Evolving best practices for transcriptome-wide association studies accelerate discovery of gene-phenotype links.

Autor: Torres-Rodríguez JV; Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA; Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA; Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA., Li D; Xianghu Laboratory, Hangzhou, 311231, China., Schnable JC; Quantitative Life Sciences Initiative, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA; Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA; Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA. Electronic address: schnable@unl.edu.
Jazyk: angličtina
Zdroj: Current opinion in plant biology [Curr Opin Plant Biol] 2024 Dec 02; Vol. 83, pp. 102670. Date of Electronic Publication: 2024 Dec 02.
DOI: 10.1016/j.pbi.2024.102670
Abstrakt: Transcriptome-wide association studies (TWAS) complement genome-wide association studies (GWAS) by using gene expression data to link specific genes to phenotypes. This review examines 37 TWAS studies across eight plant species, evaluating the impact of methodological choices on outcomes using maize and soybean datasets. Large sample sizes and synchronized sample collection for gene expression measurement appear to significantly increase power for discovering gene-phenotype linkages, while matching tissue, stage, and environment may matter much less than previously believed, making it feasible to reuse large and well-collected expression datasets across multiple studies. The development of statistical approaches and computational tools specifically optimized for plant TWAS data will ultimately be needed, but further potential remains to adapt advances developed in GWAS to TWAS contexts.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: James Schnable reports financial support was provided by US Department of Energy. James Schnable reports was provided by National Institute of Food and Agriculture. James Schnable reports financial support was provided by Advanced Research Projects Agency-Energy. James Schnable reports a relationship with Google LLC that includes: employment. James Schnable reports a relationship with Dryland Genetics that includes: board membership and equity or stocks. James Schnable reports a relationship with Data2Bio that includes: equity or stocks. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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