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EN
Quadratic partial regression coefficients were estimated for the inbreeding level on five performance traits (body weight, average egg weight, age at first egg, percentage of fertilized eggs, and hatchability of set eggs) of two strains of laying hens. Data on 5631 of H77 layers and 3563 of N88 layers from nine consecutive generations were analysed. Only dams were accounted for. Partial regression coefficients were estimated by REML with a single-trait animal model, which included fixed effects (generation and hatching period) and random effects (additive genetic and error effects). The mean inbreeding level was 0.87% in strain H77 and 1.08% in strain N88. The inbreeding effects were analysed based on the quadratic partial regression equations. A slight inbreeding depression was found for all the traits analysed in N88. In strain H77, negative effects of inbreeding were only noted for body weight and average egg weight. The small inbreeding effects shown here resulted from a relatively low level of homozygosity in the populations studied. The strains were found to differ in the effects of inbreeding. It is worth pointing out that differences were noted both between the inbreeding depression estimated from the partial linear regression equation and the quadratic partial regression equation, as well as different inbreeding levels.
EN
In the case of noninbred and unselected populations with linkage equilibrium, the additive and dominance genetic effects are uncorrelated and the variance-covariance matrix of the second component is simply a product of its variance by a matrix that can be computed from the numerator relationship matrix A. The aim of this study is to present a new approach to estimate the dominance part with a reduced set of equations and hence a lower computing cost. The method proposed is based on the processing of the residual terms resulting from the BLUP methodology applied to an additive animal model. Best linear unbiased prediction of the dominance component 'delta' is almost identical to the one given by the full mixed model equations. Based on this approach, an algorithm for restricted maximum likelihood (REML) estimation of the variance components is also presented. By way of illustration, two numerical examples are given and a comparison between the parameters estimated with the expectation maximization (EM) algorithm and those obtained by the proposed algorithm is made. The proposed algorithm is iterative and yields estimates that are close to those obtained by EM, which is also iterative.
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