The Effect of Holstein Sire Predicted Transmitting Ability on the Performance of Their Daughters in Isfahan Herds

Document Type : Research Articles

Authors

• Rabie Rahbar ¹
• Rohullah Abdullahpour ²
• Ali Sadeghi-Sefidmazgi ³

¹ Department of Agriculture, Payame Noor University (PNU), Tehran, Iran

² Department of Animal Science, Islamic Azad University, Qaemshahr Branch, Mazandaran, Iran.

³ Department of Animal Science, College of Agriculture ‎and Natural Resources, University of Tehran, Karaj, Iran.

10.22067/ijasr.2023.82164.1139

Abstract

Introduction: one of the most important and practical tools in increasing the production and profit of dairy cattle herds is to determine the exact aims of breeding, suitable genetic selection and breeding of premium cows. Based on animal breeding, breeders should make sure that the difference in the genetic merit of one or several traits leads to the difference in the phenotypic performance of the animal. Therefore, they want to reliably identify and select superior sires within and between breeds. Considering the importance of bulls in dairy herds and the use of their semen on a very wide level, this study aims to compare the information of genetic-economic indicators and productive, reproductive and health traits in the semen catalog of bulls with the actual performance of their daughters in Isfahan herds.
Materials and Methods: In this research, the information of 16 Holstein dairy herds located in Isfahan province during the years 2002 to 2017, was used. The number of productive livestock in the target herds was between 1000 and 5000 heads. Finally, 18,559 cows from the first to the fifth period of lactation, obtained from artificial insemination, which had known sires, were used. The genetic evaluation information of each sire that was used includes predicted transmitting ability (PTA), genetic-economic indicators: lifetime net merit (LNM$) and lifetime fluid merit (LFM$), production traits: milk, fat, protein, reproductive traits: sire conception rate (SCR), daughter pregnancy rate (DPR), daughter calving ease (DCE), daughter stillbirth (DSB) and health traits: production life (PL), somatic cell score (SCS), resistance to mastitis disease (Mas), metritis (Met) and retained placenta (RP) was based on the official evaluation of August 2019 by the USA council on dairy cattle breeding. Mixed and generalized linear models were used to assess the relationship between sire's PTA and daughters’ record. Sire's PTA were milk, fat, protein, daughter pregnancy rate, production life, somatic cell score, daughter calving ease, daughter stillbirth and lifetime net merit and lifetime fluid merit indices. Daughter records were milk-305, fat-305 and protein-305, open days, calving interval, age of first calving, number of productive days, calf birth weight, somatic cell score, number of inseminations per pregnancy, history of dystocia, stillbirth, metritis, retained placenta and mastitis traits. In this study, the analysis of data was done using R software and lme4 and lmerTest software packages.
Results and Discussion: Based on the results, PTA milk and SCR of most of the bulls used in herds were average, while they were lower values for LNM$ and LFM$ indices and PTA, Fat, Pro and PL traits and higher values for SCS and DSB traits. Correlation between indices and PTA of different traits of sires showed that the highest correlation was between LNM$ and LFM$ indices and the lowest was between SCR and DSB, Milk and SCR and RP and SCR traits. The correlation between sires' PTA and their daughters' performance in productive, reproductive and health traits showed that the correlation level was in the range between -0.19 and 0.16. Also, the regression coefficients of productive, reproductive and health traits of daughters were estimated based on the PTA of their sires in the studied herds, which ranged from -32.273 (between the PTA of daughter calving ease and milk-305) and 3.679 (between PTA fat and milk-305). The estimation of the odds ratio of some traits from sires' PTA on the daughters' health and reproductive (classifiable) traits showed that values were close to one for mastitis resistance, daughters' pregnancy rate and production life traits from sires' PTA which indicates the low effects of sires' PTA on their daughters' performance. However, the estimation of the odds ratio of PTA sires, related to metritis disease resistance, somatic cell score, resistance to retained placental and daughters calving ease traits had inverse relationship with the metritis and retained placental diseases (0.85, 0.67 and 0.74) and direct relationship with dystocia (1.23), respectively. The transmitting ability of sires is predicted based on the performance of their daughters in dairy cattle breeding farms, the data of those farms were available to the semen production company, the difference between conditions such as climate and breeding management of the mentioned herds and the herds of this study, can be affected the interaction of genotype and environment.
Conclusion: In this study, the correlation coefficients between random variables of sires' PTA for productive traits (milk, fat and protein) with their daughters' performance were calculated to be higher compared to reproductive and health traits. Also, the rate of OR of resistance to metritis and RP diseases, SCS and DCE, showed the effects of sires' PTA on the performance of their daughters. Therefore, according to the results, it is possible to take more advantage from sires predicted transmitting ability for milk, fat, protein, DPR and SCS in proper selection of semen and improving the phenotypic performance of daughters. While available information in genetic evaluations of other traits were less reliable, probably due to low heritability or high genetic by environment interactions.

