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Cited by 261Genetic Associations and EpidemiologyForensic and Genetic ResearchIntergenerational and Educational Inequality Studies

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Genetic correlates of social stratification in Great Britain

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Abdellaoui, Abdel, Hugh-Jones, David, Yengo, Loic, Kemper, Kathryn E., Nivard, Michel G., Veul, Laura, Holtz, Yan, Zietsch, Brendan P., Frayling, Timothy M., Wray, Naomi R., Yang, Jian, Verweij, Karin J. H., Visscher, Peter M.·2019·Nature Human Behaviour
DOI 10.1038/s41562-019-0757-5

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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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Figure 1 The geographic distributions (birthplace) of the first five PCs, Moran's I and empirical p-values for Moran's I. P-values denoted in green are significant after Bonferroni correction.
Figure 1 The geographic distributions (birthplace) of the first five PCs, Moran's I and empirical p-values for Moran's I. P-values denoted in green are significant after Bonferroni correction.CC-BY-NC-ND · From the PDF
Figure 2 Geographic distribution (birthplace) and Moran's I values for polygenic scores of four major psychiatric disorders (based on GWASs from the Psychiatric Genomics Consortium (PGC): schizophrenia 28 , bipolar 29 , MDD 30 , and ADHD 31 ) and alcohol use 32 before (top row) and after (bottom row) regressing out 100 ancestry-informative PCs. Green p-values are significant after FDR correction.
Figure 2 Geographic distribution (birthplace) and Moran's I values for polygenic scores of four major psychiatric disorders (based on GWASs from the Psychiatric Genomics Consortium (PGC): schizophrenia 28 , bipolar 29 , MDD 30 , and ADHD 31 ) and alcohol use 32 before (top row) and after (bottom row) regressing out 100 ancestry-informative PCs. Green p-values are significant after FDR correction.CC-BY-NC-ND · From the PDF
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CC-BY-NC-ND · From the PDF
Figure 4 Geographic distribution (birthplace) of the educational attainment (EA) polygenic scores before and after regressing out 100 PCs, and the geographic distribution of Townsend indices from 1971 and 2011. The black lines indicate coal mining areas.
Figure 4 Geographic distribution (birthplace) of the educational attainment (EA) polygenic scores before and after regressing out 100 PCs, and the geographic distribution of Townsend indices from 1971 and 2011. The black lines indicate coal mining areas.CC-BY-NC-ND · From the PDF
Figure 5 The average and standard errors of the 16 geographically clustered polygenic scores (ordered by Moran's I) for four migration groups: born in coal field area and moved out, born in coal mining area and stayed, born outside of coal mining area and moved to coal mining area, born outside of coal mining area and stayed out. All polygenic scores shown are standardized residuals after regressing out 100 ancestry-informative PCs. ANOVAs were conducted for every polygenic score to test the presence of group differences, which were all significant with the least significant FDR corrected p-value of 1 × 10 -4 for conscientiousness.
Figure 5 The average and standard errors of the 16 geographically clustered polygenic scores (ordered by Moran's I) for four migration groups: born in coal field area and moved out, born in coal mining area and stayed, born outside of coal mining area and moved to coal mining area, born outside of coal mining area and stayed out. All polygenic scores shown are standardized residuals after regressing out 100 ancestry-informative PCs. ANOVAs were conducted for every polygenic score to test the presence of group differences, which were all significant with the least significant FDR corrected p-value of 1 × 10 -4 for conscientiousness.CC-BY-NC-ND · From the PDF
Figure 6 Genetic correlations based on LD score regression. 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 of coal mining, religiousness, and regional political preference. The blue stars next to the trait names indicate that UK Biobank was part of the GWAS of the trait. See Supplementary Figure 23 for the standard errors and Supplementary Table 4 for the list of GWASs that the summary statistics of the complex traits were derived from.
