains”
BackgroundPublic human microbiome data dominated by highly developed countries
Abdill, R. J., Adamowicz, E. M., & Blekhman, R. (2021) · bioRxiv · doi:10.1101/2021.09.02.458641 · Cited by 35
Tracing Lung Cancer Risk Factors Through Mutational Signatures in Never-Smokers
Landi, M. T., Synnott, N. C., Rosenbaum, J., Zhang, T., Zhu, B., Shi, J., Wei, Z., Kebede, M., Sang, J., Choi, J., et al. (2020) · American Journal of Epidemiology · doi:10.1093/aje/kwaa234 · Cited by 41
“Of note, germline data from this multiethnic population will increase the diversity of existing large-scale genome-wide association studies (GWAS) (>78% of participants in published GWAS are of European ancestry (48), and approximately 71.8% of these samples are collected from the United States, the United Kingdom, an…”
BackgroundInfluentialPedersen, S., Lindstrom, R., Powe, P. M., Louie, K., & Escobar-Viera, C. S. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21070758 · Cited by 34
The Psychiatric Genomics Consortium: Discoveries and Directions
Arpana Agrawal, A., Bulik, C. M., Abebe, D., Andreassen, O. A., Atkinson, E. G., Choi, K. W., Corvin, A. P., Davies, H. L., Davis, L. K., Docherty, A. R., et al. (2025) · The Lancet Psychiatry · doi:10.1016/s2215-0366(25)00124-5 · Cited by 40
“16,17 A global approach is critical to enhance the performance and optimization of PGS—indices of genetic risk for an individual relative to the population—where incorporating GWAS of different ancestries in the training of PGS has been demonstrated to improve their performance in independent…”
Self-citationGene-level analysis reveals the genetic aetiology and therapeutic targets of schizophrenia
Dang, X., Teng, Z., Yang, Y., Li, W., Liu, J., Hui, L., Zhou, D.-S., Gong, D., Dai, S.-S., Li, Y., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-024-02091-4 · Cited by 29
“34).”
Human genetics and epigenetics of alcohol use disorder
Zhou, H., & Gelernter, J. E. (2024) · Journal of Clinical Investigation · doi:10.1172/jci172885 · Cited by 48
“(b) Some studies have endeavored to include samples in multiple ancestries (55, 56, 63, 64, 75, 80), but the sample sizes in the non-European ancestries are smaller than sample sizes in the European ancestries — a common issue in human genetic studies (53, 54).”
Racialising genetic risk: assumptions, realities, and recommendations
Cerdeña, J. P., Grubbs, V., & Non, A. L. (2022) · The Lancet · doi:10.1016/s0140-6736(22)02040-2 · Cited by 44
Insights into attention-deficit/hyperactivity disorder from recent genetic studies
Brikell, I., Burton, C. L., Mota, N. R., & Martin, J. (2021) · Psychological Medicine · doi:10.1017/s0033291721000982 · Cited by 37
“This will be particularly important when genetic studies begin the much-needed inclusion of diverse ancestries in order to ensure equity of research outcomes and clinical benefit in ADHD, but also to improve prediction accuracy and understanding of biology (Peterson et al., 2019).”
BackgroundImpact of cross-ancestry genetic architecture on GWASs in admixed populations
Mester, R., Hou, K., Ding, Y., Meeks, G. L., Burch, K. S., Bhattacharya, A., Henn, B. M., & Paşaniuc, B. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.05.001 · Cited by 41
Smit, D. J. A., Andreassen, O. A., Boomsma, D. I., Burwell, S. J., Chorlian, D. B., de Geus, E. J. C., Elvsåshagen, T., Gordon, R. L., Harper, J., Hegerl, U., et al. (2021) · Brain and Behavior · doi:10.1002/brb3.2188 · Cited by 34
“For oscillation power, the effects may be not as crucial, however, for connectivity measures like coherence, substantial effects are expected (Peterson et al., 2019).”
