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- Title
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; eQTLGen Consortium; Agbessi, Mawussé; Ahsan, Habibul; Alves, Isabel; Andiappan, Anand Kumar; Arindrarto, Wibowo; Awadalla, Philip; Beutner, Frank; Jan Bonder, Marc; Boomsma, Dorret I.
- 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. Here, the authors present a TWAS framework OTTERS that adapts multiple polygenic risk score methods to estimate eQTL weights from summary-level eQTL data. Both simulation and real studies show OTTERS is powerful across a wide range of genetic architectures.
- Subjects
DISEASE risk factors; MONOGENIC &; polygenic inheritance (Genetics); OTTERS; SAMPLE size (Statistics)
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-36862-w