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Cited by 314Genetic Associations and EpidemiologyRace, Genetics, and SocietyForensic and Genetic Research

Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis

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Haworth, Simon, Mitchell, Ruth, Corbin, Laura, Wade, Kaitlin H., Dudding, Tom, Budu-Aggrey, Ashley, Carslake, David, Hemani, Gibran +5 more·2019·Nature Communications
DOI 10.1038/s41467-018-08219-1PMC PMC6338768

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Abstract

Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when making inference from genotype data in large studies.

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  • GWAS associations for some phenotypes estimated from samples of unrelated individuals also capture effects of demography (assortative mating and population stratification) and indirect genetic effects of relatives.

    “However, there is growing evidence that GWAS associations for some phenotypes estimated from samples of unrelated individuals also capture effects of demography7,8 (assortative mating9–11 and population stratification12) and indirect genetic effects of relatives13–19 (Fig. 1).”

    From Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects (2022) · cites this paper as [12]

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Research works printed but not linked: 39

  1. [1]Cardon LR Palmer LJ Population stratification and spurious allelic association Lancet 2003 361 598 604 10.1016/S0140-6736(03)12520-2 12598158 · DOI
  2. [2]Price AL Principal components analysis corrects for stratification in genome-wide association studies Nat. Genet. 2006 38 904 10.1038/ng1847 16862161 · DOI · Paper page
  3. [3]Bouaziz M Ambroise C Guedj M Accounting for population stratification in practice: a comparison of the main strategies dedicated to genome-wide association studies PLoS One 2011 6 e28845 10.1371/journal.pone.0028845 22216125 PMC3244428 · DOI
  4. [4]Pe'er I Yelensky R Altshule D Daly MJ Estimation of the multiple testing burden for genomewide association studies of nearly all common variants Genet. Epidemiol. 2008 32 381 385 10.1002/gepi.20303 18348202 · DOI · Paper page
  5. [6]Dandine-Roulland C Accuracy of heritability estimations in presence of hidden population stratification Sci. Rep. 2016 6 26471 10.1038/srep26471 27220488 PMC4879529 · DOI
  6. [8]Sohail, M. et al. Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies. Preprint at bioRxiv10.1101/355057 (2018).
  7. [9]Berg, J. J. et al. Reduced signal for polygenic adaptation of height in UK Biobank. Preprint at bioRxiv10.1101/354951 (2018). PMC6428572 30895923
  8. [11]Collins R What makes UK Biobank special? Lancet 2012 379 1173 1174 10.1016/S0140-6736(12)60404-8 22463865 · DOI · Paper page
  9. [12]Smith GD Hemani G Mendelian randomization: genetic anchors for causal inference in epidemiological studies Hum. Mol. Genet. 2014 23 R89 R98 10.1093/hmg/ddu328 25064373 PMC4170722 · DOI · Paper page
  10. [13]Paaby AB Rockman MV The many faces of pleiotropy Trends Genet. 2013 29 66 73 10.1016/j.tig.2012.10.010 23140989 PMC3558540 · DOI
  11. [14]Lawlor DA Harbord RM Sterne JAC Timpson N Smith GD Mendelian randomization: using genes as instruments for making causal inferences in epidemiology Stat. Med. 2008 27 1133 1163 10.1002/sim.3034 17886233 · DOI · Paper page
  12. [15]Boyd A Cohort Profile: The ‘Children of the 90s’-the index offspring of the Avon Longitudinal Study of Parents and Children Int. J. Epidemiol. 2013 42 111 127 10.1093/ije/dys064 22507743 PMC3600618 · DOI · Paper page
  13. [16]Fraser A Cohort Profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort Int. J. Epidemiol. 2013 42 97 110 10.1093/ije/dys066 22507742 PMC3600619 · DOI · Paper page
  14. [18]Leslie S The fine-scale genetic structure of the British population Nature 2015 519 309 10.1038/nature14230 25788095 PMC4632200 · DOI
  15. [20]Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. Preprint at bioRxiv10.1101/166298 (2017).
  16. [21]Fry A Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population Am. J. Epidemiol. 2017 186 1026 1034 10.1093/aje/kwx246 28641372 PMC5860371 · DOI · Paper page
  17. [22]Taylor AE Exploring the association of genetic factors with participation in the Avon Longitudinal Study of Parents and Children Int. J. Epidemiol. 2018 47 1207 1216 10.1093/ije/dyy060 29800128 PMC6124613 · DOI · Paper page
  18. [23]Cole SR Illustrating bias due to conditioning on a collider Int. J. Epidemiol. 2010 39 417 420 10.1093/ije/dyp334 19926667 PMC2846442 · DOI · Paper page
  19. [24]Munafò MR Tilling K Taylor AE Evans DM Davey Smith G Collider scope: when selection bias can substantially influence observed associations Int. J. Epidemiol. 2018 47 226 235 10.1093/ije/dyx206 29040562 PMC5837306 · DOI · Paper page
  20. [25]Chang CC Second-generation PLINK: rising to the challenge of larger and richer datasets Gigascience 2015 4 7 10.1186/s13742-015-0047-8 25722852 PMC4342193 · DOI · Paper page
  21. [26]Mistry, S., Harrison, J. R., Smith, D. J., Escott-Price, V. & Zammit, S. The use of polygenic risk scores to identify phenotypes associated with genetic risk of schizophrenia: Systematic review. Schizophr Res. 197, 2–8 (2017). 29129507
  22. [27]Abraham G Genomic prediction of coronary heart disease Eur. Heart J. 2016 37 3267 3278 10.1093/eurheartj/ehw450 27655226 PMC5146693 · DOI · Paper page
  23. [28]Okbay A Genome-wide association study identifies 74 loci associated with educational attainment Nature 2016 533 539 10.1038/nature17671 27225129 PMC4883595 · DOI · Paper page
  24. [29]Locke AE Genetic studies of body mass index yield new insights for obesity biology Nature 2015 518 197 U401 10.1038/nature14177 25673413 PMC4382211 · DOI · Paper page
  25. [30]Wood AR Defining the role of common variation in the genomic and biological architecture of adult human height Nat. Genet 2014 46 1173 1186 10.1038/ng.3097 25282103 PMC4250049 · DOI · Paper page
  26. [31]Wood SN Stable and efficient multiple smoothing parameter estimation for generalized additive models J. Am. Stat. Assoc. 2004 99 673 686 10.1198/016214504000000980 · DOI
  27. [34]Kanai M Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases Nat. Genet 2018 50 390 400 10.1038/s41588-018-0047-6 29403010 · DOI · Paper page
  28. [35]Robinson MR Population genetic differentiation of height and body mass index across Europe Nat. Genet 2015 47 1357 10.1038/ng.3401 26366552 PMC4984852 · DOI
  29. [36]Turchin MC Evidence of widespread selection on standing variation in Europe at height-associated SNPs Nat. Genet. 2012 44 1015 1019 10.1038/ng.2368 22902787 PMC3480734 · DOI · Paper page
  30. [37]Berg JJ Coop G A population genetic signal of polygenic adaptation PLoS Genet. 2014 10 e1004412 10.1371/journal.pgen.1004412 25102153 PMC4125079 · DOI · Paper page
  31. [39]Domingue BW Rehkopf DH Conley D Boardman JD Geographic clustering of polygenic scores at different stages of the life course RSF 2018 4 137 149 PMC6368254 30740524
  32. [41]Curtis D Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia Psychiatr. Genet 2018 28 85 89 10.1097/YPG.0000000000000206 30160659 · DOI · Paper page
  33. [42]Kong A The nature of nurture: effects of parental genotypes Science 2018 359 424 428 10.1126/science.aan6877 29371463 · DOI · Paper page
  34. [43]Smith MW O’Brien SJ Mapping by admixture linkage disequilibrium: advances, limitations and guidelines Nat. Rev. Genet 2005 6 623 632 10.1038/nrg1657 16012528 · DOI
  35. [44]Heckerman D Linear mixed model for heritability estimation that explicitly addresses environmental variation Proc. Natl. Acad. Sci. USA 2016 113 7377 7382 10.1073/pnas.1510497113 27382152 PMC4941438 · DOI
  36. [46]Delaneau O Marchini J Zagury JF A linear complexity phasing method for thousands of genomes Nat. Methods 2012 9 179 181 10.1038/nmeth.1785 22138821 · DOI · Paper page
  37. [48]Hellenthal G A genetic atlas of human admixture history Science 2014 343 747 751 10.1126/science.1243518 24531965 PMC4209567 · DOI · Paper page
  38. [49]Lawson D van Dorp L Falush D A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots Nat. Commun. 2018 9 3258 10.1038/s41467-018-05257-7 30108219 PMC6092366 · DOI · Paper page
  39. [53]Hemani G The MR-Base platform supports systematic causal inference across the human phenome eLife 2018 7 e34408 10.7554/eLife.34408 29846171 PMC5976434 · DOI · Paper page

