Sanad, S., Gharib, M. (2021). GENETIC EVALUATION AND PRINCIPAL COMPONENTS ANALYSIS FOR MILK TRAITS IN HOLSTEIN FRIESIAN CATTLE. Egyptian Journal of Animal Production, 58(3), 113-121. doi: 10.21608/ejap.2021.89486.1019
Safaa Sanad; Mahmud Gharib. "GENETIC EVALUATION AND PRINCIPAL COMPONENTS ANALYSIS FOR MILK TRAITS IN HOLSTEIN FRIESIAN CATTLE". Egyptian Journal of Animal Production, 58, 3, 2021, 113-121. doi: 10.21608/ejap.2021.89486.1019
Sanad, S., Gharib, M. (2021). 'GENETIC EVALUATION AND PRINCIPAL COMPONENTS ANALYSIS FOR MILK TRAITS IN HOLSTEIN FRIESIAN CATTLE', Egyptian Journal of Animal Production, 58(3), pp. 113-121. doi: 10.21608/ejap.2021.89486.1019
Sanad, S., Gharib, M. GENETIC EVALUATION AND PRINCIPAL COMPONENTS ANALYSIS FOR MILK TRAITS IN HOLSTEIN FRIESIAN CATTLE. Egyptian Journal of Animal Production, 2021; 58(3): 113-121. doi: 10.21608/ejap.2021.89486.1019
GENETIC EVALUATION AND PRINCIPAL COMPONENTS ANALYSIS FOR MILK TRAITS IN HOLSTEIN FRIESIAN CATTLE
Animal Production Research Institute (APRI), Agriculture Research Center (ARC), Egypt
Abstract
The aim of the current study was to estimate genetic principal components analysis for milk traits of breeding value (BV) in Holstein Friesian (HF). A total number of 2067 records cow from 80 sires and 439 dams; during 10 consecutive years that included the four seasons for each year and six parities from the commercial farms nearly the Nile Delta, Egypt. Studied traits were total milk yield (TMY), lactation period (LP), calving interval (CI), number of services per conception (NSPC) and days open (DO). Data for milk traits (MT) were analyzed using a single trait animal model program used to estimate genetic parameters, in addition to using a method principal components analysis (PCA) program, which aims to increase the accuracy of estimating genetic evaluation. The heritability (h2a) estimates were 0.20±0.001, 0.22±0.002, 0.02±0.001, 0.04 ± 0.001 and 0.05±0.020 for TMY, LP, CI, NSPC and DO, respectively. The total variance of breeding values was 67.1, in which 46.6% and 20.5% were explained by PC1 and PC2, respectively. Two principal components (1&2) were estimated by BV. Equations for PCA were: PC1= 0.273 TMY + 0.342 LP + 0.371 CI + 0.318 NSPC - 0.004 DO, and PC2= 0.213 TMY - 0.069 LP - 0.146 CI + 0.045 NSPC + 0.949 DO. The results of genetic PCA indicate that milk traits were highly significant, also improve TMY. Improved all traits under study would be expected to use analysis PC1 and PC2 provides to overcome the multicollinearity problem while predicting the future TMY, thus achieving an increased economic return.
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