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Genetic correlates of social stratification in Great Britain

Nature Human Behaviour volume 3, pages 1332–1342 (2019)Cite this article

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Abstract

Human DNA polymorphisms vary across geographic regions, with the most commonly observed variation reflecting distant ancestry differences. Here we investigate the geographic clustering of common genetic variants that influence complex traits in a sample of ~450,000 individuals from Great Britain. Of 33 traits analysed, 21 showed significant geographic clustering at the genetic level after controlling for ancestry, probably reflecting migration driven by socioeconomic status (SES). Alleles associated with educational attainment (EA) showed the most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals who leave coal mining areas carry more EA-increasing alleles on average than those in the rest of Great Britain. The level of geographic clustering is correlated with genetic associations between complex traits and regional measures of SES, health and cultural outcomes. Our results are consistent with the hypothesis that social stratification leaves visible marks in geographic arrangements of common allele frequencies and gene–environment correlations.

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Fig. 1: Geographic distributions (birthplace) of the first five PCs, Moran’s I and empirical P values for Moran’s I.
Fig. 2: Moran’s I of five phenotypes and 33 SBLUP polygenic scores computed using the average polygenic score per region in 378 local authority regions (n = 320,940 unrelated individuals).
Fig. 3: Geographic distribution (birthplace) of EA polygenic scores, after regressing out 100 PCs (n = 320,940 unrelated individuals), and Townsend indices from 1971 and 2011.
Fig. 4: Geographically clustered polygenic scores (n = 16; ordered by Moran’s I) for the four migration groups.
Fig. 5: Polygenic scores and EA outcomes over time.
Fig. 6: Comparisons between the results of the RGWASs on EA from census data and from an individual-level EA GWAS that excluded British participants.

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Data availability

This research was conducted using data from the UK Biobank resource (application number 12514) and dbGaP (accession number: phs000674). UK Biobank data can be accessed on request once a research project has been submitted and approved by the UK Biobank committee. dbGaP data can also be accessed on request once a research project has been submitted and approved by dbGaP. The regional measures that have been analysed are publicly available and can be downloaded using the links provided in the Methods.

Code availability

Custom R code used for statistical analyses (for example, the computation of Moran’s I) is available from the corresponding authors on request.

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Acknowledgements

This research was supported by the Australian National Health and Medical Research Council (1107258, 1078901, 1078037, 1056929, 1048853 and 1113400) and the Sylvia and Charles Viertel Charitable Foundation (Senior Medical Research Fellowship). A.A. and K.J.H.V. are supported by the Foundation Volksbond Rotterdam. A.A. and M.G.N. are supported by ZonMw grants 849200011 and 531003014 from The Netherlands Organisation for Health Research and Development. B.P.Z. received funding from the Australian Research Council (FT160100298). The research was conducted using data from the UK Biobank Resource (application number: 12514) and dbGaP (accession number: phs000674). The Genetic Epidemiology Research on Adult Health and Aging study was supported by grant RC2 AG036607 from the National Institutes of Health, as well as grants from the Robert Wood Johnson Foundation, Ellison Medical Foundation, Wayne and Gladys Valley Foundation and Kaiser Permanente. The authors thank the Kaiser Permanente Medical Care Plan, Northern California Region members who participated in the Kaiser Permanente Research Program on Genes, Environment and Health. This study was conducted using UK Biobank resources under application number 12514. UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and Northwest Regional Development Agency. It also received funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands

    Abdel Abdellaoui, Laura Veul & Karin J. H. Verweij

  2. Department of Economics, University of East Anglia, Norwich, UK

    David Hugh-Jones

  3. Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia

    Loic Yengo, Kathryn E. Kemper, Yan Holtz, Naomi R. Wray, Jian Yang & Peter M. Visscher

  4. Department of Biological Psychology, VU University, Amsterdam, The Netherlands

    Michel G. Nivard

  5. School of Psychology, University of Queensland, Brisbane, Queensland, Australia

    Brendan P. Zietsch

  6. Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK

    Timothy M. Frayling

  7. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia

    Naomi R. Wray, Jian Yang & Peter M. Visscher

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  1. Abdel Abdellaoui
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Contributions

A.A., D.H.-J. and P.M.V. conceived and designed the study. A.A., D.H.-J., L.Y. and K.E.K. analysed the data. A.A. wrote the manuscript and produced the figures. D.H.-J., L.Y., K.E.K., M.G.N., L.V., Y.H., B.P.Z., T.M.F., N.R.W., J.Y., K.J.H.V. and P.M.V. provided significant feedback on the analyses and the manuscript. P.M.V. supervised the project.

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Correspondence to Abdel Abdellaoui or Peter M. Visscher.

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Extended data

Extended Data Fig. 1 Variation explained by regional differences of uncorrected polygenic scores.

Linear mixed model results, with phenotype or polygenic score (without regressing out 100 PCs) as a dependent variable and region as random effect (N = 320,940 unrelated individuals). Left: Local Authorities (~380 regions); Middle: MSOA (~5,300 regions), Right: Coal mining Regions (fitted as a binary variable). Red: Birth Place; Green: Current Address; Yellow = significant after FDR correction.

