Nature Communications (Mar 2023)

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

  • Qile Dai,
  • Geyu Zhou,
  • Hongyu Zhao,
  • Urmo Võsa,
  • Lude Franke,
  • Alexis Battle,
  • Alexander Teumer,
  • Terho Lehtimäki,
  • Olli T. Raitakari,
  • Tõnu Esko,
  • eQTLGen Consortium,
  • Michael P. Epstein,
  • Jingjing Yang

DOI
https://doi.org/10.1038/s41467-023-36862-w
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 13

Abstract

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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.