Roux O, Gevrey M, Arvanitakis L, Gers C, Bordat D, Legal L
Laboratoire Dynamique de la Biodiversité, Université Paul Sabatier Toulouse III, UMR-CNRS 5172, Bat 4R3, 31062 Toulouse cedex 9, France.
Mol Phylogenet Evol. 2007 Apr;43(1):240-50. doi: 10.1016/j.ympev.2006.09.017. Epub 2006 Oct 7.
The diamondback moth (DBM), Plutella xylostella (L.) is considered as the most destructive pest of Brassicaceae crops world-wide. Its migratory capacities and development of insecticide resistance in many populations leads to more difficulties for population management. To control movement of populations and apparitions of resistance carried by resistant migrant individuals, populations must be identified using genetic markers. Here, seven different ISSR markers have been tested as a tool for population discrimination and genetic variations among 19 DBM populations from Canada, USA, Brazil, Martinique Island, France, Romania, Austria, Uzbekistan, Egypt, Benin, South Africa, Réunion Island, Hong Kong, Laos, Japan and four localities in Australia were assessed. Two classification methods were tested and compared: a common method of genetic distance analyses and a novel method based on an advanced statistical method of the Artificial Neural Networks' family, the Self-Organizing Map (SOM). The 188 loci selected revealed a very high variability between populations with a total polymorphism of 100% and a global coefficient of gene differentiation estimated by the Nei's index (Gst) of 0.238. Nevertheless, the largest part of variability was expressed among individuals within populations (AMOVA: 73.71% and mean polymorphism of 94% within populations). Genetic differentiation among the DBM populations did not reflect geographical distances between them. The two classification methods have given excellent results with less than 1.3% of misclassified individuals. The origin of the high genetic differentiation and efficiency of the two classification methods are discussed.
小菜蛾(DBM),即小菜蛾(Plutella xylostella (L.)),被认为是全球十字花科作物中最具破坏性的害虫。其迁徙能力以及许多种群中抗药性的发展给种群管理带来了更多困难。为了控制种群的移动以及抗性迁移个体所携带的抗性的出现,必须使用遗传标记来识别种群。在此,七种不同的ISSR标记已被测试,作为一种区分种群和评估遗传变异的工具,对来自加拿大、美国、巴西、马提尼克岛、法国、罗马尼亚、奥地利、乌兹别克斯坦、埃及、贝宁、南非、留尼汪岛、中国香港、老挝、日本以及澳大利亚四个地区的19个小菜蛾种群进行了评估。测试并比较了两种分类方法:一种是遗传距离分析的常用方法,另一种是基于人工神经网络家族的先进统计方法——自组织映射(SOM)的新方法。所选择的188个位点显示出种群之间非常高的变异性,总多态性为100%,通过Nei指数(Gst)估计的全球基因分化系数为0.238。然而,大部分变异性表现在种群内的个体之间(分子方差分析:种群内为73.71%,平均多态性为94%)。小菜蛾种群之间的遗传分化并未反映它们之间的地理距离。两种分类方法都取得了优异的结果,错误分类的个体不到1.3%。文中讨论了高遗传分化的起源以及两种分类方法的有效性。