Development and Application of Tools for Genetic Analysis of Clonal Populations

Research on the population genetics of microbial organisms requires the use of specialized analyses designed for clonal organisms to avoid violating the assumptions of traditional population genetic models. The tools necessary for performing these analyses existed as a set of unrelated software with non-overlapping capabilities and did not cover all aspects of analysis. This meant that researchers not only had to reshape their data into different formats for each analysis, but they also had to switch computing platforms, thus creating a drain in time, and increasing the risk of propagating human error into the analysis. To address this problem, we created the software package poppr, written in the R statistical language, available on all computing platforms. This package is designed for analysis of clonal, partially clonal, and sexual populations, empowering researchers to perform their work in a reproducible manner. We additionally demonstrate the utility of poppr for both plant pathological and theoretical questions by using real-world and simulated data. In chapter 4, we apply these new tools to demonstrate evidence for at least two origins for the outbreak of the Sudden Oak Death pathogen, Phytophthora ramorum in Curry County, Oregon. In chapter 5, we use poppr to assess the power of the index of association with clone-correction, showing that clone-correction has the potential to reduce the power of detecting clonal reproduction. All of the software and analyses in this work were performed in an open and reproducible framework, serving as an example of the power of reproducible research in plant pathology

Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction

Many microbial, fungal, or oomcyete populations violate assumptions for population genetic analysis because these populations are clonal, admixed, partially clonal, and/or sexual. Furthermore, few tools exist that are specifically designed for analyzing data from clonal populations, making analysis difficult and haphazard. We developed the R package poppr providing unique tools for analysis of data from admixed, clonal, mixed, and/or sexual populations. Currently, poppr can be used for dominant/codominant and haploid/diploid genetic data. Data can be imported from several formats including GenAlEx formatted text files and can be analyzed on a user-defined hierarchy that includes unlimited levels of subpopulation structure and clone censoring. New functions include calculation of Bruvo’s distance for microsatellites, batch-analysis of the index of association with several indices of genotypic diversity, and graphing including dendrograms with bootstrap support and minimum spanning networks. While functions for genotypic diversity and clone censoring are specific for clonal populations, several functions found in poppr are also valuable to analysis of any populations. A manual with documentation and examples is provided. Poppr is open source and major releases are available on CRAN: http://cran.r-project.org/package=poppr. More supporting documentation and tutorials can be found under ‘resources’ at: http://grunwaldlab.cgrb.oregonstate.edu/.Keywords: Clone correction, Microbiology, Genotypic diversity, Genetics, Bootstrap, Computational Science, Bioinformatics, Population genetics, Hierarchy, Mycology, Clonality, Permutation, Minimum spanning networks, Index of association, Bruvo’s distanceThis is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by PeerJ. The published article can be found at: https://peerj.com/

Population structure and phenotypic variation of \u3ci\u3eSclerotinia sclerotiorum\u3c/i\u3e from dry bean (\u3ci\u3ePhaseolus vulgaris\u3c/i\u3e) in the United States

The ascomycete pathogen Sclerotinia sclerotiorum is a necrotrophic pathogen on over 400 known host plants, and is the causal agent of white mold on dry bean. Currently, there are no known cultivars of dry bean with complete resistance to white mold. For more than 20 years, bean breeders have been using white mold screening nurseries (wmn) with natural populations of S. sclerotiorum to screen new cultivars for resistance. It is thus important to know if the genetic diversity in populations of S. sclerotiorum within these nurseries (a) reflect the genetic diversity of the populations in the surrounding region and (b) are stable over time. Furthermore, previous studies have investigated the correlation between mycelial compatibility groups (MCG) and multilocus haplotypes (MLH), but none have formally tested these patterns.We genotyped 366 isolates of S. sclerotiorum from producer fields and wmn surveyed over 10 years in 2003–2012 representing 11 states in the United States of America, Australia, France, and Mexico at 11 microsatellite loci resulting in 165 MLHs. Populations were loosely structured over space and time based on analysis of molecular variance and discriminant analysis of principal components, but not by cultivar, aggressiveness, or field source. Of all the regions tested, only Mexico (n = 18) shared no MLHs with any other region. Using a bipartite network-based approach, we found no evidence that the MCGs accurately represent MLHs. Our study suggests that breeders should continue to test dry bean lines in several wmn across the United States to account for both the phenotypic and genotypic variation that exists across regions

Epidemic curves made easy using the R package incidence.

The epidemiological curve (epicurve) is one of the simplest yet most useful tools used by field epidemiologists, modellers, and decision makers for assessing the dynamics of infectious disease epidemics. Here, we present the free, open-source package incidence for the R programming language, which allows users to easily compute, handle, and visualise epicurves from unaggregated linelist data. This package was built in accordance with the development guidelines of the R Epidemics Consortium (RECON), which aim to ensure robustness and reliability through extensive automated testing, documentation, and good coding practices. As such, it fills an important gap in the toolbox for outbreak analytics using the R software, and provides a solid building block for further developments in infectious disease modelling. incidence is available from https://www.repidemicsconsortium.org/incidence

Population Structure of Pythium irregulare, P. ultimum, and P. sylvaticum in Forest Nursery Soils of Oregon and Washington

