ali ashrafian; nasser emam jomeh kashan; Rostam AbdolahiArpanahi; Mohammad Bagher Sayyadnejad
Volume 20, Issue 3 , November 2018, , Pages 401-409
Abstract
In order to determine the optimum number of test-day records for the progeny test program of Holstein bulls, 732,140 milk yield test-day were used. These milk yield test-days, which were related to 73,214 first parity dairy cows belonging to 62 herds, had been collected by the Animal Breeding Center ...
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In order to determine the optimum number of test-day records for the progeny test program of Holstein bulls, 732,140 milk yield test-day were used. These milk yield test-days, which were related to 73,214 first parity dairy cows belonging to 62 herds, had been collected by the Animal Breeding Center of Iran from 1992 to 2016. The correlation of predicted breeding value (EBV) of bulls from ten test-day of their daughters compared with EBV predicted from different number of recorded test-days. The Correlation of predicted EBV from ten test-days with EBV from even, odd, (second, fifth, seventh), (second, fifth, tenth) and (second, sixth) test-day records were estimated to be 0.99, 0.98, 0.98, 0.97 and 0.94 respectively. The results showed that to reduce cost of recording, number of records and generation interval in EBV prediction of bulls with random regression model it is possible to use only second, fifth and seventh test-days instead of ten test-days.
Yahya Mohammadi; Mohammad Mahdi Shariati; Saeed Zerehdaran; Mohammad Razmkabir; Mohammad Bagher Sayyadnejad; Mohammad Bagher Zandi
Volume 18, Issue 1 , April 2016, , Pages 1-11
Abstract
Genomic Selection (GS) is a tool for prediction of breeding values for quantitative traits. For a successful application of GS, accuracy of predicted genomic breeding value (GEBV) is a key issue to consider. Here we investigated the accuracy of GEBV in 345 genotyped Iranian Holstein cattle. The study ...
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Genomic Selection (GS) is a tool for prediction of breeding values for quantitative traits. For a successful application of GS, accuracy of predicted genomic breeding value (GEBV) is a key issue to consider. Here we investigated the accuracy of GEBV in 345 genotyped Iranian Holstein cattle. The study was performed on milk, fat, protein yield and somatic cell count. Four methods G-BLUP, Bayes B, Reproducing kernel Hilbert Spaces (RKHS) and Neural Networks (NN) were used to predict genomic breeding values and their accuracies. The GEBV accuracies varied between 0.39 for somatic cell count to 0.73 for fat yield. Bayes B gave the highest accuracies among methods. Bayes B and non- parametric methods tended to produce inflated predictions (slope of the regression of GBV on EBV greater than 1). However, in all traits, lower estimates of MSE were obtained using G- BLUP. Bayes B regression model are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions.