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Peer-reviewedCC BY 4.0Journal of Animal Science and Biotechnology

Leveraging artificial intelligence for livestock genetics and breeding

  • Di Zhu1
  • Abbygail Wells2,3
  • Rasmus Bak Stephansen1
  • Hao Cheng4
  • Jan Lassen1,5
  • Georgios Banos2
  • Mike Coffey2
  • Lingzhao Fang1
  • Bingjie Li1
5 affiliations
  1. 1Center for Quantitative Genetics and Genomics (QGG), Aarhus University, 8000 Aarhus, Denmark
  2. 2Animal Breeding & Genomics, Scotland’s Rural College (SRUC), Edinburgh, EH9 3JG UK
  3. 3The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG UK
  4. 4Department of Animal Science, University of California, Davis, Davis, CA 95616 USA
  5. 5VikingGenetics, 8960 Randers, Denmark
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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.

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Cite

BibTeXRIS
BibTeX
@article{zhu2026leveraging,
  title = {{Leveraging artificial intelligence for livestock genetics and breeding}},
  author = {Di Zhu and Abbygail Wells and Rasmus Bak Stephansen and Hao Cheng and Jan Lassen and Georgios Banos and Mike Coffey and Lingzhao Fang and Bingjie Li},
  journal = {Journal of Animal Science and Biotechnology},
  year = {2026},
  month = nov,
  doi = {10.1186/s40104-026-01527-3},
  url = {https://doi.org/10.1186/s40104-026-01527-3},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Leveraging artificial intelligence for livestock genetics and breeding
AU  - Di Zhu
AU  - Abbygail Wells
AU  - Rasmus Bak Stephansen
AU  - Hao Cheng
AU  - Jan Lassen
AU  - Georgios Banos
AU  - Mike Coffey
AU  - Lingzhao Fang
AU  - Bingjie Li
PY  - 2026
DA  - 2026/11/08
JO  - Journal of Animal Science and Biotechnology
DO  - 10.1186/s40104-026-01527-3
UR  - https://doi.org/10.1186/s40104-026-01527-3
AB  - 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.
SN  - 1674-9782
SN  - 2049-1891
ER  -