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Genetics and the geography of health, behaviour and attainment

Abstract

Young people’s life chances can be predicted by characteristics of their neighbourhood1. Children growing up in disadvantaged neighbourhoods exhibit worse physical and mental health and suffer poorer educational and economic outcomes than children growing up in advantaged neighbourhoods. Increasing recognition that aspects of social inequalities tend, in fact, to be geographical inequalities2,3,4,5 is stimulating research and focusing policy interest on the role of place in shaping health, behaviour and social outcomes. Where neighbourhood effects are causal, neighbourhood-level interventions can be effective. Where neighbourhood effects reflect selection of families with different characteristics into different neighbourhoods, interventions should instead target families or individuals directly. To test how selection may affect different neighbourhood-linked problems, we linked neighbourhood data with genetic, health and social outcome data for >7,000 European-descent UK and US young people in the E-Risk and Add Health studies. We tested selection/concentration of genetic risks for obesity, schizophrenia, teen pregnancy and poor educational outcomes in high-risk neighbourhoods, including genetic analysis of neighbourhood mobility. Findings argue against genetic selection/concentration as an explanation for neighbourhood gradients in obesity and mental health problems. By contrast, modest genetic selection/concentration was evident for teen pregnancy and poor educational outcomes, suggesting that neighbourhood effects for these outcomes should be interpreted with care.

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Fig. 1: Children with a higher genetic risk had more health and social problems by 18 years of age.
Fig. 2: Quantification of E-Risk families’ neighbourhood disadvantage using ACORN and a composite ecological-risk index.
Fig. 3: Neighbourhood gradients in obesity, mental health problems, teen pregnancy, poor educational qualifications, NEET status and in the genetic risk for these phenotypes.
Fig. 4: Age-at-first-birth and education polygenic score association with neighbourhood mobility in the Add Health study.

Data availability

The E-Risk data set reported in the current article is not publicly available owing to a lack of informed consent and ethical approval, but is available on request by qualified scientists. Requests require a concept paper describing the purpose of data access, ethical approval at the applicant’s institution and provision for secure data access. We offer secure access on the Duke University and King’s College London campuses. All data analysis scripts and results files are available for review. The Add Health data can be accessed through the Add Health study. Details are available through the Carolina Population Center as described here: https://www.cpc.unc.edu/projects/addhealth/documentation. Genotype data are available through dbGaP.

Code availability

All data analysis scripts and results files are available for review.

References

  1. Woods, L. M. et al. Geographical variation in life expectancy at birth in England and Wales is largely explained by deprivation. J. Epidemiol. Community Health 59, 115–120 (2005).

    Article  Google Scholar 

  2. Chetty, R. et al. The association between income and life expectancy in the United States, 2001–2014. JAMA 315, 1750–1766 (2016).

    CAS  Article  Google Scholar 

  3. Luo, Z.-C. et al. Disparities in birth outcomes by neighborhood income: temporal trends in rural and urban areas, British Columbia. Epidemiology 15, 679–686 (2004).

    Article  Google Scholar 

  4. Sampson, R. J. Urban sustainability in an age of enduring inequalities: advancing theory and ecometrics for the 21st-century city. Proc. Natl Acad. Sci. USA 114, 8957–8962 (2017).

    CAS  Article  Google Scholar 

  5. Newton, J. N. et al. Changes in health in England, with analysis by English regions and areas of deprivation, 1990–2013: a systematic analysis for the global burden of disease study 2013. Lancet 386, 2257–2274 (2015).

    Article  Google Scholar 

  6. Sampson, R. J., Morenoff, J. D. & Gannon-Rowley, T. Assessing ‘neighborhood effects’: social processes and new directions in research. Annu. Rev. Sociol. 28, 443–478 (2002).

    Article  Google Scholar 

  7. Oakes, J. M. The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology. Soc. Sci. Med. 58, 1929–1952 (2004).

    Article  Google Scholar 

  8. White, J. S. et al. Long-term effects of neighbourhood deprivation on diabetes risk: quasi-experimental evidence from a refugee dispersal policy in Sweden. Lancet Diabetes Endocrinol. 4, 517–524 (2016).

    Article  Google Scholar 

  9. Ludwig, J. et al. Long-term neighborhood effects on low-income families: evidence from moving to opportunity. Am. Econ. Rev. 103, 226–231 (2013).

    Article  Google Scholar 

  10. Chetty, R. & Hendren, N. The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates https://doi.org/10.3386/w23002 (National Bureau of Economic Research, 2016).

  11. Chetty, R., Hendren, N. & Katz, L. F. The effects of exposure to better neighborhoods on children: new evidence from the moving to opportunity experiment. Am. Econ. Rev. 106, 855–902 (2016).

    Article  Google Scholar 

  12. Arcaya, M. C. et al. Role of health in predicting moves to poor neighborhoods among Hurricane Katrina survivors. Proc. Natl Acad. Sci. USA 111, 16246–16253 (2014).