Figure 6 Genetic correlations based on LD score regression. 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 of coal mining, religiousness, and regional political preference. The blue stars next to the trait names indicate that UK Biobank was part of the GWAS of the trait. See Supplementary Figure 23 for the standard errors and Supplementary Table 4 for the list of GWASs that the summary statistics of the complex traits were derived from.CC-BY-NC-ND · From the PDF
a CC-BY-NC-ND 4.0 International license. certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under bioRxiv preprint doi: https://doi.org/10.1101/457515; this version posted October 30, 2018. The copyright holder for this preprint (which was not
a CC-BY-NC-ND 4.0 International license. certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under bioRxiv preprint doi: https://doi.org/10.1101/457515; this version posted October 30, 2018. The copyright holder for this preprint (which was notCC-BY-NC-ND · From the PDF
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CC-BY-NC-ND · From the PDF
Supplementary Figure 3: Geographic distribution and Moran's I values for polygenic scores (PS) based on GWASs on the first 5 ancestry-informative PCs from the GERA dataset. The upper five maps display uncorrected polygenic score, while the five maps below display the residuals of the polygenic scores after regressing out 100 PCs. Green p-values are significant after FDR correction.
Supplementary Figure 3: Geographic distribution and Moran's I values for polygenic scores (PS) based on GWASs on the first 5 ancestry-informative PCs from the GERA dataset. The upper five maps display uncorrected polygenic score, while the five maps below display the residuals of the polygenic scores after regressing out 100 PCs. Green p-values are significant after FDR correction.CC-BY-NC-ND · From the PDF
Supplementary Figure 4: Moran's I of polygenic scores based on GWASs on the first 5 ancestry-informative PCs from the GERA dataset computed using the average polygenic score per region in 378 local authority regions. The Figure shows the Moran's I of the polygenic scores uncorrected for PCs (red) and corrected for 100 PCs (green), where orange means a significant FDR corrected p-value < .05 (corrected for 20 tests).
Supplementary Figure 4: Moran's I of polygenic scores based on GWASs on the first 5 ancestry-informative PCs from the GERA dataset computed using the average polygenic score per region in 378 local authority regions. The Figure shows the Moran's I of the polygenic scores uncorrected for PCs (red) and corrected for 100 PCs (green), where orange means a significant FDR corrected p-value < .05 (corrected for 20 tests).CC-BY-NC-ND · From the PDF
Supplementary Figure 5: Regional educational attainment (EA) on MSOA level obtained from census data (Office of National Statistics) plotted against the average EA in UK Biobank corrected for age, year of birth, and sex (left plot; R 2 = 40%) and EA polygenic scores corrected for 100 PCs (right plot; R 2 = 20%). All measures have been standardized to have mean 0 and SD 1.
Supplementary Figure 5: Regional educational attainment (EA) on MSOA level obtained from census data (Office of National Statistics) plotted against the average EA in UK Biobank corrected for age, year of birth, and sex (left plot; R 2 = 40%) and EA polygenic scores corrected for 100 PCs (right plot; R 2 = 20%). All measures have been standardized to have mean 0 and SD 1.CC-BY-NC-ND · From the PDF
Supplementary Figure 6: The average Townsend indices from 1971 to 2011 for coal fields and regions without coal.
Supplementary Figure 6: The average Townsend indices from 1971 to 2011 for coal fields and regions without coal.CC-BY-NC-ND · From the PDF
Supplementary Figure 7: The standardized regression coefficients and standard errors for the 16 geographically clustered polygenic scores (ordered by Moran's I) of the associations with the Townsend indices of 1971 to 2011 of the birth places of the subjects (N = 349,982 unrelated subjects). All polygenic scores shown are standardized residuals after regressing out 100 PCs. * = p < .05, ** = p < .01, *** = p < .001.
Supplementary Figure 7: The standardized regression coefficients and standard errors for the 16 geographically clustered polygenic scores (ordered by Moran's I) of the associations with the Townsend indices of 1971 to 2011 of the birth places of the subjects (N = 349,982 unrelated subjects). All polygenic scores shown are standardized residuals after regressing out 100 PCs. * = p < .05, ** = p < .01, *** = p < .001.CC-BY-NC-ND · From the PDF
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Supplementary Figure 9: The average and standard errors for the 16 geographically clustered polygenic scores (ordered by Moran's I) within coal mining regions vs the rest of Great Britain based on the birth place of the participants and on the current addresses separately. All polygenic scores shown are standardized residuals after regressing out 100 ancestry-informative PCs. The differences between Coal Fields and No Coal are all significant with an FDR corrected p-value < .05.