Keywords

• Semen
• Genetic merit
• Dairy cow
• Daughters’ performance

Main Subjects

• Genetics & breeding

---

©2023 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

1. Albarrán-Portillo, B., & Pollott, G. E. (2013). The relationship between fertility and lactation characteristics in Holstein cows on United Kingdom commercial dairy farms. Journal of Dairy Science, 96(1), 635-646. https://doi.org/3168/jds.2012-5632.
2. Ali, A., & Shook, G. (1980). An optimum transformation for somatic cell concentration in milk. Journal of Dairy Science, 63, 487-490. https://doi.org/3168/jds.S0022-0302(80)82959-6.
3. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. https://doi.org/18637/jss.v067.i01. 
4. Bohlouli, M., Shodja, J., & Alijani, S. (2013). Investigation of interaction between genotype and production level in Iranian Holstein dairy cattle using test day records. Journal of Ruminant Research, 1, 93-108.
5. Chang, Y. M., Gonza´lez-Recio, O., Weigel, K. A., & Fricke, P. M. (2007). Genetic analysis of the twenty-one-day pregnancy rate in us holsteins using an ordinal censored threshold model with unknown voluntary waiting period. Journal of Dairy Science, 90, 1987-1997. https://doi.org/3168/jds.2006-359.
6. Cochran, S. D., Cole, J. B., Null, D. J., & Hansen, P. J. (2013). Discovery of single nucleotide polymorphisms in candidate genes associated with fertility and production traits in Holstein cattle. BMC Genetics, 14, 49. https://doi.org/1186/1471-2156-14-49.  
7. Coleman, J., Pierce, K. M., Berry, D. P., Brennan, A., & Horan, B. (2009). The influence of genetic selection and feed system on the reproductive performance of spring-calving dairy cows within future pasture-based production systems. Journal of Dairy Science, 92, 5258–5269. https://doi.org/3168/jds.2009-2108.
8. Craig, H. J. B., Stachowicz, K., Black, M., Parry, M., Burke, C., Meier, S., & Amer, P. (2018). Genotype by environment interactions in fertility traits in New Zealand dairy cows. Journal of Dairy Science, 101, 10991–11003. https://doi.org/3168/jds.2017-14195.
9. Cranford, J. L., & Pearson, R. E. (2001). Relationships of sire predicted transmitting ability for somatic cell score with measures of daughter performance. Journal of Dairy Science, 84, 1501–1507.
10. Dechow, D., Goodling, R. C., & Rhode, S. P. (2012). The effect of sire selection on cow mortality and early lactation culling in adverse and favorable cow survival environments. Preventive Veterinary Medicine, 103, 228-233. https://doi.org/10.1016/j.prevetmed.2011.09.020.
11. Dematawewa, C. M. P., & Berger, P. J. (1998). Genetic and phenotypic parameters for 305-day yield, fertility, and survival in Holsteins. Journal of Dairy Science, 81, 2700-2709. https://doi.org/3168/jds.S0022-0302(98)75827-8.
12. Dunne, F. L., McParland, S., Kelleher, M. M., Walsh, S. W., & Berry, D. P. (2019). How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit. Journal of Dairy Science, 102, 5295–5304. https://doi.org/3168/jds.2018-16119. 
13. Effa, K., Hunde, D., Shumiye, M., & Silasie, R. H. (2013). Analysis of longevity traits and lifetime productivity of crossbred dairy cows in the tropical highlands of Ethiopia. Journal of Cell and Animal Biology, 7, 138-143. https://doi.org/5897/JCAB2013.0375.
14. Gobikrushanth, M., Macmillan, K., Hipkin, D., & Colazo, M. G. (2020). The relationships among sire’s predicted transmitting ability for daughter pregnancy rate and cow conception rate and daughter’s reproductive performance in Canadian Holstein cows. Theriogenology, 149, 117-122. https://doi.org/1016/j.theriogenology.2020.03.026.
