Revolutionizing Animal Breeding: Quantifying the Impact of Genomic Selection, CRISPR, and Artificial Intelligence on Animal Breeding

Mina Boktar

doi:10.64636/ar.22

Authors

Mina Boktar Department of Animal and Poultry Production, Faculty of Agriculture, Sohag university, Sohag, Egypt. https://orcid.org/0009-0008-0602-0244

Competing Interests

The author declare that there are no conflicts of interest.

Corresponding Author

Mina Boktar

minakhair999@gmail.com ; https://orcid.org/0009-0008-0602-0244

DOI:

https://doi.org/10.64636/ar.22

Abstract

Genetic selection is a cornerstone of animal breeding, playing a vital role in enhancing livestock productivity, health, and sustainability. This review critically examines the evolution of selection methodologies in farm animals, with a primary focus on the quantitative impacts of modern genomic and biotechnological tools. It traces the transition from traditional phenotypic approaches and Best Linear Unbiased Prediction (BLUP) to the widespread implementation of Genomic Selection (GS). The review synthesizes key research findings that demonstrate quantifiable improvements, such as the doubling of genetic gain for milk yield in dairy cattle following the adoption of GS. Furthermore, it delves into the application of genome-editing technologies like CRISPR/Cas9, highlighting specific improvements like the development of PRRS-resistant pigs and heat-tolerant slick cattle, which have recently received regulatory approval for commercialization. It also explores the absorption of Artificial Intelligence (AI) and machine learning for analyzing complex datasets and enhancing prediction accuracy. It is also explored, showcasing their potential to revolutionize breeding decisions. While celebrating these advancements, the review addresses the concomitant ethical, regulatory, and economic challenges that require careful navigation by both scientists and policymakers. By providing a comprehensive overview supported by recent and impactful examples, it emphasizes the necessity of integrating advanced genetic technologies to build more efficient, resilient, and sustainable livestock production systems for the future.

Keywords:

Genetic Selection, Livestock Improvement, Phenotypic Selection, Genome Editing, CRISPR/Cas9, Artificial Intelligence

References

Agrimi, U., Conte, M., Morelli, L., Di Bari, M. A., Di Guardo, G., Ligios, C., Antonucci, G., Aufiero, G. M., Pozzato, N., Mutinelli, F., Nonno, R., & Vaccari, G. (2003). Animal transmissible spongiform encephalopathies and genetics. Veterinary research communications, 27 Suppl 1, 31–38. https://doi.org/10.1023/b:verc.0000014115.18327.26

Al-Gebouri, F. G., & Eidan, S. M. (2024). Metabolites and semen characteristics in different bulls fertility. Iraqi Journal of Agricultural Sciences, 55(Special), 206-216. https://doi.org/10.36103/ijas.v55iSpecial.1899

Al Kalaldeh, M., Gibson, J., Duijvesteijn, N. et al. (2019). Using imputed whole-genome sequence data to improve the accuracy of genomic prediction for parasite resistance in Australian sheep. Genet Sel Evol 51, 32. https://doi.org/10.1186/s12711-019-0476-4

Andersson, L., & Georges, M. (2004). Domestic-animal genomics: Deciphering the genetics of complex traits. Nature Reviews Genetics, 5(3), 202–212. https://doi.org/10.1038/nrg1294

Boichard, D., & Brochard, M. (2012). New phenotypes for new breeding goals in dairy cattle. Animal, 6(4), 544–550. https://doi.org/10.1017/S1751731112000018

Bourdon, R. M. (2000). Understanding animal breeding (2nd ed.). Upper Saddle River, NJ, USA: Prentice Hall.

