Animal Science and Food Technology

Latent phenotype potential: Modelling response to selection for dairy cattle productivity traits considering genetic correlations

Sergiy Ruban, Viktor Danshyn, Oleksandr Borshch, Mykola Zbroi, Olena Fedota
Download article
Abstract

The aim of the study was to analyse the dynamics of changes in economically important traits of dairy cattle: milk fat and protein content, daughter pregnancy rate, productive longevity, residual feed intake, live weight, somatic cell score, when modelling selection for dairy productivity traits such as milk yield, milk fat and protein content. The research material consisted of data from a reference herd of Holstein cows at the “Terezyne” dairy farm (Kyiv region, Ukraine). The total number of cows observed was 14,712 across seven lactations. MRS software was used for modelling. When selecting for milk yield at selection differentials of 100 kg, 300 kg and 500 kg, the genetic response of the next generation for milk yield was 31.59 kg, 94.77 kg and 157.97 kg, respectively. At the same time, there was an increase in such traits as productive longevity, from 0.59 months to 2.94 months, live weight – from 0.36 to 1.79 kg, and the score for somatic cell content in milk – from 0.03 to 0.13. Genetic changes in milk fat and protein content, signs of daughter fertility and residual feed intake had reverse values. Significant phenotypic correlations were obtained between the milk yield of first-calf heifers and milk fat content (-0.2985), milk fat content (+0.9631), milk protein content (-0.2642), milk protein (+0.9924), open days period (+0.0989), productive longevity (+0.0989) and live weight (+0.2199). There were mixed levels of correlation in the daughters of sires between milk yield and milk protein content from -0.1229 to +0.1708, and between milk yield and open days from -0.0726 to +0.1836. Modelling of possible changes in genetic correlations in the range from -0.10 to -0.95 between milk yield and daughter pregnancy rate (DPR) was performed. The rates of correlated response when selecting for milk yield, depending on the values of genetic correlation between these traits, affected the reduction in the level of pregnancy in cows from -0.51 to almost -5.0 points. The impact on the strength and direction of the established relationships can contribute to the stabilisation or deterioration of reproductive traits when selecting for milk yield. The proposed modelling method makes it possible to predict changes in the main traits used for selection, depending on the genetic correlations between them