BackgroundSelf-citationImplicit bias of encoded variables: frameworks for addressing structured bias in EHR–GWAS data
Dueñas, H. R., Seah, C., Johnson, J. S., & Huckins, L. M. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa192 · Cited by 31
The importance of family-based sampling for biobanks
Davies, N. M., Hemani, G., Neiderhiser, J. M., Martin, H. C., Mills, M. C., Visscher, P. M., Yengo, L., Young, A. I., & Keller, M. C. (2024) · Nature · doi:10.1038/s41586-024-07721-5 · Cited by 34
“Such mixed ancestry GWASs can also aid in “fine mapping” of signals by exploiting differences in the intercorrelations between SNPs across ancestry groups to clarify which genomic variants are causal and which are merely correlated 55 .”
Yap, C. X., Vo, D., Heffel, M. G., Bhattacharya, A., Wen, C. H., Yang, Y., Kemper, K. E., Zeng, J., Zheng, Z., Zhu, Z., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn7655 · Cited by 29
Metadata
- Authors
- 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., Carey, C. E., Martin, A. R., Meyers, J. L., Su, J., Chen, J., Edwards, A. C., Kalungi, A., Koen, N., Majara, L., Schwarz, E., Smoller, J. W., Stahl, E. A., Sullivan, P. F., Vassos, E., Mowry, B., Prieto, M. L., Cuellar-Barboza, A., Bigdeli, T. B., Edenberg, H. J., Huang, H., & Duncan, L. E.
- Affiliations
- Virginia Commonwealth University (US)
- University College London (GB)
- Broad Institute (US)
- Stanford Medicine (US)
- The University of Queensland (AU)
- King's College London (GB)
- Karolinska Institutet (SE)
- Brown University (US)
- SUNY Downstate Health Sciences University (US)
- Arizona State University (US)
- Heidelberg University (DE)
- Massachusetts General Hospital (US)
- Icahn School of Medicine at Mount Sinai (US)
- University of North Carolina at Chapel Hill (US)
- Mayo Clinic (US)
- Indiana University School of Medicine (US)
- Corresponding author
- Peterson, Roseann E.
- ORCID
- Peterson, Roseann E.
- Kuchenbaecker, Karoline
- Walters, Raymond K.
- Chen, Chia-Yen
- Popejoy, Alice B.
- Periyasamy, Sathish
- Lam, Max
- Iyegbe, Conrad
- Strawbridge, Rona J.
- Brick, Leslie
- Carey, Caitlin E.
- Martin, Alicia R.
- Meyers, Jacquelyn L.
- Su, Jinni
- Chen, Junfang
- Edwards, Alexis C.
- Kalungi, Allan
- Koen, Nastassja
- Majara, Lerato
- Schwarz, Emanuel
- Smoller, Jordan W.
- Stahl, Eli A.
- Sullivan, Patrick F.
- Vassos, Evangelos
- Mowry, Bryan
- Prieto, Miguel L.
- Bigdeli, Tim B.
- Edenberg, Howard J.
- Huang, Hailiang
- Duncan, Laramie E.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 179
- Issue
- 3
- Pages
- 589-603
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6939869
- OpenAlex
- W2970480306
- Cited by
- 797
- References
- 89
- Citations by year
- 2026: 110 · 2025: 134 · 2024: 153 · 2023: 158 · 2022: 117 · 2021: 82 · 2020: 41 · 2019: 2
- FWCI
- 8.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, other-oa)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- SUNY Digital Repository Support (State University of New York System) (submittedVersion, cc-by-nc-nd)
- IUScholarWorks (Indiana University) (submittedVersion)
- Funders
- Vetenskapsrådet (D0886501)
- UK Research and Innovation (MR/S003061/1, K99MH117229)
- National Alliance for Research on Schizophrenia and Depression (22604)
- National Institutes of Health (K01 MH113848, MH109532, MH109536, MH109539, U01 MH109528, MH109501, MH109514, MH109499)
- European Commission
- Horizon 2020
- Royal Irish Academy (610307)
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University
- Wellcome Trust (212360/Z/18/Z, 5U01MH109539)
- 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
- genome-wide association study, population diversity, admixed populations, genetic ancestry, genetic architecture, methodological considerations, underrepresented groups, genetic research ethics
- MeSH terms
- Data Accuracy, Human Genetics, Genetics, Population, Humans, Pedigree, Genetic Variation, Genome-Wide Association Study, Genotyping Techniques
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 ›
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