Use of Random regression model for modeling growth trajectory in Jamunapari goat in the semi-arid region of India

Abstract The objective of the study was to plot the trajectory of the growth curve in Jamunapari goat using B-spline (BS) function and compare it with the Legendre polynomial (LP) function of the random regression model (RRM) and also to assess the genotype and environment interaction for growth traits. The data was collected from 8763 descendants of 1845 dams and 339 sires. The RRM used direct genetic, direct maternal, permanent environment for animal and maternal permanent environmental effects in different orders of fit. The B-spline model (BSL6H5) was more robust compared to LP-RRM. The heritability (h2) estimates were 0.23 ± 0.03, 0.26 ± 0.02, 0.22 ± 0.02, 0.19 ± 0.02, 0.22 ± 0.02, respectively for birth weight (BW), three-month weight (3MW), six-month weight (6MW), nine-month weight (9MW) and twelve-month weight (12MW) from the best model. The h2 estimate indicated the further scope of genetic improvement through selection. The maternal proportion of variance was accounting for 3 to 8% variance across growth trajectory indicating the low influence of maternal effects that reduced significantly post-weaning. However, we observed that the animal permanent environment accounted for 45 to 69% of variance across growth trajectory. The genetic correlations between 6MW, 9MW, and 12MW were high and positive indicating scope for early selection. The genetic trends at 3, 6, 9, and 12 months of age were 0.065 kg, 0.082 kg, 0.098 kg, and 0.121 kg per year, respectively, indicating a favorable impact of selection for growth traits. The ranks of the sires at different time points based on breeding values through B-spline RRM were more robust as compared with estimates from the conventional animal model and LP-RRM, where the presence of genotype by environment (GxE) interaction resulted in significant re-ranking of sires for 9MW and 12MW. The B-spline RRM approach resulted in a pragmatic solution for the problem of GxE interaction across growth trajectories. We conclude that the B-spline RRM should be used for genetic evaluation of Jamunapari goats in the future for unbiased, accurate, and consistent prediction of breeding values.