Keywords

genetic parameters; phenotypic correlations; dairy productivity; reproduction

Suggested citation
Ruban, S., Danshyn, V., Borshch, O., Zbroi, M., & Fedota, O. (2025). Latent phenotype potential: Modelling response to selection for dairy cattle productivity traits considering genetic correlations. Animal Science and Food Technology, 16(4), 9-27. https://doi.org/10.31548/animal.4.2025.9
References
  1. Akash, Hoque, M., Mondal, S., & Adusumilli, S. (2022). Sustainable livestock production and food security. In S. Mondal & R.L. Singh (Eds.), Emerging issues in climate smart livestock production (pp. 71-90). Cambridge, MA: Academic Press. doi: 10.1016/B978-0-12-822265-2.00011-9.
  2. Berro, I., Lado, B., Nalin, R.S., Quincke, M., & Gutiérrez, L. (2019). Training population optimization for genomic selection. Plant Genome, 12(3), 1-14. doi: 10.3835/plantgenome2019.04.0028.
  3. Bouquet, A., Slagboom, M., Thomasen, J., Friggens, N.N.C., Kargo, M., & Puillet, L. (2022). Mechanistic-based prediction of selection response on resilience and feed efficiency traits in dairy cattle. In Proceedings of 12th world congress on genetics applied to livestock production (pp. 268-271). Rotterdam: HAL. doi: 10.3920/978-90-8686-940-4_55.
  4. Brito, L.F., Bedere, N., Douhard, F., Oliveira, H.R., Arnal, M., Peñagaricano, F., Schinckel, A.P., Baes, C.F., & Miglior, F. (2021). Review: Genetic selection of high-yielding dairy cattle toward sustainable farming systems in a rapidly changing world. Animal, 15(1), article number 100292. doi: 10.1016/j.animal.2021.100292.
  5. Brito, L.F., et al. (2020). Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle. Canadian Journal of Animal Science, 100(4), 587-604. doi: 10.1139/cjas-2019-0193.
  6. Caballero, A. (2020). Quantitative genetics. Cambridge: Cambridge University Press. doi: 10.1017/9781108630542.
  7. Cuyabano, B.C.D., Croiseau, P., Shokor, F., Motta, M.R., Aguerre, S., & Mattalia, S. (2024). Genetic correlations: A parameter or a latent phenotype in genetic evaluations? Interbull Bulletin, 60, 179-186.
  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), 44-53. 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(1), 14-22. doi: 10.15232/aas.2022-02345.
  10. Egger-Danner, C., Cole, J.B., Pryce, J.E., Gengler, N., Heringstad, B., Bradley, A., & Stock, K.F. (2015). Invited review: Overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits. Animal, 9(2), 191-207. doi: 10.1017/S1751731114002614.
  11. Fedota, O., Babalian, V., Ryndenko, V., Belyaev, S., & Belozоrov, I. (2020). Lactose tolerance and risk of multifactorial diseases on the example of gastrointestinal tract and bone tissue pathologies. Georgian Med News, 303, 109-113.
  12. Fedota, O., Puzik, N., Skrypkina, I., Babalyan, V., Mitiohlo, L., Ruban, S., Belyaev, S., Borshch, O.О., & Borshch, O.V. (2022). Single nucleotide polymorphism C994g of the cytochrome P450 gene possess pleiotropic effects in Bos taurus, L. Acta Biologica Szegediensis, 66(1), 7-15. doi: 10.14232/abs.2022.1.7-15.
  13. International Committee for Animal Recording (ICAR). (2014). ICAR recording guidelines. Retrieved from https://pecuaria.pt/docs/Guidelines_2014.pdf.
  14. Koutouzidou, G., Ragkos, A., & Melfou, K. (2022). Evolution of the structure and economic management of the dairy cow sector. Sustainability, 14(18), article number 11602. doi: 10.3390/su141811602.
  15. Lee, M., & Seo, S. (2021). Wearable wireless biosensor technology for monitoring cattle: A review. Animals, 11(10), article number 2779. doi: 10.3390/ani11102779.
  16. Lopez-Villalobos, N., Wiles, P., & Udy, G. (2024). The value of genetic improvement evaluated using a whole of enterprise market model. Dairy, 5(3), 372-383. doi: 10.3390/dairy5030030.
  17. Ma, L., Sonstegard, T.S., Cole, J.B., VanTassell, C.P., Wiggans, G.R., Crooker, B.A., Tan, C., Prakapenka, D., Liu, G.E., & Da, Y. (2019). Genome changes due to artificial selection in U.S. Holstein cattle. BMC Genomics, 20, article number 128. doi: 10.1186/s12864-019-5459-x.
  18. Madilindi, M.A., Zishiri, O.T., Dube, B., & Banga, C.B. (2022). Technological advances in genetic improvement of feed efficiency in dairy cattle: A review. Livestock Science, 258, article number 104871. doi: 10.1016/j.livsci.2022.104871.
  19. Martins, B.M., Mendes, A.L.C., Silva, L.F., Moreira, T.R., Costa, J.H.C., Rotta, P.P., Chizzotti, M.L., & Marcondes, M.I. (2020). Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements. Livestock Science, 236, article number 104054. doi: 10.1016/j.livsci.2020.104054.
  20. Misztal, I., & Lourenco, D. (2024). Potential negative effects of genomic selection. Journal of Animal Science, 102, article number skae155. doi: 10.1093/jas/skae155.
  21. Misztal, I., Tsuruta, S., Lourenco, D., Masuda, Y., Aguilar, I., Legarra, A., & Vitezica, Z. (2024). Manual for BLUPF90 family of programs. Athens, USA: University of Georgia.
  22. 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.
  23. Nadri, S., Sadeghi-Sefidmazgi, A., Zamani, P., Ghorbani, G.R., & Toghiani, S. (2023). Implementation of feed efficiency in Iranian Holstein breeding program. Animals, 13(7), article number 1216. doi: 10.3390/ani13071216.
  24. Nascimento, B.M., Cavani, L., Caputo, M.J., Marinho, M.N., Borchers, M.R., Wallace, R.L., Santos, J.E.P., White, H.M., Penagaricano, F., & Weigel, K. (2024). Genetic relationships between behavioral traits and feed efficiency traits in lactating Holstein cows. Journal of Dairy Science, 107(10), 8141-8149. doi: 10.3168/jds.2023-24526.
  25. Neethirajan, S. (2023). Artificial intelligence and sensor technologies in dairy livestock export: Charting a digital transformation. Sensors, 23(16), article number 7045. doi: 10.3390/s23167045.
  26. Niehoff, T.A.M., ten Napel, J., Bijma, P., Pook, T., Wientjes, Y.C.J., Hegedűs, B., & Calus, M.P.L. (2024). Improving selection decisions with mating information by accounting for Mendelian sampling variances looking two generations ahead. Genetics Selection Evolution, 56, article number 41. doi: 10.1186/s12711-024-00899-2.
  27. Nogoev, A., Azhibekov, A., Derkenbaev, S., & Ilyaz kyzy, Zh. (2025). Crossbreeding – the main method of increasing beef production in Kyrgyzstan. Bulletin of the Kyrgyz National Agrarian University, 23(3), 10-20. doi: 10.63621/bknau./3.2025.10.
  28. Olasege, B.S., van den Berg, I., Haile-Mariam, M., Ho, P.N., Oh, Z.Y., Porto-Neto, L.R., Hayes, B.J., Price, J.E., & Fortes, M.R.S. (2024). Dissecting loci that underpin the genetic correlations between production, fertility, and urea traits in Australian Holstein cattle. Animal Genetics, 55(4), 540-558. doi: 10.1111/age.13455.
  29. Satoh, M. (2024). Prediction of response to truncated selection based on BLUP of breeding values and its prediction accuracy. Animal Science Journal, 95(1), article number e13928. doi: 10.1111/asj.13928.
  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.B., Neupane, M., Toghiani, S., Gaddis, K.L., & Tempelman, R.J. (2021). Net merit as a measure of lifetime profit: 2021 revision. Washington, D.C.: USDA.
  32. Walsh, B., & Lynch, M. (2018). Evolution and selection of quantitative traits. Oxford: Oxford Academic. doi: 10.1093/oso/9780198830870.001.0001.
  33. Weller, J.I., Ezra, E., Seroussi, E., & Gershoni, M. (2023). Genetic and genomic analysis of cow mortality in the Israeli Holstein population. Genes, 14(3), article number 588. doi: 10.3390/genes14030588.
  34. Weller, J.I., Gershoni, M., & Ezra, E. (2022). Breeding dairy cattle for female fertility and production in the age of genomics. Veterinary Sciences, 9(8), article number 434. doi: 10.3390/vetsci9080434.
  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. Xu, S. (2022). Quantitative genetics. Cham: Springer. doi: 10.1007/978-3-030-83940-6.