Pythium species are important soilborne pathogens occurring in the forest nursery industry of the Pacific Northwest. However, little is known about their genetic diversity or population structure and it is suspected that isolates are moved among forest nurseries on seedling stock and shared field equipment. In order to address these concerns, a total of 115 isolates of three Pythium species (P. irregulare, P. sylvaticum, and P. ultimum) were examined at three forest nurseries using simple sequence repeat (SSR) and amplified fragment length polymorphism (AFLP) markers. Analyses revealed distinct patterns of intraspecific variation for the three species. P. sylvaticum exhibited the most diversity, followed by P. irregulare, while substantial clonality was found in P. ultimum. For both P. irregulare and P. sylvaticum, but not P. ultimum, there was evidence for significant variation among nurseries. However, all three species also exhibited at least two distinct lineages not associated with the nursery of origin. Finally, evidence was found that certain lineages and clonal genotypes, including fungicide-resistant isolates, are shared among nurseries, indicating that pathogen movement has occurred

grunwaldlab/metacoder: metacoder 0.1.3

metacoder 0.1.3 Mostly minor improvements and bug fixes. Larger changes are waiting on the taxa package to be done, which will be the new home of the taxmap class and the associated dplyr-like manipulating functions like filter_taxa. Improvements Provided helpful error message when the evaluation nested too deeply: infinite recursion / options(expressions=)? occurs due to too many labels being printed. heat_tree: improved how the predicted bondries of text is calcuated, so text with any rotation, justification, or newlines influences margins correctly (i.e. does not get cut off). heat_tree: Can now save multiple file outputs in different formats at once Minor changes heat_tree now gives a warning if infinite values are given to it extract_taxonomy: There is now a warning message if class regex does not match (issue #123) heat_tree: Increased lengend text size and reduced number of labels extract_taxonomy: added batch_size option to help deal with invalid IDs better Added CITATION file Breaking changes The heat_tree option margin_size funcion now takes four values instead of 2. Bug fixes heat_tree: Fixed bug when color is set explicitly (e.g. "grey") instead of raw numbers and the legend is not removed. Now a mixure of raw numbers and color names can be used. Fixed bugs caused by dplyr version update Fixed bug in heat_tree that made values not in the input taxmap object not associate with the right taxa. See this post. extract_taxonomy: Fixed an error that occured when not all inputs could be classified and sequences were supplied Fixed bug in primersearch that cased the wrong primer sequence to be returned when primers match in the reverse direction Fixed a bug in parse_mothur_summary where "unclassified" had got changed to "untaxmap" during a search and replace Fixed outdated example code for extract_taxonomy Fixed a bug in mutate_taxa and mutate_obs that made replacing columns result in new columns with duplicate names

Best Practices for Population Genetic Analyses

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

Population Genetics in R.

This primer provides a concise introduction to conducting applied analyses of population genetic data in R, with a special emphasis on non-model populations including clonal or partially clonal organisms. It provides a valuable resource for tackling the nitty-gritty analysis of populations that do not necessarily conform to textbook genetics and might or might not be in Hardy-Weinberg equilibrium. While this primer does not require extensive knowledge of programming in R, the user is expected to install R and all packages required for this primer. Please note that this primer is still being written and will be changing as we continue writing it. Please provide us feedback on any errors you might find or suggestions for improvement. The primer is currently published at http://grunwaldlab.github.io/Population_Genetics_in_R/index.htm

carpentries/lesson-transition: All Official Carpentries Lessons Transitioned

This represents the final milestone in the lesson transition. We have successfully completed the transition of all > 50 lessons and overview pages for The Carpentries. This additionally contains one new lesson that migrated to The Carpentries lab: https://github.com/carpentries-lab/good-enough-practice

Population structure and phenotypic variation of \u3ci\u3eSclerotinia sclerotiorum\u3c/i\u3e from dry bean (\u3ci\u3ePhaseolus vulgaris\u3c/i\u3e) in the United States

The ascomycete pathogen Sclerotinia sclerotiorum is a necrotrophic pathogen on over 400 known host plants, and is the causal agent of white mold on dry bean. Currently, there are no known cultivars of dry bean with complete resistance to white mold. For more than 20 years, bean breeders have been using white mold screening nurseries (wmn) with natural populations of S. sclerotiorum to screen new cultivars for resistance. It is thus important to know if the genetic diversity in populations of S. sclerotiorum within these nurseries (a) reflect the genetic diversity of the populations in the surrounding region and (b) are stable over time. Furthermore, previous studies have investigated the correlation between mycelial compatibility groups (MCG) and multilocus haplotypes (MLH), but none have formally tested these patterns.We genotyped 366 isolates of S. sclerotiorum from producer fields and wmn surveyed over 10 years in 2003–2012 representing 11 states in the United States of America, Australia, France, and Mexico at 11 microsatellite loci resulting in 165 MLHs. Populations were loosely structured over space and time based on analysis of molecular variance and discriminant analysis of principal components, but not by cultivar, aggressiveness, or field source. Of all the regions tested, only Mexico (n = 18) shared no MLHs with any other region. Using a bipartite network-based approach, we found no evidence that the MCGs accurately represent MLHs. Our study suggests that breeders should continue to test dry bean lines in several wmn across the United States to account for both the phenotypic and genotypic variation that exists across regions