OTTERS: a powerful TWAS framework leveraging summary-level reference data
Dai, Qile; Zhou, Geyu; Zhao, Hongyu; Võsa, Urmo; Franke, Lude; Battle, Alexis; Teumer, Alexander; Lehtimäki, Terho; Raitakari, Olli T.; Esko, Tõnu; Esko, Tõnu; Epstein, Michael P.; Yang, Jingjing (2023-03)
Dai, Qile
Zhou, Geyu
Zhao, Hongyu
Võsa, Urmo
Franke, Lude
Battle, Alexis
Teumer, Alexander
Lehtimäki, Terho
Raitakari, Olli T.
Esko, Tõnu
Esko, Tõnu
Epstein, Michael P.
Yang, Jingjing
03 / 2023
Nature Communications
1271
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202304173788
https://urn.fi/URN:NBN:fi:tuni-202304173788
Kuvaus
Peer reviewed
Tiivistelmä
<p>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.</p>
Kokoelmat
- TUNICRIS-julkaisut [20689]