Multiomic meta-analysis suggests a correlation between steroid hormone-related genes and litter size in goats

Abstract

Litter size is a key indicator of production performance in livestock. However, its genetic basis in goats remains poorly understood. In this work, a genome-wide selection sweep analysis (GWSA) on 100 published goat genomes with different litter rates was performed for the first time to identify candidate genes related to kidding rate. This analysis was combined with the public RNA-sequencing data of ovary tissues (follicular phase) from high- and low-yielding goats. A total of 2278 genes were identified by GWSA. Most of these genes were enriched in signaling pathways related to ovarian follicle development and hormone secretion. Moreover, 208 differentially expressed genes between groups were obtained from the ovaries of goats with different litter sizes. These genes were substantially enriched in the cholesterol and steroid synthesis signaling pathways. Meanwhile, the weighted gene co-expression network was used to perform modular analysis of differentially expressed genes. The results showed that seven modules were reconstructed, of which one module showed a very strong correlation with litter size (r = −0.51 and p-value <0.001). There were 51 genes in this module, and 39 hub genes were screened by Pearson's correlation coefficient between core genes > 0.4, correlation coefficient between module members > 0.80 and intra-module connectivity ≥5. Finally, based on the results of GWSA and hub gene Venn analysis, seven key genes (ACSS2, HECW2, KDR, LHCGR, NAMPT, PTGFR and TFPI) were found to be associated with steroid synthesis and follicle growth development. This work contributes to understanding of the genetic basis of goat litter size and provides theoretical support for goat molecular breeding.