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; Mawussé Agbessi; Habibul Ahsan; Isabel Alves; Anand Kumar Andiappan; Wibowo Arindrarto; Philip Awadalla; 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; Coen D.A. Stehouwer; Michael P. Epstein; Jinying Yang; eQTLgen Consortium

doi:10.1038/s41467-023-36862-w

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, Mawussé Agbessi, Habibul Ahsan, Isabel Alves, Anand Kumar Andiappan, Wibowo Arindrarto, Philip Awadalla, Frank Beutner, Marc Jan Bonder, Dorret I. Boomsma, Mark W. ChristiansenAnnique 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, Coen D.A. Stehouwer, Michael P. Epstein^*, Jinying Yang^*, eQTLgen Consortium

^*Corresponding author for this work

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

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.Here, the authors present a TWAS framework OTTERS that adapts multiple polygenic risk score methods to estimate eQTL weights from summary-level eQTL data. Both simulation and real studies show OTTERS is powerful across a wide range of genetic architectures.

Original language	English
Article number	1271
Number of pages	13
Journal	Nature Communications
Volume	14
Issue number	1
DOIs	https://doi.org/10.1038/s41467-023-36862-w
Publication status	Published - 7 Mar 2023

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

Cite this

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., Beutner, F., Jan Bonder, M., Boomsma, D. I., ... eQTLgen Consortium (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

@article{deea0125faaa4b678ef8b1c4e8bfdbcc,

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.Here, the authors present a TWAS framework OTTERS that adapts multiple polygenic risk score methods to estimate eQTL weights from summary-level eQTL data. Both simulation and real studies show OTTERS is powerful across a wide range of genetic architectures.",

author = "Qile Dai and Geyu Zhou 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 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 Lili Milani and Montgomery, {Grant W.} and Martina M{\"u}ller-Nurasyid and Matthias Nauck and Stehouwer, {Coen D.A.} and Epstein, {Michael P.} and Jinying Yang and {eQTLgen Consortium}",

note = "Funding Information: The authors thank Dr Greg Gibson from Georgia Tech for his insightful comments and discussion that help the development and improve the quality of this manuscript. This work was supported by National Institutes of Health grant awards R35GM138313 (Q.D., J.Y.), RF1AG071170 (Q.D., M.P.E.), and Estonian Research Council Grant PUT (PRG1291) for T.E. NIH/NIA grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, R01AG56352, U01AG32984, U01AG46152, U01AG61356, the Illinois Department of Public Health, the Translational Genomics Research Institute support the generation of the ROS/MAP data led by A.S.B., P.L.D.J. and D.A.B. The Young Finns Study has been financially supported by the Academy of Finland: grants 322098, 286284, 134309 (Eye), 126925, 121584, 124282, 255381, 256474, 283115, 319060, 320297, 314389, 338395, 330809, and 104821, 129378 (Salve), 117797 (Gendi), and 141071 (Skidi), the Social Insurance Institution of Finland, Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001), Juho Vainio Foundation, Paavo Nurmi Foundation, Finnish Foundation for Cardiovascular Research, Finnish Cultural Foundation, The Sigrid Juselius Foundation, Tampere Tuberculosis Foundation, Emil Aaltonen Foundation, Yrj{\"o} Jahnsson Foundation, Signe and Ane Gyllenberg Foundation, Diabetes Research Foundation of Finnish Diabetes Association, EU Horizon 2020 (grant 755320 for TAXINOMISIS and grant 848146 for To Aition), European Research Council (grant 742927 for MULTIEPIGEN project), Tampere University Hospital Supporting Foundation, and Finnish Society of Clinical Chemistry and the Cancer Foundation Finland. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: {\textcopyright} 2023, The Author(s).",

year = "2023",

month = mar,

day = "7",

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

language = "English",

volume = "14",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

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Dai, Q, Zhou, G, 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, 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, Milani, L, Montgomery, GW, Müller-Nurasyid, M, Nauck, M, Stehouwer, CDA, Epstein, MP, Yang, J & eQTLgen Consortium 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

T1 - OTTERS

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

AU - Dai, Qile

AU - Zhou, Geyu

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 - 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 - Milani, Lili

AU - Montgomery, Grant W.

AU - Müller-Nurasyid, Martina

AU - Nauck, Matthias

AU - Stehouwer, Coen D.A.

AU - Epstein, Michael P.

AU - Yang, Jinying

AU - eQTLgen Consortium

N1 - Funding Information: The authors thank Dr Greg Gibson from Georgia Tech for his insightful comments and discussion that help the development and improve the quality of this manuscript. This work was supported by National Institutes of Health grant awards R35GM138313 (Q.D., J.Y.), RF1AG071170 (Q.D., M.P.E.), and Estonian Research Council Grant PUT (PRG1291) for T.E. NIH/NIA grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, R01AG56352, U01AG32984, U01AG46152, U01AG61356, the Illinois Department of Public Health, the Translational Genomics Research Institute support the generation of the ROS/MAP data led by A.S.B., P.L.D.J. and D.A.B. The Young Finns Study has been financially supported by the Academy of Finland: grants 322098, 286284, 134309 (Eye), 126925, 121584, 124282, 255381, 256474, 283115, 319060, 320297, 314389, 338395, 330809, and 104821, 129378 (Salve), 117797 (Gendi), and 141071 (Skidi), the Social Insurance Institution of Finland, Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001), Juho Vainio Foundation, Paavo Nurmi Foundation, Finnish Foundation for Cardiovascular Research, Finnish Cultural Foundation, The Sigrid Juselius Foundation, Tampere Tuberculosis Foundation, Emil Aaltonen Foundation, Yrjö Jahnsson Foundation, Signe and Ane Gyllenberg Foundation, Diabetes Research Foundation of Finnish Diabetes Association, EU Horizon 2020 (grant 755320 for TAXINOMISIS and grant 848146 for To Aition), European Research Council (grant 742927 for MULTIEPIGEN project), Tampere University Hospital Supporting Foundation, and Finnish Society of Clinical Chemistry and the Cancer Foundation Finland. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2023, The Author(s).

PY - 2023/3/7

Y1 - 2023/3/7

N2 - 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.Here, the authors present a TWAS framework OTTERS that adapts multiple polygenic risk score methods to estimate eQTL weights from summary-level eQTL data. Both simulation and real studies show OTTERS is powerful across a wide range of genetic architectures.

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.Here, the authors present a TWAS framework OTTERS that adapts multiple polygenic risk score methods to estimate eQTL weights from summary-level eQTL data. Both simulation and real studies show OTTERS is powerful across a wide range of genetic architectures.

U2 - 10.1038/s41467-023-36862-w

DO - 10.1038/s41467-023-36862-w

M3 - Article

C2 - 36882394

SN - 2041-1723

VL - 14

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 1271

ER -