Revealing cryptic spatial patterns in genetic variability by a new multivariate method
Revealing cryptic spatial patterns in genetic variability by a new multivariate method
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DOI：
10.1038/hdy.2008.34
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发表时间：
2008-07-01
期刊：
影响因子：
3.8
通讯作者：
Pontier, D.
中科院分区：
文献类型：
--
作者：
Jombart, T.;Devillard, S.;Pontier, D.
人们越来越关注在遗传学研究中考虑景观信息。在景观变量中，空间通常被认为是最重要的变量之一。为了揭示空间模式，统计方法应该是空间显式的，也就是说，它应该直接考虑空间信息作为调整模型或优化标准的组成部分。在本文中，我们提出了一种新的空间显式多变量方法，即空间主成分分析（sPCA），利用个体或群体的等位基因频率数据来研究遗传变异的空间模式。该分析不需要数据满足 Hardy-Weinberg 期望或基因座之间存在连锁平衡。 sPCA 产生的分数总结了个体（或群体）之间的遗传变异性和空间结构。全局结构（斑块、序列和中间体）与局部结构（邻居之间的强烈遗传差异）和随机噪声分开。提出了两种统计测试来检测两种类型模式的存在。作为说明，使用斯堪的纳维亚棕熊 (Ursus arctos) 的模拟数据集和真实地理参考微卫星数据对主成分分析 (PCA) 和 sPCA 的结果进行了比较。 sPCA 在揭示空间遗传模式方面比 PCA 表现更好。所提出的方法在免费软件 R 的 adegenet 包中实现。
Increasing attention is being devoted to taking landscape information into account in genetic studies. Among landscape variables, space is often considered as one of the most important. To reveal spatial patterns, a statistical method should be spatially explicit, that is, it should directly take spatial information into account as a component of the adjusted model or of the optimized criterion. In this paper we propose a new spatially explicit multivariate method, spatial principal component analysis (sPCA), to investigate the spatial pattern of genetic variability using allelic frequency data of individuals or populations. This analysis does not require data to meet Hardy - Weinberg expectations or linkage equilibrium to exist between loci. The sPCA yields scores summarizing both the genetic variability and the spatial structure among individuals (or populations). Global structures (patches, clines and intermediates) are disentangled from local ones (strong genetic differences between neighbors) and from random noise. Two statistical tests are proposed to detect the existence of both types of patterns. As an illustration, the results of principal component analysis (PCA) and sPCA are compared using simulated datasets and real georeferenced microsatellite data of Scandinavian brown bear individuals (Ursus arctos). sPCA performed better than PCA to reveal spatial genetic patterns. The proposed methodology is implemented in the adegenet package of the free software R.