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Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes

Nature Geneticsvolume 51pages327334 (2019) | Download Citation


We analysed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status (SES), air pollution and climate) in each phenotype. Mean heritability (h2 = 0.311) and shared environmental variance (c2 = 0.088) were higher than variance attributed to specific environmental factors such as zip-code-level SES (varSES = 0.002), daily air quality (varAQI = 0.0004), and average temperature (vartemp = 0.001) overall, as well as for individual phenotypes. We found significant heritability and shared environment for a number of comorbidities (h2 = 0.433, c2 = 0.241) and average monthly cost (h2 = 0.290, c2 = 0.302). All results are available using our Claims Analysis of Twin Correlation and Heritability (CaTCH) web application.

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Data Availability

The data that support the findings of this study are available from Aetna Insurance, but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Please contact N. Palmer ( for inquiries about the Aetna dataset. Summary data are, however, available from the authors upon reasonable request and with permission of Aetna Insurance. Code for analysis, generation of figures and figure files is available at

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We thank K. Fox of Aetna, Inc., N. Palmer of Harvard Medical School, and I. Kohane of Harvard Medical School for support and providing access to the Aetna Insurance Claims Data. We are grateful to L. O’Connor and A. Price for helpful discussion. This research was supported by the Australian National Health and Medical Research Council (1078037 and 1113400), National Institutes of Health NIEHS (R00ES23504 and R21ES205052), the National Science Foundation (1636870), and the Sylvia & Charles Viertel Charitable Foundation.

Author information

Author notes

  1. These authors jointly supervised this work: Peter M. Visscher, Chirag J. Patel.


  1. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    • Chirag M. Lakhani
    • , Braden T. Tierney
    • , Arjun K. Manrai
    •  & Chirag J. Patel
  2. Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA

    • Braden T. Tierney
  3. Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA

    • Arjun K. Manrai
  4. Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia

    • Jian Yang
    •  & Peter M. Visscher
  5. Queensland Brain Institute, The University of Queensland, Brisbane, Australia

    • Jian Yang
    •  & Peter M. Visscher


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All authors contributed extensively to the work presented in this paper. C.M.L., P.M.V., and C.J.P. designed experiments, analysed data, and wrote the manuscript. B.T.T. developed the Shiny App for analysis. B.T.T., A.K.M., and J.Y. contributed to iterative improvement of the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Peter M. Visscher or Chirag J. Patel.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–11, Supplement Notes 1–6 and Supplementary Tables 1–4 and 6

  2. Reporting Summary

  3. Supplementary Table 5

    Comparison of h2 estimates from claims analysis to h2 estimates from 81 published studies, including the method of estimation

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