Estimates of genetic parameters for growth traits in Kermani sheep

Birth weight (BW), weaning weight (WW), 6-month weight (W6), 9-month weight (W9) and yearling weight (YW) of Kermani lambs were used to estimate genetic parameters. The data were collected from Shahrbabak Sheep Breeding Research Station in Iran during the period of 1993–1998. The fixed effects in the model were lambing year, sex, type of birth and age of dam. Number of days between birth date and the date of obtaining measurement of each record was used as a covariate. Estimates of (co)variance components and genetic parameters were obtained by restricted maximum likelihood, using single and two-trait animal models. Based on the most appropriate fitted model, direct and maternal heritabilities of BW, WW, W6, W9 and YW were estimated to be 0.10 ± 0.06 and 0.27 ± 0.04, 0.22 ± 0.09 and 0.19 ± 0.05, 0.09 ± 0.06 and 0.25 ± 0.04, 0.13 ± 0.08 and 0.18 ± 0.05, and 0.14 ± 0.08 and 0.14 ± 0.06 respectively. Direct and maternal genetic correlations between the lamb weights varied between 0.66 and 0.99, and 0.11 and 0.99. The results showed that the maternal influence on lamb weights decreased with age at measurement. Ignoring maternal effects in the model caused overestimation of direct heritability. Maternal effects are significant sources of variation for growth traits and ignoring maternal effects in the model would cause inaccurate genetic evaluation of lambs

Simple hierarchical and general nonlinear growth modeling in sheep

Differential equations and advanced statistical models have been used to predict growth phenomena. In the present study, general nonlinear growth functions such as von Bertalanffy, Gompertz, logistic, and Brody, along with hierarchical modeling were applied to investigate the phenotypic growth pattern of Iranian Lori-Bakhtiari sheep. Growth data from 1410 Lori- Bakhtiari lambs were used in the present study. The results showed that the Brody function outperformed the other three nonlinear growth functions. In addition, including hierarchical growth modeling results allowed the adoption of many random effect structures, suggesting that hierarchical growth modeling has a useful role in growth data modeling. This method provides an estimation of growth parameters based on individual animals, improving individual growth selection. The results suggest this approach for growth modeling. Combining the strength of individual growth modeling with general growth modeling, e.g., von Bertalanffy, Gompertz, logistic, and Brody would be deeply appealing in the future. In this regard, dealing with sheep growth phenomenon using pure mathematical models, i.e. grey system theory models that could be new powerful prediction tools for breeders and experts, has not been done yet. However, running the analysis on large datasets will require significantly higher computational power than is ordinarily available