Abstract
Simulations were used to study the influence of model adequacy and data structure on the estimation of genetic parameters for traits governed by direct and maternal effects. To test model adequacy, several data sets were simulated according to different underlying genetic assumptions and analysed by comparing the correct and incorrect models. Results showed that omission of one of the random effects leads to an incorrect decomposition of the other components. If maternal genetic effects exist but are neglected, direct heritability is overestimated, and sometimes more than double. The bias depends on the value of the genetic correlation between direct and maternal effects. To study the influence of data structure on the estimation of genetic parameters, several populations were simulated, with different degrees of known paternity and different levels of genetic connectedness between flocks. Results showed that the lack of connectedness affects estimates when flocks have different genetic means because no distinction can be made between genetic and environmental differences between flocks. In this case, direct and maternal heritabilities are under-estimated, whereas maternal environmental effects are overestimated. The insufficiency of pedigree leads to biased estimates of genetic parameters.
Highlights
The animal model is extensively used for predicting genetic values and estimating genetic parameters, because the optimum combined use of all relationships and performances improves accuracy
Empirical standard deviations between replicates varied between 0.02 and 0.04 for heritabilities of direct and maternal effects. They were higher for the genetic correlation, when true values tended to zero (AMR0, AMR0C) and when direct and maternal heritabilities were small
With the analysis model AMR, for the first population, the direct heritability and the genetic correlation were correctly estimated, but the maternal heritability was overestimated (h2Am = 0.36)
Summary
The animal model is extensively used for predicting genetic values and estimating genetic parameters, because the optimum combined use of all relationships and performances improves accuracy. Concerning maternally influenced traits, there is still discrepancy between the theoretical studies about genetic parameter estimation and practical applications. The reasons for this can be problems of convergence with variance components estimation software, or data structure (for example incomplete pedigree), or unavailability of efficient techniques (software or hardware) as is the case in some developing countries. Quintanilla Aguado [39] studied the importance of the models on maternal effects analysis by fitting an environmental correlation between the dam and the offspring. These previous publications reported biases when using incorrect models. We quantify this bias for different values of true genetic parameters
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