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

Dai, Q.; Zhou, G.; Zhao, H.; Vosa, U.; Franke, L.; Battle, A.; Teumer, A.; Lehtimaki, T.; Raitakari, O.; Esko, T.; eQTLGen Consortium,; Epstein, M. P.; Yang, J.

doi:10.1101/2022.03.30.486451

bioRxiv · 10.1101/2022.03.30.486451

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

Dai, Q.·Zhou, G.·Zhao, H.·Vosa, U.·Franke, L.·Battle, A.·Teumer, A.·Lehtimaki, T.·Raitakari, O.·Esko, T.·eQTLGen Consortium,·Epstein, M. P.·Yang, J.

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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.

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Dai, Q., Zhou, G., Zhao, H., Vosa, U., Franke, L., Battle, A., Teumer, A., Lehtimaki, T., Raitakari, O., Esko, T., eQTLGen Consortium,, Epstein, M. P., Yang, J.. 2022-04-01. OTTERS: A powerful TWAS framework leveraging summary-level reference data. https://doi.org/10.1101/2022.03.30.486451

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