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This study was conducted to estimate the additive genetic components of calf mortality in the first month of age, calving difficulty, and birth weight in Holstein dairy cows in the central regions of Iran. The records comprised 61,200 calves born between 1990 and 2011 from 60 dairy herds. Different threshold-linear models in three groups of univariate, bivariate, and multivariate models were used. The frequency of calf mortality was 2.6%. Distribution of calving difficulty score was 65.12% in the first category (no assistance), 30.66% in the second, 3.12% in the third, and 1.1% in the fourth (major assistance). Averages of birth weight and dam age were 40.34 kg and 769.4 days, respectively. Direct Heritability estimation for calf mortality varied from 0.005 to 0.027. The estimated heritability for calving difficulty ranged from 0.032 to 0.050. Heritability for birth weight was estimated about 0.22. The results of this study showed that there were genetic variations for all traits. Although there was no strong additive genetic correlation between the traits, an environmental correlation between mortality and other traits was observed. Results suggested that implementation of threshold models for mortality trait was more favorable, but they were not reflected in genetic analysis of calving difficulty records. Furthermore, current findings indicated that benefit from the use of multi-traits models for genetic evaluation of postnatal mortality depended on the methodology (linear or threshold model) used for mortality trait.

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This study was designed to investigate the effects of physical forms and hay contents of diets on gene expression of insulin-like growth factor (IGF) I and II in rumen epithelium of Holstein calves. Twelve male calves were assigned to 4 treatments: ground (GR), texturized (TX), pellet (PL), and ground+10% forage (GF). Calves were weaned on day 50 of age and then slaughtered on day 70 after birth. Rumen epithelial tissue samples were immediately collected for quantification of mRNA abundance. Results indicated that only IGF I expression was influenced by the dietary treatments. A significant (p<0.05) correlation between IGF I expression and each of histological parameters denoted as length of rumen villi and diameter of keratinocyte layer was observed. No significant correlation between IGF II expression and rumen histological parameters was found (p>0.05). Regarding the results, higher IGF I expression in PL and TX treatments despite the low growth rate might be due to the challenging condition of developing rumen in calves. In fact, the rumen tissue attempted to maintain rumen pH at least by induction of a higher IGF I expression.
 

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Four nonlinear models including Logistic, Gompertz-Laird, Richards, and von Bertalanffy were compared to achieve the best prediction of growth parameters describing the growth curve in a crossbred chicken population. Growth data (weekly body weights of chicken from birth to 84 days of age) were collected on 303 birds (174 females and 129 males) of F₂ cross of the Arian line broiler chicken (Line B) and Urmia native chicken. Some statistical criteria such as Akaike Information Criterion (AIC), Corrected Akaike Information Criterion for small sample sizes (AICc), and Bayesian Information Criterion (BIC) were used to find the best model. The results showed that the estimated values of the initial weight (W₀) and final Weight (W_f) in male were significantly (P< 0.01) higher than the female birds in all models. The average estimated initial weight calculated by Gompertz-Laird (0.038 kg) was closer to the average observed initial weight (0.044 kg). Regardless of sex of the birds, the calculated age (t_i) and Weight (W_i) at the inflection point were relatively the same in Gompertz-Laird, Richards and von Bertalanffy models, indicating that the growth patterns described by these models are similar. Meanwhile, the different t_i and W_i values between the sexes in the four models revealed the different growth pattern in males and females. The goodness of fit indices (R² and adjusted R²) were higher than 0.97 in all models, indicating that these models could appropriately be fitted on the growth data. However, based on the AIC, AICc, and BIC criteria, Gompertz-Laird model showed better performance, therefore, it was chosen as the best model to analyze the growth pattern in crossbred of .

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The aims of this investigation were to compare the accuracy and bias of prediction of Estimated Breeding Values (EBV) for Average Daily Gain (ADG) at 2-4 weeks old by employing pedigree-based BLUP and single-step Genomic BLUP (ssGBLUP) techniques. Additionally, the study aimed to identify the optimal minor allele frequencies (MAF) threshold for pre-selecting SNPs for genetic prediction. The present investigation utilized a total of 488 F2 broiler chickens, which were derived from the crossbreeding of fast-growing Arian chickens and slow-growing native chickens from Urmia, Iran. These chickens were between 2-4 weeks old at the time of the study. Samples were genotyped using the Illumina 60K chicken Beadchip. In order to examine the impact of MAF on prediction accuracy, a total of 48,379 quality-controlled SNPs were categorized into five subgroups based on their MAF values: 0.05-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4, and 0.4-0.5. The findings substantiated the dominance of ssGBLUP over conventional BLUP techniques. The average accuracy of GP improved by 1.96, 3.87, and 2.12% using ssGBLUP compared to BLUP method for ADG at 2-4 weeks of age, respectively. Using a specific MAF bin and a subset of SNPs based on age group significantly enhanced the accuracy of genomic prediction for ADG traits. Current results highlighted that the pre-selection of SNPs based on allele frequency may provide a reasonable compromise between accuracy of results, number of independent variables to be considered and computing requirements.