Grünwald N J, Everhart S E, Knaus B J, Kamvar Z N
First and third authors: Horticultural Crop Research Unit, USDA-ARS, Corvallis, OR; and second and fourth authors: Department of Botany and Plant Pathology, Oregon State University, Corvallis.
Phytopathology. 2017 Sep;107(9):1000-1010. doi: 10.1094/PHYTO-12-16-0425-RVW. Epub 2017 Jul 27.
Population genetic analysis is a powerful tool to understand how pathogens emerge and adapt. However, determining the genetic structure of populations requires complex knowledge on a range of subtle skills that are often not explicitly stated in book chapters or review articles on population genetics. What is a good sampling strategy? How many isolates should I sample? How do I include positive and negative controls in my molecular assays? What marker system should I use? This review will attempt to address many of these practical questions that are often not readily answered from reading books or reviews on the topic, but emerge from discussions with colleagues and from practical experience. A further complication for microbial or pathogen populations is the frequent observation of clonality or partial clonality. Clonality invariably makes analyses of population data difficult because many assumptions underlying the theory from which analysis methods were derived are often violated. This review provides practical guidance on how to navigate through the complex web of data analyses of pathogens that may violate typical population genetics assumptions. We also provide resources and examples for analysis in the R programming environment.
群体遗传分析是了解病原体如何出现和适应的有力工具。然而,确定群体的遗传结构需要一系列微妙技能的复杂知识,而这些知识在关于群体遗传学的书籍章节或综述文章中往往没有明确阐述。什么是好的采样策略?我应该采集多少分离株?我如何在分子检测中纳入阳性和阴性对照?我应该使用什么标记系统?本综述将试图解决许多这类实际问题,这些问题往往无法从关于该主题的书籍或综述中轻易找到答案,而是来自与同事的讨论和实践经验。对于微生物或病原体群体来说,另一个复杂情况是经常观察到克隆性或部分克隆性。克隆性总是使群体数据分析变得困难,因为许多分析方法所基于的理论的假设常常被违反。本综述提供了关于如何在可能违反典型群体遗传学假设的病原体数据分析复杂网络中导航的实用指南。我们还提供了在R编程环境中进行分析的资源和示例。