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Cited by 797Genetic Associations and EpidemiologyRace, Genetics, and SocietyForensic and Genetic Research

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Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations

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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., Cuellar-Barboza, Alfredo, Bigdeli, Tim B., Edenberg, Howard J., Huang, Hailiang, Duncan, Laramie E.·2019·Cell
DOI 10.1016/j.cell.2019.08.051PMC PMC6939869
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Abstract

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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Research works printed but not linked: 62

  1. Ahmad, 2017, Inclusion of Population-specific Reference Panel from India to the 1000 Genomes Phase 3 Panel Improves Imputation Accuracy, Sci. Rep., 7, 6733, 10.1038/s41598-017-06905-6 · DOI
  2. Akinhanmi, 2018, Racial disparities in bipolar disorder treatment and research: a call to action, Bipolar Disord., 20, 506, 10.1111/bdi.12638 · DOI
  3. Banda, 2015, Characterizing Race/Ethnicity and Genetic Ancestry for 100,000 Subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort, Genetics, 200, 1285, 10.1534/genetics.115.178616 · DOI
  4. Barbeira, 2018, Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics, Nat. Commun., 9, 1825, 10.1038/s41467-018-03621-1 · DOI · Paper page
  5. Barrett, 2006, Evaluating coverage of genome-wide association studies, Nat. Genet., 38, 659, 10.1038/ng1801 · DOI
  6. Beaton, 2017, Engaging Māori in biobanking and genomic research: a model for biobanks to guide culturally informed governance, operational, and community engagement activities, Genet. Med., 19, 345, 10.1038/gim.2016.111 · DOI
  7. Bigdeli, 2017, Genetic effects influencing risk for major depressive disorder in China and Europe, Transl. Psychiatry, 7, e1074, 10.1038/tp.2016.292 · DOI
  8. Bomba, 2017, The impact of rare and low-frequency genetic variants in common disease, Genome Biol., 18, 77, 10.1186/s13059-017-1212-4 · DOI
  9. Brick, 2019, Shared additive genetic variation for alcohol dependence among subjects of African and European ancestry, Addict. Biol., 24, 132, 10.1111/adb.12578 · DOI
  10. Brown, 2016, Transethnic Genetic-Correlation Estimates from Summary Statistics, Am. J. Hum. Genet., 99, 76, 10.1016/j.ajhg.2016.05.001 · DOI · Paper page
  11. Bycroft, 2018, The UK Biobank resource with deep phenotyping and genomic data, Nature, 562, 203, 10.1038/s41586-018-0579-z · DOI · Paper page
  12. Campbell, 2017, Using iterative learning to improve understanding during the informed consent process in a South African psychiatric genomics study, PLoS ONE, 12, e0188466, 10.1371/journal.pone.0188466 · DOI
  13. Claw, 2018, A framework for enhancing ethical genomic research with Indigenous communities, Nat. Commun., 9, 2957, 10.1038/s41467-018-05188-3 · DOI
  14. Conomos, 2018, Genome-Wide Control of Population Structure and Relatedness in Genetic Association Studies via Linear Mixed Models with Orthogonally Partitioned Structure, bioRxiv
  15. de Vries, 2015, The H3Africa policy framework: negotiating fairness in genomics, Trends Genet., 31, 117, 10.1016/j.tig.2014.11.004 · DOI
  16. Duncan, 2014, Mind the gap: why many geneticists and psychological scientists have discrepant views about gene-environment interaction (G×E) research, Am. Psychol., 69, 249, 10.1037/a0036320 · DOI
  17. Edenberg, 2018, Alcohol Dehydrogenases, Aldehyde Dehydrogenases, and Alcohol Use Disorders: A Critical Review, Alcohol. Clin. Exp. Res., 42, 2281, 10.1111/acer.13904 · DOI
