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Parrish RL, Buchman AS, Tasaki S, Wang Y, Avey D, Xu J, De Jager PL, Bennett DA, Epstein MP, Yang J. SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.06.20.23291605. [PMID: 37425698 PMCID: PMC10327185 DOI: 10.1101/2023.06.20.23291605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.
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Dai Q, Zhou G, Zhao H, Võsa U, Franke L, Battle A, Teumer A, Lehtimäki T, Raitakari OT, Esko T, Epstein MP, Yang J. OTTERS: a powerful TWAS framework leveraging summary-level reference data. Nat Commun 2023; 14:1271. [PMID: 36882394 PMCID: PMC9992663 DOI: 10.1038/s41467-023-36862-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 02/20/2023] [Indexed: 03/09/2023] Open
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.
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Affiliation(s)
- Qile Dai
- Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA, 30322, USA
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Geyu Zhou
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06511, USA
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06511, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 50090, Tartu, Estonia
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, The Netherlands
- Oncode Institute, 3521 AL, Utrecht, The Netherlands
| | - Alexis Battle
- Department of Computer Science, and Departments of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, 17489, Greifswald, Germany
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Centre for Cardiovascular Disease Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33520, Finland
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, 20520, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, 20521, Turku, Finland
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 50090, Tartu, Estonia
| | - Michael P Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA.
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA.
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