Modern approaches and prospects for the genetic evaluation of dairy cattle in breeding programmes

Viktor Danshyn, Sergiy Ruban, Volodymyr Nazarenko
Abstract

The evaluation of breeding value for economically important traits is a key tool in modern systems of genetic improvement in dairy cattle. This analytical study aimed to describe the current algorithm for genetic evaluation in dairy cattle and to identify promising directions for potential improvement in the near future. Genetic evaluation methods have undergone significant development, evolving from mass daughter-dam comparisons, direct and improved herdmate comparisons, and modified contemporary comparison, to more complex approaches such as the Animal Model and genomic evaluation using mathematical techniques such as BLUP and REML. The implementation of modern genomic selection programmes requires a substantial restructuring of the entire organisational system of breeding. The presence of reference populations, with ongoing monitoring of genetic and phenotypic traits, is a fundamental requirement. It is noted that a general trend in modern dairy farming is the increasing number of traits considered in selection to account for both observable traits (such as milk yield and composition) and “hidden” traits (such as health status, reproductive efficiency, productive longevity, and feed conversion efficiency), all of which significantly influence production economics. A comparative analysis of the monitored livestock population and productivity indicators in Ukraine and ICAR member countries was carried out, revealing key limitations within the national breeding system. It was established that genomic evaluation enables the shortening of generation intervals and the doubling of the rate of genetic progress in milk yield. The practical value of the study lies in providing scientifically grounded guidelines for developing an effective system of genetic evaluation and breeding resource management in Ukraine

