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Evaluating the contribution of genetics and familial shared environment to common disease using the UK Biobank

Nature Genetics volume 48, pages 980–983 (2016)Cite this article

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Abstract

Genome-wide association studies have detected many loci underlying susceptibility to disease, but most of the genetic factors that contribute to disease susceptibility remain unknown. Here we provide evidence that part of the 'missing heritability' can be explained by an overestimation of heritability. We estimated the heritability of 12 complex human diseases using family history of disease in 1,555,906 individuals of white ancestry from the UK Biobank. Estimates using simple family-based statistical models were inflated on average by ∼47% when compared with those from structural equation modeling (SEM), which specifically accounted for shared familial environmental factors. In addition, heritabilities estimated using SNP data explained an average of 44.2% of the simple family-based estimates across diseases and an average of 57.3% of the SEM-estimated heritabilities, accounting for almost all of the SEM heritability for hypertension. Our results show that both genetics and familial environment make substantial contributions to familial clustering of disease.

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Figure 1: Heritability estimates using SEM family-based models and SNPs.

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References

  1. Falconer, D.S. Inheritance of liability to certain diseases estimated from incidence among relatives. Annal. Hum. Genet. 29, 51–76 (1965).

    Article  Google Scholar 

  2. Manolio, T.A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Tenesa, A. & Haley, C.S. The heritability of human disease: estimation, uses and abuses. Nat. Rev. Genet. 14, 139–149 (2013).

    Article  CAS  PubMed  Google Scholar 

  4. Allen, N. et al. UK Biobank: current status and what it means for epidemiology. Health Policy Technol 1, 123–126 (2012).

    Article  Google Scholar 

  5. UK Biobank Coordinating Centre. UK Biobank: protocol for a large-scale prospective epidemiological resource (Protocol number UKBB-PROT-09-06 (Main Phase)) (UK Biobank, 2007).

  6. Kaprio, J. et al. Concordance for type 1 (insulin-dependent) and type 2 (non-insulin-dependent) diabetes mellitus in a population-based cohort of twins in Finland. Diabetologia 35, 1060–1067 (1992).

    Article  CAS  PubMed  Google Scholar 

  7. Lichtenstein, P. et al. Environmental and heritable factors in the causation of cancer—analyses of cohorts of twins from Sweden, Denmark, and Finland. N. Engl. J. Med. 343, 78–85 (2000).

    Article  CAS  PubMed  Google Scholar 

  8. Iliadou, A. et al. Repeated blood pressure measurements in a sample of Swedish twins: heritabilities and associations with polymorphisms in the renin–angiotensin–aldosterone system. J. Hypertens. 20, 1543–1550 (2002).

    Article  CAS  PubMed  Google Scholar 

  9. Gatz, M. et al. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry 63, 168–174 (2006).

    Article  PubMed  Google Scholar 

  10. Zdravkovic, S., Wienke, A., Pedersen, N.L. & de Faire, U. Genetic influences on angina pectoris and its impact on coronary heart disease. Eur. J. Hum. Genet. 15, 872–877 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Hallberg, J. et al. Interaction between smoking and genetic factors in the development of chronic bronchitis. Am. J. Respir. Crit. Care Med. 177, 486–490 (2008).

    Article  PubMed  Google Scholar 

  12. Korja, M. et al. Genetic epidemiology of spontaneous subarachnoid hemorrhage: Nordic Twin Study. Stroke 41, 2458–2462 (2010).

    Article  PubMed  Google Scholar 

  13. 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).

    Article  CAS  PubMed  Google Scholar 

  14. Annest, J.L., Sing, C.F., Biron, P. & Mongeau, J.G. Familial aggregation of blood pressure and weight in adoptive families. II. Estimation of the relative contributions of genetic and common environmental factors to blood pressure correlations between family members. Am. J. Epidemiol. 110, 492–503 (1979).

    Article  CAS  PubMed  Google Scholar 

  15. Mancuso, N. et al. The contribution of rare variation to prostate cancer heritability. Nat. Genet. 48, 30–35 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zaitlen, N. et al. Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. PLoS Genet. 9, e1003520 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Liu, C. et al. Revisiting heritability accounting for shared environmental effects and maternal inheritance. Hum. Genet. 134, 169–179 (2015).

    Article  PubMed  Google Scholar 

  19. Aschard, H., Vilhjálmsson, B.J., Joshi, A.D., Price, A.L. & Kraft, P. Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am. J. Hum. Genet. 96, 329–339 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Tada, H. et al. Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur. Heart J. 37, 561–567 (2016).

    Article  CAS  PubMed  Google Scholar 

  21. Risch, N. Linkage strategies for genetically complex traits. I. Multilocus models. Am. J. Hum. Genet. 46, 222–228 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Falconer, D.S. & Mackay, T.F. Introduction to Quantitative Genetics (Longman, 1996).

  23. Dempster, E.R. & Lerner, I.M. Heritability of threshold characters. Genetics 35, 212–236 (1950).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Boker, S. et al. OpenMx: an open source extended structural equation modeling framework. Psychometrika 76, 306–317 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Canela-Xandri, O., Law, A., Gray, A., Woolliams, J.A. & Tenesa, A. A new tool called DISSECT for analysing large genomic data sets using a Big Data approach. Nat. Commun. 6, 10162 (2015).

    Article  CAS  PubMed  Google Scholar 

  27. Stuart, A. & Ord, J.K. Kendall's Advanced Theory of Statistics (Hoder Arnold, 1994).

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Acknowledgements

We thank I. White for his helpful comments. This research has been conducted using the UK Biobank resource and funded by the Roslin Institute Strategic Programme Grant from the BBSRC (BB/J004235/1). C.S.H. and A.T. also acknowledge funding from the MRC. SNP heritability estimates were calculated using the ARCHER UK National Supercomputing Service.

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Authors and Affiliations

  1. Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, UK

    María Muñoz, Ricardo Pong-Wong, Oriol Canela-Xandri, Konrad Rawlik, Chris S Haley & Albert Tenesa

  2. MRC Human Genetics Unit at the MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK

    Chris S Haley & Albert Tenesa

Authors
  1. María Muñoz
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  2. Ricardo Pong-Wong
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  3. Oriol Canela-Xandri
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  4. Konrad Rawlik
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  5. Chris S Haley
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  6. Albert Tenesa
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Contributions

A.T. and C.S.H. conceived and designed the study. M.M. and A.T. performed the statistical analysis. O.C.-X. and K.R. carried out the SNP filtering and quality control. M.M., C.S.H. and A.T. wrote the manuscript. R.P.-W. performed the simulations and contributed ideas and quantitative genetics expertise. All authors read and approved the manuscript.

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Correspondence to Albert Tenesa.

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The authors declare no competing financial interests.

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Supplementary Figures 1 and 2, Supplementary Tables 1–16 and Supplementary Note. (PDF 665 kb)

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Muñoz, M., Pong-Wong, R., Canela-Xandri, O. et al. Evaluating the contribution of genetics and familial shared environment to common disease using the UK Biobank. Nat Genet 48, 980–983 (2016). https://doi.org/10.1038/ng.3618

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