Title: | Population Genetic Data Analysis Using Genepop |
---|---|
Description: | Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples. |
Authors: | François Rousset [aut, cre, cph] , Jimmy Lopez [ctb], Alexandre Genin [ctb], Khalid Belkhir [ctb] |
Maintainer: | François Rousset <[email protected]> |
License: | CeCILL-2 |
Version: | 1.2.2 |
Built: | 2024-11-02 06:34:40 UTC |
Source: | CRAN |
A distribution of the Genepop software as an R package. The included C++ sources are suitable for compilation as a stand-alone executable. A shiny interface is also included. Genepop performs three main tasks: it computes exact tests for Hardy-Weinberg equilibrium (test_HW
), for population differentiation (test_diff
) and for genotypic disequilibrium among pairs of loci (test_LD
); it computes estimates of F-statistics (Fst
), null allele frequencies (nulls
), allele size-based statistics for microsatellites, etc., and of number of immigrants by Barton & Slatkin's (1986) private allele method (Nm_private
); It performs analyses of isolation by distance from pairwise comparisons of individuals or groups (ibd
), including confidence intervals for "neighborhood size". It also provides various data conversion
and manipulation
utilities.
R package originally developed by Jimmy Lopez and Khalid Belkhir from the C++ sources of the Genepop executable version 4.6 (2016; Rousset 2008).
Main reference for current maintained version of Genepop:
Rousset, F. (2008). Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Res. 8: 103-106.
Original Genepop publication:
Raymond, M. & Rousset, F., 1995b. GENEPOP Version 1.2: population genetics software for exact tests and ecumenicism. J. Hered. 86: 248-249.
Methods implemented in Genepop:
Barton, N. H. & Slatkin, M., 1986. A quasi-equilibrium theory of the distribution of rare alleles in a subdivided population. Heredity 56: 409-415.
Brookfield, J. F. Y., 1996. A simple new method for estimating null allele frequency from heterozygote deficiency. Mol. Ecol. 5: 453-455.
Goudet, J., Raymond, M., de Meeus, T. & Rousset, F., 1996. Testing differentiation in diploid populations. Genetics 144: 1931-1938.
Guo, S. W. & Thompson, E. A., 1992. Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 48: 361-372.
Kalinowski, S. T. & Taper, M. L., 2006. Maximum likelihood estimation of the frequency of null alleles at microsatellite loci. Conserv. Genetics 7:991-995.
Louis, E. J. & Dempster, E. R., 1987. An exact test for Hardy-Weinberg and multiple alleles. Biometrics 43: 805-811.
Mantel, N., 1967. The detection of disease clustering and a generalized regression approach. Cancer Research 27: 209-220.
Michalakis, Y. & Excoffier, L., 1996. A generic estimation of population subdivision using distances between alleles with special interest to microsatellite loci. Genetics 142: 1061-1064.
Raymond, M. & Rousset, F., 1995a. An exact test for population differentiation.Evolution 49: 1283-1286.
Robertson, A. & Hill, W. G., 1984. Deviations from Hardy-Weinberg proportions: sampling variances and use in estimation of inbreeding coefficients. Genetics 107: 703-718.
Rousset, F., 1996. Equilibrium values of measures of population subdivision for stepwise mutation processes. Genetics 142: 1357-1362.
Rousset, F., 1997. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145: 1219-1228.
Rousset, F., 2000. Genetic differentiation between individuals. J. Evol. Biol. 13:58-62.
Rousset, F. & Raymond, M., 1995. Testing heterozygote excess and deficiency. Genetics 140: 1413-1419.
Watts, P. C., Rousset, F., Saccheri, I. J., Leblois, R., Kemp, S. J. & Thompson, D. J., 2007. Compatible genetic and ecological estimates of dispersal rates in insect (Coenagrion mercuriale: Odonata: Zygoptera) populations: analysis of 'neighbourhood size' using a more precise estimator. Mol. Ecol. 16: 737-751.