Supplementary Figure 9: The average and standard errors for the 16 geographically clustered polygenic scores (ordered by Moran's I) within coal mining regions vs the rest of Great Britain based on the birth place of the participants and on the current addresses separately. All polygenic scores shown are standardized residuals after regressing out 100 ancestry-informative PCs. The differences between Coal Fields and No Coal are all significant with an FDR corrected p-value < .05.CC-BY-NC-ND · From the PDF
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Supplementary Figure 11: The standardized regression coefficients and standard errors for the 8 significantly clustering polygenic scores (clumped, i.e., based on independent SNPs with p-values <.05; ordered by Moran's I) of the associations with the Townsend indices of 1971 to 2011 of the birth places of the subjects (N = 349,982 unrelated subjects). All polygenic scores shown are standardized residuals after regressing out 100 PCs.
Supplementary Figure 11: The standardized regression coefficients and standard errors for the 8 significantly clustering polygenic scores (clumped, i.e., based on independent SNPs with p-values <.05; ordered by Moran's I) of the associations with the Townsend indices of 1971 to 2011 of the birth places of the subjects (N = 349,982 unrelated subjects). All polygenic scores shown are standardized residuals after regressing out 100 PCs.CC-BY-NC-ND · From the PDF
Supplementary Figure 12: The standardized regression coefficients and standard errors for the 8 significantly clustering polygenic scores (clumped, i.e., based on independent SNPs with p-values <.05; ordered by Moran's I) of the associations with the Townsend indices of 1971 to 2011 of the current address of the subjects (N = 349,982 unrelated subjects). All polygenic scores shown are standardized residuals after regressing out 100 PCs.
Supplementary Figure 12: The standardized regression coefficients and standard errors for the 8 significantly clustering polygenic scores (clumped, i.e., based on independent SNPs with p-values <.05; ordered by Moran's I) of the associations with the Townsend indices of 1971 to 2011 of the current address of the subjects (N = 349,982 unrelated subjects). All polygenic scores shown are standardized residuals after regressing out 100 PCs.CC-BY-NC-ND · From the PDF
Supplementary Figure 13: The average and standard errors for 8 significantly clustering polygenic scores (clumped, i.e., based on independent SNPs with p-values <.05; ordered by Moran's I) within coal mining regions vs the rest of Great Britain for the birth place and the current address. All polygenic scores shown are standardized residuals after regressing out 100 ancestry-informative PCs. The differences between Coal Fields and No Coal are significant for all scores except conscientiousness for both current address and birth place with an FDR corrected p-value < .05.
Supplementary Figure 13: The average and standard errors for 8 significantly clustering polygenic scores (clumped, i.e., based on independent SNPs with p-values <.05; ordered by Moran's I) within coal mining regions vs the rest of Great Britain for the birth place and the current address. All polygenic scores shown are standardized residuals after regressing out 100 ancestry-informative PCs. The differences between Coal Fields and No Coal are significant for all scores except conscientiousness for both current address and birth place with an FDR corrected p-value < .05.CC-BY-NC-ND · From the PDF
Supplementary Figure 14: The average and standard errors for 8 significantly clustering polygenic scores (clumped, i.e., based on independent SNPs with p-values <.05; ordered by Moran's I) for four migration groups: born in coal field area and moved out, born in coal field area and stayed, born outside of coal field area and moved to coal field area, born outside of coal field area and stayed out. All polygenic scores shown are standardized residuals after regressing out 100 ancestry-informative PCs. All polygenic scores show significant group differences after (FDR corrected p < .05), except conscientiousness.
