  We propose a Model-Based Clustering (MBC) method combined with loci selection
using multi-allelic loci genetic data. The loci selection problem is regarded
as a model selection problem and models in competition are compared with the
Bayesian Information Criterion (BIC). The resulting procedure selects the
subset of clustering loci, the number of clusters, estimates the proportion of
each cluster and the allelic frequencies within each cluster. We prove that the
selected model converges in probability to the true model under a single
realistic assumption as the size of the sample tends to infinity. The proposed
method named MixMoGenD (Mixture Model using Genetic Data) was implemented using
c++ programming language. Numerical experiments on simulated data sets was
conducted to highlight the interest of the proposed loci selection procedure.
