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Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics

Author

Listed:
  • Ruowang Li

    (University of Pennsylvania)

  • Rui Duan

    (Harvard T.H. Chan School of Public Health)

  • Xinyuan Zhang

    (University of Pennsylvania)

  • Thomas Lumley

    (University of Auckland)

  • Sarah Pendergrass

    (Biomedical and Translational Informatics Institute)

  • Christopher Bauer

    (Biomedical and Translational Informatics Institute)

  • Hakon Hakonarson

    (Children’s Hospital of Philadelphia)

  • David S. Carrell

    (Kaiser Permanente Washington Health Research Institute)

  • Jordan W. Smoller

    (Massachusetts General Hospital)

  • Wei-Qi Wei

    (Vanderbilt University Medical Centre)

  • Robert Carroll

    (Vanderbilt University Medical Centre)

  • Digna R. Velez Edwards

    (Vanderbilt University)

  • Georgia Wiesner

    (Vanderbilt University)

  • Patrick Sleiman

    (Children’s Hospital of Philadelphia)

  • Josh C. Denny

    (Vanderbilt University Medical Centre)

  • Jonathan D. Mosley

    (Vanderbilt University Medical Centre)

  • Marylyn D. Ritchie

    (Department of Genetics, Perelman School of Medicine, University of Pennsylvania)

  • Yong Chen

    (University of Pennsylvania)

  • Jason H. Moore

    (University of Pennsylvania)

Abstract

Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients’ data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases.

Suggested Citation

  • Ruowang Li & Rui Duan & Xinyuan Zhang & Thomas Lumley & Sarah Pendergrass & Christopher Bauer & Hakon Hakonarson & David S. Carrell & Jordan W. Smoller & Wei-Qi Wei & Robert Carroll & Digna R. Velez E, 2021. "Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20211-2
    DOI: 10.1038/s41467-020-20211-2
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