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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...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
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
Descripción
Sumario: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.