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Classification of common human diseases derived from shared genetic and environmental determinants

Nature Genetics volume 49, pages 1319–1325 (2017)Cite this article

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Abstract

In this study, we used insurance claims for over one-third of the entire US population to create a subset of 128,989 families (481,657 unique individuals). We then used these data to (i) estimate the heritability and familial environmental patterns of 149 diseases and (ii) infer the genetic and environmental correlations for disease pairs from a set of 29 complex diseases. The majority (52 of 65) of our study's heritability estimates matched earlier reports, and 84 of our estimates appear to have been obtained for the first time. We used correlation matrices to compute environmental and genetic disease classifications and corresponding reliability measures. Among unexpected observations, we found that migraine, typically classified as a disease of the central nervous system, appeared to be most genetically similar to irritable bowel syndrome and most environmentally similar to cystitis and urethritis, all of which are inflammatory diseases.

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Figure 1: Information on study population, results of model selection, and analysis of heritability of 149 diseases.
Figure 2: Genetic and environmental correlations between diseases.

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Acknowledgements

We thank E. Gannon, R. Melamed, R. Mork, and M. Rzhetsky for numerous comments on earlier versions of the manuscript. This work was funded by the DARPA Big Mechanism program under ARO contract W911NF1410333, by National Institutes of Health grants R01HL122712, 1P50MH094267, and U01HL108634-01, and by a gift from Liz and Kent Dauten.

Author information

Authors and Affiliations

  1. Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, Illinois, USA

    Kanix Wang

  2. Institute of Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA

    Kanix Wang, Hallie Gaitsch & Andrey Rzhetsky

  3. Microsoft Research, Redmond, Washington, USA

    Hoifung Poon

  4. Vanderbilt Genetics Institute, Vanderbilt University, School of Medicine, Nashville, Tennessee, USA

    Nancy J Cox

  5. Department of Medicine, Department of Human Genetics, and Computation Institute, University of Chicago, Chicago, Illinois, USA

    Andrey Rzhetsky

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  1. Kanix Wang
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Contributions

All authors contributed extensively to the work presented in this paper. K.W. and A.R. designed experiments, analyzed data, and wrote the manuscript; K.W., H.G., and H.P. performed computational experiments; and N.J.C., H.G., and H.P. contributed to iterative improvement of the manuscript.

Corresponding author

Correspondence to Andrey Rzhetsky.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Environmental effects estimates.

(a) Common couple environment effects. (b) Common sibling environment effects. (c) Unique environment effects. Bar color in the bar plots indicates biological systems associated with each disease, consistent throughout all figures.

Supplementary Figure 2 Testing dependence of heritability estimates on age of onset; heritability distributions, sorted by biological system.

(a) Histograms and density plots of heritability estimates by biological system. (b) Heritability estimate versus disease age of onset for biological systems with more than three diseases, with linear fits indicated by solid lines.

Supplementary Figure 3 Positive correlations between phenotypic and genetic correlations and between phenotypic and environmental correlations.

Supplementary Figure 4 Classification trees: ICD-9 versus phenotypic correlations.

(a) A classification of diseases that corresponds to a subset of ICD-9 taxonomy. (b) Disease classification constructed from phenotypic correlations between diseases; distances between diseases were calculated as 1 – correlation.

Supplementary Figure 5 Neighbor-joining classifications showing the 29 conditions’ nosologies inferred from genetic and environmental correlations presented on the left and the right trees, respectively.

For both classifications, we defined the distance between diseases as 1 – correlation. Because we estimated a posterior distribution for each correlation estimate, we were able to sample 10,000 distance sets using posterior distributions for pairwise correlations. For each of these samples, we estimated a classification and computed reliability measures for individual classification topology partitions (each integer number on the tree indicates the percentage of trees out of 10,000 in which this particular partition was present). Disease labels are colored according to associated biological systems, consistent with other figures. Note that, while the genetic and environmental trees are significantly different, both are stable, as the bootstrap-like numbers indicate.

Supplementary Figure 6 Estimates of age-related increase in disease liability for seven late-onset conditions (aneurysm, atherosclerosis, benign colon neoplasm, cataract, cerebrovascular disease, keratosis, and osteoarthritis).

Error bars show 1 s.d., and LOcally WEighted Scatter-plot Smoother (LOWESS) curve fits are shown with solid lines.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 and Supplementary Tables 3 and 5–8 (PDF 1883 kb)

Supplementary Table 1

Acronyms, biological systems, prevalence percentages and standard errors for 149 studied diseases. (XLSX 74 kb)

Supplementary Table 2

Heritability and preventability estimates and standard deviations for 149 studied diseases. (XLSX 83 kb)

Supplementary Table 4

Pairwise estimates and standard deviations of genetic, environmental and phenotypic correlations for 29 diseases. (XLSX 64 kb)

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Wang, K., Gaitsch, H., Poon, H. et al. Classification of common human diseases derived from shared genetic and environmental determinants. Nat Genet 49, 1319–1325 (2017). https://doi.org/10.1038/ng.3931

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