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

: 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

: 2023

: Nature Communications

: Nature communications

: Nat Commun

: 14

: 1

: 2041-1723

DOI: https://doi.org/10.1038/s41467-023-36862-w

: https://www.nature.com/articles/s41467-023-36862-w

: 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.

s41467-023-36862-w.pdf