A single nucleotide polymorphism-derived regulatory gene network underlying puberty in 2 tropical breeds of beef...

Harsh tropical environments impose serious challenges on poorly adapted species. In beef cattle, tropical adaptation in the form of temperature and disease resistance, coupled with acclimatization to seasonal and limited forage, comes at a cost to production efficiency. Prominent among these costs is delayed onset of puberty, a challenging phenotype to manipulate through traditional breeding mechanisms. Recently, system biology approaches, including gene networks, have been applied to the genetic dissection of complex phenotypes. We aimed at developing and studying gene networks underlying cattle puberty. Our starting material comprises the association results of ~50,000 SNP on 22 traits, including age at puberty, and 2 cattle breed populations: Brahman (n = 843) and Tropical Composite (n = 866). We defined age at puberty as the age at first corpus luteum (AGECL). By capturing the genes harboring mutations minimally associated (P < 0.05) to AGECL or to a set of traits related with AGECL, we derived a gene network for each breed separately and a third network for the combined data set. At the intersection of the 3 networks, we identified candidate genes and pathways that were common to both breeds. Resulting from these analyses, we identified an enrichment of genes involved in axon guidance, cell adhesion, ErbB signaling, and glutamate activity, pathways that are known to affect pulsatile release of GnRH, which is necessary for the onset of puberty. Furthermore, we employed network connectivity and centrality parameters along with a regulatory impact factor metric to identify the key transcription factors (TF) responsible for the molecular regulation of puberty. As a novel finding, we report 5 TF (HIVEP3, TOX, EYA1, NCOA2, and ZFHX4) located in the network intersecting both breeds and interacting with other TF, forming a regulatory network that harmonizes with the recent literature of puberty. Finally, we support our network predictions with evidence derived from gene expression in hypothalamic tissue of adult cows.