Supplementary Figure 14: The average and standard errors for 8 significantly clustering polygenic scores (clumped, i.e., based on independent SNPs with p-values <.05; ordered by Moran's I) for four migration groups: born in coal field area and moved out, born in coal field area and stayed, born outside of coal field area and moved to coal field area, born outside of coal field area and stayed out. All polygenic scores shown are standardized residuals after regressing out 100 ancestry-informative PCs. All polygenic scores show significant group differences after (FDR corrected p < .05), except conscientiousness.CC-BY-NC-ND · From the PDF
Supplementary Figure 15: Linear Mixed Model results, with 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.
Supplementary Figure 15: Linear Mixed Model results, with 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.CC-BY-NC-ND · From the PDF
Supplementary Figure 16: 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.
Supplementary Figure 16: 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.CC-BY-NC-ND · From the PDF
Image 23
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Supplementary Figure 18: Manhattan plots of the two GC-corrected GWASs on regional educational attainment (EA). The suggestive significance threshold (blue line) is set at 5 × 10 -8 , and the genome-wide significance threshold (red line) is set at 1 × 10 -8 .
Supplementary Figure 18: Manhattan plots of the two GC-corrected GWASs on regional educational attainment (EA). The suggestive significance threshold (blue line) is set at 5 × 10 -8 , and the genome-wide significance threshold (red line) is set at 1 × 10 -8 .CC-BY-NC-ND · From the PDF
Image 25
CC-BY-NC-ND · From the PDF
Supplementary Figure 20: Scatterplots of the genetic correlations with 64 complex traits from Supplementary Figure 16. Each dot represents a complex trait. The correlations between the rg's from the regional EA GWAS at local authority level and the individual EA GWAS (plot to the left) is .99. The correlations between the rg's from the regional EA GWAS at MSOA level and the individual EA GWAS (plot to the right) is also .99.
Supplementary Figure 20: Scatterplots of the genetic correlations with 64 complex traits from Supplementary Figure 16. Each dot represents a complex trait. The correlations between the rg's from the regional EA GWAS at local authority level and the individual EA GWAS (plot to the left) is .99. The correlations between the rg's from the regional EA GWAS at MSOA level and the individual EA GWAS (plot to the right) is also .99.CC-BY-NC-ND · From the PDF
Image 27
CC-BY-NC-ND · From the PDF
Supplementary Figure 22: Distributions of regional-level phenotypes. The distributions show all subjects included in the GWASs, where all subjects from the same region were assigned the same phenotypic value.
Supplementary Figure 22: Distributions of regional-level phenotypes. The distributions show all subjects included in the GWASs, where all subjects from the same region were assigned the same phenotypic value.CC-BY-NC-ND · From the PDF
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Supplementary Figure 23: Manhattan plots of the GC-corrected GWAS on the presence of coal fields in the birthplace and the current address, the proportion of religious vs nonreligious inhabitants, the Brexit referendum of 2016, and the regional outcomes on general election results of 1970 and 2015. The suggestive significance threshold (blue line) is set at 5 × 10 -8 , and the genome-wide significance threshold (red line) is set at 1 × 10 -8 .
Supplementary Figure 23: Manhattan plots of the GC-corrected GWAS on the presence of coal fields in the birthplace and the current address, the proportion of religious vs nonreligious inhabitants, the Brexit referendum of 2016, and the regional outcomes on general election results of 1970 and 2015. The suggestive significance threshold (blue line) is set at 5 × 10 -8 , and the genome-wide significance threshold (red line) is set at 1 × 10 -8 .CC-BY-NC-ND · From the PDF
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Supplementary Figure 24: The significance of enrichment for ten cell type groups based on heritability partitioning with LD score regression. The dashed line at -log10(P) = 3.53 is the Bonferroni adjusted significance threshold (adjusted for 170 tests).
Supplementary Figure 24: The significance of enrichment for ten cell type groups based on heritability partitioning with LD score regression. The dashed line at -log10(P) = 3.53 is the Bonferroni adjusted significance threshold (adjusted for 170 tests).CC-BY-NC-ND · From the PDF
Supplementary Figure 25: Standard errors of the genetic correlations based on LD score regression from Figure 6. Colored is significant after FDR correction. The green numbers in the left part of the Figure below the diagonal of 1's are the standard errors of the phenotypic correlations between the regional outcomes of coal mining, religiousness, and regional political preference. The blue stars next to the trait names indicate that UK Biobank was part of the GWAS of the trait.
