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:10.1038/s41467-023-36862-w

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

Nat Commun. 2023 Mar 7;14(1):1271. doi: 10.1038/s41467-023-36862-w.

Authors

Qile Dai^{1

2}, Geyu Zhou³, Hongyu Zhao^{3

4}, Urmo Võsa⁵, Lude Franke^{6

7}, Alexis Battle⁸, Alexander Teumer⁹, Terho Lehtimäki¹⁰, Olli T Raitakari^{11

12

13}, Tõnu Esko⁵; eQTLGen Consortium; Michael P Epstein¹⁴, Jingjing Yang¹⁵

Collaborators

eQTLGen Consortium:
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

Affiliations

¹ Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA, 30322, USA.
² Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA.
³ Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06511, USA.
⁴ Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA.
⁵ Estonian Genome Centre, Institute of Genomics, University of Tartu, 50090, Tartu, Estonia.
⁶ Department of Genetics, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, The Netherlands.
⁷ Oncode Institute, 3521 AL, Utrecht, The Netherlands.
⁸ Department of Computer Science, and Departments of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
⁹ Institute for Community Medicine, University Medicine Greifswald, 17489, Greifswald, Germany.
¹⁰ Department of Clinical Chemistry, Fimlab Laboratories and Finnish Centre for Cardiovascular Disease Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33520, Finland.
¹¹ Centre for Population Health Research, University of Turku and Turku University Hospital, 20520, Turku, Finland.
¹² Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520, Turku, Finland.
¹³ Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, 20521, Turku, Finland.
¹⁴ Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA. mpepste@emory.edu.
¹⁵ Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA. jingjing.yang@emory.edu.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Animals
Multifactorial Inheritance
Otters*
Risk Factors
Sample Size
Transcriptome

Abstract

Publication types

MeSH terms

Grants and funding