genepopedit

The goal of genepopedit is to provide a simple and flexible tool for manipulating large multi-locus genotype datasets in R.

** Note input parameters for genepopedit versions > 1.0.0.5 are now all completely lowercase. **

Use genepopedit to subset a SNP dataset by:

• removing specified loci.

• removing specified populations.

• removing specified individuals.

• renaming populations.

• grouping populations.

• reordering populations.

• extracting meta-data:

  • population names.
  • population counts.
  • sample IDs.
  • loci names.
  • allele calls.
  • allele frequencies by population grouping.
  • loci linkage and global Weir and Cockerham’s Fst.
  • lists of unlinked loci maximizing pairwise global Weir and Cockerham’s Fst.

• create datasets for training, assignment, and outlier detection, according to a population stratified random sample.

• convert Genepop to STRUCTURE, FSTAT, NEWHYBRIDS, ASSIGNER, BGC, TREEMIX, COLONY, Genetic Stock Identification (gsi_sim), HZAR, and flattened/unflattened format.

• simulate individual genotypes using pooled DNA allele frequencies.

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Fig 1. genepopedit workflow including data preparation, diagnostics, manipulation and transformation. Files, functions, and function operations are denoted by black, grey and blue text, respectively. Function inputs and outputs are denoted by dashed and solid lines, respectively.

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Requirement:
genepopedit functions through the manipulation of multi-locus diploid SNP files structured in the Genepop file format link. Specifically, we use the three number format (e.g. 110110) where the six digits correspond to the alleles of a given locus for an individual. Locus names can be listed in the first row separated by columns or each on their own row so that the total number of rows in the Genepop file equals:

nrows = nLOCI + nINDIVIDUALS + nPOPULATIONS - 1

or

nrows* = nLOCI + nINDIVIDUALS + nPOPULATIONS 

* if STACKS version is not specified

For example:

A three locus dataset with two populations and four individuals per population with the STACKS version specified

STACKS Version 1.0
Loci_1
Loci_2
Loci_3
Pop
BON_01 ,  120120 110110 110110
BON_02 ,  100100 110110 110110
BON_03 ,  100100 110110 110110
BON_04 ,  100100 110110 110110
Pop
TAG_01 ,  120120 110110 110110
TAG_02 ,  120120 110110 110110
TAG_03 ,  120120 110110 110110
TAG_04 ,  120120 110110 110110

Alternatively, the loci names can be read in the first row as a single character separated by commas.

Loci_1,Loci_2,Loci,3
Pop
BON_01 ,  120120 110110 110110
BON_02 ,  100100 110110 110110
BON_03 ,  100100 110110 110110
BON_04 ,  100100 110110 110110
Pop
TAG_01 ,  120120 110110 110110
TAG_02 ,  120120 110110 110110
TAG_03 ,  120120 110110 110110
TAG_04 ,  120120 110110 110110

In both formats each row is read in as a single character vector. Sample IDs have the population and sample number separated by a “_”. Between sample ID and the loci is conventional Genepop separator " , " (space,space space). Note if your population label is note separated from the sample number in the Sample IDs refer to the help section for . subset_genepop_rename

Note that input and output “path” variables all require the FULL file path. Relative paths will not work with genepopedit functions.

Most molecular based file formats can be converted to and from Genepop using conversion programs such as the R package adegenet or the program PGDspider.

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Installation

You can install genepopedit as a R package using the following 2 steps. Before installation it is recommended that you open up a new R session without any packages loaded. During the devtools installation process some package dependencies may be downloaded or updated. Installation errors can arise if an older version of a package dependency is already loaded.

Step 1 Install the R package devtools

if (!require("devtools")) install.packages("devtools") # to install

Step 2 Install genepopedit:

#install the diveRsity dependency from Github ** this package may no longer be supported by CRAN, depending on your R Version **
#install the package from *Github*
devtools::install_github("kkeenan02/diveRsity")
devtools::install_github("rystanley/genepopedit") 
library(genepopedit) # load the library

Step 3 Install PGDspider and plink. Note these programs are required for the use of genepop_toploc() and PGDspideR() only!

• PGDspider link
• plink link

For the time being, this function only works with Plink 1.9. Use of Plink 2.0 will create function issues.

** Note that appveyor build ‘failure’ is linked to an error in the build check and not genepopedit itself. This package has been tested on Windows, Linux (Unbuntu & Mint), and IOS operating systems. **

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Contributions:

genepopedit was written in collaboration:

• Ryan Stanley https://github.com/rystanley - Corresponding developer and maintainer
• Ian Bradbury https://bradburygeneticslab.com/
• Nick Jeffery https://github.com/NickJeff13
• Brendan Wringe https://github.com/bwringe
• Sarah Lehnert https://github.com/SarahLehnert

Support funding was inpart provided by the Canadian Healthy Oceans Nework CHONe

This package has been developed to be used by anyone who is looking for a more efficient and repeatable method to manipulate large multi-locus datasets. The package is open, and I encourage you to tinker and look for improvements. I will do my best to respond to any inquiries, add additional functions and-or functionality, and improve the efficiency of the package.

• If you don’t understand something, please let me know:
  (ryan.stanley at dfo-mpo.gc.ca).
• Any ideas on how to improve the functionality are very much appreciated.
• If you spot a typo, feel free to edit and send a pull request.

