Received 22.11.2022
Revised 16.01.2023
Accepted 28.02.2023
Retrieved from Volume 27, No. 1, 2023
Pages 20 -29
Abstract
An important problem in modern dairy cattle breeding is the achievement of a high level of genetic progress in economically important traits through the implementation of effective breeding programs. For this purpose, genomic selection is currently used in many countries of the world. The aim of the study was to investigate possibilities of use of genomic selection in dairy cattle breeding in Ukraine. On the basis of analysis of “Catalogue of sires of dairy and dual-purpose breeds for reproduction of cows in 2020” (sperm of these sires was used in Ukraine) two methods of breeding value estimation were compared: 1) traditional method based on pedigree and performance of progeny; 2) genomic method based on effects of SNPs. Considerable advantage of sires with genomic evaluations was proved. These sires excel sires with traditional evaluation for milk yield by 1.6 times, for fat percentage by 2.2 times, for fat yield by 1.7 times, for protein percentage by 2.1 times and for protein yield by 1.7 times. Using estimates of breeding values of sires pare-wise genetic correlations between main genetic traits were computed. The negative genetic relationship between milk yield and fat and protein percentages was revealed. Values of energy corrected milk (ECM) of daughters and dams of sires across breeds and countries of origin were calculated. It was shown that dams of sires of Holstein and Jersey breeds had highest values of energy corrected milk (9,132.0 kg and 8,041 kg, respectively) while dams of sires of Ukrainian Black-and-White dairy breed had lowest values of this trait (5,848.1 kg). According to country-of-origin daughters of sire’s form USA, Canada and the Netherlands had highest values of energy corrected milk. Values of response to selection using traditional breeding program and genomic selection were compared. It was proved that by means of shortening generation intervals on pathways of genetic improvement “sires of bulls”, “sires of cows” and “dams of bulls” using genomic selection it is possible to increase rate of genetic progress for milk yield from 100.1 kg to 180.0 kg that is by 80%
Keywords:
dairy breed; energy corrected milk; genetic improvement; genetic correlation; response to selection; genomic evaluation[1] Ahmadi, N., & Bartholomé, J. (Eds.). (2022). Genomic prediction of complex traits. Methods and protocols. Totowa: Humana Press.
[2] ARRIVE guidelines. (n.d.). Retrieved from https://arriveguidelines.org/.
[3] 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.
[4] Doublet, A.-C., Croiseau, P., Fritz, S., Michenet, A., Hozé, C., Danchin‑Burge, C., Laloë, D., & Restoux, G. (2019). The impact of genomic selection on genetic diversity and genetic gain in three French dairy cattle breeds. Genetics Selection Evolution, 51, 52. doi: 10.1186/s12711-019-0495-1.
[5] 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.
[6] Gutierrez-Reinoso, M.A., Aponte, P.M., & Garcia-Herreros, M. (2021). Genomic analysis, progress and future perspectives in dairy cattle selection: A review. Animals, 11(3), 599. doi: 10.3390/ani11030599.
[7] Hagan, B.A., & Cue, R. (2019). Generation intervals in Canadian dairy cattle herds. Canadian Journal of Animal Science, 100(1), 175-183. doi: 10.1139/cjas-2019-0053.
[8] Hossein-Zadeh, N.G. (2017). Application of growth models to describe the lactation curves for test-day milk production in Holstein cows. Journal of Applied Animal Research, 45(1), 145-151. doi: 10.1080/09712119.2015.1124336.
[9] Lee, Y.-M., Dang, C.-G., Alam, M.Z., Kim, Y.-S., Cho, K.-H., Park, K.-D., & Kim, J.-J. (2020). The effectiveness of genomic selection for milk production traits of Holstein dairy cattle. Asian-Australasian Journal of Animal Science, 33(3), 382-389. doi: 10.5713/ajas.19.0546.
[10] Lembeye, F., López-Villalobos, N., & Uribe, H. (2022). Potential response from selection schemes based on progeny testing and genomic selection for the Chilean dairy cattle under pastoral systems: A deterministic simulation. Journal of Dairy Research, 89(3), 231-235. doi: 10.1017/S0022029922000504.
[11] 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(7), 790. doi: 10.3390/genes11070790.
[12] Lozada-Soto, E.A., Tiezzi, F., Jiang, J., Cole, J.B., VanRaden, P.M., & Maltecca, C. (2022). Genomic characterization of autozygosity and recent inbreeding trends in all major breeds of US dairy cattle. Journal of Dairy Science, 105(11), 8956-8971. doi: 10.3168/jds.2022-22116.
[13] Makanjuola, B.O., Miglior, F., & Abdalla, E.A. (2020). Effect of genomic selection on rate of inbreeding and coancestry and effective population size of Holstein and Jersey cattle populations. Journal of Dairy Science, 103(6), 5183-5199. doi: 10.3168/jds.2019-18013.
[14] Mäntysaari, E.A., Koivula, M., & Strandén, I. (2020). Symposium review: Single-step genomic evaluations in dairy cattle. Journal of Dairy Science, 103(6), 5314-5326. doi: 10.3168/jds.2019-17754.
[15] Marjanovic, J., Hulsegge, B., & Calus, M.P.L. (2021). Relatedness between numerically small Dutch Red dairy cattle populations and possibilities for multibreed genomic prediction. Journal of Dairy Science, 104(4), 4498-4506. doi: 10.3168/jds.2020-19573.
[16] Misztal, I., Lourenco, D., & Legarra, A. (2020). Current status of genomic evaluation. Journal of Dairy Science, 98(4), 1-14. doi: 10.1093/jas/skaa101.
[17] Norwegian Red breeding program. (2021). Retrieved from https://www.norwegianred.com/about-norwegian-red/norwegian-red-breeding-program/.
[18] Obšteter, J., Jenko, J., & Gorjanc, G. (2021). Genomic selection for any dairy breeding program via optimized investment in phenotyping and genotyping. Frontiers in Genetics, 12, 637017. doi: 10.3389/fgene.2021.637017.
[19] Polupan, Y.P., Gladiy, M.V., Basovskiy, D.M., Germanchuk, S.G., Biriukova, O.D., Pryima, S.V., Podoba, B.E., & Romanova, O.V. (2020). Catalogue of dairy and dual-purpose service sires of Ukraine. Kyiv: NAASU.
[20] Ruban, S. & Danshin, V. (2019). Modern methods of animal breeding. Kyiv: NUELS.
[21] 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.
[22] Simm, G., Pollott, G., Mrode, R., Houston, R., & Marshall, K. (2021). Genetic improvement of farmed animals. Wallingford: CABI. doi: 10.1079/9781789241723.0000.
[23] van den Berg, I., Meuwissen, T.H.E., MacLeod, I.M., & Goddard, M.E. (2019). Predicting the effect of reference population on the accuracy of within, across, and multibreed genomic prediction. Journal of Dairy Science, 102(4), 3155-3174. doi: 10.3168/jds.2018-15231.
[24] 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
[25] 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.
[26] Wiggans, G.R., & Carrillo, J.A. (2022). Genomic selection in United States dairy cattle. Frontiers in Genetics, 13, 994466. doi: 10.3389/fgene.2022.994466.
[27] Zhang, M., Luo, H., Xu, L., Shi, Y., Zhou, J., Wang, D., Zhang, X., Huang, X., & Wang, Y. (2022). Genomic selection for milk production traits in Xinjiang brown cattle. Animals, 12(2), 136. doi: 10.3390/ani12020136.