Computational Identification of Milk Trait Regulation Through Transcription Factor Cooperation in Murciano-Granadina Goats

Abstract

The Murciano-Granadina goat (MUG) is a renowned dairy breed, known for its adaptability and resilience, as well as for its exceptional milk traits characterized by high protein and fat content, along with low somatic cell counts. These traits are governed by complex biological processes, crucial in shaping phenotypic diversity. Thus, it is imperative to explore the factors regulating milk production and lactation for this breed. In this study, we investigated the genetic architecture of seven milk traits in MUGs, employing a two-step computational analysis to examine genotype–phenotype associations. Initially, a random forest algorithm identified the relative importance of each single-nucleotide polymorphism (SNP) in determining the traits of interest. The second step applied an information theory-based approach to exploring the complex genetic architecture of quantitative milk traits, focusing on epistatic interactions that may have been overlooked in the first step. These approaches allowed us to identify an almost distinct set of candidate genes for each trait. In contrast, by analyzing the promoter regions of these genes, we revealed common regulatory networks among the milk traits under study. These findings are crucial for understanding the molecular mechanisms underlying gene regulation, and they highlight the pivotal role of transcription factors (TFs) and their preferential interactions in the development of these traits. Notably, TFs such as DBP, HAND1E47, HOXA4, PPARA, and THAP1 were consistently identified for all traits, highlighting their important roles in immunity within the mammary gland and milk production during lactation.

Keywords

• Murciano-Granadina
• milk traits
• random forest
• transcription factors
• mammary gland