Extended Data Fig. 2 Variation explained by regional differences of ancestry-corrected polygenic scores.

Linear mixed model results, with phenotype or polygenic score (after regressing out 100 PCs) as a dependent variable and region as random effect (N = 320,940 unrelated individuals). Left: Local Authorities (~380 regions); Middle: MSOA (~5,300 regions), Right: Coal mining Regions (fitted as a binary variable). Red: Birth Place; Green: Current Address; Yellow = significant after FDR correction.

Extended Data Fig. 3 Variation explained by regional differences of ancestry-informative PCs.

Linear mixed model results, with PCs as a dependent variable and region as random effect (N = 320,940 unrelated individuals). Left: Local Authorities (~380 regions); Middle: MSOA (~5,300 regions), Right: Coal mining Regions (fitted as a binary variable). Red: Birth Place; Green: Current Address; Yellow = significant after FDR correction.

Extended Data Fig. 4 Associations between polygenic scores and regional measures of socio-economic outcomes.

The standardized effect size estimates of robust linear regressions of polygenic scores on regional measures of socio-economic outcomes in unrelated UK Biobank participants of European descent (N ~320k). The polygenic scores are all standardized residuals after regressing out 100 PCs. Every individual was given the value of their region. Significant effects are colored, whereby the significance threshold is based on FDR correction across all tests shown in all four panels. All SEs were ≤ .002.

Extended Data Fig. 5 Associations between polygenic scores and regional measures of nutrition and health.

The standardized effect size estimates of robust linear regressions of polygenic scores on regional measures of nutrition and health outcomes in unrelated UK Biobank participants of European descent (N ~320k). The polygenic scores are all standardized residuals after regressing out 100 PCs. Every individual was given the value of their region. Significant effects are colored, whereby the significance threshold is based on FDR correction across all tests shown in all four panels. All SEs were ≤ .002.

Extended Data Fig. 6 Associations between polygenic scores and regional measures of religiosity and political preference.

The standardized effect size estimates of robust linear regressions of polygenic scores on regional measures of religiosity and election outcomes in unrelated UK Biobank participants of European descent (N ~320k). The polygenic scores are all standardized residuals after regressing out 100 PCs. Every individual was given the value of their region. Significant effects are colored, whereby the significance threshold is based on FDR correction across all tests shown in all four panels. All SEs were ≤ .002.

Extended Data Fig. 7 Associations between polygenic scores and individual-level phenotypes.

The standardized effect size estimates of robust linear regressions of polygenic scores on individual level phenotypes in unrelated UK Biobank participants of European descent (N ~320k). The polygenic scores are all standardized residuals after regressing out 100 PCs. Significant effects are colored, whereby the significance threshold is based on FDR correction across all tests shown in all four panels. All SEs were ≤ .002.

Extended Data Fig. 8 Genetic correlations between regional measures of socio-economic outcomes and a range of complex traits and diseases.

Genetic correlations (above) and their SEs (below) based on LD score regression for the RGWASs on SES-related traits. Colored is significant after FDR correction. The green numbers in the left part of the Figure below the diagonal of 1’s are the phenotypic correlations between the regional outcomes. The blue stars next to the trait names indicate that UK Biobank was part of the GWAS of the trait. See Supplementary Table 3 for the list of GWASs that the summary statistics of the complex traits were derived from.

Extended Data Fig. 9 Genetic correlations between regional measures of health- and nutrition and a range of complex traits and diseases.

Genetic correlations (above) and their SEs (below) based on LD score regression for the RGWASs on health- and nutrition-related traits. Colored is significant after FDR correction. The green numbers in the left part of the Figure below the diagonal of 1’s are the phenotypic correlations between the regional outcomes. The blue stars next to the trait names indicate that UK Biobank was part of the GWAS of the trait. See Supplementary Table 3 for the list of GWASs that the summary statistics of the complex traits were derived from.

Extended Data Fig. 10 Genetic correlations between regional measures of religiosity and political preference and a range of complex traits and diseases.

Genetic correlations (above) and their SEs (below) based on LD score regression for the RGWASs on ideology-related traits (religion and political preference). Colored is significant after FDR correction. The green numbers in the left part of the Figure below the diagonal of 1’s are the phenotypic correlations between the regional outcomes. The blue stars next to the trait names indicate that UK Biobank was part of the GWAS of the trait. See Supplementary Table 3 for the list of GWASs that the summary statistics of the complex traits were derived from.

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General summary and frequently asked questions, Supplementary Notes, Supplementary References, Supplementary Tables 1–3 and Supplementary Figs. 1–24.

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Abdellaoui, A., Hugh-Jones, D., Yengo, L. et al. Genetic correlates of social stratification in Great Britain. Nat Hum Behav 3, 1332–1342 (2019). https://doi.org/10.1038/s41562-019-0757-5

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