Mammary Gland Multi-Omics Data Reveals New Genetic Insights into Milk Production Traits in Dairy Cattle

Although many sequence variants have been discovered in cattle, deciphering the relationship between genome and phenome remains a significant challenge. In this study, we defined functional classes, including mammary-specific genes, lactation-associated genes, novel long non-coding RNAs, miRNAs, RNA editing sites, DNA methylation, histone modifications, eQTLs, and sQTLs, and estimated their contributions to genetic variation for milk production traits using 3 million variants in 23,566 Holstein bulls. Sequence variants in the 5'UTR, synonymous, and splicing regions captured more variance for milk production traits than those in other genomic regions. Variants within specific genes related to lactating mammary tissue explained more genetic variance than those in other mammary tissues. Genetic variance was enriched in these lactation-up-regulated DEGs and down-regulated DEGs with small changes. We proposed a novel strategy for selecting candidate miRNAs by identifying overlaps that exhibit a significant number of mRNA targets with negative correlations, as well as a relatively large genetic variance explained by these targets. Notably, bta-miR-193-5p and bta-miR-345-5p were confirmed to enhance concentrations of total protein, triglycerides, and β-casein, as well as promote the proliferation of mammary epithelial cells. Furthermore, we found that mammary enhancers explained more genetic variance for milk production traits than repressive regulatory elements, while minor changes in DNA methylation accounted for more variance than larger changes. Finally, we constructed a new SNP panel, which improved the reliabilities of genomic predictions by 0.22%. Dividing routine SNPs into two groups based on functional classes improved the reliabilities by 0.21%. Overall, incorporating prior biological knowledge of the mammary gland directly enhances our understanding of the genetic architecture underlying milk production and improves the reliability of genomic predictions for milk production traits.