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

Nature Genetics volume 51, pages 327–334 (2019)Cite this article

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An Author Correction to this article was published on 27 February 2019

This article has been updated

Abstract

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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Fig. 1: Geographic distribution of 56,396 twin pairs in CaTCH and an example of environmental data aggregation on a zip code basis.
Fig. 2: Estimates of twin statistics across functional domains and individual basis for 56,396 twin pairs in CaTCH among all 560 phenotypes.
Fig. 3: Comparison of h2 estimates in CaTCH to published literature and estimates for cost and comorbidities in CaTCH.
Fig. 4: Comparison of h2/c2 estimates from 56,396 twin pairs among 560 phenotypes in CaTCH to 5,169,880 twin pairs among 9,568 phenotypes in MaTCH (Supplementary Table 1).

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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 (nathan_palmer@hms.harvard.edu) 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 https://github.com/cmlakhan/twinInsurance.

Change history

  • 27 February 2019

    In the version of this article initially published, in Fig. 4b, the shared environmental variance (c2) values for all MaTCH functional domains except ‘all traits’ were erroneously estimated because of a coding error. Figure 4 has been revised to include corrected c2 estimates in the data in panel b as well as the number of phenotypes in CaTCH and MaTCH functional domains in the y axes of panels a and b; the Fig. 4 legend and the description of Fig. 4b in the Results section have also been revised to describe these changes. In addition, the erroneous term ‘depravity index’, appearing throughout the article’s main text, Fig. 1, Supplementary Fig. 10 and the Supplementary Note, should have read ‘deprivation index’. The errors have been corrected in the HTML and PDF versions of the article. Images of the original figure are shown in the correction notice.

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Acknowledgements

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.

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Author notes

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

Authors and Affiliations

  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

Authors
  1. Chirag M. Lakhani
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  4. Jian Yang
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Contributions

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.

Corresponding authors

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

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The authors declare no competing interests.

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Supplementary information

Supplementary Text and Figures

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

Reporting Summary

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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Lakhani, C.M., Tierney, B.T., Manrai, A.K. et al. Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes. Nat Genet 51, 327–334 (2019). https://doi.org/10.1038/s41588-018-0313-7

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