Table 1.
The annotation of significant SNPs.
Fig 1.
Genome-wide association analysis of LMA trait in the Huaxi cattle population.
(A) Manhattan plot. The red dotted lines indicated the significant threshold (P = 0.05/605,671). (B) QQ plot.
Fig 2.
Identification of DEGs in the high LMA and low LMA groups.
(A) PCA of the samples. Green and red dots indicated samples with high LMA and low LMA, respectively. (B) Volcano plot of DEGs. Red dots represented significantly up-regulated genes; dark-green dots indicated significantly down-regulated genes; black dots indicated genes with no significant effect. (C) Heatmap of DEGs. H: high LMA group; L: low LMA group.
Fig 3.
Functional enrichment analysis of DEGs.
(A) BP, CC, and MF showed the top ten terms, respectively; (B) The top 15 KEGG pathways enrichment of significant DEGs.
Fig 4.
(A) Sample clustering tree. (B) Soft threshold estimation based on adjacency matrix. (C) Dynamic cutting tree algorithm to partition gene modules. (D) Modular-trait heatmap of correlation between gene modules and LMA. Each module contains the corresponding correlation coefficient and p-value.
Fig 5.
(A) Hub gene within the sienna3 module. (B) Hub gene within the lightcyan1 module. (C) Intersection genes obtained by MCC, MNC, and Degree algorithms in the sienna3 module. (D) Intersection genes obtained by MCC, MNC, and Degree algorithms in the lightcyan1 module. (E) The intersection of genes selected by three algorithms and DEGs.