Package 'StAMPP'

Title: Statistical Analysis of Mixed Ploidy Populations
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.
Authors: LW Pembleton
Maintainer: LW Pembleton <[email protected]>
License: GPL-3
Version: 1.6.3.9000
Built: 2025-01-25 03:04:00 UTC
Source: https://github.com/lpembleton/stampp

Help Index

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 <lpembleton at barenbrug.com>

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 <lpembleton at barenbrug.com>

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, "standard")
# 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 <lpembleton at barenbrug.com>

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 <lpembleton at barenbrug.com>

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 <lpembleton at barenbrug.com>

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, measure = "standard")

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
measure

a character string defining the distance measure to use: "standard" for the Neis standard genetic distance 1972 or "DA" for Neis DA distance 1983.

Value

A object of class matrix which contains the genetic distance between each population or individual

Author(s)

Luke Pembleton <lpembleton at barenbrug.com>

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, "standard")
# Calculate genetic distance between populations
potato.D.pop <- stamppNeisD(potato.freq, TRUE, "standard")

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 <lpembleton at barenbrug.com>

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, "standard")
# Export the genetic distance matrix in Phylip format
## Not run: stamppPhylip(potato.D.pop, file="potato_distance.txt")