AI Article Synopsis
- Transcriptome-wide association studies (TWAS) enhance genome-wide association studies (GWAS) by utilizing gene expression data to connect genes with specific traits.
- A review of 37 TWAS studies in eight plant species highlights that large sample sizes and timely collection of gene expression data significantly boost the ability to identify gene-phenotype relationships.
- While factors like tissue type and environmental conditions may be less critical than once thought, there’s a need for tailored statistical methods and tools for TWAS in plants, with opportunities to adapt successful GWAS techniques.
Article 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.
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| http://dx.doi.org/10.1016/j.pbi.2024.102670 | DOI Listing |
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