Big data analytics

GWAS for improving productivity and disease resistance in livestock

Abstract

This paper reviews the methodological advancements, statistical models, and technological platforms underpinning genome-wide association studies (GWAS), highlighting its transformative role in livestock breeding programs. A significant focus is placed on the integration of high-throughput genotyping and phenotyping, linkage disequilibrium models, and multiomics data to enhance genomic prediction and selection accuracy. The study also emphasizes GWAS applications in Indian livestock, particularly Vrindavani cattle and indigenous pig breeds, for traits such as milk composition, mineral content, and immune response to classical swine fever (CSF) vaccination. Despite its strengths, GWAS faces limitations including reliance on existing SNP panels, stringent significance thresholds, and challenges in pinpointing causal variants. The application of mixed models, principal component analysis, and CNV analysis are discussed as strategies to mitigate these issues. The findings underscore GWAS as a pivotal tool for unravelling the genetic architecture of economically important traits, paving the way for precision breeding and improved animal health management. Future prospects lie in exploring gene–gene and gene–environment interactions to bridge the gap of missing heritability and to optimize trait-specific breeding strategies.