Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach
Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach
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DOI：
10.1007/s10592-012-0411-5
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发表时间：
2013-04-01
影响因子：
2.2
通讯作者：
Manel, Stephanie
中科院分区：
文献类型：
--
作者：
Bothwell, Helen;Bisbing, Sarah;Manel, Stephanie
人们普遍认为，大多数植物种群是适应当地的。然而，了解环境力量如何引起适应性遗传变异是保护遗传学的一个挑战，对于在快速变化的气候条件下保护物种至关重要。环境变化、地理历史和人口统计学过程都对空间结构的遗传变异有贡献，然而目前很少有模型试图将这些混杂效应分开。为了说明使用空间显式模型识别潜在的适应性位点的好处，我们比较了离群位点检测方法与最近开发的景观遗传方法。我们分析了157个基因座的高山草本植物Gentiana nivalis收集在欧洲阿尔卑斯山的样品。主坐标的邻居矩阵（PCNM），特征向量，量化多尺度的空间变异存在于一个数据集，被纳入一个景观遗传的方法与AFLP频率与23个环境变量。出现了四个主要发现。1)15个位点与至少一个预测变量显著相关（R（adj）（2）> 0.5）。2)包括PCNM变量的模型比没有空间变量的模型多确定了8个潜在的适应位点。3)与离群值检测方法相比，景观遗传方法检测到4个相同的位点加上11个额外的位点。4)温度、降水量和太阳辐射是导致G.尼瓦利斯本文提出的技术提供了一种有效的方法，用于识别潜在的适应性遗传变异和相关的环境力量的选择，提供了一个重要的一步，在全球变化下的非模式物种的保护。
It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental variation, phylogeographic history, and population demographic processes all contribute to spatially structured genetic variation, however few current models attempt to separate these confounding effects. To illustrate the benefits of using a spatially-explicit model for identifying potentially adaptive loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi-scale spatial variation present in a data set, were incorporated into a landscape genetic approach relating AFLP frequencies with 23 environmental variables. Four major findings emerged. 1) Fifteen loci were significantly correlated with at least one predictor variable (R (adj) (2) > 0.5). 2) Models including PCNM variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major environmental factors driving potentially adaptive genetic variation in G. nivalis. Techniques presented in this paper offer an efficient method for identifying potentially adaptive genetic variation and associated environmental forces of selection, providing an important step forward for the conservation of non-model species under global change.