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

Qile Dai; Geyu Zhou; eQTLgen Consortium; Hongyu Zhao; Urmo Võsa; Lude Franke; Alexis Battle; Alexander Teumer; Terho Lehtimäki; Olli T. Raitakari; Tõnu 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; Markus Scholz; Joyce van Meurs; Joost Verlouw; Jian Yang; Jingjing Yang

doi:10.1038/s41467-023-36862-w

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

Qile Dai, Geyu Zhou, eQTLgen Consortium, Hongyu Zhao, Urmo Võsa, Lude Franke, Alexis Battle, Alexander Teumer, Terho Lehtimäki, Olli T. Raitakari, Tõnu Esko, Mawussé Agbessi, Habibul Ahsan, Isabel Alves, Anand Kumar Andiappan, Wibowo Arindrarto, Philip Awadalla, Alexis Battle, Frank Beutner, Marc Jan BonderDorret 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, Markus Scholz, Joyce van Meurs, Joost Verlouw, Jian Yang, Jingjing Yang^*

^*Corresponding author for this work

Internal Medicine

Research output: Contribution to journal › Article › Academic › peer-review

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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.

Original language	English
Article number	1271
Journal	Nature Communications
Volume	14
Issue number	1
DOIs	https://doi.org/10.1038/s41467-023-36862-w
Publication status	Published - 7 Mar 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

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10.1038/s41467-023-36862-wLicence: CC BY

OTTERSFinal published version, 2.86 MBLicence: CC BY

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Dai, Q., Zhou, G., eQTLgen Consortium, 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), Article 1271. https://doi.org/10.1038/s41467-023-36862-w

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title = "OTTERS: a powerful TWAS framework leveraging summary-level reference data",

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.",

author = "Qile Dai and Geyu Zhou and {eQTLgen Consortium} and Hongyu Zhao and Urmo V{\~o}sa and Lude Franke and Alexis Battle and Alexander Teumer and Terho Lehtim{\"a}ki and Raitakari, {Olli T.} and T{\~o}nu Esko and Mawuss{\'e} Agbessi and Habibul Ahsan and Isabel Alves and Andiappan, {Anand Kumar} and Wibowo Arindrarto and Philip Awadalla and Alexis Battle and Frank Beutner and {Jan Bonder}, Marc and Boomsma, {Dorret I.} and Christiansen, {Mark W.} and Annique Claringbould and Patrick Deelen and Fav{\'e}, {Marie Julie} and Timothy Frayling and Gharib, {Sina A.} and Greg Gibson and Heijmans, {Bastiaan T.} and Gibran Hemani and Rick Jansen and Mika K{\"a}h{\"o}nen and Anette Kalnapenkis and Silva Kasela and Johannes Kettunen and Yungil Kim and Holger Kirsten and Peter Kovacs and Knut Krohn and Jaanika Kronberg and Viktorija Kukushkina and Zoltan Kutalik and Bernett Lee and Markus Loeffler and Marigorta, {Urko M.} and Hailang Mei and Markus Scholz and {van Meurs}, Joyce and Joost Verlouw and Jian Yang and Jingjing Yang",

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doi = "10.1038/s41467-023-36862-w",

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Dai, Q, Zhou, G, eQTLgen Consortium, Zhao, H, Võsa, U, Franke, L, Battle, A, Teumer, A, Lehtimäki, T, Raitakari, OT, Esko, T, Agbessi, M, Ahsan, H, Alves, I, Andiappan, AK, Arindrarto, W, Awadalla, P, Battle, A, Beutner, F, Jan Bonder, M, Boomsma, DI, Christiansen, MW, Claringbould, A, Deelen, P, Favé, MJ, Frayling, T, Gharib, SA, Gibson, G, Heijmans, BT, Hemani, G, Jansen, R, Kähönen, M, Kalnapenkis, A, Kasela, S, Kettunen, J, Kim, Y, Kirsten, H, Kovacs, P, Krohn, K, Kronberg, J, Kukushkina, V, Kutalik, Z, Lee, B, Loeffler, M, Marigorta, UM, Mei, H, Scholz, M, van Meurs, J , Verlouw, J, Yang, J & Yang, J 2023, 'OTTERS: a powerful TWAS framework leveraging summary-level reference data', Nature Communications, vol. 14, no. 1, 1271. https://doi.org/10.1038/s41467-023-36862-w

TY - JOUR

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T2 - a powerful TWAS framework leveraging summary-level reference data

AU - Dai, Qile

AU - Zhou, Geyu

AU - eQTLgen Consortium

AU - Zhao, Hongyu

AU - Võsa, Urmo

AU - Franke, Lude

AU - Battle, Alexis

AU - Teumer, Alexander

AU - Lehtimäki, Terho

AU - Raitakari, Olli T.

AU - Esko, Tõnu

AU - Agbessi, Mawussé

AU - Ahsan, Habibul

AU - Alves, Isabel

AU - Andiappan, Anand Kumar

AU - Arindrarto, Wibowo

AU - Awadalla, Philip

AU - Battle, Alexis

AU - Beutner, Frank

AU - Jan Bonder, Marc

AU - Boomsma, Dorret I.

AU - Christiansen, Mark W.

AU - Claringbould, Annique

AU - Deelen, Patrick

AU - Favé, Marie Julie

AU - Frayling, Timothy

AU - Gharib, Sina A.

AU - Gibson, Greg

AU - Heijmans, Bastiaan T.

AU - Hemani, Gibran

AU - Jansen, Rick

AU - Kähönen, Mika

AU - Kalnapenkis, Anette

AU - Kasela, Silva

AU - Kettunen, Johannes

AU - Kim, Yungil

AU - Kirsten, Holger

AU - Kovacs, Peter

AU - Krohn, Knut

AU - Kronberg, Jaanika

AU - Kukushkina, Viktorija

AU - Kutalik, Zoltan

AU - Lee, Bernett

AU - Loeffler, Markus

AU - Marigorta, Urko M.

AU - Mei, Hailang

AU - Scholz, Markus

AU - van Meurs, Joyce

AU - Verlouw, Joost

AU - Yang, Jian

AU - Yang, Jingjing

PY - 2023/3/7

Y1 - 2023/3/7

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AB - 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.

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DO - 10.1038/s41467-023-36862-w

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AN - SCOPUS:85149555455

SN - 2041-1723

VL - 14

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 1271

ER -

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

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