Random regression models to explore genetic variation and genetic variability in the growth curve of Baluchi lambs

Publication date: Available online 1 October 2018Source: Meta GeneAuthor(s): Farhad Ghafouri-Kesbi, Mohsen GholizadehAbstractThe aim was to apply Random Regression Model (RRM) to describe growth curve of Baluchi lambs. Data was retrieved from an experimental population of Baluchi sheep and consisted of body weight records from 50 to 400 days of age. Regressions for the direct and maternal random effects in the RRM were modeled using different combinations of legendre polynomials with orders from one (i.e., simple repeatability model, SRM) to four. Mean trends were also modeled through a quadratic regression on orthogonal polynomials of age. Homogeneity and heterogeneity of error variance were considered along growth trajectory. The Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) were computed to compare different models. According to AIC and BIC values, simple repeatability model was the worse model and resulted in greater estimates of error variances. A RRM with legendre polynomials of orders 4, 4, 4, and 4 for direct additive genetic, direct permanent environment, maternal additive genetic and maternal permanent environmental effects was selected as the parsimonious model. The AIC and BIC values significantly decreased when maternal effects excluded from the parsimonious model. Direct heritability (h2) decreased from 0.32 at 50 days of age to 0.09 at 90 days of age and increased thereafter to a peak at 400 days of age (0.42). Direct per...

Source: Meta Gene - October 5, 2018 Category: Genetics & Stem Cells Source Type: research