Brito, L. F., Oliveira, H. R., McConn, B. R., Schinckel, A. P., Arrazola, A., Marchant-Forde, J. N., & Johnson, J. S. (2020). Large-scale phenotyping of livestock welfare in commercial production systems: A new frontier in animal breeding. Frontiers in Genetics, 11, 793. https://doi.org/10.3389/fgene.2020.00793

Burrow, H. M., Mrode, R., Mwai, A. O., Coffey, M. P., & Hayes, B. J. (2021). Challenges and Opportunities in Applying Genomic Selection to Ruminants Owned by Smallholder Farmers. Agriculture, 11(11), 1172. https://doi.org/10.3390/agriculture11111172

Carlson, D. F., Lancto, C. A., Zang, B., Kim, E. S., Walton, M., Oldeschulte, D., ... & Fahrenkrug, S. C. (2016). Production of hornless dairy cattle from genome-edited cell lines. Nature Biotechnology, 34(5), 479–481. https://doi.org/10.1038/nbt.3560

Chafai, N., Hayah, I., Houaga, I., & Badaoui, B. (2023). A review of machine learning models applied to genomic prediction in animal breeding. Frontiers in genetics, 14, 1150596. https://doi.org/10.3389/fgene.2023.1150596

Clark, M., & Tilman, D. (2017). Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice. Environmental Research Letters, 12(6), 064016. https://doi.org/10.1088/1748-9326/aa6cd7

Collard, B. C. Y., & Mackill, D. J. (2008). Marker-assisted selection: An approach for precision plant breeding in the twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1491), 557–572. https://doi.org/10.1098/rstb.2007.2170

Cuellar, C. J., et al. (2024). Consequences of gene editing of PRLR on thermotolerance, growth, and male reproduction in cattle. FASEB BioAdvances, 6(8), 223–234. https://doi.org/10.1096/fba.2024-00029

Cyranoski, D. (2016). CRISPR gene-editing tested in a person for the first time. Nature, 539(7630), 479. https://doi.org/10.1038/nature.2016.20988

Dekkers J. C. (2004). Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. Journal of animal science, 82 E-Suppl, E313–E328. https://doi.org/10.2527/2004.8213_supplE313x

Doudna, J. A., & Charpentier, E. (2014). The new frontier of genome engineering with CRISPR-Cas9. Science, 346(6213), 1258096. https://doi.org/10.1126/science.1258096

Eidan, S. M., & Khudhir, S. A. (2023). Association between atp1a1 gene polymorphisms with semen characteristics in Holstein bulls. Iraqi Journal of Agricultural Sciences, 54(2), 330-337. https://doi.org/10.36103/ijas.v54i2.1706

Falconer, D. S., & Mackay, T. F. C. (1996). Introduction to quantitative genetics (4th ed.). Harlow, Essex, United Kingdom: Longman.

FAO. (2015). The Second Report on the State of the World’s Animal Genetic Resources for Food and Agriculture. Food and Agriculture Organization of the United Nations.

FDA. (2024). FDA Approves Gene-Edited Pigs for Food and Biomedical Use. U.S. Food and Drug Administration.

Frewer, L. J., van der Lans, I. A., Fischer, A. R., Reinders, M. J., Menozzi, D., Zhang, X., van den Berg, I., & Zimmermann, K. L. (2013). Public perceptions of agri-food applications of genetic modification – a systematic review and meta-analysis. Trends in Food Science & Technology, 30(2), 142–152. https://doi.org/10.1016/j.tifs.2013.01.003

García-Ruiz, A., Cole, J. B., VanRaden, P. M., Wiggans, G. R., Ruiz-López, F. J., & Van Tassell, C. P. (2016). Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proceedings of the National Academy of Sciences, 113(28), E3995–E4004. https://doi.org/10.1073/pnas.1519061113

Goddard, M. E., & Hayes, B. J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics, 10(6), 381–391. https://doi.org/10.1038/nrg2575

Gonzalez-Recio, O., Rosa, G. J. M., & Gianola, D. (2014). Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits. Livestock Science, 166, 217–231. https://doi.org/10.1016/j.livsci.2014.05.036

Habier, D., Fernando, R. L., & Dekkers, J. C. M. (2007). The impact of genetic relationship information on genome-assisted breeding values. Genetics, 177(4), 2389–2397. https://doi.org/10.1534/genetics.107.081190

Hayes, B. J., Bowman, P. J., Chamberlain, A. J., & Goddard, M. E. (2009). Invited review: Genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science, 92(2), 433–443. https://doi.org/10.3168/jds.2008-1646

