Joost S, Bonin A, Bruford M W, Després L, Conord C, Erhardt G, Taberlet P
Istituto di Zootecnica, Università Cattolica del S.Cuore, via E. Parmense 84, 29100 Piacenza, Italy.
Mol Ecol. 2007 Sep;16(18):3955-69. doi: 10.1111/j.1365-294X.2007.03442.x.
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.
检测基因组中的适应性位点至关重要，因为这使得了解基因组的多大比例或哪些基因正受到自然选择的塑造成为可能。已经开发了几种统计方法，利用分子数据来揭示受选择的基因组区域。在本文中，我们提出一种从环境角度解决这个问题的方法，以补充群体遗传学所获得的结果。我们引入一种基于空间分析应用的新方法来检测自然选择的特征，并借助地理信息系统（GIS）、环境变量和分子数据。进行了多个单变量逻辑回归，以检验标记位点的等位基因频率与环境变量之间的关联。这种空间分析方法（SAM）与当前的群体基因组学方法类似，因为它旨在扫描数百个标记，以评估与数百个环境变量的假定关联。在这里，通过应用于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。发现了将位点与环境参数相关联的统计信号，并且这些位点与中性位点的理论分布相比表现异常。这个新工具的作用不仅在于能够识别受选择的位点，还在于能够建立关于可能施加选择压力的生态因素的假设。未来，这样的方法可能会加速在群体水平上寻找功能基因的过程。
