The aim of this study was to estimate genetic parameters for environmental sensitivities in milk yield and composition of Iranian Holstein cows using the double hierarchical generalized linear model (DHGLM) method. Data set included test-day productive records of cows which were provided by the Animal Breeding Center and Promotion of Animal Products of Iran during 1983 to 2014. In the DHGLM method, a random regression model was fitted which included two parts of mean and residual variance. A random regression model (mean model) and a residual variance model were used to study the genetic variation of micro-environmental sensitivities. In order to consider macro-environmental sensitivities, DHGLM was extended using a reaction norm model, and a sire model was applied. Based on the mean model, additive genetic variances for the mean were 38.25 for milk yield, 0.23 for fat yield and 0.03 for protein yield in the first lactation, respectively. Based on the residual variance model, additive genetic variances for residual variance were 0.039 for milk yield, 0.030 for fat yield and 0.020 for protein yield in the first lactation, respectively. Estimates of genetic correlation between milk yield and macro- and micro-environmental sensitivities were 0.660 and 0.597 in the first lactation, respectively. The results of this study indicated that macro- and micro-environmental sensitivities were present for milk production traits of Iranian Holsteins. High genetic coefficient of variation for micro-environmental sensitivities indicated the possibility of reducing environmental variation and increase in uniformity via selection.

Using Kermani sheep, the current study estimated (co)variance components and genetic parameters for average daily gain, Kleiber ratio, growth e ciency and relative growth rate. Data were analyzed by the average information restricted maximum likelihood (AI-REML) method using six animal models with different combinations of direct and maternal effects. The best-tting model was determined after testing for improvement in log-likelihood values. The estimates of h2 for average daily gain (ADG), Klieber ratio (KR), growth e ciency (GE) and relative growth rate (RGR) in pre-and post-weaning phases were 0.13 ± 0.6 and 0.

To cite this article: Jamshid Ehsaninia (2021) Estimates of (co)variance components and genetic parameters for pre-weaning body weight traits and Kleiber ratio in Sangsari sheep breed, Italian

No abstract

Using Kermani sheep, the current study estimated (co)variance components and genetic parameters for average daily gain, Kleiber ratio, growth efficiency and relative growth rate. Data were analyzed by the average information restricted maximum likelihood (AI-REML) method using six animal models with different combinations of direct and maternal effects. The best-fitting model was determined after testing for improvement in log-likelihood values. The estimates of h2 for average daily gain (ADG), Klieber ratio (KR), growth efficiency (GE) and relative growth rate (RGR) in pre- and post-weaning phases were 0.13 ± 0.6 and 0.17 ± 0.02, 0.12 ± 0.04, and 0.16 ± 0.03; 0.05 ± 0.05 and 0.07 ± 0.03 and 0.06 ± 0.02 and 0.07 ± 0.01, respectively. Maternal heritabilities (m2) ranged from 0.03 ± 0.01 for relative growth rate in pre-weaning phase to 0.11 ± 0.04 for average daily gain in post-weaning period. The maternal permanent environmental component (Pe2) accounted for 3–13% to the phenotypic variance for all the studied traits. Estimated values of additive coefficient of variations (CVA) ranged from 2.79% for relative growth rate at six months of age to 23.74% for growth efficiency at yearling age. Genetic and phenotypic correlations among traits were ranged from − 0.687 to 0.946 and − 0.648 to 0.918, respectively. The result indicated that selection for growth rate and efficiency-related traits would also be less effective in achieving genetic change, because there was little additive genetic variation among Kermani lambs.