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OTTERS: a powerful TWAS framework leveraging summary-level reference data
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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992663/ https://www.ncbi.nlm.nih.gov/pubmed/36882394 http://dx.doi.org/10.1038/s41467-023-36862-w |
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author | Dai, Qile Zhou, Geyu Zhao, Hongyu Võsa, Urmo Franke, Lude Battle, Alexis Teumer, Alexander Lehtimäki, Terho Raitakari, Olli T. Esko, Tõnu Epstein, Michael P. Yang, Jingjing |
author_facet | Dai, Qile Zhou, Geyu Zhao, Hongyu Võsa, Urmo Franke, Lude Battle, Alexis Teumer, Alexander Lehtimäki, Terho Raitakari, Olli T. Esko, Tõnu Epstein, Michael P. Yang, Jingjing |
author_sort | Dai, Qile |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9992663 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99926632023-03-09 OTTERS: a powerful TWAS framework leveraging summary-level reference data Dai, Qile Zhou, Geyu Zhao, Hongyu Võsa, Urmo Franke, Lude Battle, Alexis Teumer, Alexander Lehtimäki, Terho Raitakari, Olli T. Esko, Tõnu Epstein, Michael P. Yang, Jingjing Nat Commun Article 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. Nature Publishing Group UK 2023-03-07 /pmc/articles/PMC9992663/ /pubmed/36882394 http://dx.doi.org/10.1038/s41467-023-36862-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Dai, Qile Zhou, Geyu Zhao, Hongyu Võsa, Urmo Franke, Lude Battle, Alexis Teumer, Alexander Lehtimäki, Terho Raitakari, Olli T. Esko, Tõnu Epstein, Michael P. Yang, Jingjing OTTERS: a powerful TWAS framework leveraging summary-level reference data |
title | OTTERS: a powerful TWAS framework leveraging summary-level reference data |
title_full | OTTERS: a powerful TWAS framework leveraging summary-level reference data |
title_fullStr | OTTERS: a powerful TWAS framework leveraging summary-level reference data |
title_full_unstemmed | OTTERS: a powerful TWAS framework leveraging summary-level reference data |
title_short | OTTERS: a powerful TWAS framework leveraging summary-level reference data |
title_sort | otters: a powerful twas framework leveraging summary-level reference data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992663/ https://www.ncbi.nlm.nih.gov/pubmed/36882394 http://dx.doi.org/10.1038/s41467-023-36862-w |
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