%0 Journal Article
%T TWAS facilitates gene-scale trait genetic dissection through gene expression, structural variations, and alternative splicing in soybean.
%A Li D
%A Wang Q
%A Tian Y
%A Lyv X
%A Zhang H
%A Hong H
%A Gao H
%A Li YF
%A Zhao C
%A Wang J
%A Wang R
%A Yang J
%A Liu B
%A Schnable PS
%A Schnable JC
%A Li YH
%A Qiu LJ
%J Plant Commun
%V 0
%N 0
%D 2024 Jun 25
%M 38918950
%F 8.625
%R 10.1016/j.xplc.2024.101010
%X A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.