Advances in Animal and Veterinary Sciences

Research Article
Adv. Anim. Vet. Sci. 9(8): 1113-1122
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Sherif A. Moawed1*, Mohamed M. Osman2, El-Houssainy A. Rady3, Khairy M. El-Bayomi4, Ahmed F. Farag1

1Department of Animal Wealth Development (Biostatistics Division), Faculty of Veterinary Medicine, Suez Canal University, Ismailia 41522, Egypt; 2Department of Animal Wealth Development, Faculty of Veterinary Medicine, Suez Canal University, Ismailia 41522, Egypt; 3Department of Applied Statistics and Econometrics, Institute of Statistical Studies and Research, Cairo University, Cairo, Egypt; 4Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Zagazig, Egypt.

Abstract | This study was conducted to estimate genetic parameters and breeding values (EBVs) for milk yield (MY), peak yield (PY), lactation length (LL), days open (DO), calving interval (CI), and services per conception (SC) of Holstein dairy cattle. The direct genetic, maternal genetic and maternal permanent environmental effects were separately evaluated. Furthermore, the principle components analysis (PCA) was applied to explore the relationships among the animal EBVs. Genetic parameters were estimated using the multi-trait restricted maximum likelihood methodology by incorporating six different models that either included or excluded maternal effects. The best model was selected based on the likelihood ratio test. In this context of the research, a total of 18221 cows were assessed for records between 2007 and 2018. Out of the six animal models, the fourth model was chosen as the best model, because it had the smallest -2 Log Likelihood value. The range of direct heritability values were 0.21-0.35, 0.02-0.30, 0.15-0.33, 0.04-0.18, 0.05-0.18, and 0.05-0.15 for MY, PY, LL, DO, CI, and SC, respectively. The estimated maternal heritabilities were lower than direct heritabilities informed by all models. However, models 4 and 6 showed the greatest increase in maternal heritability, for all traits. PCA reduced the standardized EBVs of traits into two components, explaining 75.04 % of the total genetic variance. The EBVs of MY, LL, DO, SC, and CI highly associated with PC1, whereas those of PY is closely connected with PC2. In conclusion, the selection indices could be planned based on two PCs instead of all traits.

Keywords | Animal model, Dairy cattle, Estimated breeding values, Maternal effects, estimated breeding values, principle components analysis