| Type: | Package |
| Title: | Statistical Analysis of Mixed Ploidy Populations |
| Depends: | R (≥ 3.2.0), pegas |
| Imports: | parallel, doParallel, foreach, adegenet, methods, utils |
| Version: | 1.6.3 |
| Date: | 2021-07-30 |
| Author: | LW Pembleton |
| Maintainer: | LW Pembleton <lwpembleton@gmail.com> |
| Description: | Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects allowing integration with other packages such adegenet. Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. <doi:10.1111/1755-0998.12129> for the appropriate citation and user manual. Thank you in advance. |
| URL: | https://github.com/lpembleton/StAMPP |
| License: | GPL-3 |
| RoxygenNote: | 7.1.1 |
| Encoding: | UTF-8 |
| NeedsCompilation: | no |
| Packaged: | 2021-08-07 15:40:14 UTC; lp40 |
| Repository: | CRAN |
| Date/Publication: | 2021-08-08 04:20:05 UTC |
Example genotype input format
Description
A data frame containing Solcap potato genotype data in tetraploid and diploid format as an small example of the input format required by StAMPP
Usage
data(potato)
Format
A data frame with 30 rows and 48 variables:
- Sample
Sample names
- Pop
Population name
- Ploidy
Ploidy level
- Format
Format of genotype data
- solcap_snp_c1_1
genotype data
- solcap_snp_c1_1000
genotype data
- solcap_snp_c1_10000
genotype data
- solcap_snp_c1_10001
genotype data
- solcap_snp_c1_10011
genotype data
- solcap_snp_c1_10012
genotype data
- solcap_snp_c1_10031
genotype data
- solcap_snp_c1_10042
genotype data
- solcap_snp_c1_10050
genotype data
- solcap_snp_c1_10054
genotype data
- solcap_snp_c1_10109
genotype data
- solcap_snp_c1_10130
genotype data
- solcap_snp_c1_10157
genotype data
- solcap_snp_c1_10202
genotype data
- solcap_snp_c1_10252
genotype data
- solcap_snp_c1_10253
genotype data
- solcap_snp_c1_10255
genotype data
- solcap_snp_c1_1029
genotype data
- solcap_snp_c1_10295
genotype data
- solcap_snp_c1_10297
genotype data
- solcap_snp_c1_10351
genotype data
- solcap_snp_c1_10384
genotype data
- solcap_snp_c1_10397
genotype data
- solcap_snp_c1_10457
genotype data
- solcap_snp_c1_10491
genotype data
- solcap_snp_c1_10492
genotype data
- solcap_snp_c1_10494
genotype data
- solcap_snp_c1_10579
genotype data
- solcap_snp_c1_10646
genotype data
- solcap_snp_c1_10669
genotype data
- solcap_snp_c1_10715
genotype data
- solcap_snp_c1_10737
genotype data
- solcap_snp_c1_10743
genotype data
- solcap_snp_c1_10762
genotype data
- solcap_snp_c1_10855
genotype data
- solcap_snp_c1_10873
genotype data
- solcap_snp_c1_10879
genotype data
- solcap_snp_c1_10900
genotype data
- solcap_snp_c1_10932
genotype data
- solcap_snp_c1_1094
genotype data
- solcap_snp_c1_11137
genotype data
- solcap_snp_c1_11144
genotype data
- solcap_snp_c1_11196
genotype data
- solcap_snp_c1_11206
genotype data
Source
The example genotype data is a subset of data from the publically avaliable Solcap potato dataset which was re-scored in GenomeStudio in diploid and tetraploid formats
Smaller example genotype input format
Description
A data frame containing Solcap potato genotype data in tetraploid and diploid format as an small example of the input format required by StAMPP
Usage
data(potato.mini)
Format
A data frame with 6 rows and 48 variables:
- Sample
Sample names
- Pop
Population name
- Ploidy
Ploidy level
- Format
Format of genotype data
- solcap_snp_c1_1
genotype data
- solcap_snp_c1_1000
genotype data
- solcap_snp_c1_10000
genotype data
- solcap_snp_c1_10001
genotype data
- solcap_snp_c1_10011
genotype data
- solcap_snp_c1_10012
genotype data
- solcap_snp_c1_10031
genotype data
- solcap_snp_c1_10042
genotype data
- solcap_snp_c1_10050
genotype data
- solcap_snp_c1_10054
genotype data
- solcap_snp_c1_10109
genotype data
- solcap_snp_c1_10130
genotype data
- solcap_snp_c1_10157
genotype data
- solcap_snp_c1_10202
genotype data
- solcap_snp_c1_10252
genotype data
- solcap_snp_c1_10253
genotype data
- solcap_snp_c1_10255
genotype data
- solcap_snp_c1_1029
genotype data
- solcap_snp_c1_10295
genotype data
- solcap_snp_c1_10297
genotype data
- solcap_snp_c1_10351
genotype data
- solcap_snp_c1_10384
genotype data
- solcap_snp_c1_10397
genotype data
- solcap_snp_c1_10457
genotype data
- solcap_snp_c1_10491
genotype data
- solcap_snp_c1_10492
genotype data
- solcap_snp_c1_10494
genotype data
- solcap_snp_c1_10579
genotype data
- solcap_snp_c1_10646
genotype data
- solcap_snp_c1_10669
genotype data
- solcap_snp_c1_10715
genotype data
- solcap_snp_c1_10737
genotype data
- solcap_snp_c1_10743
genotype data
- solcap_snp_c1_10762
genotype data
- solcap_snp_c1_10855
genotype data
- solcap_snp_c1_10873
genotype data
- solcap_snp_c1_10879
genotype data
- solcap_snp_c1_10900
genotype data
- solcap_snp_c1_10932
genotype data
- solcap_snp_c1_1094
genotype data
- solcap_snp_c1_11137
genotype data
- solcap_snp_c1_11144
genotype data