Weir, B. S., 1996. Genetic Data Analysis II. Sinauer, Sunderland, Mass.
Weir, B. S. & Cockerham, C. C., 1984. Estimating F-statistics for the analysis of population structure. Evolution 38: 1358-1370.
Allele and genotype frequencies per locus and per sample. See this section of the Genepop executable documentation for more information on the statistical methods.
basic_info(inputFile, outputFile = "", verbose = interactive())
basic_info(inputFile, outputFile = "", verbose = interactive())
inputFile |
The path of the input file, in Genepop format |
outputFile |
character: The path of the output file |
verbose |
logical: whether to print some information |
The path of the output file is returned invisibly.
locinfile <- genepopExample('sample.txt') basic_info(locinfile,'sample.txt.INF') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
locinfile <- genepopExample('sample.txt') basic_info(locinfile,'sample.txt.INF') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
This removes “temporary files” created by Genepop, but also output files, so it should be used only when one no longer needs the latter files. This function asumes that the input file name contains only alphanumeric, dot, or underscore characters.
clean_workdir( otherfiles = NULL, path = ".", suffixes = c("GRA", "ISO", "MIG", "PRI", "DAT", "DG", "DIV", "D", "DIS", "FST", "NUL", "RHO", "2G2", "G", "GE", "GE2", "INF", "MSD", "TAB", "ST2"), in. = TRUE, cmdline = TRUE )
clean_workdir( otherfiles = NULL, path = ".", suffixes = c("GRA", "ISO", "MIG", "PRI", "DAT", "DG", "DIV", "D", "DIS", "FST", "NUL", "RHO", "2G2", "G", "GE", "GE2", "INF", "MSD", "TAB", "ST2"), in. = TRUE, cmdline = TRUE )
otherfiles |
Character vector(s): one or more names of files to be removed and not matched by the other arguments (such as the input file, or some output files not identified by their suffix, as shown in the Example). |
path |
character vector: path from where files should be removed. |
suffixes |
Character vector(s): suffixes of files to be removed (useful for output files with readily identifiable suffixes). |
in. |
boolean: whether to remove the |
cmdline |
boolean: whether to remove the |
# Removing files possibly written by other examples in the documentation: clean_workdir(otherfiles=c("sample.txt", "Dsample.txt", "w2.txt", "PEL1600withCoord.txt", "Rhesus.txt", "structest.txt"))
# Removing files possibly written by other examples in the documentation: clean_workdir(otherfiles=c("sample.txt", "Dsample.txt", "w2.txt", "PEL1600withCoord.txt", "Rhesus.txt", "structest.txt"))
Performs an exact conditional contingency-table test. There are many other ways of doing this in R but this function replicates the functionality of earlier genepop code analysing a contingency table provided in a file with ad hoc format. See this section of the Genepop executable documentation for more information on the statistical methods.
struc( inputFile, settingsFile = "", dememorization = 10000, batches = 100, iterations = 5000, verbose = interactive() )
struc( inputFile, settingsFile = "", dememorization = 10000, batches = 100, iterations = 5000, verbose = interactive() )
inputFile |
character: The path of the input file. This file should be in an ad hoc format |
settingsFile |
character: The path of the settings file |
dememorization |
integer: length of dememorization step of Markov chain algorithm |
batches |
integer: Number of batches |
iterations |
integer: Iterations per batch |
verbose |
logical: whether to print some information |
The path of the output file is returned invisibly.
locinfile <- genepopExample('structest.txt') struc(locinfile) if ( ! interactive()) clean_workdir(otherfiles='structest.txt')
locinfile <- genepopExample('structest.txt') struc(locinfile) if ( ! interactive()) clean_workdir(otherfiles='structest.txt')
Converts input files from genepop format to some other formats (some maybe only of historical interest): Fstat, two Biosys formats. and linkdos. See this section of the Genepop executable documentation for more information on the statistical methods.
