A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation
A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation
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DOI：
10.1111/j.1365-294x.2007.03442.x
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发表时间：
2007-09-01
影响因子：
4.9
通讯作者：
Taberlet, P.
中科院分区：
文献类型：
--
作者：
Joost, S.;Bonin, A.;Taberlet, P.
基因组中适应性位点的检测是必不可少的，因为它提供了了解基因组的比例或哪些基因是由自然选择形成的可能性。一些统计方法已经发展起来，利用分子数据来揭示选择下的基因组区域。在本文中，我们提出了一种从环境角度解决这一问题的方法，以补充群体遗传学的结果。在地理信息系统、环境变量和分子数据的基础上，提出了一种基于空间分析的自然选择特征检测方法。对标记位点的等位基因频率与环境变量之间的关联进行了多重单变量逻辑回归检验。这种空间分析方法（SAM）类似于目前的群体基因组学方法，因为它旨在扫描数百个标记来评估与数百个环境变量的假定关联。通过对松象鼻虫和绵羊品种的研究，我们证明了SAM结果与使用群体遗传学方法获得的结果之间有很强的对应关系。我们发现了与环境参数相关的基因座的统计信号，与中性基因座的理论分布相比，这些基因座的表现是非典型的。这个新工具的贡献不仅是允许识别选择下的位点，而且还建立了关于可能施加选择压力的生态因素的假设。在未来，这种方法可能会加速在种群水平上寻找功能基因的过程。
The detection of adaptive loci in the genome is essential as it gives the possibility of understanding what proportion of a genome or which genes are being shaped by natural selection. Several statistical methods have been developed which make use of molecular data to reveal genomic regions under selection. In this paper, we propose an approach to address this issue from the environmental angle, in order to complement results obtained by population genetics. We introduce a new method to detect signatures of natural selection based on the application of spatial analysis, with the contribution of geographical information systems (GIS), environmental variables and molecular data. Multiple univariate logistic regressions were carried out to test for association between allelic frequencies at marker loci and environmental variables. This spatial analysis method (SAM) is similar to current population genomics approaches since it is designed to scan hundreds of markers to assess a putative association with hundreds of environmental variables. Here, by application to studies of pine weevils and breeds of sheep we demonstrate a strong correspondence between SAM results and those obtained using population genetics approaches. Statistical signals were found that associate loci with environmental parameters, and these loci behave atypically in comparison with the theoretical distribution for neutral loci. The contribution of this new tool is not only to permit the identification of loci under selection but also to establish hypotheses about ecological factors that could exert the selection pressure responsible. In the future, such an approach may accelerate the process of hunting for functional genes at the population level.