- solcap_snp_c1_11196
genotype data
- solcap_snp_c1_11206
genotype data
Source
The example genotype data is a subset of data from the publically avaliable Solcap potato dataset which was re-scored in GenomeStudio in diploid and tetraploid formats
Convert StAMPP genotype data to genlight object
Description
Converts a StAMPP formated allele frequency data frame generated from the stamppConvert function to a genlight object for use in other packages
Usage
stampp2genlight(geno, pop = TRUE)
Arguments
geno |
a data frame containing allele frequency data generated from stamppConvert |
pop |
logical. True if population IDs are present in the StAMPP genotype data, False if population IDs are absent. |
Details
StAMPP only exports to genlight objects as they are able to handle mixed ploidy datasets unlike genpop and genloci objects. The genlight object allows the intergration between StAMPP and other common R packages such as ADEGENET
Value
A object of class genlight which contains genotype data, individual IDs, population IDs (if present) and ploidy levels
Author(s)
Luke Pembleton <luke.pembleton at agriculture.vic.gov.au>
Examples
# import genotype data and convert to allele frequecies
data(potato.mini, package="StAMPP")
potato.freq <- stamppConvert(potato.mini, "r")
# Convert the StAMPP formatted allele frequency data frame to a genlight object
potato.genlight <- stampp2genlight(potato.freq, TRUE)
Analysis of Molecular Variance
Description
Calculates an AMOVA based on the genetic distance matrix from stamppNeisD() using the amova() function from the package PEGAS for exploring within and between population variation
Usage
stamppAmova(dist.mat, geno, perm = 100)
Arguments
dist.mat |
the matrix of genetic distances between individuals generated from stamppNeisD() |
geno |
a data frame containing allele frequency data generated from stamppConvert, or a genlight object containing genotype data, individual IDs, population IDs and ploidy levels |
perm |
the number of permutations for the tests of hypotheses |
Details
Uses the formula distance ~ populations, to calculate an AMOVA for population differentiation and within & between population variation. This function uses the amova function from the PEGAS package.
Value
An object of class "amova" which is a list containing a table of sum of square deviations (SSD), mean square deviations (MSD) and the number of degrees of freedom as well as the variance components
Author(s)
Luke Pembleton <luke.pembleton at agriculture.vic.gov.au>
References
Paradis E (2010) pegas: an R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419-420. <doi:10.1093/bioinformatics/btp696>
Examples
# import genotype data and convert to allele frequecies
data(potato.mini, package="StAMPP")
potato.freq <- stamppConvert(potato.mini, "r")
# Calculate genetic distance between individuals
potato.D.ind <- stamppNeisD(potato.freq, FALSE)
# Calculate AMOVA
stamppAmova(potato.D.ind, potato.freq, 100)
Import and Convert
Description
Imports biallelic AB formated or allele A frequency genotype data. If the data is in imported in biallelic AB format this function also converts it to allele frequencies
Usage
stamppConvert(genotype.file, type = "csv")
Arguments
genotype.file |
the genotype input file. This should be a R matrix object or a file path for a csv file containing the genotype data in either bialleleic AB format or allele 'A' frequency format, or a genlight object containing genotype data |
type |
the type of file the genotype data is being imported from; "csv" = comma seperated file, "r" = data frame in the R workspace, "genlight" = genlight object. |
Value
An object of class data.frame which contains allele frequency data for use in other StAMPP functions
Author(s)
Luke Pembleton <luke.pembleton at agriculture.vic.gov.au>
Examples
# Import example data into the R workspace
data(potato.mini, package="StAMPP")
# Convert to allele frequencies
potato.freq <- stamppConvert(potato.mini, "r")
Fst Computation
Description
This function calculates pairwise Fst values along with confidence intervals and p-values between populations according to the method proposed by Wright(1949) and updated by Weir and Cockerham (1984)
Usage
stamppFst(geno, nboots = 100, percent = 95, nclusters = 1)
Arguments
geno |
a data frame containing allele frequency data generated from stamppConvert, or a genlight object containing genotype data, individual IDs, population IDs and ploidy levels |
nboots |
number of bootstraps to perform across loci to generate confidence intervals and p-values |
percent |
the percentile to calculate the confidence interval around |
nclusters |
number of proccesor treads or cores to use during calculations. |
Details
If possible, using multiple processing threads or cores is recommended to assist in calculating Fst values over a large number of bootstraps.