conversion(inputFile, format, outputFile = "", verbose = interactive())
conversion(inputFile, format, outputFile = "", verbose = interactive())
inputFile |
The path of the input file, in Genepop format |
format |
Character string: must be one of |
outputFile |
character: The path of the output file |
verbose |
logical: whether to print some information |
The path of the output file is returned invisibly.
locinfile <- genepopExample('sample.txt') conversion(locinfile, format='Fstat', 'sample.txt.DAT') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
locinfile <- genepopExample('sample.txt') conversion(locinfile, format='Fstat', 'sample.txt.DAT') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
Exact conditional contingency-table tests for genic or genotypic differentiation. A single test for all populations, or distinct tests for all pairs of populations, may be computed. See this section of the Genepop executable documentation for more information on the statistical methods.
test_diff( inputFile, genic = TRUE, pairs = FALSE, outputFile = "", settingsFile = "", dememorization = 10000, batches = 100, iterations = 5000, verbose = interactive() )
test_diff( inputFile, genic = TRUE, pairs = FALSE, outputFile = "", settingsFile = "", dememorization = 10000, batches = 100, iterations = 5000, verbose = interactive() )
inputFile |
The path of the input file, in Genepop format |
genic |
logical: whether to perform genic or genotypic tests |
pairs |
logical: whether to test differentiation between all pairs of ppulation, or to perform a single global test |
outputFile |
character: The path of the output file |
settingsFile |
character: The path of the settings file |
dememorization |
integer: length of dememorization step of Markov chain algorithm |
batches |
integer: Number of batches |
iterations |
integer: Iterations per batch |
verbose |
logical: whether to print some information |
The path of the output file is returned invisibly.
locinfile <- genepopExample('sample.txt') test_diff(locinfile,outputFile='sample.txt.GE') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
locinfile <- genepopExample('sample.txt') test_diff(locinfile,outputFile='sample.txt.GE') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
Evaluates Fst or related measures based on allele sizes, for all populations of for all pairs of populations. See this section of the Genepop executable documentation for more information on the statistical methods.
Fst( inputFile, sizes = FALSE, pairs = FALSE, outputFile = "", dataType = "Diploid", verbose = interactive() )
Fst( inputFile, sizes = FALSE, pairs = FALSE, outputFile = "", dataType = "Diploid", verbose = interactive() )
inputFile |
The path of the input file, in Genepop format |
sizes |
logical: whether to estimate allele-size based statistics, or identity-based Fst |
pairs |
whether to estimate differentiation between all pairs of populations, or to compute a global estimate for all populations |
outputFile |
character: The path of the output file |
dataType |
character: The haploid and diploid data |
verbose |
logical: whether to print some information |
The path of the output file is returned invisibly.
locinfile <- genepopExample('sample.txt') Fst(locinfile, outputFile= 'sample.txt.DIV') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
locinfile <- genepopExample('sample.txt') Fst(locinfile, outputFile= 'sample.txt.DIV') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
Evaluates Fis and gene diversities, or related measures based on allele sizes. See this section of the Genepop executable documentation for more information on the identity-based statistical methods, and this one for allele-size based ones.
genedivFis( inputFile, sizes = FALSE, outputFile = "", dataType = "Diploid", verbose = interactive() )
genedivFis( inputFile, sizes = FALSE, outputFile = "", dataType = "Diploid", verbose = interactive() )
inputFile |
The path of the input file, in Genepop format |
sizes |
logical: whether to compute statistics based on allele size, or not. |
outputFile |
character: The path of the output file |
dataType |
character: The haploid and diploid data |
verbose |
logical: whether to print some information |
The path of the output file is returned invisibly.
locinfile <- genepopExample('sample.txt') genedivFis(locinfile,outputFile = 'sample.txt.DIV') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
locinfile <- genepopExample('sample.txt') genedivFis(locinfile,outputFile = 'sample.txt.DIV') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
This function is used to copy an example file to the user's directory. It should not be used when analysing one's own data!
genepopExample(filename)
genepopExample(filename)
filename |
The name of an example file from the Genepop distribution. |
Returns the filename
Call an experimental GUI for Genepop
GUI()
GUI()
The return value of a 'shiny::runApp()' call.
