Genomic Selection in Livestock Breeding: Opportunities for Improving Productivity and Disease Resistance

Abstract

Genomic selection has emerged as a transformative approach in livestock breeding, enabling the rapid and precise improvement of productivity and disease resistance traits. By leveraging high-density genomic markers and advanced statistical models, breeders can predict the genetic potential of animals with unprecedented accuracy, reducing generation intervals and accelerating genetic gains. This study reviews current methodologies, including single-step genomic best linear unbiased prediction (ssGBLUP) and machine learning–enhanced models, highlighting their application across major livestock species such as cattle, sheep, pigs, and poultry. Evidence indicates that genomic selection not only enhances growth, milk, and reproductive performance but also contributes significantly to resistance against prevalent diseases, thereby improving herd health and sustainability. Despite these benefits, challenges remain, including high genotyping costs, the need for robust reference populations, and ethical considerations related to genetic diversity. The study underscores the potential of integrating genomic selection with conventional breeding programs and emerging biotechnologies to achieve resilient, high-performing livestock populations. Future research should focus on optimizing genomic prediction models, exploring multi-trait selection strategies, and assessing long-term impacts on genetic diversity and animal welfare.