Identification of Co-Expression Modules of Cotton Plant Height-Related Genes Based on Weighted Gene Co-Expression Network Analysis

Abstract

Plant height (PH) is a vital agronomic trait that significantly affects cotton yield and facilitates mechanized harvesting. Gaining insights into the genetic regulatory mechanisms governing plant height is fundamental to advancing cotton breeding. In this study, cotton plants were treated with varying concentrations of mepiquat chloride, resulting in significant differences in plant height compared to the control group. Transcriptome data from 18 treated cotton stem tissue samples were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), leading to the identification of 21 gene co-expression modules. Of these, eight modules exhibited positive correlations, while 13 modules showed negative correlations with plant height. A co-expression network comprising 20,409 valid genes was constructed and visualized using Cytoscape 3.9.1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that these modules are associated with biologically significant pathways, including oxidoreductase activity, transcriptional regulator activity, catalytic activity, and phosphate hydrolase activity. Further analysis of gene connectivity within these modules identified 10 core genes (Gohir.D03G105600, Gohir.A03G060400, Gohir.A05G333400, Gohir.D09G243300, Gohir.D12G213500, Gohir.A04G065185, Gohir.A04G039105, Gohir.D08G127760, Gohir.A09G103348, and Gohir.A04G039120) and enabled the establishment of a gene interaction regulation network. Functional predictions suggest that these candidate genes may play key roles in the regulation of cotton plant height. This study offers theoretical insights into the molecular mechanisms underlying cotton plant height and provides valuable references for breeding new cotton varieties with optimized plant heights.

Keywords

• mepiquat chloride
• cotton
• plant height regulation
• weighted gene co-expression network analysis
• core gene