Bayesian clustering using hidden Markov random fields in spatial population genetics
Bayesian clustering using hidden Markov random fields in spatial population genetics
复制标题
DOI：
10.1534/genetics.106.059923
复制
发表时间：
2006-10-01
期刊：
影响因子：
3.3
通讯作者：
Guillot, Gilles
中科院分区：
文献类型：
--
作者：
Francois, Olivier;Ancelet, Sophie;Guillot, Gilles
我们引入了一个新的贝叶斯聚类算法研究人口结构，使用单独的地理参考多位点数据集。该算法是基于隐马尔可夫随机场的概念，模型的空间依赖性在集群成员的水平。我们认为，（i）马尔可夫链蒙特卡罗过程可以有效地实现该算法，（ii）它可以检测等位基因频率的显著地理不连续性并调节聚类的数量，（iii）它可以检查在不使用空间先验的情况下获得的聚类是否对等位基因频率的不连续地理变异的假设具有鲁棒性，和（iv）它可以减少获得精确分配所需的基因座数目。我们说明和讨论的实施问题与斯堪的纳维亚棕熊和人类CEPH多样性面板数据集。
We introduce a new Bayesian clustering algorithm for studying population structure using individually geo-referenced multilocus data sets. The algorithm is based on the concept of hidden Markov random field, which models the spatial dependencies at the cluster membership level. We argue that (i) a Markov chain Monte Carlo procedure call implement the algorithm efficiently, (ii) it can detect significant geographical discontinuities in allele frequencies and regulate the number of clusters, (iii) it call check whether the clusters obtained without the use of spatial priors are robust to the hypothesis of discontinuous geographical variation in allele frequencies, and (iv) it can reduce the number of loci required to obtain accurate assignments. We illustrate and discuss the implementation issues with the Scandinavian brown bear and the human CEPH diversity panel data set.