Genetic fingerprinting of salmon louse ( Lepeophtheirus salmonis ) populations in the North-East Atlantic using a random forest classification approach
Genetic fingerprinting of salmon louse ( Lepeophtheirus salmonis ) populations in the North-East Atlantic using a random forest classification approach
复制标题
使用随机森林分类方法对东北大西洋鲑鱼虱 (Lepeophtheirus Salmonis) 种群进行基因指纹分析
DOI：
10.1101/179218
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Jacobs A
中科院分区：
文献类型：
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作者：
Jacobs A
海虱对世界范围内的鲑鱼水产养殖构成重大威胁。群体遗传分析一直显示北南极洲鲑虱的最小群体遗传结构，令人沮丧的努力，以跟踪虱子的人口和改善有针对性的控制措施。本研究的目的是测试简化代表性文库测序（IIb-RAD测序）结合随机森林机器学习算法的能力，以定义用于虱子种群精细区分的标记。我们确定了1286强有力的支持SNPs之间的四个L。鲑鱼种群来自爱尔兰、苏格兰和北方挪威。基于全SNP数据集，仅观察到弱全局结构。随机森林机器学习算法的应用确定了98个歧视性SNP，这些SNP显著改善了种群分配，增加了全局遗传结构，并导致了显著的遗传种群分化。在定向选择下发现的大部分SNP也被鉴定为高度歧视性的。我们的数据表明，它是可能区分nearbyL。研究人员还认为，鲑鱼种群中存在着适当的标记选择方法，这种差异可能具有适应性基础。我们讨论了这些数据，海虱适应人类和环境的压力，以及新的方法来跟踪和预测海虱扩散。
Caligid sea lice represent a significant threat to salmonid aquaculture worldwide. Population genetic analyses have consistently shown minimal population genetic structure in North AtlanticLepeophtheirus salmonis, frustrating efforts to track louse populations and improve targeted control measures. The aim of this study was to test the power of reduced representation library sequencing (IIb-RAD sequencing) coupled with random forest machine learning algorithms to define markers for fine-scale discrimination of louse populations. We identified 1286 robustly supported SNPs among fourL. salmonispopulations from Ireland, Scotland and Northern Norway. Only weak global structure was observed based on the full SNP dataset. The application of a random forest machine-learning algorithm identified 98 discriminatory SNPs that dramatically improved population assignment, increased global genetic structure and resulted in significant genetic population differentiation. A large proportion of SNPs found to be under directional selection were also identified to be highly discriminatory. Our data suggest that it is possible to discriminate between nearbyL. salmonispopulations given suitable marker selection approaches, and that such differences might have an adaptive basis. We discuss these data in light of sea lice adaption to anthropogenic and environmental pressures as well as novel approaches to track and predict sea louse dispersal.