    CAS  Article  Google Scholar 

  13. Oakes, J. M., Andrade, K. E., Biyoow, I. M. & Cowan, L. T. Twenty years of neighborhood effect research: an assessment. Curr. Epidemiol. Rep. 2, 80–87 (2015).

    Article  Google Scholar 

  14. Buka, S. L., Brennan, R. T., Rich-Edwards, J. W., Raudenbush, S. W. & Earls, F. Neighborhood support and the birth weight of urban infants. Am. J. Epidemiol. 157, 1–8 (2003).

    Article  Google Scholar 

  15. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  Article  Google Scholar 

  16. Schizophrenia Working Group of the Psychiatric Genomics Consortium Biological insights from 108 schizophrenia-associated genetic loci. Nature 51, 421–427 (2014).

  17. Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet. 48, 1462–1472 (2016).

    CAS  Article  Google Scholar 

  18. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    CAS  Article  Google Scholar 

  19. Odgers, C. L., Caspi, A., Bates, C. J., Sampson, R. J. & Moffitt, T. E. Systematic social observation of children’s neighborhoods using Google Street View: a reliable and cost-effective method. J. Child Psychol. Psychiatry 53, 1009–1017 (2012).

    Article  Google Scholar 

  20. Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).

    CAS  Article  Google Scholar 

  21. Belsky, D. W. et al. Polygenic risk, rapid childhood growth, and the development of obesity: evidence from a 4-decade longitudinal study. Arch. Pediatr. Adolesc. Med. 166, 515–521 (2012).

    Article  Google Scholar 

  22. Belsky, D. W. et al. Polygenic risk and the developmental progression to heavy, persistent smoking and nicotine dependence: evidence from a 4-decade longitudinal study. JAMA Psychiatry 70, 534–542 (2013).

    Article  Google Scholar 

  23. Agerbo, E. et al. Polygenic risk score, parental socioeconomic status, family history of psychiatric disorders, and the risk for schizophrenia: a Danish population-based study and meta-analysis. JAMA Psychiatry 72, 635–641 (2015).

    Article  Google Scholar 

  24. Mujahid, M. S., Diez Roux, A. V., Morenoff, J. D. & Raghunathan, T. Assessing the measurement properties of neighborhood scales: from psychometrics to ecometrics. Am. J. Epidemiol. 165, 858–867 (2007).

    Article  Google Scholar 

  25. Harris, K. M. et al. Social, behavioral, and genetic linkages from adolescence into adulthood. Am. J. Public Health 103, S25–S32 (2013).

    Article  Google Scholar 

  26. Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat. Genet. 43, 519–525 (2011).

    CAS  Article  Google Scholar 

  27. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  Article  Google Scholar 

  28. Sariaslan, A. et al. Schizophrenia and subsequent neighborhood deprivation: revisiting the social drift hypothesis using population, twin and molecular genetic data. Transl Psychiatry 6, e796 (2016).

    CAS  Article  Google Scholar 

  29. Martin, J. et al. Association of genetic risk for schizophrenia with nonparticipation over time in a population-based cohort study. Am. J. Epidemiol. 183, 1149–1158 (2016).

    Article  Google Scholar 

  30. Gage, S. H., Smith, G. D. & Munafò, M. R. Schizophrenia and neighbourhood deprivation. Transl Psychiatry 6, e979 (2016).

    CAS  Article  Google Scholar 

  31. Sharkey, P. Neighborhoods, cities, and economic mobility. RSF 2, 159–177 (2016).

    Article  Google Scholar 

  32. Sharkey, P. Stuck in Place: Urban Neighborhoods and the End of Progress Toward Racial Equality (Univ. Chicago Press, 2013).

  33. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    CAS  Article  Google Scholar 

  34. Polderman, T. J. C. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).

    CAS  Article  Google Scholar 

  35. Hill, W. D. et al. Molecular genetic contributions to social deprivation and household income in UK Biobank. Curr. Biol. 26, 3083–3089 (2016).

    CAS  Article  Google Scholar 

  36. Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

    CAS  Article  Google Scholar 

  37. Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).

    CAS  Article  Google Scholar 

  38. Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).

    Article  Google Scholar 

  39. Hartwig, F. P., Davies, N. M., Hemani, G. & Davey Smith, G. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int. J. Epidemiol. 45, 1717–1726 (2016).

    Article  Google Scholar 

  40. Trouton, A., Spinath, F. M. & Plomin, R. Twins Early Development Study (TEDS): a multivariate, longitudinal genetic investigation of language, cognition and behavior problems in childhood. Twin Res. Hum. Genet. 5, 444–448 (2002).

    Article  Google Scholar 

  41. Moffitt, T. E. & E-Risk Team Teen-aged mothers in contemporary Britain. J. Child Psychol. Psychiatry 43, 727–742 (2002).

    Article  Google Scholar 

  42. Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  Google Scholar 

  43. 1000 Genomes Project Consortium et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Article  Google Scholar 

  44. Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

    CAS  Article  Google Scholar 

  45. Lapouse, R., Monk, M. A. & Terris, M. The drift hypothesis and socioeconomic differentials in schizophrenia. Am. J. Public Health Nations Health 46, 978–986 (1956).

