| چکیده انگلیسی مقاله |
Introduction and Objectives
Native chicken breeds have gradually developed over time under the influence of artificial and natural selection, which has led to a wide phenotypic variation between them. Mazandaran native chicken is one of the most important breeds of native chicken that is raised in the north of Iran, mainly in Mazandaran province. Eggs are a rich source of high quality protein, fat and natural vitamins. Accurate and unbiased estimation of genetic parameters is very necessary to make selection decisions in breeding programs that lead to positive genetic improvement in egg production. Traditional selection models that ignore maternal and permanent environmental effects lead to overestimation of additive genetic variance and thus high heritability estimates. Genetic evaluation methods using best linear unbiased prediction (BLUP) are usually performed under autosomal inheritance. However, there are reports that some traits of economic importance are under sex chromosomal inheritance, which should be considered in genetic evaluation. It has been reported that ignoring sex-dependent genetic components can lead to a decrease in the accuracy of genetic evaluation. Estimating the genetic parameters of egg production is necessary to understand the genetic architecture and explore suitable biological traits to improve egg production. However, information in this regard is quite limited for indigenous breeds. In the present study, using the phenotypic and pedigree data of Mazandaran native chicken, the variance components and genetic parameters were investigated considering the non-additive genetic effects in different genetic models, which can be a basis for Mazandaran native chicken breeding.
Materials and methods
The phenotypic and pedigree data used in this study were collected in Mazandaran Native Chicken Breeding Center located in the north of Iran (Mazandaran Province). The pedigree was checked using CFC software and possible errors were identified. The studied traits included EW1: average egg weight on the first day of laying, EW28: average egg weight at 28 weeks, EW32: average egg weight at 32 weeks of age, AEW: average egg weight until the 32nd week of laying. The GLM procedure in SAS statistical software (2004) was used to determine the significant fixed effect to be included in the final animal models. Based on this, hatch number and the generation effects were included as significant fixed effects (p<0.05) in the final evaluation model. The contribution of autosomal and sex-dependent additive genetic effects in the genetic architecture of each trait was evaluated using WOMBAT software. For this purpose, the non-autosomal additive genetic relationship matrix (S) and its inverse (S-1) were constructed using the nadiv software package in the R environment. Then, the matrix inverse (S-1) was manually entered into the WOMBAT software using the GIN plugin. Twelve univariate animal models were analyzed to identify autosomal additive genetic effects, sex-linked additive genetic effects, and maternal effects (both genetic effects and permanent environmental effects) to assess the significance of the contribution of each random effect. The model with the lowest AIC value was identified as the best model.
Results
Based on AIC values, the best genetic model for the average egg weight for the first day of laying was model 11 including both maternal effects (genetic and permanent environment). Also, the most appropriate model for EW28, EW32 and AEW was model 7, which included maternal genetic effects in addition to direct additive genetic effects, and covariance between direct and maternal genetic effects. The results showed that the phenotypic variance of egg weight in native chickens of Mazandaran was not influenced by sex-linked additive genetic effects. The data set used, the breeds investigated, the methods used in data analysis are effective in identifying the best model among different studies. The results showed that with increasing age, the additive genetic effect increased and maternal effects decreased. Maternal effects accounted for only a small portion of the phenotypic variance, but ignoring these effects leads to bias and overestimation of genetic parameters, which compromises breeding goals. Estimates of autosomal heritability for studied traits for EW1, EW28, EW32 and AEW were equal to 0.36, 0.48, 0.46 and 0.45, respectively. The effect of adding random variables to models has shown that direct heritability estimates increase when the maternal genetic effect is ignored; therefore, removing the maternal genetic effect increased the additive direct genetic effect. In this case, it appears that some of the maternal genetic variation is incorporated into the additive direct genetic effect. In all investigated traits, using optimal models, the direct heritability (h2) was higher than the ratio of maternal heritability (m2) and the ratio of maternal environmental effects (c2). In addition, m2 and c2 were generally very small, and decreased with age. The results showed a negative genetic correlation between direct and maternal effects in chickens. This negative estimate may be due to the model used or the data structure. Assessing maternal effects requires extensive data on dams and granddams. This kind of data should include information on the number of dams with their own performance records, progeny records per dams, and data recorded over multiple generations. Furthermore, distinguishing between the maternal genetic component and permanent maternal environmental effects requires repeated records of dams and the presence of these dams in the data set.
Conclusion
In total, the results of the present study showed that the genetic variance of the egg weight trait in Mazandaran native chickens is not affected by non-autosomal additive genetic effects. Maternal effects significantly contributed to the genetic variance of the studied traits. Also, the negative correlation between direct additive and maternal genetic effects indicated antagonism between these effects. Therefore, consideration of maternal effects along with covariance between direct and maternal additive genetic effects is recommended in genetic evaluation and breeding programs. |