标题： 基于模型的聚类使用多等位基因位点数据和位点选择 显示英文标题

标题： Model-Based Clustering using multi-allelic loci data with loci selection

摘要： 我们提出了一种基于模型的聚类（MBC）方法，并结合了使用多等位基因遗传数据的位点选择。 位点选择问题被视为模型选择问题，竞争模型通过贝叶斯信息准则（BIC）进行比较。 由此产生的过程选择聚类位点的子集、聚类的数量、估计每个聚类的比例以及每个聚类内的等位基因频率。 在样本大小趋于无穷大时，我们证明了在单一现实假设下，所选模型以概率收敛到真实模型。 该方法名为MixMoGenD（使用遗传数据的混合模型），使用C++编程语言实现。 针对模拟数据集进行了数值实验，以突出所提出的位点选择程序的兴趣。 显示英文摘要

摘要： 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.