Pull request how-to:

• Click the edit this page on the sidebar.
• Make the changes using github’s in-page editor and save.
• Submit a pull request and include a brief description of your changes. (e.g. “spelling errors” or “indexing error”).

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Citation

Full package description and citation now available at Molecular Ecology Resources http://onlinelibrary.wiley.com/doi/10.1111/1755-0998.12569/abstract

A Zenodo DOI is also avaiable for the most recent release of genepopedit:

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Diagnostics

genepop_detective.R

• extract quick meta-data from Genepop file
  • Population names
  • Population counts
  • Sample IDs
  • Loci
  • Alleles

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
variable	variable to report.

• variable=“Pops” -> returns vector of population names (default)
• variable=“PopNum” -> returns population summary table
• variable=“Loci” -> returns vector of loci names
• variable=“Inds” -> returns vector of Sample IDs
• variable=“All” -> returns an s4 container with “Pops”,“Loci”,“Inds” & “PopNum”
• variable=“Allele” -> returns vector of Alleles (*note this might include missing values)

genepop_allelefreq.R

Calculate allele frequencies (major) for a given set of loci aggregated by population grouping.

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
popgroup	population grouping using the “Pop” delimiter (Default: NULL) or a dataframe or path to a csv. The grouping dataframe should have two columns, the first corresponding to the population name and the second to an aggregation vector of common groups. Each population can only be assigned to one group.
wide	logical (default: FALSE) defining whether the output should be cast in ‘wide’ format. Note that ‘wide’ format is accepted as the input for alleleotype_genepop().

genepop_filter_maf.R

Filter loci by global minor allele frequency. For the time being, this function only works with Plink 1.9. Use of Plink 2.0 will create function issues.

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
where.plink	A file path to the PLINK installation folder.
where.pgdspider	A file path to the PGDspider installation folder.
maf	Minor allele frequency cutoff (default = 0.05)
path	the filepath & filename of output.

genepop_toploci.R

This function will return the the panel with the highest Fst for unlinked loci ordered by Fst. Returned loci have the highest Fst from the original panel and are not linked to any other loci in the returned panel. Note this function requires the installation and access to both PGDspider for format conversion & PLINK for linkage calculations. For the time being, this function only works with Plink 1.9. Use of Plink 2.0 will create function issues. See installation instructions here

example use

Variable name	Input
genepop	A path to a Genepop file or a dataframe read into the workspace of a Genepop file.
where.plink	A file path to the PLINK installation folder.
where.pgdspider	A file path to the PGDspider installation folder.
r2.theshold	Linkage threshold based on correlation between loci. Value must be between 0 & 1. Default: 0.2 matched to PLINK defaults.
fst.threshold	The minimum r^2 threshold to consider a pair of loci to be considered linked (default: 0.05).
ld.window	The number of sequential loci to be considered for linkage. This sliding window is based on the input order of loci in the input data. Default (NULL) will calcualte linkage among all loci. This variable should not be changed unless the order and proximity of loci on the genome is known and integrated into the order within the input data.
ldpop	A string which denotes which of the populations you wish to calculate linkage disequilibrium from. The default is to calculate LD among all (“All”) populations. Population names must match those returned by genepop_detective.
allocate.pgd.ram	An integer defining how many GB of ram will be made available to PGD spider during calculation of LD. Default (1 GB) should be sufficient for most calculations. Note that for Windows based platforms the limitation for PGDspider is ~1 GB. If a value other than default is specified on Windows allocate.pgd.ram will be set to 1 GB.
return.workspace	Logical query (default: TRUE) sepecifying whether to return the output to the workspace.
save.output	Logical query (default: FALSE) to save the output to the same location as the file being analyzed. Each of the outputs of the function will be saved as a separate file with the file name of the orginal data appended with “Linkages”, “Loci-FST”, and “Unlinked-Loci-FST” for the pairwise linked loci along with their r^2, all loci with their global Fst, and only the top unlinked loci with their Fst respectively.

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Manipulation

subset_genepop.R

• remove or keep a subset of loci
• remove populations

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
subs	vector loci names of interest (default: NULL)
keep	logical whether to keep loci specified by subs (default: TRUE) or to keep remaining loci.
spop	populations to be retained (default: NULL).
path	the filepath & filename of output.

subset_genepop_rename.R

• subset and-or rename populations

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
nameframe	a dataframe or path to a csv detailing the original and any edited population names or sampleIDs.
renumber	logical (default: FALSE) whether the sample numbers are to be replaced.
meta	character defining which meta information is being edited. Options are “Pop” (default) or “Ind” for populations or sampleIDs respectively. This parameter must be specified.
path	the filepath & filename of output.

subset_genepop_aggregate.R

• group populations based on Genepop population tags

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
subs	vector loci names of interest (default: NULL)
keep	logical whether to keep loci specified by subs (default: TRUE) or to keep remaining loci.
agpopframe	a dataframe or path to a csv detailing the population names & populations to be aggregated.
path	the filepath & filename of output.

subset_genepop_individuals.R

• remove or keep a subset of individuals

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
indiv	vector sample IDs of interest.
keep	logical whether to delete sample IDs specified by indiv (default: TRUE) or delete all other IDs.
path	the filepath & filename of output.

genepop_ID.R

• function to add ‘_’ between the population and sample number of a sample ID. This format (e.g. BON_01) is required for the remaining genepopedit functions. If your data already fits this format then this function need not be used.