Supplementary Figure 25: Standard errors of the genetic correlations based on LD score regression from Figure 6. Colored is significant after FDR correction. The green numbers in the left part of the Figure below the diagonal of 1's are the standard errors of the phenotypic correlations between the regional outcomes of coal mining, religiousness, and regional political preference. The blue stars next to the trait names indicate that UK Biobank was part of the GWAS of the trait.CC-BY-NC-ND · From the PDF
Supplementary Figure 26: Genetic correlations (r g ) and their standard errors (SE) based on LD score regression between regional political outcomes, within higher and lower SES groups. Colored is significant after FDR correction. The blue stars next to the trait names indicate that UK Biobank was part of the GWAS of the trait.
Supplementary Figure 26: Genetic correlations (r g ) and their standard errors (SE) based on LD score regression between regional political outcomes, within higher and lower SES groups. Colored is significant after FDR correction. The blue stars next to the trait names indicate that UK Biobank was part of the GWAS of the trait.CC-BY-NC-ND · From the PDF

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  1. Townsend, P., Phillimore, P. & Beattie, A. Health and Deprivation: Inequality and the North (Routledge, 1988).

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Metadata

Authors
Abdellaoui, A., Hugh-Jones, D., Yengo, L., Kemper, K. E., Nivard, M. G., Veul, L., Holtz, Y., Zietsch, B. P., Frayling, T. M., Wray, N. R., Yang, J., Verweij, K. J. H., & Visscher, P. M.
Affiliations
  • Amsterdam University Medical Centers (NL)
  • University of East Anglia (GB)
  • The University of Queensland (AU)
  • Vrije Universiteit Amsterdam (NL)
  • University of Exeter (GB)
Corresponding author
  • Abdellaoui, Abdel
  • Visscher, Peter M.
ORCID
Venue
Nature Human Behaviour
Publisher
Springer Science and Business Media LLC
Published
2019-10-21
Volume
3
Issue
12
Pages
1332-1342
ISSN
2397-3374 (ISSN-L)
DOI
10.1038/s41562-019-0757-5
OpenAlex
W2980548847
Cited by
261
References
100
Citations by year
2026: 18 · 2025: 27 · 2024: 34 · 2023: 34 · 2022: 42 · 2021: 41 · 2020: 49 · 2019: 14
FWCI
22.15
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Human Behaviour (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Pure Amsterdam UMC (publishedVersion, other-oa)
  • VU Research Portal (publishedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Royal Devon and Exeter Research Repository (Royal Devon and Exeter) (submittedVersion)
  • UEA Digital Repository (University of East Anglia) (submittedVersion)
Funders
  • Ellison Medical Foundation
  • Robert Wood Johnson Foundation
  • Wayne and Gladys Valley Foundation
  • Kaiser Permanente
  • Wellcome Trust
  • Scottish Government
  • Llywodraeth Cymru
  • Stichting Volksbond Rotterdam
  • British Heart Foundation
  • Diabetes UK
  • ZonMw (849200011, 531003014)
  • Northwest Regional Development Agency
  • National Institutes of Health
  • Medical Research Council (1107258, 1113400, 1078037, 1078901)
  • Australian Research Council (FT160100298, 1113400)
  • National Health and Medical Research Council (1113400, 1078901, 1056929, 1107258, 1078037, 1048853)
  • National Institute on Aging (RC2AG036607)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
  • Social Sciences › Sociology and Political Science › Intergenerational and Educational Inequality Studies
Keywords
social stratification, educational attainment, socioeconomic status, gene-environment correlation, migration, coal mining areas, polygenic risk score, complex traits, regional health disparities, Great Britain
MeSH terms
Adipose Tissue, Alleles, Body Height, Educational Status, Emigration and Immigration, United Kingdom, Health Status, Humans, Phenotype, Social Class, Body Mass Index, Cluster Analysis, Multifactorial Inheritance, Polymorphism, Single Nucleotide, White People, Geographic Mapping

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