Correlations between complex human phenotypes vary by genetic background, gender, and environment

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Creator
  • Elgart, M.
    • Other Affiliation: Harvard Medical School
  • Goodman, M.O.
    • Other Affiliation: Harvard Medical School
  • Isasi, C.
    • Other Affiliation: Albert Einstein College of Medicine
  • Chen, H.
    • Other Affiliation: University of Texas Health Science Center at Houston
  • Morrison, A.C.
    • Other Affiliation: University of Texas Health Science Center at Houston
  • de Vries, P.S.
    • Other Affiliation: University of Texas Health Science Center at Houston
  • Xu, H.
    • Other Affiliation: University of Maryland School of Medicine
  • Manichaikul, A.W.
    • Other Affiliation: University of Virginia
  • Guo, X.
    • Other Affiliation: Harbor-UCLA Medical Center
  • Franceschini, N.
    • Gillings School of Global Public Health, Department of Epidemiology
  • Psaty, B.M.
    • Other Affiliation: University of Washington
  • Rich, S.S.
    • Other Affiliation: University of Virginia School of Medicine
  • Rotter, J.I.
    • Other Affiliation: The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center
  • Lloyd-Jones, D.M.
    • Other Affiliation: Northwestern University
  • Fornage, M.
    • Other Affiliation: The University of Texas Health Science Center at Houston
  • Correa, A.
    • Other Affiliation: University of Mississippi Medical Center
  • Heard-Costa, N.L.
    • Other Affiliation: Boston University and National Heart Lung and Blood Institute's Framingham Heart Study
  • Vasan, R.S.
    • Other Affiliation: Boston University
  • Hernandez, R.
    • Other Affiliation: University of California, San Francisco
  • Kaplan, R.C.
    • Other Affiliation: Albert Einstein College of Medicine
  • Redline, S.
    • Other Affiliation: Brigham and Women's Hospital
  • Sofer, T.
    • Other Affiliation: Harvard T.H. Chan School of Public Health
  • The Trans-Omics for Precision Medicine (TOPMed) Consortium
Abstract
  • We develop a closed-form Haseman-Elston estimator for genetic and environmental correlation coefficients between complex phenotypes, which we term HEc, that is as precise as GCTA yet ∼20× faster. We estimate genetic and environmental correlations between over 7,000 phenotype pairs in subgroups from the Trans-Omics in Precision Medicine (TOPMed) program. We demonstrate substantial differences in both heritabilities and genetic correlations for multiple phenotypes and phenotype pairs between individuals of self-reported Black, Hispanic/Latino, and White backgrounds. We similarly observe differences in many of the genetic and environmental correlations between genders. To estimate the contribution of genetics to the observed phenotypic correlation, we introduce “fractional genetic correlation” as the fraction of phenotypic correlation explained by genetics. Finally, we quantify the enrichment of correlations between phenotypic domains, each of which is comprised of multiple phenotypes. Altogether, we demonstrate that the observed correlations between complex human phenotypes depend on the genetic background of the individuals, their gender, and their environment.
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Resource type
  • Article
License
  • Attribution-NonCommercial-NoDerivs 4.0 International
Journal title
  • Cell Reports Medicine
Journal volume
  • 3
Journal issue
  • 12
Language
  • English
Version
  • Publisher
Funder
  • National Heart, Lung, and Blood Institute, NHLBI: R21HL145425, R35HL135818
  • Johnson and Johnson, J&J
ISSN
  • 2666-3791
Publisher
  • Cell Press

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