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ARS Home » Pacific West Area » Corvallis, Oregon » Horticultural Crops Research Unit » Research » Publications at this Location » Publication #336479
Title: Best practices for population genetic analyses

Author
item Grunwald, Niklaus - Nik
item EVERHART, SYDNEY - University Of Nebraska
item Knaus, Brian
item KAMVAR, ZHIAN - Oregon State University

Submitted to: Phytopathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/9/2017
Publication Date: 7/27/2017
Citation: Grunwald, N.J., Everhart, S.E., Knaus, B.J., Kamvar, Z.N. 2017. Best practices for population genetic analyses. Phytopathology. 107(9):1000-1010. https://doi.org/10.1094/PHYTO-12-16-0425-RVW.
DOI: https://doi.org/10.1094/PHYTO-12-16-0425-RVW

Interpretive Summary: 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.

Technical Abstract: 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.