15. Lombard, J. E., Garry, F. B., Tomlinson, S. M., & Garber, L. P. (2007). Impacts of Dystocia on Health and Survival of Dairy Calves. Journal of Dairy Science, 90, 1751-1760. https://doi.org/10.3168/jds.2006-295.
16. Lopez-Villalobos, N., Wiles, P. G., & Garrick, D. J. (2020). Sire selection and genetic improvement of dairy cattle assuming pure market competition. Journal of Dairy Science, 103, 4532-4544. https://doi.org/10.3168/jds.2019-17582.
17. Newton, J. E., Goddard, M. E., Phuong, H. N., Axford, M. A., Ho, C. K. M., Nelson, N. C., Waterman, C. F., Hayes, B. J., & Pryce, J. E. (2017). High genetic merit dairy cows contribute more to farm profit: Case studies of 3 Australian dairy herds. Association for the Advancement of Animal Breeding and Genetics, 22, 19–22.
18. Norman, D., Wright, J. R., Hubbard, S. M., Kuhn, M. T., & Miller, R. H. (2007). Genetic selection for reproduction: current reproductive status of the national herd: application of selection indexes for dairy producers. pp. 69-78. In: Dairy Cattle Reproductive Conference, Denver.
19. Oikonomou, G., Cook, N. B., & Bicalho, R. C. (2013). Sire predicted transmitting ability for conformation and yield traits and previous lactation incidence of foot lesions as risk factors for the incidence of foot lesions in Holstein cows. Journal of Dairy Science, 96(6), 3713-3722. https://doi.org/3168/jds.2012-6308.  
20. Ortega, M. S., Denicol, A. C., Cole, J. B., Null, D. J., Taylor, J. F., Schnabel, R. D., & Hansen, P. J. (2017). Association of single nucleotide polymorphisms in candidate genes previously related to genetic variation in fertility with phenotypic measurements of reproductive function in Holstein cows. Journal of Dairy Science, 100, 3725-3734. https://doi.org/10.3168/jds.2016-12260.
21. O’Sullivan, M., Horan, B., Pierce, K. M., McParland, S., O’Sullivan, K., & Buckley, F. (2019). Milk production of Holstein-Friesian cows of divergent Economic Breeding Index evaluated under seasonal pasture-based management. Journal of Dairy Science, 102, 2560–2577. https://doi.org/10.3168/jds.2018-15559.
22. Ramsbottom, G., Cromie, A. R., Horan, B., & Berry, D. P. (2012). Relationship between dairy cow genetic merit and profit on commercial spring calving dairy farms. Animal, 6, 1031–1039. https://doi.org/10.1017/S1751731111002503.
23. R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
24. Rodrigo, C. B., Carla, F., Rob, G., & Georgios, O. (2014). The effect of sire predicted transmitting ability for production traits on fertility, survivability, and health of Holstein dairy cows. Theriogenology, 81, 257-265. https://doi.org/1016/j.theriogenology.2013.09.023.
25. Tsuruta, S., Misztal, I., & Lawlor, T. J. (2005). Changing definition of productive life in US Holsteins: Effect on genetic correlations. Journal of Dairy Science, 88, 1156-1165. https://doi.org/3168/jds.S0022-0302(05)72782-X.
26. VanRaden, P. M., Dematawewa, C. M. B., Pearson, R. E., & Tooker, M. E. (2006). Productive life including all lactations and longer lactations with diminishing credits. Journal of Dairy Science, 89(8), 3213-3220. https://doi.org/3168/jds.S0022-0302(06)72596-6.
27. Veronese, , Marques, O., Moreira, R., Belli, A. L., Bisinotto, R. S., Bilby, T. R., Penagaricano, F., & Chebel, R. C. (2019). Genomic merit for reproductive traits. I: estrous characteristics and fertility in Holstein heifers. Journal of Dairy Science, 102, 6624-6638. https://doi.org/10.3168/jds.2018-15205.