Multi-omics integration deciphers the metabolic-genetic regulatory network underlying milk composition in dairy goats

Milk composition in dairy goats, an economically important trait, is coordinately governed by complex metabolic networks and genetic factors. This study employed extreme phenotype grouping and multi-omics analysis of Xinong Saanen dairy goats to systematically elucidate the metabolic and genetic regulatory mechanisms underlying milk fat, SNF, protein, and lactose. Using widely targeted metabolomics, we identified 795 milk metabolites. Differential metabolite analysis revealed 57, 94, 50, and 58 significantly altered metabolites in milk fat, SNF, protein, and lactose, respectively. Subsequent metabolomic GWAS of these metabolites demonstrated significant genetic signals for 17 milk fat-associated metabolites, annotating 330 candidate genes (e.g., JAK2, LIPC, LRP1B). Similarly, 33 SNF-associated metabolites exhibited heritable signals linked to 177 candidate genes (including DGAT2, TGFB1, and NPAS3). For the protein-associated metabolites, 9 showed significant signals corresponding to 18 candidate genes (e.g., SRP54, CABYR, PRRX1), and 6 lactose-associated metabolites carried heritable signals that were mapped to 47 key candidate genes (such as HMGCS1, RXRA, and ADCK1). Collectively, this work identifies critical metabolites and candidate genes governing distinct milk components, deciphers the genetic-metabolic regulatory network influencing milk composition traits from a multidimensional perspective, and provides novel targets for the precise molecular breeding of high-quality goat milk.