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

Author

Listed:
  • Qile Dai

    (Emory University School of Public Health
    Emory University School of Medicine)

  • Geyu Zhou

    (Yale University)

  • Hongyu Zhao

    (Yale University
    Yale School of Public Health)

  • Urmo Võsa

    (University of Tartu)

  • Lude Franke

    (University Medical Center Groningen
    Oncode Institute)

  • Alexis Battle

    (Johns Hopkins University)

  • Alexander Teumer

    (University Medicine Greifswald)

  • Terho Lehtimäki

    (Tampere University)

  • Olli T. Raitakari

    (University of Turku and Turku University Hospital
    University of Turku
    Turku University Hospital)

  • Tõnu Esko

    (University of Tartu)

  • Michael P. Epstein

    (Emory University School of Medicine)

  • Jingjing Yang

    (Emory University School of Medicine)

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.

Suggested Citation

  • Qile Dai & Geyu Zhou & Hongyu Zhao & Urmo Võsa & Lude Franke & Alexis Battle & Alexander Teumer & Terho Lehtimäki & Olli T. Raitakari & Tõnu Esko & Michael P. Epstein & Jingjing Yang, 2023. "OTTERS: a powerful TWAS framework leveraging summary-level reference data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36862-w
    DOI: 10.1038/s41467-023-36862-w
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    References listed on IDEAS

    as
    1. Tian Ge & Chia-Yen Chen & Yang Ni & Yen-Chen Anne Feng & Jordan W. Smoller, 2019. "Polygenic prediction via Bayesian regression and continuous shrinkage priors," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    2. Geyu Zhou & Hongyu Zhao, 2021. "A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics," PLOS Genetics, Public Library of Science, vol. 17(7), pages 1-17, July.
    3. Arjun Bhattacharya & Yun Li & Michael I Love, 2021. "MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 17(3), pages 1-30, March.
    4. Ping Zeng & Xiang Zhou, 2017. "Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
    5. Helian Feng & Nicholas Mancuso & Alexander Gusev & Arunabha Majumdar & Megan Major & Bogdan Pasaniuc & Peter Kraft, 2021. "Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies," PLOS Genetics, Public Library of Science, vol. 17(4), pages 1-21, April.
    6. Qianqian Zhang & Florian Privé & Bjarni Vilhjálmsson & Doug Speed, 2021. "Improved genetic prediction of complex traits from individual-level data or summary statistics," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    7. Nicholas Mancuso & Simon Gayther & Alexander Gusev & Wei Zheng & Kathryn L. Penney & Zsofia Kote-Jarai & Rosalind Eeles & Matthew Freedman & Christopher Haiman & Bogdan Pasaniuc, 2018. "Large-scale transcriptome-wide association study identifies new prostate cancer risk regions," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    8. Zhongshang Yuan & Huanhuan Zhu & Ping Zeng & Sheng Yang & Shiquan Sun & Can Yang & Jin Liu & Xiang Zhou, 2020. "Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    9. Kevin L Keys & Angel C Y Mak & Marquitta J White & Walter L Eckalbar & Andrew W Dahl & Joel Mefford & Anna V Mikhaylova & María G Contreras & Jennifer R Elhawary & Celeste Eng & Donglei Hu & Scott Hun, 2020. "On the cross-population generalizability of gene expression prediction models," PLOS Genetics, Public Library of Science, vol. 16(8), pages 1-28, August.
    10. Lijoi, Antonio & Prunster, Igor & Walker, Stephen G., 2005. "On Consistency of Nonparametric Normal Mixtures for Bayesian Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1292-1296, December.
    11. Chen Cao & Bowei Ding & Qing Li & Devin Kwok & Jingjing Wu & Quan Long, 2021. "Power analysis of transcriptome-wide association study: Implications for practical protocol choice," PLOS Genetics, Public Library of Science, vol. 17(2), pages 1-20, February.
    12. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    13. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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