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
path	the filepath & filename of output.

genepop_reorder.R

• Return genepop file with populations ordered sequentially according to specified population order.

example use

Variable name | Input
--------------|---------------------------------
genepop | a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
reorder | vector of populaton names which will be used to reorder the output file. Note population names must be unique(n=1) and match those returned by genepop_detective().
path | the filepath & filename of output.

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Simulation

alleleotype_genepop.R

• A tool to simulate individual genotypes using pooled sequencing allele frequencies and randomization.

example use

Variable name	Input
input	complete file path to the input file to be converted or an object in the workspace. The first column should denote populations and adjacent columns the allele frequency (major or minor) for each loci (named in the column header).
numsim	the number of simulated individuals to be returned per population (default: 100).
path	the filepath & filename of output.

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#Sampling

genepop_sample.R

• Subsample individuals from a genepop file using population based random stratified sampling.

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file
nsample	object which defines sampling.

• nsample (integer) -> (e.g. nsample = 5) nsample will be sampled at random from each strata (population).
• nsample (fraction) -> (e.g. nsample = 0.5) a fraction will be sampled at random from each strata (population).
• nsample (integer per population) -> (e.g. number varying per population) nsample (column 2) will be sampled at random from each strata (column 1 population).
• nsample (fraction per population) -> (e.g. fraction varying per population) a fraction (column 2) will be sampled at random from each strata (column 1 population).

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Transformation

genepop_structure.R

• Convert Genepop format to Structure format.

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
popgroup	popgroup is a dataframe or path to a csv. column one specifies the population and column two has the numeric grouping. If NULL (default) then population levels will be assigned an individual group.
locusnames	logical (default=FALSE). Specify TRUE if you want the locus names from your Genepop file to be the first row in your Structure files.
path	the filepath & filename of output.

genepop_fstat.R

• Convert Genepop format to fstat format.

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file
path	the filepath & filename of output.
addworkspace	logical to specify whether to save fstat conversion to path (default) or to workspace as Output_fstat object.

genepop_newhybrids.R

• Convert Genepop format to NewHybrids format.

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file
path	the filepath & filename of output.

genepop_assigner.R

• Convert Genepop format to assigner format.

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
popgroup	popgroup is a dataframe or path to a csv. column one specifies the population and column two has the numeric grouping. If NULL (default) then population levels will be assigned an individual group.
path	the filepath & filename of output.

genepop_colony.R

• Convert Genepop from GENEPOP file to the input files (2) required by Colony
  For the time being, this function only works with Plink 1.9. Use of Plink 2.0 will create function issues.

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
where.pgdspider	A file path to the PGDspider installation folder.
where.plink	A file path to the PLINK installation folder.
denote.missing	The value that denotes missing data in your input file (default: “000”).
allocate.pgd.ram	An integer defining how many GB of ram will be made available to PGD spider during calculation of LD. Default (1 GB) should be sufficient for most calculations. Note that for Windows based platforms the limitation for PGDspider is ~1 GB. If a value other than default is specified on Windows allocate.pgd.ram will be set to 1 GB.
path	file path to directory where the Colony files (4) will be saved.

genepop_bgc.R

• Convert Genepop format to Bayesian Genomic Clines (BGC) format (3 files).

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
popdef	popgroup is a dataframe or path to a csv. This dataframe contains two columns. Column 1 corresponds to the population names. The next column has the grouping classification corresponding to each population defining parental 1 (“P1”) parental 2 (“P2”) and admixed (“Admixed”) populations.
fname	collective name assigned to each output the output files (3).
path	the path to directory where the BGC files will be saved.

genepop_treemix.R

• Convert Genepop to format required for first step in TREEMIX conversion. For the time being, this function only works with Plink 1.9. Use of Plink 2.0 will create function issues.

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
where.pgdspider	A file path to the PGDspider installation folder.
where.plink	A file path to the PLINK installation folder.
allocate.pgd.ram	An integer defining how many GB of ram will be made available to PGD spider during calculation of LD. Default (1 GB) should be sufficient for most calculations. Note that for Windows based platforms the limitation for PGDspider is ~1 GB. If a value other than default is specified on Windows allocate.pgd.ram will be set to 1 GB.
keep_inter	A logical vector specifying whether the intermediate conversion steps (.map,.ped, and clustering file) should be returned to the path.
path	the path to directory where the TREEMIX files will be saved.

genepop_GSIsim.R

• Convert Genepop format to the format required for Genetic Stock Identification analysis (GSI)

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.
path	the filepath & filename of output.

genepop_hzar.R

• Convert Genepop format to HZAR format.

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file
distances	A dataframe or path to a text file with your distances between populations. Should contain 2 columns-Populations and Distances.There should be the same number of populations as in the Genepop file.
path	the filepath and filename of output.

genepop_flatten.R

• Convert Genepop format to a flattened dataframe:
  • Sample IDs
  • Population
  • Sample number
  • Loci

example use

Variable name	Input
genepop	a path to a Genepop file or a dataframe read into the workspace of a Genepop file.

genepop_unflatten.R

• Convert flattened dataframe to genepop format:

example use

Variable name	Input
df	data.frame object in workspace. First column is the sampleID (e.g. “BON_01”) the remaining columns are Loci.
path	the filepath & filename of output.

