Leveraging artificial intelligence for livestock genetics and breeding

Abstract The rapid development of artificial intelligence (AI) is transforming modern society including animal genetics and breeding which is essential for meeting global nutritional needs in supplying milk, meat, and eggs among the most complete sources of protein and micronutrients. Although animal breeding has a long history of using data-driven prediction methods, the application of modern AI approaches remains at a relatively early stage and shows substantial potential for wide-ranging applications in animal genetics and breeding. This study aims to review the current use of AI tools in livestock species across three major applications of high interest: AI-assisted phenotyping, AI-enabled dissection of complex-trait biology (focusing on AI-assisted GWAS and AI-for-omics), and AI-assisted genomic prediction. In addition, we discussed emerging methods and tools developed in human genetics that are not yet widely adopted in farm animals but may offer translational potential for future livestock studies. Looking ahead, AI breakthroughs in livestock genetics and breeding are likely to be driven by increases in data scale and resolution, enhanced computational capacities, standardized data infrastructure and benchmarking, and systematic method development and validation. From a livestock genetic improvement perspective, the key value of AI will lie in integrating AI-generated inputs, including improved phenotyping and biological insights, into practical breeding decisions through statistical modeling.