Best Practices for Population Genetic Analyses

Abstract

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.

Genetic structure and population diversity in the wheat sharp eyespot pathogen Rhizoctonia cerealis in the Willamette Valley, Oregon, USA

“…As mentioned above, a considerable number of isolates were isolated from the same tillers and lesions. Therefore, to eliminate the bias caused by a large number of clonal isolates (Li et al , ; Grünwald et al , ), a clone‐corrected dataset was used to perform the HWE analysis and LD test.…”

“…Pairwise Nei’s genetic distance (Nei’s D ), which assumes an infinite allele model (Grünwald et al , ), and Nei’s genetic identity (Nei’s I ; Nei, ) were calculated using GenAlEx v. 6.5 (Smouse & Peakall, ) to determine the genetic similarity among subpopulations collected from different fields. Nei’s I ranges from 0 to 1; the closer Nei’s I is to 1, the higher the genetic similarity of two subpopulations.…”

“…The nonparametric significance test with 999 permutations was conducted using ade4 (Dray & Dufour, ) to test the significance of the AMOVA. Two clone‐corrected datasets (Li et al , ; Grünwald et al , ) were analysed, one including all seven subpopulations (7pops) and the other one excluding the subpopulation BF17 (6pops). These two datasets were individually tested by AMOVA because the subpopulation BF17 did not contain any within‐lesion samples.…”

Novel insights on colonization routes and evolutionary potential of Colletotrichum kahawae, a severe pathogen of Coffea arabica

“…Therefore, it is crucial to understand the mode of reproduction of a pathogen in order to infer its evolutionary potential and ability to infect new hosts and areas, which sometimes proves to be a challenging task, mainly because the sexual reproduction of the pathogen may be cryptic/rare and difficult to observe in the field, leading to misclassifications (Dutech et al, 2010;Haag et al, 2013). Recently, as a result of high-throughput sequencing, the reproduction mode of several pathogens has been reclassified (Plissonneau et al, 2017), proving the ability of population genetics to increase our knowledge of the reproductive system, the complex demographic scenario of the pathogen and the adaptive evolution of plant pathogens in agro-ecosystems (Grünwald et al, 2017;.…”

Genetic diversity and population structure of Lasiodiplodia theobromae from different hosts in northeastern Brazil and Mexico

“…Knowledge obtained from genetic analysis of plant pathogen populations may be relevant for breeding and fungicide resistance management programmes (McDonald & Linde, 2002). Population genetics can be used to identify genetic patterns and processes of pathogen emergence and re-emergence (Gr€ unwald et al, 2017). A better understanding of these processes is necessary to predict and mitigate their impact.…”