Compute variants of the exact conditional test for Hardy-Weinberg genotypic proportions. The tests differ by their test statistics. HWtable_analysis
handles a single table of genotype counts, and test_HW
requires a standard genepop input file. See this section of the Genepop-executable documentation for more information on the statistical methods.
test_HW( inputFile, which = "Proba", outputFile = "", settingsFile = "", enumeration = FALSE, dememorization = 10000, batches = 20, iterations = 5000, verbose = interactive() ) HWtable_analysis( inputFile, which = "Proba", settingsFile = "", enumeration = FALSE, dememorization = 10000, batches = 20, iterations = 5000, verbose = interactive() )
test_HW( inputFile, which = "Proba", outputFile = "", settingsFile = "", enumeration = FALSE, dememorization = 10000, batches = 20, iterations = 5000, verbose = interactive() ) HWtable_analysis( inputFile, which = "Proba", settingsFile = "", enumeration = FALSE, dememorization = 10000, batches = 20, iterations = 5000, verbose = interactive() )
inputFile |
character: The path of the input file. For |
which |
character: |
outputFile |
character: The path of the output file |
settingsFile |
character: The path of the settings file |
enumeration |
logical: whether to compute the complete enumeration test for samples with less than 5 alleles |
dememorization |
integer: length of dememorization step of Markov chain algorithm |
batches |
integer: Number of batches |
iterations |
integer: Iterations per batch |
verbose |
logical: whether to print some information |
The path of the output file is returned invisibly.
locinfile <- genepopExample('sample.txt') test_HW(locinfile, which='deficit', 'sample.txt.D') if ( ! interactive()) clean_workdir(otherfiles='sample.txt') # Example in Guo & Thompson 1992 Table 5 locinfile <- genepopExample('Rhesus.txt') outfile <- HWtable_analysis(locinfile,which='Proba',batches = 1000,iterations = 1000) readLines(outfile)[21] #clean_workdir(otherfiles='Rhesus.txt')
locinfile <- genepopExample('sample.txt') test_HW(locinfile, which='deficit', 'sample.txt.D') if ( ! interactive()) clean_workdir(otherfiles='sample.txt') # Example in Guo & Thompson 1992 Table 5 locinfile <- genepopExample('Rhesus.txt') outfile <- HWtable_analysis(locinfile,which='Proba',batches = 1000,iterations = 1000) readLines(outfile)[21] #clean_workdir(otherfiles='Rhesus.txt')
Estimates isolation by distance by regression of genetic distance to geographical distance. See this section of the Genepop executable documentation for more information on individual-based analyses and this one for group-based analyses.
ibd( inputFile, outputFile = "", settingsFile = "", dataType = "Diploid", statistic = "F/(1-F)", geographicScale = "2D", CIcoverage = 0.95, testPoint = 0, minimalDistance = 1e-04, maximalDistance = 1e+09, mantelPermutations = 1000, mantelRankTest = FALSE, bootstrapMethod = "ABC", bootstrapNsim = 999, verbose = interactive() )
ibd( inputFile, outputFile = "", settingsFile = "", dataType = "Diploid", statistic = "F/(1-F)", geographicScale = "2D", CIcoverage = 0.95, testPoint = 0, minimalDistance = 1e-04, maximalDistance = 1e+09, mantelPermutations = 1000, mantelRankTest = FALSE, bootstrapMethod = "ABC", bootstrapNsim = 999, verbose = interactive() )
inputFile |
The path of the input file, in Genepop format |
outputFile |
character: The path of the output file |
settingsFile |
character: The path of the settings file |
dataType |
character: |
statistic |
character: The pairwise genetic distance, either |
geographicScale |
character: gives either the scale transformation |
CIcoverage |
numeric: The coverage probability of confidence intervals |
testPoint |
numeric: Given value of the slope to be tested |
minimalDistance |
numeric: The minimal geographic distance |
maximalDistance |
numeric: The maximal geographic distance |
mantelPermutations |
numeric: The number of permutations may be specified |
mantelRankTest |
logical: whether to use ranks in the Mantel test |
bootstrapMethod |
character: which bootstrap method to use (one of "ABC", "BC" or "BCa"). |
bootstrapNsim |
integer: the number of bootstrap simulations to use (has no effect if method is "ABC"). |
verbose |
logical: whether to print some information |
The path of the output file is returned invisibly.