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Conversion using PGDspider

We have developed an R interface for PGDspider. This function will enable R to interact with PGDspider to convert among file formats not included in genepopedit conversion functions. Currently this is best accomplished using the .spid files generated by PGDspider to define the conversion process. Once a .spid file is created, genepopedit can continue to convert files provided the conversion parameters in the .spid file match those required for the conversion. This program will require installation of PGDspider. See installation instructions here

PGDspideR.R

• call conversion tools of pgdSpider through R

example use

Variable name	Input
input	complete file path to the input file to be converted.
input_format	format of the input file. This format should match the dropdown menus of pgdSpider in terms of spelling and capitalization (e.g. GENEPOP & FSTAT).
output	complete file path to the defining where the converted file will be stored.
output_format	format of the of the converted file. This format should match the dropdown menus of pgdSpider in terms of spelling and capitalization (e.g. GENEPOP & FSTAT).
spid	complete file path to the .spid file created by pgdSpider defining the conversion between input_format and output_format. Note that parameters of this .spid file must match the conversion and input file as specified by pgdSpider.
where.pgdspider	the filepath to the folder where pgdSpider installation files are stored.

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How to use genepopedit

Example data for the use in the following documentation is included within genepopedit.
The example genepop data is a GenePop data format with 10 populations, 30 individuals per population, and 100 Loci.

#Load example data in your workspace. 'GenePopData' will now be available for use.
  data("genepopedit_data")
  head(GenePopData) #View the data and the first 4 loci names. 

If reading in data to your workspace use the following read.table code specifics and associated parameters (sep=“\t”,quote=“”,stringsAsFactors=FALSE)

#Example file path to your data
  file.link <- "c:/Users/YOUR NAME/Documents/Data/genepopdata.txt"

#If your dataset has fewer than ~15,000 loci *read.table* should suffice
  GenePop <- read.table(file.link,sep="\t",quote="",stringsAsFactors=FALSE)

#If your dataset has a large amount of loci (>15,000) then we suggest using the data.table package to load in the data more efficiently.
  GenePop <- data.table::fread(file.link,header=FALSE, sep="\t",stringsAsFactors=FALSE)

However, reading in your GENEPOP data into the workspace is not required, as all functions can work with both workspace objects and file paths.

Set a directory where the output files will be saved.

output_dir <- "c:/Users/YourName/Documents/GenePopFiles/" 

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Preparation

genepop_ID

Functions available in genepopedit require that the population and sample number be separated by an _ in the sample ID (e.g. BON_01 not BON01). This can be accomplished manually using a text editor or using the function genepop_ID. genepop_ID will read in a Genepop file and look for a common character string within the sample ID vectors grouped by the Genepop “Pop” labels. This function assumes that there is a common string which unifies IDs between each of the Pop labels. If this isn’t the case, some pre-filtering of the data using a text editor will be required. Function description

#Use genepop_ID to fix the sample IDs prior to the use of genepopedit functions.
filepath="~myfile.txt" #path to 'myfile' which has sample IDs without the required _ separation. Note this can also be an object in the workspace.

genepop_ID(genepop=filepath, path=paste0(output_dir,"Genepop_SampleID_fixed.txt"))

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Diagnostics

genepop_detective

This function queries meta-data from your genepop data without having to open it in a text editor. Function returns a vector of population names, population counts, sample IDs, or Loci names. Function description

Return population names

PopNames <- genepop_detective(GenePopData, variable="Pops")

Return population counts

PopCounts <- genepop_detective(GenePopData, variable="PopNum")

Return sample IDs

SampleIDs <- genepop_detective(GenePopData, variable="Inds")

Return Loci names using the file path instead of the workspace object.

LociNames <- genepop_detective("genepopedit_examplefile.txt",variable="Loci")

Return All using the file path instead of the workspace object.

metadata <- genepop_detective("genepopedit_examplefile.txt",variable="All")
metadata$Pops #Populations
metadata$Loci #Loci names
metadata$Inds #Individual IDs
metadata$PopNum #Population counts

Return Allele values using the file path instead of the workspace object.

Alleles <- genepop_detective("genepopedit_examplefile.txt",variable="Allele")

genepop_allelefreq

This function will calculate allele frequencies based on the major allele among populations for each population group. Function description

#calculate major allele frequencies according to the default population groupings.
  AlleleFreq_default <- genepop_allelefreq(GenePopData)

#calculate major allele frequencies according to the specified population groupings.
  popgrouping <- data.frame(Name=c("AAA","BBB","CCC","DDD","EEE","FFF"),groups=c("gr1","gr1","gr2","gr2","gr3","gr3"))

  AlleleFreq_specified <- genepop_allelefreq(GenePopData, popgroup=popgrouping)

genepop_filter_maf

This function will calculate global minor allele frequencies and filter based on a user defined threshold. Function description

#Filter loci with minor allele frequencies less than 0.07.
 genepop_filter_maf(genepop="genepopedit_examplefile.txt",where.plink="c:/Users/YOURNAME/Documents/Programs/plink/",where.pgdspider="c:/Users/YOURNAME/Documents/Programs/PGDSpider_2.0.9.0/",maf=0.07,path=paste0(output_dir,"Genepop_maf_0-07.txt")) 

genepop_toploci

This function will calculate linkage disequilibrium and global Weir and Cockerham’s Fst for loci among populations. The function seeks a loci panel which maximizes global Fst for unlinked loci. The returned object is a list containing Linked loci ($Linked), global FST ($FST) and the top unlinked loci by FST ($FST_Unlinked). Note ‘linked’ loci are calcuated based on the threshold R² of 0.2 used by PLINK. This threshold (0-1) can be adjusted, though values <0.2 may be limited by computational resources. Note that the available RAM for conversions using PGDspider is limited to 1 GB on Windows based operating systems. For the time being, this function only works with Plink 1.9. Use of Plink 2.0 will create function issues. Function description

Warning messages & diagnostics will be returned to the console by PGDspider & PLINK. These warnings are expected.