Value
An object list with the components:
Fstsa matrix of pairwise Fst values between populations
Pvaluesa matrix of p-values for each of the pairwise Fst values containined in the 'Fsts' matrix
Bootstrapsa dataframe of each Fst value generated during Bootstrapping and the associated confidence intervals
If nboots<2, no bootstrapping is performed and therefore only a matrix of Fst values is returned.
Author(s)
Luke Pembleton <luke.pembleton at agriculture.vic.gov.au>
References
Wright S (1949) The Genetical Structure of Populations. Annals of Human Genetics 15, 323-354. <doi:10.1111/j.1469-1809.1949.tb02451.x> Weir BS, Cockerham CC (1984) Estimating F Statistics for the ANalysis of Population Structure. Evolution 38, 1358-1370. <doi:10.2307/2408641>
Examples
# import genotype data and convert to allele frequecies
data(potato.mini, package="StAMPP")
potato.freq <- stamppConvert(potato.mini, "r")
# Calculate pairwise Fst values between each population
potato.fst <- stamppFst(potato.freq, 100, 95, 1)
Genomic Relationship Calculation
Description
This function calculates a genomic relationship matrix following the method decribed by Yang et al (2010)
Usage
stamppGmatrix(geno)
Arguments
geno |
a data frame containing allele frequency data generated from stamppConvert, or a genlight object containing genotype data, individual IDs, population IDs and ploidy levels |
Value
An object of class matrix which contains the genomic relationship values between each individual
Author(s)
Luke Pembleton <luke.pembleton at agriculture.vic.gov.au>
References
Yang J, Benyamin B, McEvoy BP, et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42, 565-569. <doi:10.1038/ng.608>
Examples
# import genotype data and convert to allele frequecies
data(potato.mini, package="StAMPP")
potato.freq <- stamppConvert(potato.mini, "r")
# Calculate genomic relationship values between each individual
potato.fst <- stamppGmatrix(potato.freq)
Genetic Distance Calculation
Description
This function calculates Nei's genetic distance (Nei 1972) between populations or individuals
Usage
stamppNeisD(geno, pop = TRUE)
Arguments
geno |
a data frame containing allele frequency data generated from stamppConvert, or a genlight object containing genotype data, individual IDs, population IDs and ploidy levels |
pop |
logical. True if genetic distance should be calculated between populations, false if it should be calculated between individual |
Value
A object of class matrix which contains the genetic distance between each population or individual
Author(s)
Luke Pembleton <luke.pembleton at agriculture.vic.gov.au>
References
Nei M (1972) Genetic Distance between Populations. The American Naturalist 106, 283-292.
Examples
# import genotype data and convert to allele frequecies
data(potato.mini, package="StAMPP")
potato.freq <- stamppConvert(potato.mini, "r")
# Calculate genetic distance between individuals
potato.D.ind <- stamppNeisD(potato.freq, FALSE)
# Calculate genetic distance between populations
potato.D.pop <- stamppNeisD(potato.freq, TRUE)
Export to Phylip Format
Description
Converts the genetic distance matrix generated with stamppNeisD into Phylip format and exports it as a text file
Usage
stamppPhylip(distance.mat, file = "")
Arguments
distance.mat |
the matrix containing the genetic distances generated from stamppNeisD to be converted into Phylip format |
file |
the file path and name to save the Phylip format matrix as |
Details
The exported Phylip formated text file can be easily imported into sofware packages such as DARWin (Perrier & Jacquemound-Collet 2006) to be used to generate neighbour joining trees
Author(s)
Luke Pembleton <luke.pembleton at agriculture.vic.gov.au>
References
Perrier X, Jacquemound-Collet JP (2006) DARWin - Dissimilarity Analysis and Representation for Windows. Agricultural Research for Development
Examples
# import genotype data and convert to allele frequecies
data(potato.mini, package="StAMPP")
potato.freq <- stamppConvert(potato.mini, "r")
# Calculate genetic distance between populations
potato.D.pop <- stamppNeisD(potato.freq, TRUE)
# Export the genetic distance matrix in Phylip format
## Not run: stamppPhylip(potato.D.pop, file="potato_distance.txt")