  18. Fiume, 2019, Federated discovery and sharing of genomic data using Beacons, Nat. Biotechnol., 37, 220, 10.1038/s41587-019-0046-x · DOI
  19. Galinsky, 2019, Estimating cross-population genetic correlations of causal effect sizes, Genet. Epidemiol., 43, 180, 10.1002/gepi.22173 · DOI · Paper page
  20. Gilly, 2018, Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits, Nat. Commun., 9, 4674, 10.1038/s41467-018-07070-8 · DOI
  21. Grinde, 2019, Generalizing polygenic risk scores from Europeans to Hispanics/Latinos, Genet. Epidemiol., 43, 50, 10.1002/gepi.22166 · DOI
  22. 2013, The Genotype-Tissue Expression (GTEx) project, Nat. Genet., 45, 580, 10.1038/ng.2653 · DOI · Paper page
  23. Heckerman, 2016, Linear mixed model for heritability estimation that explicitly addresses environmental variation, Proc. Natl. Acad. Sci. USA, 113, 7377, 10.1073/pnas.1510497113 · DOI
  24. Hellwege, 2017, Population Stratification in Genetic Association Studies, Curr. Protoc. Hum. Genet., 95, 1.22.1
  25. Hindorff, 2018, Prioritizing diversity in human genomics research, Nat. Rev. Genet., 19, 175, 10.1038/nrg.2017.89 · DOI · Paper page
  26. Hindorff, 2018, Enhancing diversity to reduce health information disparities and build an evidence base for genomic medicine, Per. Med., 15, 403, 10.2217/pme-2018-0037 · DOI · Paper page
  27. 2005, A haplotype map of the human genome, Nature, 437, 1299, 10.1038/nature04226 · DOI · Paper page
  28. Jiang, 2014, Robust rare variant association testing for quantitative traits in samples with related individuals, Genet. Epidemiol., 38, 10, 10.1002/gepi.21775 · DOI
  29. Kelleher, 2018, Inferring the ancestry of everyone, bioRxv
  30. Kendler, 2010, Interpretation of interactions: guide for the perplexed, Br. J. Psychiatry, 197, 170, 10.1192/bjp.bp.110.081331 · DOI
  31. Kichaev, 2015, Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies, Am. J. Hum. Genet., 97, 260, 10.1016/j.ajhg.2015.06.007 · DOI
  32. Kuchenbaecker, 2018, Assessing Rare Variation in Complex Traits, Methods Mol. Biol., 1793, 51, 10.1007/978-1-4939-7868-7_5 · DOI
  33. Lee, 2013, General framework for meta-analysis of rare variants in sequencing association studies, Am. J. Hum. Genet., 93, 42, 10.1016/j.ajhg.2013.05.010 · DOI
  34. Loh, 2018, Mixed-model association for biobank-scale datasets, Nat. Genet., 50, 906, 10.1038/s41588-018-0144-6 · DOI · Paper page
  35. Luo, 2018, Estimating heritability of complex traits in admixed populations with summary statistics, bioRxiv
  36. Mahajan, 2018, Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, Nat. Genet., 50, 1505, 10.1038/s41588-018-0241-6 · DOI · Paper page
  37. Marigorta, 2013, High trans-ethnic replicability of GWAS results implies common causal variants, PLoS Genet., 9, e1003566, 10.1371/journal.pgen.1003566 · DOI
  38. Márquez-Luna, 2017, Multiethnic polygenic risk scores improve risk prediction in diverse populations, Genet. Epidemiol., 41, 811, 10.1002/gepi.22083 · DOI
  39. Medina-Gomez, 2015, Challenges in conducting genome-wide association studies in highly admixed multi-ethnic populations: the Generation R Study, Eur. J. Epidemiol., 30, 317, 10.1007/s10654-015-9998-4 · DOI
  40. Mersha, 2015, Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities, Hum. Genomics, 9, 1, 10.1186/s40246-014-0023-x · DOI · Paper page
  41. Mills, 2019, A scientometric review of genome-wide association studies, Commun Biol, 2, 9, 10.1038/s42003-018-0261-x · DOI
  42. Minster, 2016, A thrifty variant in CREBRF strongly influences body mass index in Samoans, Nat. Genet., 48, 1049, 10.1038/ng.3620 · DOI