    CAS  Article  Google Scholar 

  46. Murray, R. M., Jones, P. B., Susser, E., Os, J. V. & Cannon, M. The Epidemiology of Schizophrenia (Cambridge Univ. Press, 2002).

  47. Euesden, J., Lewis, C. M. & O’Reilly, P. F. PRSice: polygenic risk score software. Bioinformatics 31, 1466–1468 (2015).

    CAS  Article  Google Scholar 

  48. Hamer, D. & Sirota, L. Beware the chopsticks gene. Mol. Psychiatry 5, 11–13 (2000).

    CAS  Article  Google Scholar 

  49. Price, A. L., Zaitlen, N. A., Reich, D. & Patterson, N. New approaches to population stratification in genome-wide association studies. Nat. Rev. Genet. 11, 459–463 (2010).

    CAS  Article  Google Scholar 

  50. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  Google Scholar 

  51. Conley, D. et al. Assortative mating and differential fertility by phenotype and genotype across the 20th century. Proc. Natl Acad. Sci. USA 113, 6647–6652 (2016).

    CAS  Article  Google Scholar 

  52. Sampson, R. J., Raudenbush, S. W. & Earls, F. Neighborhoods and violent crime: a multilevel study of collective efficacy. Science 277, 918–924 (1997).

    CAS  Article  Google Scholar 

  53. Sampson, R. J., Morenoff, J. D. & Earls, F. Beyond social capital: spatial dynamics of collective efficacy for children. Am. Sociol. Rev. 64, 633–660 (1999).

    Article  Google Scholar 

  54. Waist Circumference and Waist–Hip Ratio: Report of a WHO Expert Consultation http://www.who.int/nutrition/publications/obesity/WHO_report_waistcircumference_and_waisthip_ratio/en/ (WHO, 2008).

  55. Scantlebury, R. & Moody, A. in Health Survey for England, 2014 (eds. Craig, R., Fuller, E. & Mindell, J.) Vol. 1 Ch. 9 (The Health and Social Care Information Centre, 2014).

  56. Schaefer, J. D. et al. Adolescent victimization and early-adult psychopathology: approaching causal inference using a longitudinal twin study to rule out noncausal explanations. Clin. Psychol. Sci. 6, 352–371 (2018).

    Article  Google Scholar 

  57. Caspi, A. & Moffitt, T. E. All for one and one for all: mental disorders in one dimension. Am. J. Psychiatry 175, 831–844 (2018).

    Article  Google Scholar 

  58. Goldman-Mellor, S. et al. Committed to work but vulnerable: self-perceptions and mental health in NEET 18-year olds from a contemporary British cohort. J. Child Psychol. Psychiatry 57, 196–203 (2016).

    Article  Google Scholar 

  59. Brown, J. NEET: Young people Not in Education, Employment Or Training (House of Commons, 2016).

  60. The Haplotype Reference Consortium. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

  61. Domingue, B. W. et al. The social genome of friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health. Proc. Natl Acad. Sci. USA 115, 702–707 (2018).

    CAS  Article  Google Scholar 

  62. Billy, J. O., Wenzlow, A. T. & Grady, W. R. User Documentation for the Add Health Contextual Database (Seattle Battelle Center Public Health Research, 1998).

  63. Morales, L. & Monbureau, T. Add Health Wave IV Contextual Data (Add Health, 2013).

  64. Belsky, D. W. et al. The genetics of success: how single-nucleotide polymorphisms associated with educational attainment relate to life-course development. Psychol. Sci. 27, 957–972 (2016).

    Article  Google Scholar 

  65. Power and Sample Size https://www.stata.com/features/power-and-sample-size/ (Stata, 2017).

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Acknowledgements

The E-Risk Study is funded by the Medical Research Council (UKMRC grant G1002190). Additional support was provided by NICHD grant HD077482, Google and by the Jacobs Foundation. The Add Health study is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development grants P01HD31921, R01HD073342 and R01HD060726, with cooperative funding from 23 other federal agencies and foundations. D.W.B. and C.L.O. were supported by fellowships from the Jacobs Foundation. C.L.O. is supported by the Canadian Institute for Advanced Research. B.W.D. is supported by the Russell Sage Foundation award 961704. We are grateful to the E-Risk study mothers and fathers, the twins and the twins’ teachers, and the Add Health study participants and their parents for their participation. Our thanks to CACI, Google Street View and to members of the E-Risk team for their dedication, hard work and insights. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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D.W.B., A.C., T.E.M. and C.L.O. designed the research. A.C., T.E.M., L.A., C.L.O. and K.M.H. collected the data. Data were analysed by D.W.B., B.W.D., R.M.H., D.L.C. and J.P. All authors reviewed drafts and provided critical feedback and approved the final manuscript.

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Correspondence to Daniel W. Belsky or Candice L. Odgers.

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Belsky, D.W., Caspi, A., Arseneault, L. et al. Genetics and the geography of health, behaviour and attainment. Nat Hum Behav 3, 576–586 (2019). https://doi.org/10.1038/s41562-019-0562-1

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