Identification of Candidate Forage Yield Genes in Sorghum (Sorghum bicolor L.) Using Integrated Genome-Wide Association Studies and RNA-Seq - Wang, Lihua; Liu, Yanlong; Gao, Li; Yang, Xiaocui; Zhang, Xu; Xie, Shaoping; Chen, Meng; Wang, Yi-Hong; Li, Jieqin; Shen, Yixin (Frontiers in Plant Science, 2022)
The genetic dissection of forage yield traits is critical to the development of sorghum as a forage crop. In the present study, a genome-wide association study was performed using 245 sorghum accessions and 85,585 SNP markers for four forage yield traits, namely plant height (PH), tiller number (TN), stem diameter (SD), and fresh weight per plant (FW). A total of 338 SNPs, or quantitative trait nucleotides (QTNs), were associated with the four traits, and 21 of these were detected in at least two environments, including four QTNs for PH, ten for TN, six for SD, and one for FW. To identify candidate genes, dynamic transcriptome expression profiling was performed at four stages of sorghum development. One hundred and six differentially expressed genes that were enriched in hormone signal transduction pathways were found in all stages. Weighted gene correlation network analysis for PH and SD indicated that eight modules were significantly correlated with PH and that three modules were significantly correlated with SD. The blue module had the highest positive correlation with PH and SD, and the turquoise module had the highest negative correlation with PH and SD. Ultimately, eight candidate genes were identified through the integration of genome-wide association studies and RNA sequencing. Sobic.004G143900, an indole-3-glycerol phosphate synthase gene that is involved in indoleacetic acid biosynthesis, was down-regulated as sorghum plants increased in height and was identified in the blue module, and Sobic.003G375100, an SD candidate gene, encoded a DNA repair RAD52-like protein 1 that plays a critical role in DNA repair-linked cell cycle progression. These findings demonstrate that the integrative analysis of omics data is a promising approach for identifying candidate genes for complex traits.