OTTERS: a powerful TWAS framework leveraging summary-level reference data
Publication date
2023-12
Authors
Dai, Qile
Zhou, Geyu
Võsa, Urmo
Franke, Lude
Battle, Alexis
Teumer, Alexander
Lehtimäki, Terho
Raitakari, Olli T.
Esko, Tõnu
Deelen, Patrick
Editors
Advisors
Supervisors
Document Type
Article
Metadata
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License
cc_by
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.
Keywords
General Chemistry, General Biochemistry,Genetics and Molecular Biology, General Physics and Astronomy
Citation
Dai, Q, Zhou, G, Võsa, U, Franke, L, Battle, A, Teumer, A, Lehtimäki, T, Raitakari, O T, Esko, T, Deelen, P & eQTLGen Consortium 2023, 'OTTERS : a powerful TWAS framework leveraging summary-level reference data', Nature Communications, vol. 14, no. 1, 1271. https://doi.org/10.1038/s41467-023-36862-w