QUANTITATIVE TRAIT LOCI (QTL) MAPPING AND GENOMIC SELECTION FOR MASTITIS RESISTANCE IN HOLSTEIN-FRIESIAN CATTLE USING WHOLE-GENOME SEQUENCING

Abstract

Mastitis, a pervasive and economically significant disease in dairy cattle, demands an integrated diagnostic and preventative strategy due to its complex etiology involving host genetics, immune response, and microbial factors. This study employed a multi-omics and computational framework to elucidate the genomic, immunological, and microbial determinants of mastitis resistance in Holstein-Friesian cows. Through whole-genome sequencing and QTL mapping, we identified several SNPs associated with mastitis resilience. Simultaneously, cytokine profiling revealed elevated IL-1β and TNF-α levels in infected cattle, which were statistically correlated with somatic cell count (SCC) and pathogen burden. Microbiome analysis via 16S rRNA sequencing indicated reduced microbial diversity and increased prevalence of Staphylococcus aureus and E. coli in mastitis-affected milk samples. Nine detailed tables demonstrated these trends across various animal groups, and twelve figures, including hybrid plots, bar graphs, and scatter charts, visualized the intricate relationships among host immunity, microbial community structure, and genomic variations. Machine learning models—especially ensemble classifiers—exhibited strong predictive performance (accuracy > 90%) for mastitis risk assessment using integrated features. Explainable AI techniques validated model transparency and feature importance. These results underscore the utility of genomics-driven precision breeding and computational diagnostics in controlling mastitis. The study provides a scalable, reproducible methodology for enhancing mastitis resistance, contributing to improved dairy herd productivity and sustainability.