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

Qile Dai, Geyu Zhou, Hongyu Zhao, Urmo Võsa, Lude Franke, Alexis Battle, Alexander Teumer, Terho Lehtimäki, Olli T. Raitakari, Tõnu Esko, Michael P. Epstein () and Jingjing Yang ()
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Qile Dai: Emory University School of Public Health
Geyu Zhou: Yale University
Hongyu Zhao: Yale University
Urmo Võsa: University of Tartu
Lude Franke: University Medical Center Groningen
Alexis Battle: Johns Hopkins University
Alexander Teumer: University Medicine Greifswald
Terho Lehtimäki: Tampere University
Olli T. Raitakari: University of Turku and Turku University Hospital
Tõnu Esko: University of Tartu
Michael P. Epstein: Emory University School of Medicine
Jingjing Yang: Emory University School of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-36862-w

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