GENETIC EVALUATION AND GENETIC TREND OF GROWTH IN MAKOUEI SHEEP VIA RANDOM REGRESSION
1Department of Animal Science, Astara Branch, Islamic Azad University, Astara, Iran.
*Corresponding author’s Email: email@example.com
The aim of this study was to estimate covariance functions for additive genetic, direct and maternal permanent environmental effects on the growth from 1 to 270 days of age using random regression models on Legendre polynomials (LPs). For this aim, 8733 body weight records of 1973 lambs from 110 sires and 550dams were collected between 1990 and 2014 from the rearing and breeding station of Makooei sheep in Makoo (36°, 35′S and 48°, 22′E) of West Azerbaijan province. Contemporary groups (age of dam, sex of lamb, type of birth and year-season of birth) and fixed regression of body weight on age were considered as fixed parts of the models. Random regressions of direct additive genetic, maternal and direct permanent environmental were random parts of the models. The assumptions about the distribution of the residual variance were compared. The variances increased along the trajectory from 0.25 to 2.03, 0.11 to 14.85 and 0.08 to 2.26 for direct additive genetic, direct and maternal permanent environmental effect, respectively. Low to high estimates of direct heritability (0.10-0.42) and moderate to high estimates of coefficient of permanent environmental effects (0.18 to 0.77) were obtained, while estimates of maternal environmental effect (c2) (0.06 to 0.21) was low and moderate in all ages. This finding indicated distinct environments a trait is influenced by sets of different genes and these genes are expressed at variable intensities according to the degree of similarity or difference within and between environments. Despite low, positive genetic trends were found for growth traits and genetic trend estimates for growth performance revealed that selection decisions made during the breeding program effectively improved the body weight traits.
Keywords: Makooei Sheep, Body Weight, Random Regression Model, Variance Components, Legendre Polynomial.