A1 Refereed original research article in a scientific journal
OTTERS: a powerful TWAS framework leveraging summary-level reference data
Authors: Dai Qile, Zhou Geyu, Zhao Hongyu, Võsa Urmo, Franke Lude, Battle Alexis, Teumer Alexander, Lehtimäki Terho, Raitakari Olli T., Esko Tõnu; Consortium eQTLGen; Epstein Michael P., Yang Jingjing
Publisher: Springer Nature
Publication year: 2023
Journal: Nature Communications
Journal name in source: Nature communications
Journal acronym: Nat Commun
Volume: 14
Issue: 1
ISSN: 2041-1723
eISSN: 2041-1723
DOI: https://doi.org/10.1038/s41467-023-36862-w
Web address : https://www.nature.com/articles/s41467-023-36862-w
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/179554558
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.
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