A Bayesian approach to the identification of panmictic populations and the assignment of individuals
A Bayesian approach to the identification of panmictic populations and the assignment of individuals
复制标题
DOI：
10.1017/s001667230100502x
复制
发表时间：
2001-08-01
期刊：
影响因子：
--
通讯作者：
Belkhir, K
中科院分区：
文献类型：
--
作者：
Dawson, KJ;Belkhir, K
我们提出了基于可能性的方法，根据样本中个体在共显性标记基因座上的基因类型，将样本中的个体分配给源群体。假设源群体处于Hardy-Weinberg和连锁平衡状态，但这些源群体的等位基因组成甚至样本中所代表的源群体的数量都是不确定的。感兴趣的参数是抽样个体集合的划分，这是由将个体分配到源群体而引起的。我们提出了一种最大似然方法，然后给出了一种更强大的贝叶斯方法来估计这种样本划分。一般来说，对支持样本每一种可能分割的证据进行评估是不可行的。此外，当样本中的个体数量很大时，甚至可能不可能单独评估支持每个最合理的分区的证据，因为可能有许多难以分配的个体。为了克服这些问题，我们使用样本划分的后验分布的低维边缘(共同分配概率)来作为相似性的度量，然后应用分层聚类算法来识别其分配由后验分布很好地支持的个体聚类。二叉树提供了后验分布对层次中每个聚类的支持程度的直观表示。这些方法也适用于感兴趣的参数是集合的划分的其他问题。由于协同分配概率与源群体的任意标记无关，因此避免了以前贝叶斯方法的标记切换问题。
We present likelihood-based methods for assigning the individuals in a sample to source populations, on the basis of their genotypes at co-dominant marker loci. The source populations are assumed to be at Hardy-Weinberg and linkage equilibrium, but the allelic composition of these source populations and even the number of source populations represented in the sample are treated as uncertain. The parameter of interest is the partition of the set of sampled individuals, induced by the assignment of individuals to source populations. We present a maximum likelihood method, and then a more powerful Bayesian approach for estimating this sample partition. In general, it will not be feasible to evaluate the evidence supporting each possible partition of the sample. Furthermore, when the number of individuals in the sample is large, it may not even be feasible to evaluate the evidence supporting, individually, each of the most plausible partitions because there may be many individuals which are difficult to assign. To overcome these problems, we use low-dimensional marginals (the 'co-assignment probabilities') of the posterior distribution of the sample partition as measures of 'similarity', and then apply a hierarchical clustering algorithm to identify clusters of individuals whose assignment together is well supported by the posterior distribution, A binary tree provides a visual representation of how well the posterior distribution supports each cluster in the hierarchy. These methods are applicable to other problems where the parameter of interest is a partition of a set. Because the co-assignment probabilities are independent of the arbitrary labelling of source populations, we avoid the label-switching problem of previous Bayesian methods.