#Evaluate Fst and linkage among all loci and return an unlinked panel optimized by global Fst. 
#To calculate LD we use PLINK which requires a file conversion using PGDspider. 

  TopLoci <- genepop_toploci(genepop="genepopedit_examplefile.txt",
  where.plink="c:/Users/YOURNAME/Documents/Programs/plink/",
  where.pgdspider="c:/Users/YOURNAME/Documents/Programs/PGDSpider_2.0.9.0/",fst.threshold = 0.04)
  
#Linked loci
  TopLoci$Linked
  
#Loci FST among populations
  TopLoci$FST
  
#Maximized FST by unlinked loci
  TopLoci$FST_Unlinked
  

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Manipulation

subset_genepop

Create a new Genepop file which only contains Loci 3, 15, 23, 49, 62, 81, 88, & 94. This useful if, for example, you identify loci which are potentially under selection and want a parsed dataset. Here our loci are named in the Loci# (1:100). Function description

#inspect the names of the loci within the Genepop data
  genepop_detective(GenePopData,"Loci")

#subset the Genepop file and 'keep' the specified loci names.
  subloci <-c("Loci03","Loci15","Loci23","Loci49","Loci62","Loci81","Loci88","Loci94")
  
  subset_genepop(genepop= GenePopData, keep = TRUE, subs = subloci, path = paste0(output_dir,"Genepop_Loci_selection.txt"))

You can also choose all loci not in the vector specified using keep = FALSE.

#subset the Genepop file and do not 'keep' the specified 'subs'
  subset_genepop(genepop= GenePopData, keep = FALSE, subs = subloci, path = paste0(output_dir,"Genepop_Loci_neutral.txt"))

You can also remove populations within this function. Here we again select loci under selection, but remove populations 3 & 7 (CCC/GGG). To do this we create a list of all populations we want to keep
(e.g. AAA, BBB, CCC … JJJ)

#use genepop_detective to find the population names
  PopNames <- genepop_detective(GenePopData,"Pops")

#create a list of populations you want to keep
  #manual
  PopKeep <- c("AAA","BBB","DDD","EEE","FFF","HHH","III","JJJ")
#or
  #if you have fewer pops to remove
  PopKeep <- setdiff(PopNames, c("CCC","GGG"))
  
  #vector of loci to keep
  subloci <- c("Loci03","Loci15","Loci23","Loci49","Loci62","Loci81","Loci88","Loci94")
  
  subset_genepop(genepop= GenePopData, keep = TRUE, subs = subloci, spop = PopKeep, path = paste0(output_dir,"Genepop_Loci_selection_subpop.txt"))

subset_genepop_rename

Now change the names of some of the populations. In this example we will rename populations DDD & HHH to YYY & ZZZ. Function description

#view the population names
  genepop_detective(GenePopData,"Pops")

#create a dataframe for renaming. 
#column 1 = the original pop names.
#column 2 = the new names. 
  PopRename <- data.frame(oPop = c("AAA","BBB","CCC","DDD","EEE","FFF","GGG","HHH","III","JJJ"),
  newname = c("AAA","BBB","CCC","YYY","EEE","FFF","GGG","ZZZ","III","JJJ"))

#rename populations
  subset_genepop_rename(genepop= GenePopData, path = paste0(output_dir,"Genepop_renamed.txt"),nameframe = PopRename)

Populations can also be grouped by a common name. The result is similar to subset-genepop-aggregate except that population names will be replaced. Here it is useful to enable renumbering (renumber = TRUE) or there individuals with the same number will be assigned the same ID, if populations are combined.

#view the population names
  genepop_detective(GenePopData,"Pops")

#create a dataframe for renaming. 
#column 1 = the original pop names.
#column 2 = the new names. 
  PopRename_group <- data.frame(oPop = c("AAA","BBB","CCC","DDD","EEE","FFF","GGG","HHH","III","JJJ"),newname = c("Pop1","Pop1","Pop1","Pop1","Pop1","Pop2","Pop2","Pop2","Pop2","Pop2"))

#rename populations
  subset_genepop_rename(genepop= GenePopData, nameframe = PopRename_group, renumber = TRUE, meta="Pop",path = paste0(output_dir,"Genepop_renamed_renumbered.txt"))

Alternatively the sampleIDs can be renamed directly.

