Evolving best practices for transcriptome-wide association studies accelerate discovery of gene-phenotype links
J Vladimir Torres-Rodríguez1, Delin Li2, James C Schnable1
1Quantitative 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.
Abstract:
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.