Cited by

Sorted by citations

Authors
Haworth, S., Mitchell, R., Corbin, L., Wade, K. H., Dudding, T., Budu-Aggrey, A., Carslake, D., Hemani, G., Paternoster, L., Smith, G. D., Davies, N., Lawson, D. J., & J. Timpson, N.
Affiliations
University of Bristol (GB)
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-01-18
Volume
10
Issue
1
Pages
333-333
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-018-08219-1
PMC
PMC6338768
OpenAlex
W2910199421
Cited by
314
References
8
Citations by year
2026: 10 · 2025: 26 · 2024: 51 · 2023: 34 · 2022: 49 · 2021: 51 · 2020: 54 · 2019: 31 · 2018: 8
FWCI
27.92
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Explore Bristol Research (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Wellcome Trust (102215/2/13/2), 208806, Z/17/Z, MC_UU_12013/1, 102215, 202802/Z/16/Z, 104125/Z/14/Z, 208806/Z/17/Z, 102215/2/13/, C18281/A19169, WT104125MA, 201237, 201237/Z/16/Z, 202802)
  • Cancer Research UK (C18281/, C18281 / A19169, A19169, 19169)
  • National Institute for Health and Care Research (C18281/A19169, MC_UU_12013/1, 202802/Z/16/Z)
  • University of Oxford
  • University of Bristol (MC_UU_12013/1, C18281/A19169, MC_UU_12013/3, 102215/2/13/2, MC_UU_12013/4, MC_UU_12013)
  • Medical Research Council (102215, MC_UU_12013, MC_PC_19009, 202802/Z/16/Z, ref: 102215/2/13/2, MC_UU_12013/4, MC_PC_15018, MCUU12013/1, MC_PC_17228, 102215/2/13/2, MC_UU_12013/3, MC_UU_00011/1)
  • Economic and Social Research Council (ES/N000757/1, 102215/2/13/2)
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
UK Biobank, birthplace, genetic score, genetic variants, principal components, health outcomes, complex traits, population stratification
MeSH terms
Epidemiology, Genotype, Geography, United Kingdom, Humans, Phenotype, Genetic Variation, Sequence Alignment, Biological Specimen Banks, Multifactorial Inheritance, Pregnant People, Genome-Wide Association Study

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