#create a renaming vector (column 1 = old column 2 = new)
  IndRename_group <- data.frame(old = c("AAA_10","BBB_10","CCC_10","DDD_10"),new = c("AAA_99","BBB_99","CCC_99","DDD_99"))

#rename sampleIDs
  subset_genepop_rename(genepop= GenePopData, nameframe = IndRename_group, meta="Ind",path = paste0(output_dir,"Genepop_ID_renamed.txt"))

If your sample IDs are not split between the population and the sample number using an _ then you can rename the populations before using any subsetting functions. Note that in this example because there is already a _ separating populations the

#view the sample IDs
PopNames <- genepop_detective(GenePopData,"Inds")
PopNames_fixed <- paste0(PopNames,"_") #add the underscore seperator

#create a dataframe for renaming. 
  PopRename_fixed <- data.frame(oPop = PopNames, newname = PopNames_fixed)

#rename populations * note here you will keep the original sample #s (renumber = F)
  subset_genepop_rename(genepop= GenePopData, path = paste0(output_dir,"Genepop_renamed_renumbered.txt"),nameframe = PopRename_fixed, renumber = FALSE)

subset_genepop_aggregate

Now lets change group some populations together. In this example we will combine populations DDD/EEE & FFF/HHH and remove populations JJJ/GGG (not listed in column 1 of PopRename). No loci will be removed from this dataframe (subs = NULL). This is useful when you don’t want a clustering program (e.g. STRUCTURE or Bayescan) to assume differences among population groups. Function description

#Use genepop_detective to find the meta-data for populations
  genepop_detective(GenePopData,"Pops")

#create a dataframe for renaming. 
#column 1 = the original pop names and the list of pops required.
#column 2 = Grouping variables. 
  PopAggregate <-data.frame(oP = c("AAA","BBB","CCC","DDD","EEE","FFF","HHH","III"),
  agname = c("AAA","Pop1","CCC","Pop1","DDD","Pop2","HHH","Pop2"))

#re-cast Genepop format to group populations based on 'PopAggregate'
  subset_genepop_aggregate(genepop= GenePopData, subs = NULL, path = paste0(output_dir,"Genepop_grouped.txt"),agpopframe = PopAggregate)

Now that we have two grouped populations, we can use the population rename function to give them a common name.

#read in the grouped Genepop file
  GenePopData2 <-read.table(paste0(output_dir,"Genepop_grouped.txt"),header = FALSE, sep = "\t", quote = "", stringsAsFactors = FALSE)

#investigate the population parameters
  genepop_detective(GenePopData2,"Pops")

#we can see that "BBB" & "DDD" and "FFF" & "III" are at the end but still have different names. 

#create renaming frame
  PopRename <- data.frame(oPop = c("AAA","CCC","EEE","HHH","BBB","DDD","FFF","III"),
  newname = c("AAA","CCC","EEE","HHH","Group1","Group1","Group2","Group2"))

#Rename the grouped populations
  subset_genepop_rename(genepop= GenePopData2, nameframe = PopRename, path = paste0(output_dir,"Genepop_grouped_renamed.txt"))
                 
                 

subset_genepop_individual

Remove some individual samples from the dataframe. Here we consider the ‘individual’ ID to be an alpha-numeric code which at the start of each row of data. The keep parameter defines whether the specified list is removed or retained (default: keep = FALSE).
This function is best run at the beginning of the analysis prior to removal of specific loci or populations. Function description

#Use genepop_detective to see the naming structure.
  genepop_detective(GenePopData,"Inds")

#vector of sample IDs to remove
  subid <- c("AAA_1","AAA_3","BBB_20","CCC_21","EEE_3","EEE_26","HHH_25","JJJ_4")

  subset_genepop_individual(genepop= GenePopData, indiv = subid, keep = FALSE, path = paste0(output_dir,"Genepop_IDsubset.txt"))

genepop_reorder

Reorder the genepop file sequentially according to a specified ordering (see below for examples). Function description

#Identify populaton names
  pops <- genepop_detective(genepop="Genepop_IDsubset.txt")

#reorder populatons. In this example the first and last example will be switched 
  genepop_reorder(genepop="Genepop_IDsubset.txt",reorder=pops[c(length(pops),2:(length(pops)-1),1)]),path=paste0(output_dir,"GenePop_reordered.txt")

---

Sampling

genepop_sample

Function which will allow you to create subsets of the genepop. This might be useful for creating training and leave-out datasets for assignment. Function description

#view how many individuals you have in each population
  genepop_detective(GenePopData,"PopNum")

There are several ways we can sub-sample each population

Use a fixed number (e.g. 5 from each population)

  SubSamp <- genepop_sample(GenePopData, nsample = 5)

or

Use a fixed fraction (e.g. 25% from each population)

  SubSamp <- genepop_sample(GenePopData, nsample = 0.25)

or

Use a fraction which varies for each population

#vector of populations in the Genepop file
  Pops <- genepop_detective(GenePopData,"Pops")

#create a dataframe which defines how many you want to sample from each population. Note here we create a column which has an entry for each population.
  subdf <- data.frame(Pops = Pops, nsample = c(5, 2, 3, 5, 6, 5, 2, 3, 5, 6))

  SubSamp <- genepop_sample(GenePopData, nsample = subdf)

or

Use a fraction which varies for each population

#vector of populations in the Genepop file
  Pops <- genepop_detective(GenePopData,"Pops")

#create a dataframe which defines how many you want to sample from each population. Note here we create a column which has an entry for each population.
  subdf <- data.frame(Pops = Pops, nsample = c(5, 2, 3, 5, 6, 5, 2, 3, 5, 6)/10)

  SubSamp <- genepop_sample(GenePopData, nsample = subdf)

Once you select a method to sub-sample each population, you can use the function subset-genepop-indiv to create the sampled genepop file.

