Behaviour of the additive finite locus model

A finite locus model to estimate additive variance and the breeding values was implemented using Gibbs sampling. Four different distributions for the size of the gene effects across the loci were considered: i) uniform with l oci of different effects, ii) uniform with all loci having equal effects, i ii) exponential, and iv) normal. Stochastic simulation was used to study th e influence of the number of loci and the distribution of their effect assu med in the model analysis. The assumption of loci with different and unifor mly distributed effects resulted in an increase in the estimate of the addi tive variance according to the number of loci assumed in the model of analy sis, causing biases in the estimated breeding values. When the gene effects were assumed to be exponentially distributed, the estimate of the additive variance was still dependent on the number of loci assumed in the model of analysis, but this influence was much less. When assuming that all the loc i have the same gene effects or when they were normally distributed, the ad ditive variance estimate was the same regardless of the number of loci assu med in the model of analysis. The estimates were not significantly differen t from either the true simulated values or from those obtained when using t he standard mixed model approach where an infinitesimal model is assumed. T he results indicate that if the number of loci has to be assumed a priori, the most useful finite locus models are those assuming loci with equal effe cts or normally distributed effects. (C) Inra/Elsevier, Paris.