Weighted Gene Correlation Network Analysis (WGCNA)
Groups with the greatest genetic distance and DEGs were selected for
WGCNA. The analysis was conducted using the WGCNA R package following
the provided tutorials (Langfelder & Horvath, 2008). The soft
thresholding power was determined according to the principle of a
nonscale network, and the lowest power when the correlation coefficient
reached the plateau period was used as a parameter in subsequent
analysis. A gene clustering tree was constructed according to the
correlations between the expression levels of genes and coexpression
modules were identified by dynamic tree cutting using a minimum module
size of 30. When the correlation between the modules and traits was
greater than 0.75 (p < 0.05), the modules were significantly
related to the traits. The R package clusterProfiler was used for KEGG
enrichment analysis of genes in the phenotype-related modules (Yu et
al., 2012).