Keywords

BLUP Animal Model, genomic selection, holo-omics, genome editing, biotechnology

Suggested citation
Danshyn, V., Ruban, S., & Nazarenko, V. (2025). Modern approaches and prospects for the genetic evaluation of dairy cattle in breeding programmes. Scientific Reports of the National University of Life and Environmental Sciences of Ukraine, 21(3),9-23. https://doi.org/10.31548/dopovidi/3.2025.09
References
  1. ABS Genetics. (n.d.). Bull search tool. Retrieved from https://absbullsearch.absglobal.com.
  2. Ahmadi, N., & Bartholomé, J. (Eds.). (2022). Genomic prediction of complex traits: Methods and protocolsMethods in molecular biology (vol. 2467). New York: Humana Press. doi: 10.1007/978-1-0716-2205-6.
  3. Alemu, S.W., Lopdell, T.J., Trevarton, A.J., Snell, R.G., Littlejohn, M.D., & Garrick, D.J. (2025). Comparison of genomic prediction accuracies in dairy cattle lactation traits using five classes of functional variants versus generic SNP. Genetics Selection Evolution, 57, article number 20. doi: 10.1186/s12711-025-00966-2.
  4. Bermann, M., Cesarani, A., Misztal, I., & Lourenco, D. (2022). Past, present, and future developments in single-step genomic models. Italian Journal of Animal Science, 21(1), 673-685. doi: 10.1080/1828051X.2022.2053366.
  5. Chakraborty, D., Sharma, N., Kour, S., Sodhi, S.S., Gupta, M.K., Lee, S.J., & Son, Y.O. (2022). Applications of Omics technology for livestock selection and improvement. Frontiers in Genetics, 13, article number 774113. doi: 10.3389/fgene.2022.774113.
  6. Council on Dairy Cattle Breeding. (n.d.). Retrieved from https://uscdcb.com/.
  7. Dai, H., Wu, J., Yang, H., Guo, Y., Di, H., Gao, M., & Wang, J. (2022). Construction of BHV-1 UL41 defective virus using the CRISPR/Cas9 system and analysis of viral replication properties. Frontiers in Cellular and Infection Microbiology, 12, article number 942987. doi: 10.3389/fcimb.2022.942987.
  8. Danshin, V.O., Ruban, S.Y., & Afanasenko, V.Y. (2017). Evaluation of breeding values of sires and cows in dairy breeds. The Animal Biology, 19(1), 44-52. doi: 10.15407/animbiol19.01.044.
  9. De Vries, A., Bliznyuk, N., & Pinedo, P. (2023). Invited review: Examples and opportunities for artificial intelligence (AI) in dairy farms. Applied Animal Science, 39, 14-22. doi: 10.15232/aas.2022-02345.
  10. Gim, G.-M., et al. (2023). Generation of double knockout cattle via CRISPR-Cas9 ribonucleoprotein (RNP) electroporation. Journal of Animal Science and Biotechnology, 14, article number 103. doi: 10.1186/s40104-023-00902-8.
  11. Guinan, F.L., Wiggans, G.R., Norman, H.D., Dürr, J.W., Cole, J.B., Van Tassell, C.P., Misztal, I., & Lourenco, D. (2023). Changes in genetic trends in US dairy cattle since the implementation of genomic selection. Journal of Dairy Science, 106(2), 1110-1129. doi: 10.3168/jds.2022-22205.
  12. Hopper, R.M. (Ed.). (2021). Bovine reproduction (2nd ed.). Hoboken: John Wiley & Sons, Inc.
  13. ICAR. (2023a). Annual report 2022–2023. Retrieved from https://dahd.gov.in/sites/default/files/2023-06/FINALREPORT2023ENGLISH.pdf.
  14. ICAR. (2023b). Yearly survey on the situation of milk recording systems (years 2022 and 2023) in ICAR member countries for cow, sheep and goats. Retrieved from https://www.icar.org/wp-content/uploads/documents/Survey-on-milk-recording-systems-in-cows-sheep-and-goats-2022-and-2023.pdf.
  15. Klímová, A., Kašná, E., Machová, K., Brzáková, M., Přiby, J., & Vostrý, L. (2020). The use of genomic data and imputation methods in dairy cattle breeding. Czech Journal of Animal Science, 65(12), 445-453. doi: 10.17221/83/2020‑CJAS.
  16. Koutouzidou, G., Ragkos, A., & Melfou, K. (2022). Evolution of the structure and economic management of the dairy cow sector. Sustainability, 14, article number 11602. doi: 10.3390/su141811602.
  17. Lourenco, D., Legarra, A., Tsuruta, S., Masuda, Y., Aguilar, I., & Misztal, I. (2020). Single-step genomic evaluations from theory to practice: Using SNP chips and sequence data in BLUPF90. Genes, 11, article number 790. doi: 10.3390/genes11070790.
  18. Meuwissen, T.H.E., Hayes, B.J., & Goddard, M.E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819-1829. doi: 10.1093/genetics/157.4.1819.
  19. Misztal, I., Lourenco, D., & Legarra, A. (2020). Current status of genomic evaluation. Journal of Animal Science, 98(4), article number skaa101. doi: 10.1093/jas/skaa101.
  20. Monteiro, H.F., et al. (2024). An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows. Animal Microbiome, 6, article number 5. doi: 10.1186/s42523‑024‑00289‑5.
  21. Mrode, R.A., Pocrnic, I., Gorjanc, G., & Thompson, R. (2023). Linear models for the prediction of the genetic merit of animals. Wallingford: CABI.
  22. Norman, D., Guinan, F.L., & Dürr, J.W. (2022). Genetic gains in lifetime merit indexes during the use of three genetic evaluation methodsInterbull Bulletin, 57, 111-116.
  23. Pal, A. (2022). Genome-wide association studies/SNP chips. In Protocols in advanced genomics and allied techniques. New York: Springer. doi: 10.1007/978-1-0716-1818-9_16.
  24. Ross, E.M., & Hayes, B.J. (2022). Metagenomic predictions: A review 10 years on. Frontiers in Genetics, 13, article number865765. doi: 10.3389/fgene.2022.865765.
  25. Ruban, S., & Danshin, V. (2023). Perspectives for the use of genomic selection for genetic improvement of dairy cattle in Ukraine. Ukrainian Black Sea Region Agrarian Science, 27(1), 20-29. doi: 10.56407/bs.agrarian/1.2023.20.
  26. Ruban, S.Y., Kudlay, I.M., Klymenko, A.V., Mitioglo, L.V., Tsentylo, L.V., & Tsybenko, V.G. (2021). Milk production (domestic and world experience of effective dairy farming). Bila Tserkva: PE O.V. Brovin.
  27. Scott, B.A., Haile-Mariam, M., Cocks, B.G., & Pryce, J.E. (2021). How genomic selection has increased rates of genetic gain and inbreeding in the Australian national herd, genomic information nucleus, and bulls. Journal of Dairy Science, 104(11), 11832-11849. doi: 10.3168/jds.2021‑20326.
  28. Simm, G., Pollott, G., Mrode, R., Houston, R., & Marshall, K. (2021). Genetic improvement of farmed animals. Wallingford: CABI.
  29. Van Eenennaam, A.L. (2025). Current and future uses of genetic improvement technologies in livestock breeding programs. Animal Frontiers, 15(1), 80-90. doi: 10.1093/af/vfae042.
  30. VanRaden, P.M. (2020). Symposium review: How to implement genomic selection. Journal of Dairy Science, 103(6), 5291-5301. doi: 10.3168/jds.2019-17684.
  31. VanRaden, P.M., Cole, J., & Parker Gaddis, K.L. (2021). Net merit as a measure of lifetime profit: 2021 revision. AIP Research Report.
  32. Weigel, K., Chasco, A., Pacheco, H., Sigdel, A., Guinan, F., Lauber, M., Fricke, P., & Peñagaricano, F. (2024). Genomic selection in dairy cattle: Impact and contribution to the improvement of bovine fertility. Clinical Theriogenology, 16, article number 10399. doi: 10.58292/CT.v16.10399.
  33. Weller, J.I. (2019). Genetic evaluation: Use of genomic data in large-scale genetic evaluations in dairy cattle breeding. In J. van der Werf & J. Pryce (Eds.), Advances in breeding of dairy cattle (pp. 441-474). Cambridge: Burleigh Dodds Science Publishing Limited. doi: 10.19103/AS.2019.0058.22.
  34. Wientjes, Y.C.J., Bijma, P., Calus, M.P.L., Zwaan, B.J., Vitezica, Z.G., & van den Heuvel, J. (2022). The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture. Genetics Selection Evolution, 54, article number 19. doi: 10.1186/s12711-022-00709-7.
  35. Wiggans, G.R., & Carrillo, J.A. (2022). Genomic selection in United States dairy cattle. Frontiers in Genetics, 13, article number 994466. doi: 10.3389/fgene.2022.994466.
  36. Workman, A.M., et al. (2023). First gene-edited calf with reduced susceptibility to a major viral pathogen. PNAS Nexus, 2, article number pgad125. doi: 10.1093/pnasnexus/pgad125.
  37. Xu, S. (2022). Quantitative genetics. Cham: Springer.
  38. Yuan, M., Zhang, J., Gao, Y., Yuan, Z., Zhu, Z., Wei, Y., Wu, T., Han, J., & Zhang, Y. (2021). HMEJ-based safe-harbor genome editing enables efficient generation of cattle with increased resistance to tuberculosis. Journal of Biological Chemistry, 296, article number 100497. doi: 10.1016/j.jbc.2021.100497.
  39. Zhukosky, O.M., Romanova, O.V., Mykhailenko, N.G., Pryima, S.V., & Basovsky, D.M. (Eds.). (2024). State register of breeding subjects in livestock breeding for 2023 (Vol. 2). Kyiv: M.V. Zubets Institute of Animal Breeding and Genetics of the NAAS.