Hayes, B. J., Lewin, H. A., & Goddard, M. E. (2013). The future of livestock breeding: Genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends in Genetics, 29(4), 206–214. https://doi.org/10.1016/j.tig.2012.11.009

Hazel, L. N. (1943). The genetic basis for constructing selection indexes. Genetics, 28(6), 476–490. https://doi.org/10.1093/genetics/28.6.476

Henderson, C. R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics, 31(2), 423–447. https://doi.org/10.2307/2529430

Hill, W. G. (2010). Understanding and using quantitative genetic variation. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1537), 73–85. https://doi.org/10.1098/rstb.2009.0203

Hill W. G. (2014). Applications of population genetics to animal breeding, from wright, fisher and lush to genomic prediction. Genetics, 196(1), 1–16. https://doi.org/10.1534/genetics.112.147850

Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012). A programmable dual-RNA–guided DNA endonuclease in adaptive bacterial immunity. Science, 337(6096), 816–821. https://doi.org/10.1126/science.1225829

Lillico, S. G., Proudfoot, C., Carlson, D. F., Stverakova, D., Neil, C., Blain, C., ... & Whitelaw, C. B. (2013). Live pigs produced from genome edited zygotes. Scientific Reports, 3, 2847. https://doi.org/10.1038/srep02847

Marshall, K., Gibson, J. P., Mwai, O., Mwacharo, J. M., Haile, A., Getachew, T., Mrode, R., & Kemp, S. J. (2019). Livestock genomics for developing countries: African examples in practice. Frontiers in Genetics, 10, 297. https://doi.org/10.3389/fgene.2019.00297

Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819–1829. https://doi.org/10.1093/genetics/157.4.1819

Meuwissen, T., Hayes, B., & Goddard, M. (2016). Genomic selection: A paradigm shift in animal breeding. Animal Frontiers, 6(1), 6–14. https://doi.org/10.2527/af.2016-0002

Morota, G., Ventura, R. V., Silva, F. F., Koyama, M., & Fernando, S. C. (2018). Big Data Analytics and Precision Animal Agriculture Symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science, 96(4), 1540-1550. https://doi.org/10.1093/jas/sky014

Mrode, R. A. (2014). Linear models for the prediction of animal breeding values (3rd ed.). CABI Publishing. https://doi.org/10.1079/9781780643915.0000

Nicholas, F. W. (1987). Veterinary genetics. Oxford, United Kingdom: Oxford University Press.

Niemann, H., & Kues, W. A. (2007). Transgenic farm animals: An update. Reproduction, Fertility and Development, 19(6), 762–770. https://doi.org/10.1071/RD07040

Osei-Amponsah, R., Chauhan, S. S., Leury, B. J., Cheng, L., Cullen, B., Clarke, I. J., & Dunshea, F. R. (2019). Genetic Selection for Thermotolerance in Ruminants. Animals, 9(11), 948. https://doi.org/10.3390/ani9110948

Pérez-Enciso, M., & Zingaretti, L. M. (2019). A guide on deep learning for complex trait genomic prediction. Genes, 10(7), 553. https://doi.org/10.3390/genes10070553

Pryce, J. E., & Daetwyler, H. D. (2012). Designing dairy cattle breeding schemes under genomic selection: A review of international research. Animal Production Science, 52(3), 107–114. https://doi.org/10.1071/AN11098

Shapiro, P. (2018). Clean meat: How growing meat without animals will revolutionize dinner and the world. New York, NY, USA: Gallery Books.

