Behzad Rajabi Marand; Hossein Moradi Shahrbabak; Mostafa Sadeghi; Rostam AbdolahiArpanahi
Volume 21, Issue 4 , January 2020, , Pages 419-430
Abstract
The aim of current study was to evaluate the accuracy of genomic breeding values (GEBV) for two important economical traits of milk yield and somatic cell score using SNP markers and LD-based haplotype blocks (haploblocks) by two statistical methods of GBULP and Bayes B. The data set consisted ...
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The aim of current study was to evaluate the accuracy of genomic breeding values (GEBV) for two important economical traits of milk yield and somatic cell score using SNP markers and LD-based haplotype blocks (haploblocks) by two statistical methods of GBULP and Bayes B. The data set consisted of 1654 bulls genotyped with different marker densities. When SNPs were used, the accuracy of breeding values obtained by Bayes B was better than GBLUP. In other words, for milk yield and somatic cell score traits, the prediction accuracy of GBLUP was 0.54 and 0.44 and by Bayes B was 0.58 and 0.44,respectively. For milk yield, the prediction accuracy of using haploblocks in both statistical methods was higher than the prediction accuracy using SNPs, while for the somatic cell score, this increase was more pronounced when GBLUP was used. However, when Bayes B was used this superiority was only obtained when the r2 statistic used to build the haploblocks was higher than 0.2. The results showed that the optimum level of r2 for building haploblocks depends on the trait type and its heritability. As a result, using r2 statistic more than 0.2 for building haploblocks can increase the accuracy of breeding valuesfoe both traits compared to SNP markers.
Mostafa Lotfy; farid shariatmadari; Hamed Ahmadi; Mohsen Sharafi
Volume 21, Issue 2 , July 2019, , Pages 223-232
Abstract
The purpose of this study was to develop multiple linear regression (MLR) model to predict the nitrogen-corrected true metabolizable energy (TMEn) value of wheat bran. The amount of crude fat, ash, crude protein, crude fiber (all used as % of DM) and TMEn (Kcal/kg DM) were measured in 25 wheat bran samples ...
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The purpose of this study was to develop multiple linear regression (MLR) model to predict the nitrogen-corrected true metabolizable energy (TMEn) value of wheat bran. The amount of crude fat, ash, crude protein, crude fiber (all used as % of DM) and TMEn (Kcal/kg DM) were measured in 25 wheat bran samples with 4 replicates. The force-fed method has been used to estimate TMEn and excreta were collected for 48 h. There were significant (P < 0.001) differences in chemical composition and TMEn of wheat bran samples. The average crude fat, ash, crude protein, crude fiber and TMEn content of samples was determined to be 4.80, 5.68, 16.23, 8.60 (all used as % of DM) and 2062 (Kcal/kg DM), respectively. The calculated MLR model to predict the TMEn value (Kcal/kg) based on chemical composition (% of DM) was obtained as follows: TMEn = 2364 + (19×crude protein) + (46.1×crude fat) – (63×crude fiber) – (51.1×ash). The R2 value revealed that developed model could accurately predict the TMEn of wheat bran samples (R2=0.82). Crude fat and crude protein had a positive effect on TMEn, while ash and crude fiber had a negative impact on TMEn. The sensitivity analysis on the model indicated that dietary crude fiber (%) is the most important variable in the TMEn, followed by dietary ash, crude fat and crude protein. The results suggest that the MLR model may be used to accurately estimate the TMEn value of wheat bran from its corresponding chemical composition.