Abstract

In breeding programs, using appropriate models to estimate the variance components with high accuracy is essential. Considering the non-additive genetic effects along with additive effects in evaluation analysis models can be effective in increasing the accuracy of estimates. This study was conducted to fit the best model for estimation of variance components of humoral immune responses (antibody titers against sheep red blood cells [SRBC] and Newcastle disease virus [NDV]), especially non-additive genetic effects (dominance and epistasis) in Japanese quail. The collected records of humoral immune responses were used to estimate genetic parameters using the Bayesian method via Gibbs sampling by fitting 24 different models. The best model for each trait was selected based on Deviance Information Criteria (DIC). Moreover, the residual and additive genetic variance changes were evaluated comparing the models. The results showed that dominance, epistasis, and maternal genetic effects were influenced on humeral immune responses traits except for immunoglobulin Y (IgY). The direct heritability for total antibody titer (AbT), the titer of immunoglobulin Y (IgY), the titer of immunoglobulin M (IgM) against SRBC, antibody titer against NDV (AbNDV), and immune system performance index (IgF) were 0.065, 0.208, 0.117, 0.100, and 0.132, respectively. The contribution of dominance and epistasis as a proportion of phenotypic variance for all traits ranged from 0.070 to 0.221, and 0.087 to 0.251, respectively. A reduction in residual variance were observed in all traits, moreover accuracy of estimated breeding values increased in some traits with adding non-additive genetic effects in the models. Furthermore, the additive genetic variance in the best models decreased for AbT, IgF, and AbNDV traits compared with the simple model (without non-additive genetic effects). The results suggested including the non-additive genetic effects in the genetic models for evaluation of humoral immune responses of quail to obtain precise genetic parameters.

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