Longitudinal analysis of growth traits in Rayeni goats via random regression

This study analyzed 13198 test-day body weight records of 4817 Rayeni goats, aged from birth to 365 days, collected over 20 years (1990-2010) at Baft Breeding Station, Iran. Growth trajectories were investigated using random regression (RR) models with varying degrees of Legendre polynomial (LP) functions to estimate genetic and phenotypic parameters. Genetic analyses were conducted using WOMBAT software, considering fixed effects (birth year, dam age, birth type, and sex) and random effects (direct additive genetic effects, maternal genetic effects, and animals' permanent environmental effects). RR models were compared using Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC) to determine the best fit. The optimal model (Leg723) included a seventh-order LP for direct additive genetic effects, a second-order for maternal genetic effects, a third-order for individual permanent environmental effects, and a step function with five heterogeneous classes for residual variances. Direct heritability fluctuated, increasing from birth (0.23) to weaning (0.63), then declining to nine months (0.36), and rising again at twelve months (0.47), whereas maternal heritability exhibited a decreasing trend with age. Direct genetic and phenotypic correlations between kid weights ranged from -0.27 to 0.76 and -0.02 to 0.79, respectively, and increased as the time interval between weighings decreased. This study demonstrates that RR models are effective tools for evaluating growth trajectories in Rayeni goats. Additionally, medium to high direct genetic heritability estimates from birth to one year indicate potential for genetic improvement by selecting for body weight traits in Rayeni goats reared in harsh tropical conditions.