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OTTERS: a powerful TWAS framework leveraging summary-level reference data

Author

Listed:
  • Qile Dai

    (Emory University School of Public Health
    Emory University School of Medicine)

  • Geyu Zhou

    (Yale University)

  • Hongyu Zhao

    (Yale University
    Yale School of Public Health)

  • Urmo Võsa

    (University of Tartu)

  • Lude Franke

    (University Medical Center Groningen
    Oncode Institute)

  • Alexis Battle

    (Johns Hopkins University)

  • Alexander Teumer

    (University Medicine Greifswald)

  • Terho Lehtimäki

    (Tampere University)

  • Olli T. Raitakari

    (University of Turku and Turku University Hospital
    University of Turku
    Turku University Hospital)

  • Tõnu Esko

    (University of Tartu)

  • Michael P. Epstein

    (Emory University School of Medicine)

  • Jingjing Yang

    (Emory University School of Medicine)

Abstract

Most existing TWAS tools require individual-level eQTL reference data and thus are not applicable to summary-level reference eQTL datasets. The development of TWAS methods that can harness summary-level reference data is valuable to enable TWAS in broader settings and enhance power due to increased reference sample size. Thus, we develop a TWAS framework called OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data) that adapts multiple polygenic risk score (PRS) methods to estimate eQTL weights from summary-level eQTL reference data and conducts an omnibus TWAS. We show that OTTERS is a practical and powerful TWAS tool by both simulations and application studies.

Suggested Citation

  • Qile Dai & Geyu Zhou & Hongyu Zhao & Urmo Võsa & Lude Franke & Alexis Battle & Alexander Teumer & Terho Lehtimäki & Olli T. Raitakari & Tõnu Esko & Michael P. Epstein & Jingjing Yang, 2023. "OTTERS: a powerful TWAS framework leveraging summary-level reference data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36862-w
    DOI: 10.1038/s41467-023-36862-w
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    1. Lida Wang & Chachrit Khunsriraksakul & Havell Markus & Dieyi Chen & Fan Zhang & Fang Chen & Xiaowei Zhan & Laura Carrel & Dajiang. J. Liu & Bibo Jiang, 2024. "Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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