For example if you wanted to create a training and assignment dataset in genepop format using the random stratified sampled we selected using subset-genepop-indiv:

#Create training dataset (keep = TRUE)
  subset_genepop_individual(GenePopData, indiv = SubSamp, keep = TRUE, path = paste0(output_dir,"Genepop_training.txt"))

#Create an assignment dataset using the remaining individuals (keep = FALSE)
  subset_genepop_individual(GenePopData, indiv = SubSamp, keep = FALSE, path = paste0(output_dir,"Genepop_assignment.txt"))

---

Conversion

genepop_structure

If you are interested in investigating population structure you can convert your modified Genepop object or path to saved file directly into a STRUCTURE formatted text (.str) file <>. Function description

#convert Genepop format to STRUCTURE to default groupings
  genepop_structure(genepop="Genepop_IDsubset.txt",path = paste0(output_dir,"Sturcture_IDsubset_groups.txt"))
  
# Specify population groupings (group CCC/DDD/EEE and III/JJJ)
  pGroups <- data.frame(pops = c("AAA","BBB","CCC","DDD","EEE","FFF","GGG","HHH","III","JJJ"),groups = c("1","2","3","3","3","4","5","6","7","7"))

#convert Genepop format to STRUCTURE with new groupings
  genepop_structure(genepop="Genepop_IDsubset.txt",popgroup = pGroups, path = paste0(output_dir,"Sturcture_IDsubset_groups.txt"))

genepop_fstat

If you are interested in doing calculating gene diversities and-or F-statistics with the cleaned loci datasets, you can convert from Genepop to FSTAT format
This data can be used by the R package hierfstat or the program FSTAT Function description

#convert Genepop format to FSTAT (.dat)
  genepop_fstat(genepop="Genepop_IDsubset.txt",path = paste0(output_dir,"Fstat_IDsubset.dat"))

#convert Genepop format to FSTAT but keep the data in the workspace
  genepop_fstat(genepop="Genepop_IDsubset.txt",addworkspace = TRUE)

genepop_newhybrids

If you are interested in testing for hybridization you can convert from Genepop to the format necessary for the program New Hybrids Function description

#convert Genepop format to New Hybrids format (.txt)
  genepop_newhybrids(genepop="Genepop_IDsubset.txt",path = paste0(output_dir,"NewHybrids_IDsubset.txt"))

genepop_assigner

If you are interested conducting assignment analysis, you can convert to the format required for the R package assigner Function description

#convert Genepop format to assigner format (.txt) using the populations each as their own assessment level
  genepop_assigner(genepop="Genepop_IDsubset.txt",path = paste0(output_dir,"assigner_IDsubset.txt"))
  
# Set new population assignment groupings (group CCC/DDD/EEE and III/JJJ)
  pGroups <- data.frame(pops = c("AAA","BBB","CCC","DDD","EEE","FFF","GGG","HHH","III","JJJ"),groups = c("1","2","3","3","3","4","5","6","7","7"))
   
   genepop_assigner(genepop="Genepop_IDsubset.txt",popgroup = pGroups, path = paste0(output_dir,"assigner_IDsubset_NewGroups.txt"))
  

genepop_colony

If you are interested in investigating sibship or parentage, you can convert to the format required for the software Colony. Returned are functions for translating the Colony output and those required by the maximum likelihood analysis. ‘Individual’ and ‘Loci’ conversion files can be used to match the loci and individual names formatted for input to Colony to those provided as input by the user.‘MarkerTypeErrorRT’ file defines the allele dropout rates as calculated using the ‘missing’ function in PLINK. Rates of other error types are assumed to be equal to the estimated allele dropout rates. ‘GENOTYPES’ are the geneotypes in two columns per loci format. Output from Colony is based on the input for of the
‘GENOTYPES’ file. Original loci and individual names can be assigned using the conversion output files. All files are returned to path and are named according tot he input filename. Function description

#convert Genepop format to the files necessary for Colony 
  genepop_colony(genepop="Genepop_IDsubset.txt",where.plink="c:/Users/YOURNAME/Documents/Programs/plink/",where.pgdspider="c:/Users/YOURNAME/Documents/Programs/PGDSpider_2.0.9.0/",denote.missing = "000",path = output_dir)
  

genepop_bgc

genepopedit is one of the few tools available to convert data to the format required for input into to the Bayesian estimation of Genomic Clines (BGC) format. BGC can evalute genomic clinal patterns and introgression among loci. To convert to BGC format you must specify which populations you consider to be ancestral or parental (P1, P2) and which you assume could be hybridized (admixed). Function description

#specify which populations are going to be included in the analysis and to which class they belong. Note Pops identified in P1 and-or P2 can also be specified as "Admixed" to test BGC output. 
  BGC_groups = data.frame(pops = c("AAA","BBB","CCC","DDD","EEE","FFF","GGG","HHH","III","JJJ"),groups = c("P1","P1","P1","Admixed","Admixed","Admixed","Admixed","P2","P2","P2"))

#convert Genepop to BGC input files (3). Note in this case the variable path is a path to the directory where the input files will be saved.
  genepop_bgc(genepop="Genepop_IDsubset.txt",popdef = BGC_groups, fname="BGC_IDsubset",path = output_dir)

genepop_treemix

genepopedit is one of the few tools available to convert data to the format required for input into the Treemix format. It creates a gzipped file that clusters your individuals based on the population ID of the individual’s code. Population IDs are extracted using the “_” to differentiate population from sample ID (e.g. BON_01 is population BON sample 01). If samples are not separated refer to genepop_ID. Grouping levels for populations can also be changed using subset_genepop_rename. Note that this is just the first of two steps in the process; the gzipped output from this function needs to be run through the Python script (Python 2.7+ must be installed first) that accompanies Treemix. This output from “plink2treemix.py” is then ready to run in Treemix. This Python script can be downloaded from link. Treemix is used to infer migration weight and directionality among your populations, as well as detecting population splits and mixtures. Function description

