Login / Signup

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

Qile DaiGeyu ZhouHongyu ZhaoUrmo VõsaLude H FrankeAlexis J BattleAlexander TeumerTerho LehtimäkiOlli T RaitakariTõnu Eskonull nullMichael P EpsteinJingjing Yang
Published in: Nature communications (2023)
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.
Keyphrases
  • electronic health record
  • big data
  • gene expression
  • machine learning
  • data analysis
  • rna seq
  • artificial intelligence
  • binding protein
  • deep learning