Genetic analysis of growth curve in Moghani Sheep using Bayesian and restricted maximum likelihood

This study was conducted to predict the genetic (co)variance components of growth curve parameters of Moghani sheep breed using the following information: birth weight (N = 7278), 3-mo-old weight (N = 5881), 6-mo-old weight (N = 5013), 9-mo-old weigh (N = 2819], and 12-mo-old weight (N = 2883). The growth parameters (A: maturity weight, B: growth rate, and K: maturity rate) were calculated using Gompertz, Logistic, Brody, and Von Bertalanffy nonlinear models via NLIN procedure of SAS software. The aforementioned models were compared using Akaike information criterion, root mean square error, adjusted co-efficient of determination. Also, both Bayesian (using MTGSAM) and RMEL (using WOMBAT) paradigms were adapted to predict the genetic (co)variance components of growth parameters (A, B, K) due to the best fitted growth models. It was turned out that Von Bertalanffy best fitted to the data in this study. The year of birth and lamb gender had a significant effect on maturity rate (P < 0.01). Also it turned out that within the growth parameter, with increasing (co)variance matrix complexity, the Bayesian paradigm fitted well to the data than the restricted maximum likelihood (REML) one. However, for simple animal model and across all growth parameters, REML outperformed Bayesian. In this way, the h(a)(2) predicted (0.15 & PLUSMN; 0.05), (0.11 & PLUSMN;.05), and (0.04 & PLUSMN; 0.03) for A, B, and K parameters, respectively. Practically, in terms of breeding plan, we could see that genetic improvement of growth parameters in this study is not a tractable strategy to follow up and improvement of the management and environment should be thoroughly considered. In terms of paradigm comparison, REML's bias correction bears up an advantageous approach as far as we are concerned with small sample size. To this end, REML predictions are fairly accurate but the mode of posterior distributions could be overestimated. Finally, the differences between REML and Bayesian estimates were found for all parameter data in this study. We conclude that simulation studies are necessary in order to trade off these parading in the complex random effects scenarios of genetic individual model. Lay Summary The Iran plateau is known to be the origin of many sheep species nowadays. In Iran, different production systems are operated ranging from intensive to lower-input/extensive ones. However, the majority of these sheep breeds are extensively managed, where lambs are born outside and with little intervention and generally they experience frequent drought and shortage of nutritional value of forages. Meanwhile, the weight of lambs as a whole play a major decision role in rearing or culling them. However, investigations involving the possible genetic improvement of lamb weights over different periods of time have found low genetic variations. This study serves to be comprehensive in addressing this issue in Moghani sheep breed. Fitting many different genetics models over both restricted maximum likelihood and Bayesian paradigms indicated that heritability of weight spanned 0.03 to 0.23. The low genetic variation would lead to recommendations that the improvement of Moghani lamb weights should rather be based upon modification of the environment to create conditions suitable for weight of lambs. This reflects that breeders of Moghani sheep breed have less options to tackle Iran harsh conditions using Moghani sheep genetic potentials. One of the main challenges in estimation of genetic variance components, a cornerstone of breeding schemes, is choosing the right paradigm for estimation of aforementioned components. In this study, we showed that by increasing the number and complexity of variance components, Bayesian paradigm overtakes restricted maximum likelihood in terms of explaining much better the data.