  43. Mulder, 2018, H3Africa: current perspectives, Pharm. Genomics Pers. Med., 11, 59
  44. Nelson, 2017, Imputation-Based Genomic Coverage Assessments of Current Genotyping Arrays: Illumina HumanCore, OmniExpress, Multi-Ethnic global array and sub-arrays, Global Screening Array, Omni2.5M, Omni5M, and Affymetrix UK Biobank, bioRxv
  45. Novembre, 2018, Tread Lightly Interpreting Polygenic Tests of Selection, Genetics, 208, 1351, 10.1534/genetics.118.300786 · DOI · Paper page
  46. Parker, 2016, The ethics of sustainable genomic research in Africa, Genome Biol., 17, 44, 10.1186/s13059-016-0914-3 · DOI
  47. Persyn, 2018, The impact of a fine-scale population stratification on rare variant association test results, PLoS ONE, 13, e0207677, 10.1371/journal.pone.0207677 · DOI
  48. Peterson, 2017, The Genetic Architecture of Major Depressive Disorder in Han Chinese Women, JAMA Psychiatry, 74, 162, 10.1001/jamapsychiatry.2016.3578 · DOI
  49. Peterson, 2017, The utility of empirically assigning ancestry groups in cross-population genetic studies of addiction, Am. J. Addict., 26, 494, 10.1111/ajad.12586 · DOI
  50. Petrovski, 2016, Unequal representation of genetic variation across ancestry groups creates healthcare inequality in the application of precision medicine, Genome Biol., 17, 157, 10.1186/s13059-016-1016-y · DOI · Paper page
  51. 2005, The use of racial, ethnic, and ancestral categories in human genetics research, Am. J. Hum. Genet., 77, 519, 10.1086/491747 · DOI
  52. Roden, 2011, Pharmacogenomics: the genetics of variable drug responses, Circulation, 123, 1661, 10.1161/CIRCULATIONAHA.109.914820 · DOI
  53. Schaid, 2018, From genome-wide associations to candidate causal variants by statistical fine-mapping, Nat. Rev. Genet., 19, 491, 10.1038/s41576-018-0016-z · DOI
  54. Simon, 2002, Understanding cross-national differences in depression prevalence, Psychol. Med., 32, 585, 10.1017/S0033291702005457 · DOI
  55. Sirugo, 2019, The Missing Diversity in Human Genetic Studies, Cell, 177, 26, 10.1016/j.cell.2019.02.048 · DOI · Paper page
  56. Sudmant, 2015, An integrated map of structural variation in 2,504 human genomes, Nature, 526, 75, 10.1038/nature15394 · DOI · Paper page
  57. Sul, 2018, Population structure in genetic studies: Confounding factors and mixed models, PLoS Genet., 14, e1007309, 10.1371/journal.pgen.1007309 · DOI
  58. Tang, 2015, Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs, Am. J. Hum. Genet., 97, 35, 10.1016/j.ajhg.2015.05.001 · DOI
  59. Thornton, 2012, Estimating kinship in admixed populations, Am. J. Hum. Genet., 91, 122, 10.1016/j.ajhg.2012.05.024 · DOI
  60. Zaitlen, 2014, Leveraging population admixture to characterize the heritability of complex traits, Nat. Genet., 46, 1356, 10.1038/ng.3139 · DOI · Paper page
  61. Zhang, 2015, Principal component regression and linear mixed model in association analysis of structured samples: competitors or complements?, Genet. Epidemiol., 39, 149, 10.1002/gepi.21879 · DOI
  62. Zhang, 2013, Adjusting for population stratification in a fine scale with principal components and sequencing data, Genet. Epidemiol., 37, 787, 10.1002/gepi.21764 · DOI

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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
Venue
Cell
Publisher
Elsevier BV
Published
2019-10
Volume
179
Issue
3
Pages
589-603
ISSN
0092-8674 (ISSN-L)
DOI
10.1016/j.cell.2019.08.051
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
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

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