## Not run: locinfile <- genepopExample('w2.txt') outfile <- ibd(locinfile,'w2.txt.ISO', geographicScale = 'Log', statistic='e') if ( ! interactive()) clean_workdir(otherfiles='w2.txt') locinfile <- genepopExample('PEL1600withCoord.txt') outfile <- ibd(locinfile,'PEL1600withCoord.ISO', statistic = 'SingleGeneDiv', geographicScale = '1D') if ( ! interactive()) clean_workdir(otherfiles='PEL1600withCoord.txt') ## End(Not run)
## Not run: locinfile <- genepopExample('w2.txt') outfile <- ibd(locinfile,'w2.txt.ISO', geographicScale = 'Log', statistic='e') if ( ! interactive()) clean_workdir(otherfiles='w2.txt') locinfile <- genepopExample('PEL1600withCoord.txt') outfile <- ibd(locinfile,'PEL1600withCoord.ISO', statistic = 'SingleGeneDiv', geographicScale = '1D') if ( ! interactive()) clean_workdir(otherfiles='PEL1600withCoord.txt') ## End(Not run)
Exact test for each pair of loci in each population. See this section of the Genepop executable documentation for more information on the statistical methods.
test_LD( inputFile, outputFile = "", settingsFile = "", dememorization = 10000, batches = 100, iterations = 5000, verbose = interactive() ) write_LD_tables(inputFile, outputFile = "", verbose = interactive())
test_LD( inputFile, outputFile = "", settingsFile = "", dememorization = 10000, batches = 100, iterations = 5000, verbose = interactive() ) write_LD_tables(inputFile, outputFile = "", verbose = interactive())
inputFile |
The path of the input file, in Genepop format |
outputFile |
character: The path of the output file |
settingsFile |
character: The path of the settings file |
dememorization |
integer: length of dememorization step of Markov chain algorithm |
batches |
integer: Number of batches |
iterations |
integer: Iterations per batch |
verbose |
logical: whether to print some information |
The path of the output file is returned invisibly.
## Not run: # 'dontrun' only because a bit too slow for CRAN checks locinfile <- genepopExample('sample.txt') test_LD(locinfile,'sample.txt.DIS') if ( ! interactive()) clean_workdir(otherfiles='sample.txt') ## End(Not run) locinfile <- genepopExample('sample.txt') write_LD_tables(locinfile,'sample.txt.TAB') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
## Not run: # 'dontrun' only because a bit too slow for CRAN checks locinfile <- genepopExample('sample.txt') test_LD(locinfile,'sample.txt.DIS') if ( ! interactive()) clean_workdir(otherfiles='sample.txt') ## End(Not run) locinfile <- genepopExample('sample.txt') write_LD_tables(locinfile,'sample.txt.TAB') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
Various procedures described in the linked sections of the Genepop executable documentation: diploidize haploid data, relabel_alleles, sample_haploid, and pop_to_indiv. The latter procedure converts population samples (several individuals in each population) to individual data. The names given to the individuals in the new file created (names which are to be interpreted as coordinates in a spatial analysis) may be the population coordinates (given as the name of the last individual in the original data file), or each individual's coordinates (given as the name of each individual in the original data file).