#convert Genepop to Treemix input and keep intermediary conversion files (default: FALSE)
  genepop_treemix(genepop="Genepop_IDsubset.txt",where.plink="c:/Users/YOURNAME/Documents/Programs/plink/",where.pgdspider="c:/Users/YOURNAME/Documents/Programs/PGDSpider_2.0.9.0/",keep_inter = TRUE, path = paste0(output_dir,"Treemix_IDsubset.txt"))

genepop_GSIsim

If you are interested in assessing the accuracy of a genetic stock identification analysis, given a genetic baseline, you can convert directly from GENEPOP to a GSI_sim formatted text file. genepopedit is among the few tools currenlty available to format data for GSI_sim. Function description

#convert Genepop to GSIsim format
  genepop_GSIsim(genepop="Genepop_IDsubset.txt",path = paste0(output_dir,"GSIsim_IDsubset.txt"))

genepop_hzar

If you are interested in exploring clines in allele frequency over distance, you can convert from Genepop to HZAR format.This data can be used by the R package hzar Function description

#convert Genepop format to HZAR (.csz)
  genepop_hzar(genepop="Genepop_IDsubset.txt",distances=data.frame(Pop=genepop_detective("Genepop_IDsubset.txt"), Distance=c(5,8,22,3,84,39,45.5,67,101),stringsAsFactors=FALSE), path = paste0(output_dir,"HZAR_Input.csv"))

genepop_flatten

Flatten your geneotype data for other calculations. This function will convert and return the genepop format into a dataframe. Because this function returns the dataframe, it needs to be assigned a variable ID. Note that this function does not have any subsetting capabilities. If you would like to remove loci and-or populations, this must be before hand using subset_genepop or its sister functions (see below for examples). Function description

#Flatten the dataframe
  GenePop_df<- genepop_flatten(GenePopData)

#inspect the output for the first 10 columns 
  head(GenePop_df[,1:10])

genepop_unflatten

If you are working with a flattened dataframe in your workspace you can convert it back to Genepop format. **Note that the first column of this data.frame should correspond to the sampleID (e.g. “BON_01”) and the remaining columns should be Loci. Function description

# create a Genepop format which can be used by other genepopedit functions
genepop_unflatten(GenePop_df, path = paste0(output_dir,"GenePop_UNFLATTENED.txt"))

Conversion using PGDspider

PGDspideR

There are a broad range of data formats which can be converted to and from using PGDspider. If you have multiple files which you are going to be converting using the same conversion parameters, PGDspideR.R can be useful, providing a code based interface to exploit the conversion functions of PGDspider. Function description

#convert between GENEPOP and FSTAT format using PGDspider
  pgdSpideR(input = paste0(output_dir,"Genepop_IDsubset.txt"),
  input_format="GENEPOP",
  output = paste0(output_dir,"Genepop_IDsubset_FSTAT.dat",
  output_format="FSTAT",
  spid="c:/Users/YOURNAME/Documents/spids/GENEPOP_FSTAT.spid",
  where.pgdspider="c:/Users/YOURNAME/Documents/Programs/PGDSpider_2.0.9.0/")

#Simulate genotypes using pooled DNA allele frequencies

alleleotype_genepop

Individual geneotypes can be simulated using the alleleotype_genepop() function and formatted to GENEPOP for use in conventional genomic analyses (e.g. assigner, DAPC, STRUCTURE). This function will simulate individuals for each population provided by the input file. The output can then be diagnosed, sampled, and formatted using other genepopedit functions. Function description

#simulate individual geneotypes allele frequencies stratified by population.
  #Create an example pooled DNA input file
    pooledDNA <- data.frame(Pop = c("Pop1","Pop2","Pop3","Pop4","Pop5"),Loci1 = round(runif(5, 0, 1),2),Loci2 = round(runif(5, 0, 1),2),Loci3 = round(runif(5, 0, 1),2),Loci4 = round(runif(5, 0, 1),2),Loci5 = round(runif(5, 0, 1),2),Loci6 = round(runif(5, 0, 1),2))
    
    #inspect the input
    pooledDNA

  #simulate 200 individual geneotypes for each population
    alleleotype_genepop(pooledDNA, numsim = 200, path = paste0(output_dir,"SimulatedGeneotypes.txt"))
  
  #To validate output simulate 100 (default) geneotypes per population and compare allele frequencies to those of the input file. Estimated frequencies per population and SNP should match those of the input file within +/- 1%.
  
    alleleotype_genepop(pooledDNA, numsim = 100, path = paste0(output_dir,"SimulatedGeneotypesValidate.txt"))
   
    genepop_allelefreq(paste0(output_dir,"SimulatedGeneotypesValidate.txt"))
    
  #Note you can simulate new geneotypes using the summary from genepop_allelefreq(... , wide = TRUE).
    GeneFreq <- genepop_allelefreq("Genepop_IDsubset.txt",wide = TRUE)
    
    alleleotype_genepop(GeneFreq, numsim = 100, path = paste0(output_dir,"SimulatedGeneotypes_Genepop_IDsubset.txt"))