Nature CommunicationsGeneticsPeer-reviewed journal article
OTTERS: A powerful TWAS framework leveraging summary-level reference data
Qile Dai, Geyu ZhouiD, Hongyu ZhaoiD, Urmo VõsaiD, Lude FrankeiD, Alexis J. BattleiD, Alexander TeumeriD, Terho J. LehtimäkiiD, Olli Tuomas RaitakariiD, Tõnu EskoiD, Mawussé D. T. AgbessiiD, Habibul AhsaniD, Isabel AlvesiD, Anand Kumar AndiappaniD, Wibowo ArindrartoiD, Philip AwadallaiD, Frank Beutner, Marc Jan BonderiD, Dorret Irene BoomsmaiD, Mark W. Christiansen, Annique ClaringbouldiD, Patrick DeeleniD, Marie-Julie FavéiD, Timothy M. FraylingiD, Sina A. GharibiD, , Jingjing YangiD
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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- 53
- FWCI
- 8.95
- Field percentile
- top 1%
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