Sharif-Islam, M., van der Werf, J. H. J., Wood, B. J., & Hermesch, S. (2024). The predicted benefits of genomic selection on pig breeding objectives. Journal of Animal Breeding and Genetics. https://doi.org/10.1111/jbg.12873

Shook, G. E. (2006). Major advances in determining appropriate selection goals. Journal of Dairy Science, 89(4), 1349–1361. https://doi.org/10.3168/jds.S0022-0302(06)72202-0

Si, Q. (2024). Advancements in Precision Livestock Farming: Technologies and Applications. Animal Molecular Breeding, 14(2), 1–8. https://doi.org/10.5376/amb.2024.14.0020

Tan, W., Proudfoot, C., Lillico, S. G., & Whitelaw, C. B. (2016). Gene targeting, genome editing: from Dolly to editors. Transgenic research, 25(3), 273–287. https://doi.org/10.1007/s11248-016-9932-x

U.S. Food and Drug Administration. (2022). FDA Makes Low-Risk Determination for Marketing of Products from Genome-Edited Beef Cattle. Retrieved from https://www.fda.gov/animal-veterinary/cvm-updates/fda-announces-animal-biotechnology-webinar-low-risk-intentional-genomic-alterations-animals-food-use

Van Eenennaam, A. L. (2017). Genetic modification of food animals. Current Opinion in Biotechnology, 44, 27–34. https://doi.org/10.1016/j.copbio.2016.10.007

Van Eenennaam, Alison. (2018). The Uses of Biotechnology to Improve Animal Welfare. CRC Press. https://doi.org/10.1201/b21911-8

Van Eenennaam A. L. (2019). Application of genome editing in farm animals: cattle. Transgenic research, 28(Suppl 2), 93–100. https://doi.org/10.1007/s11248-019-00141-6

VanRaden, P. M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science, 91(11), 4414–4423. https://doi.org/10.3168/jds.2007-0980

Waltz, E. (2022). How Europe’s gene-edited-food rules could change. Nature, 606(7912), 20-21.

Wang, J., Chai, J., Chen, L., Zhang, T., Long, X., Diao, S., Chen, D., Guo, Z., Tang, G., & Wu, P. (2025). Enhancing genomic prediction accuracy of reproduction traits in Rongchang pigs through machine learning. Animals, 15(4), 525. https://doi.org/10.3390/ani15040525

Whitelaw, C. B. A., Sheets, T. P., Lillico, S. G., & Telugu, B. P. (2016). Engineering large animal models of human disease. Journal of Pathology, 238(2), 247 256. https://doi.org/10.1002/path.4648

Whitelaw, B., & Lillico, S. (2022). Increasing livestock farming sustainability using genome editing technology. Biochemical Society Transactions, 44(3), 9–12. https://doi.org/10.1042/bio_2022_114

Whitworth, K. M., Rowland, R. R. R., Ewen, C. L., Trible, B. R., Kerrigan, M. A., Cino-Ozuna, A. G., ... & Prather, R. S. (2016). Gene-edited pigs are protected from porcine reproductive and respiratory syndrome virus. Nature Biotechnology, 34(1), 20–22. https://doi.org/10.1038/nbt.3434

Wiggans, G. R., VanRaden, P. M., & Cooper, T. A. (2011). The genomic evaluation system in the United States: Past, present, future. Journal of Dairy Science, 94(6), 3202–3211. https://doi.org/10.3168/jds.2010-3866

Wolt, J. D., Wang, K., & Yang, B. (2015). The regulatory status of genome-edited crops. Plant Biotechnology Journal, 14(2), 510–518. https://doi.org/10.1111/pbi.12444

Wray, N. R., Lin, T., Austin, J., McGrath, J. J., Hickie, I. B., Murray, G. K., & Visscher, P. M. (2021). From Basic Science to Clinical Application of Polygenic Risk Scores: A Primer. JAMA Psychiatry, 78(1), 101–109. https://doi.org/10.1001/jamapsychiatry.2020.3049

Type: Review

Revolutionizing Animal Breeding: Quantifying the Impact of Genomic Selection, CRISPR, and Artificial Intelligence on Animal Breeding

Authors

Corresponding Author

DOI:

Abstract

Keywords:

References

View and Download

Downloads

Published

Competing Interest

Data Availability Statement

Issue

Section

Categories

License

How to Cite

Share

Downloads

Similar Articles

Journal Highlights

Discovery and Databases

Digital Preservation

Animal Reports’ Commitment to Long-Term Digital Preservation

Keywords

Reviewer Credits

Browse