A spatial statistical model for landscape genetics
A spatial statistical model for landscape genetics
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DOI：
10.1534/genetics.104.033803
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发表时间：
2005-07-01
期刊：
影响因子：
3.3
通讯作者：
Cosson, JF
中科院分区：
文献类型：
--
作者：
Guillot, G;Estoup, A;Cosson, JF
景观遗传学是研究景观和环境特征如何影响种群遗传结构的一门新兴学科。景观遗传学的第一个关键步骤是空间检测和定位种群之间的遗传不连续性。然而，缺乏实现这一任务的有效方法。在这篇文章中，我们首先澄清什么是概念上涉及的遗传数据的空间建模。然后，我们描述了一个贝叶斯模型中实现的马尔可夫链蒙特卡罗计划，允许推理的位置，这样的遗传不连续性从个人地理参考多位点基因型，没有先验知识的人口单位和限制。在该方法中，采样个体的全球集合被建模为一个空间混合的panmictic人口，和人口的空间组织是通过彩色Voronoi镶嵌建模。除了在空间上定位遗传不连续性，该方法量化了数据集中的空间依赖量，估计了研究区域的种群数量，将个体分配到其起源种群，并检测种群之间的个体迁移，同时考虑到采样个体位置的不确定性。该方法的性能进行评估，通过模拟数据集的分析。结果显示出对于标准数据集的良好性能（例如，100个个体在10个基因座上进行基因分型，每个基因座10个等位基因），具有高但也低水平的群体分化（例如，F-ST < 0.05）。然后，该方法被应用到一组88个个体的狼獾（Gulo gulo）在美国西北部的采样和基因型在10个微卫星。
Landscape genetics is a new discipline that aims to provide information oil how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual georeferenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of Populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), With high but also low levels of population differentiation (e.g., F-ST < 0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites.