Extensive transcriptome data providing great efficacy in genetic research and adaptive gene discovery: a case study of Elymus sibiricus L. (Poaceae, Triticeae)

• Published in: Frontiers in plant science Vol. 15; p. 1457980
• Main Authors: Xiong, Yanli, Li, Daxu, Liu, Tianqi, Xiong, Yi, Yu, Qingqing, Lei, Xiong, Zhao, Junming, Yan, Lijun, Ma, Xiao
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 19.09.2024
• Subjects: adaptability; Elymus sibiricus; genome skimming; Plant Science; SNPs; transcriptome; WGCNA; SNPs; adaptability; Elymus sibiricus; WGCNA; transcriptome; genome skimming
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2024.1457980

Genetic markers play a central role in understanding genetic diversity, speciation, evolutionary processes, and how species respond to environmental stresses. However, conventional molecular markers are less effective when studying polyploid species with large genomes. In this study, we compared gene expression levels in 101 accessions of , a widely distributed allotetraploid forage species across the Eurasian continent. A total of 20,273 high quality transcriptomic SNPs were identified. In addition, 72,344 evolutionary information loci of these accessions of were identified using genome skimming data in conjunction with the assembled composite genome. The population structure results suggest that transcriptome SNPs were more effective than SNPs derived from genome skimming data in revealing the population structure of from different locations, and also outperformed gene expression levels. Compared with transcriptome SNPs, the investigation of population-specifically-expressed genes (PSEGs) using expression levels revealed a larger number of locally adapted genes mainly involved in the ion response process in the Sichuan, Inner Mongolia, and Xizang geographical groups. Furthermore, we performed the weighted gene co-expression network analysis (WGCNA) and successfully identified potential regulators of PSEGs. Therefore, for species lacking genomic information, the use of transcriptome SNPs is an efficient approach to perform population structure analysis. In addition, analyzing genes under selection through nucleotide diversity and genetic differentiation index analysis based on transcriptome SNPs, and exploring PSEG through expression levels is an effective method for analyzing locally adaptive genes.