Genomic Selection Techniques for Developing Heat-Resilient and Disease-Tolerant Livestock Breeds

Abstract

One of the biggest challenges that has been faced regarding sustainable livestock production at a global level is climate change, growing thermal stress and the rapid development of infectious diseases. Traditional breeding trends tend to be ineffective in enhancing adaptive phenomenon like heat resilience and resistance to diseases because of low heritability, lengthy generation cycle, and environmental factors. This paper introduces a genomic selection system to produce genome-wide molecular markers and predictive genomic analysis to produce heat-tolerant and disease-tolerant livestock breeds. The suggested approach combines single nucleotide polymorphism (SNP)-based genotyping, genome wide association study (GWAS), genomic estimated breeding value (GEBV) prediction, and machine learning-aided genomic analysis to select better breeding individuals among cattle, sheep, goats, and poultry groups. Multi-species phenotypic and genomic data under controlled environmental stress were used as experimental measurements. The genomic prediction model that was developed had prediction accuracies of 91.8% and 89.6% in heat resilience and disease tolerance respectively. Animals genetically selected showed better thermoregulation, survival and improved productivity as well as decreased disease occurrence than traditional breeding methods. The analysis of statistics proved a significant enhancement of adaptive livestock characteristics (p < 0.05). The suggested framework offers an efficient approach to precision livestock breeding and can make a significant contribution to climate-resilient livestock production, sustainable livestock health care and sustainable global food security in the long-term.