AeutThis data is for a population of the diploid plant pathogen Aphanomyces euteiches using AFLP as a marker system (Grünwald & Hoheisel, 2006). A total of 187 pathogen isolates were sampled hierarchically in two regions in Oregon (N = 97) and Washington (N = 90). The alleles observed were treated as dominant markers with presence or absence. Analysis included clone-correction, genotypic diversity, linkage disequilibrium using the index of association, dendrograms based on Nei’s genetic distance, and AMOVA.
monpopThis is microsatellite data for a population of the haploid plant pathogen Monilinia fructicola that causes disease within peach tree canopies (Everhart & Scherm, 2015). Entire populations within trees were sampled across 3 years (2009, 2010, and 2011) in a total of four trees, where one tree was sampled in all three years, for a total of 6 within-tree populations. Within each year, samples in the spring were taken from affected blossoms (termed “BB” for blossom blight) and in late summer from affected fruits (termed “FR” for fruit rot). There are a total of 694 isolates with 65 to 173 isolates within each canopy population that were characterized using a set of 13 microsatellite markers.
microbovThis data set contains 704 bovine samples over 30 microsatellite loci for 15 breeds, 2 species and 2 countries. From (Laloe et al., 2007).
nancycatsThis is a data set of 217 cats Felis catus L. from Nancy, France genotyped over 9 microsatellite loci. They have been divided into 17 populations with spatial coordinates in the @other slot. It comes from the adegenet package without a published reference. Likely, the source paper is here.
PinfThis microsatellite data comes from a larger data set of populations of Phytophthora infestans (Goss et al., 2014). It contains 86 individuals representing 72 multilocus genotypes that have been genotyped over 11 loci. They are grouped by continent and country. This data set is used to demonstrate clone correction and linkage disequilibrium.
PramThis data set contains populations of Phytophthora ramorum from Nurseries in California and Oregon (Goss et al., 2009) and Forests in Curry County, Oregon from 2001 to 2014 (Kamvar et al., 2015). There are 729 samples representing 98 multilocus genotypes genotyped over 5 microsatellite loci.
H3N2This is a SNP data set from the hemaglutinin segment of the H3N2 strain of seasonal influenza contained within the adegenet package. It contains 1902 samples genotyped over 125 SNP markers. It contains a wealth of information in the @other slot including year isolated, month isolated, country of origin, and more. This data set is utilized to demonstrate the DAPC function in adegenet.
Prubi_gbsWe will use a data set of 94 samples of the red raspberry pathogen Phytophthora rubi (Tabima et al., 2018). This pathogen is diploid and a fungal like Oomycete. Populations were obtained by sampling individual pathogen strains from roots of infected red raspberry in the states of California (CA), Oregon (OR), and Washington (WA). A total of 94 samples of P. rubi were sequenced using the Illumina HiSeq 3000 technology with 150 bp paired end reads and a target insert size of 500 bp. Currently, there is little information about the population structure of P. rubi in the western USA. We are interested in studying the population structure of P. rubi populations in the western US. The VCF data for this population can be downloaded from: prubi_gbs.vcf.gz.
pinfsc50For genomics examples we’ll use the pinfsc50 dataset. The pinfsc50 dataset is from a number of published P. infestans genomics projects where the data has been subset here to supercontig_1.50. This dataset is available as a stand alone R package (Knaus & Grünwald, 2017). By subsetting the data to one supercontig it creates a dataset of a size that can be conveniently used for examples. This dataset illustrates some important strengths and weaknesses of these studies. A strength is the amount of data we have for each individual. Among the weaknesses are that the samples are ‘opportunistic’ in that we have no control over the design of the experiment. Also, because of the large investment in data per sample, there is a relatively small number of samples.
Everhart S., Scherm H. 2015. Fine-scale genetic structure of Monilinia fructicola during brown rot epidemics within individual peach tree canopies. Phytopathology 105:542–549. Available at: https://doi.org/10.1094/PHYTO-03-14-0088-R
Goss EM., Larsen M., Chastagner GA., Givens DR., Grünwald NJ. 2009. Population genetic analysis infers migration pathways of phytophthora ramorum in us nurseries. PLoS Pathog 5:e1000583. Available at: http://dx.doi.org/10.1371/journal.ppat.1000583
Goss EM., Tabima JF., Cooke DEL., Restrepo S., Fry WE., Forbes GA., Fieland VJ., Cardenas M., Grünwald NJ. 2014. The Irish potato famine pathogen phytophthora infestans originated in central mexico rather than the andes. Proceedings of the National Academy of Sciences 111:8791–8796. Available at: http://www.pnas.org/content/early/2014/05/29/1401884111.abstract
Grünwald NJ., Hoheisel G-A. 2006. Hierarchical analysis of diversity, selfing, and genetic differentiation in populations of the oomycete aphanomyces euteiches. Phytopathology 96:1134–1141. Available at: http://apsjournals.apsnet.org/doi/abs/10.1094/PHYTO-96-1134
Kamvar Z., Larsen M., Kanaskie A., Hansen E., Grünwald N. 2015. Spatial and temporal analysis of populations of the sudden oak death pathogen in oregon forests. Phytopathology 105:982–989. Available at: http://dx.doi.org/10.1094/PHYTO-12-14-0350-FI
Knaus BJ., Grünwald NJ. 2017. \({V}cfr\): A package to manipulate and visualize variant call format data in R. Molecular Ecology Resources 17:44–53. Available at: http://dx.doi.org/10.1111/1755-0998.12549
Laloe D., Jombart T., Dufour A-B., Moazami-Goudarzi K. 2007. Consensus genetic structuring and typological value of markers using multiple co-inertia analysis. Genetics Selection Evolution 39:545–567. Available at: http://dx.doi.org/10.1051/gse:2007021
Tabima JF., Coffey MD., Zazada IA., Grünwald NJ. 2018. Populations of Phytophthora rubi show little differentiation and high rates of migration among states in the western United States. Molecular Plant-Microbe Interactions 31:614–622. Available at: https://doi.org/10.1094/MPMI-10-17-0258-R