diploidize(inputFile, outputFile = "", verbose = interactive()) relabel_alleles(inputFile, outputFile = "", verbose = interactive()) pop_to_indiv(inputFile, coordinates, outputFile = "", verbose = interactive()) sample_haploid(inputFile, outputFile = "", verbose = interactive())
diploidize(inputFile, outputFile = "", verbose = interactive()) relabel_alleles(inputFile, outputFile = "", verbose = interactive()) pop_to_indiv(inputFile, coordinates, outputFile = "", verbose = interactive()) sample_haploid(inputFile, outputFile = "", verbose = interactive())
inputFile |
The path of the input file, in Genepop format |
outputFile |
character: The path of the output file |
verbose |
logical: whether to print some information |
coordinates |
character: either |
locinfile <- genepopExample('sample.txt') outfile <- diploidize(inputFile = locinfile,outputFile="Dsample.txt") if ( ! interactive()) clean_workdir(c("sample.txt", "Dsample.txt"))
locinfile <- genepopExample('sample.txt') outfile <- diploidize(inputFile = locinfile,outputFile="Dsample.txt") if ( ! interactive()) clean_workdir(c("sample.txt", "Dsample.txt"))
Estimation of Nm by private allele method of Slatkin and Barton. See this section of the Genepop executable documentation for more information on the statistical methods.
Nm_private( inputFile, outputFile = "", dataType = "Diploid", verbose = interactive() )
Nm_private( inputFile, outputFile = "", dataType = "Diploid", verbose = interactive() )
inputFile |
The path of the input file, in Genepop format |
outputFile |
character: The path of the output file |
dataType |
character: The haploid and diploid data |
verbose |
logical: whether to print some information |
The path of the output file is returned invisibly.
locinfile <- genepopExample('sample.txt') Nm_private(locinfile,'sample.txt.PRI') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
locinfile <- genepopExample('sample.txt') Nm_private(locinfile,'sample.txt.PRI') if ( ! interactive()) clean_workdir(otherfiles='sample.txt')
Estimates allele frequencies (and failure rate if relevant) under dfferent assumptions: maximum likelihood assuming that there is null allele (default method), maximum likelihood assuming that apparent nulls are technical failures independent of genotype ('ApparentNulls'
), and Brookfield's (1996) estimator ('B96'
). See this section of the Genepop executable documentation for more information on the statistical methods. Genepop takes the allele with the highest number for a given locus across all populations as the null allele. For example, if you have 4 alleles plus a null allele, a null homozygote individual should be indicated as e.g. 0505
or 9999
in the input file.
nulls( inputFile, outputFile = "", settingsFile = "", nullAlleleMethod = "", CIcoverage = 0.95, verbose = interactive() )
nulls( inputFile, outputFile = "", settingsFile = "", nullAlleleMethod = "", CIcoverage = 0.95, verbose = interactive() )
inputFile |
The path of the input file, in Genepop format |
outputFile |
character: The path of the output file |
settingsFile |
character: The path of the settings file |
nullAlleleMethod |
character: |
CIcoverage |
numeric: The coverage probability of confidence interval |
verbose |
logical: whether to print some information |
The path of the output file is returned invisibly.
getVersion
returns the version number of the C++ code (the same number that identifies the C++ executable). set_restriction(TRUE)
sets the maximum number of populations and of loci to 300.
set_restriction(set = FALSE) getVersion()
set_restriction(set = FALSE) getVersion()
set |
logical: whether to set restrictions on number of populations and of loci |
Set random generator seed for Mantel test
setMantelSeed(seed)
setMantelSeed(seed)
seed |
integer: the new seed |
Set random generator seed (except for Mantel test)
setRandomSeed(seed)
setRandomSeed(seed)
seed |
integer: the new seed |