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

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

Nature Communications

10.1038/s41467-023-36862-w

https://doi.org/10.1038/s41467-023-36862-w

Qile Dai, Geyu Zhou, Hongyu Zhao, Urmo Võsa, Lude Franke, Alexis Battle, Alexander Teumer, Terho Lehtimäki, Olli T. Raitakari, Tonu Esko, , Mawussé Agbessi, Habibul Ahsan, Isabel Alves, Anand Kumar Andiappan, Wibowo Arindrarto, Philip Awadalla, Alexis Battle, Frank Beutner, Marc Jan Bonder, Dorret I. Boomsma, Mark W. Christiansen, Annique Claringbould, Patrick Deelen, Marie-Julie Favé, Timothy Frayling, Sina A. Gharib, Greg Gibson, Bastiaan T. Heijmans, Gibran Hemani, Rick Jansen, Mika Kähönen, Anette Kalnapenkis, Silva Kasela, Johannes Kettunen, Yungil Kim, Holger Kirsten, Peter Kovacs, Knut Krohn, Jaanika Kronberg, Viktorija Kukushkina, Zoltan Kutalik, Bernett Lee, Markus Loeffler, Urko M. Marigorta, Hailang Mei, Lili Milani, Grant W. Montgomery, Martina Müller-Nurasyid, Matthias Nauck, Michel G. Nivard, Brenda Penninx, Markus Perola, Natalia Pervjakova, Brandon L. Pierce, Joseph Powell, Holger Prokisch, Bruce M. Psaty, Samuli Ripatti, Olaf Rotzschke, Sina Rüeger, Ashis Saha, Markus Scholz, Katharina Schramm, Ilkka Seppälä, Eline P. Slagboom, Coen D. A. Stehouwer, Michael Stumvoll, Patrick Sullivan, Peter A. C. ‘t Hoen, Joachim Thiery, Lin Tong, Anke Tönjes, Jenny van Dongen, Maarten van Iterson, Joyce van Meurs, Jan H. Veldink, Joost Verlouw, Peter M. Visscher, Uwe Völker, Harm-Jan Westra, Cisca Wijmenga, Hanieh Yaghootka, Jian Yang, Biao Zeng, Futao Zhang, Michael P. Epstein, Jingjing Yang

05 April 2023

Dai, Q., Zhou, G., Zhao, H., Võsa, U., Franke, L., Battle, A., Teumer, A., Lehtimäki, T., Raitakari, O. T., Esko, T., Agbessi, M., Ahsan, H., Alves, I., Andiappan, A. K., Arindrarto, W., Awadalla, P., Battle, A., Beutner, F., … Yang, J. (2023). OTTERS: a powerful TWAS framework leveraging summary-level reference data. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-36862-w

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.

Attribution 4.0 International (CC BY 4.0)

There was no specific funding for the research done

https://oar.a-star.edu.sg/communities-collections/articles/22566

2041-1723

Singapore Immunology Network

https://doi.org/10.1038/s41467-023-36862-w