Type: Package
Title: Fine-Scale Population Analysis (Rewrite for Gene-Trait-Environment Interaction Analysis)
Version: 0.4
Date: 2020-02-20
Author: Reiichiro Nakamichi, Shuichi Kitada, Hirohisa Kishino
Maintainer: Reiichiro Nakamichi <nakamichi@affrc.go.jp>
Description: Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate 'GENEPOP' format data files). For more information, see Kitada, Nakamichi and Kishino (2020) <doi:10.1101/2020.01.30.927186>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
Depends: R (≥ 3.4.0)
LazyLoad: yes
NeedsCompilation: no
Encoding: UTF-8
Packaged: 2020-02-20 14:55:40 UTC; nakamichi
Repository: CRAN
Date/Publication: 2020-02-28 10:20:02 UTC

Fine-Scale Population Analysis (Rewrite for Gene-Trait-Environment Interaction Analysis)

Description

Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate 'GENEPOP' format data files). For more information, see Kitada, Nakamichi and Kishino (2020) <doi:10.1101/2020.01.30.927186>.

Author(s)

Reiichiro Nakamichi, Shuichi Kitada, Hirohisa Kishino
Maintainer: Reiichiro Nakamichi <nakamichi@affrc.go.jp>

References

Kitada S, Nakamichi R, Kishino H (2017) The empirical Bayes estimators of fine-scale population structure in high gene flow species. Mol. Ecol. Resources, 17, 1210-1222.
Nei M, Chesser RK (1983) Estimation of fixation indices and gene diversity. Annals of Human Genetics, 47, 253-259.
Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resources, 8, 103-106.
Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution, 38, 1358-1370.
Weir BS, Goudet J (2017) A Unified Characterization of Population Structure and Relatedness. Genetics, 206, 2085-2103.

Generalized least squres for regression analysis considering auto correlation.

Description

This function provides a multiple regression analysis considering auto correlation of response variable using generalized least squres method (Aitken 1934). It supports lm like format of model. A typical model has the form response ~ terms. Terms specification supports only first + second form. Cross term specification of first * second form is not supported.

Usage

GLS(model, data, omega = NULL)

Arguments

model

Symbolic description of the model to be fitted.

data

Data frame containing variables in the model.

omega

A numeric matrix of auto correlation of responce variable.

Value

coefficients

Estimated coefficient, standard error, Z value and p value of each factor.

variance

Variance-covariance matrix of estimated coefficients.

logL

Log likelihood of fitted model.

Author(s)

Reiichiro Nakamichi, Shuichi Kitada, Hirohisa Kishino

References

Aitken AC (1934) On Least-squares and Linear Combinations of Observations. Proceedings of the Royal Society of Edinburgh, 55, 42-48.

See Also

lm

Examples

# Example data of Atlantic herring
data(herring)
ah.genepop.file <- tempfile()
ah.popname.file <- tempfile()
cat(herring$genepop, file=ah.genepop.file, sep="\n")
cat(herring$popname, file=ah.popname.file, sep=" ")

# Data load
popdata <- read.GENEPOP(ah.genepop.file, ah.popname.file)

# Pop-specific FST and correlation among populations
fst.popsp <- pop_specificFST(popdata, cov=TRUE)
cov.fst.popsp <- fst.popsp$cov
sd.fst.popsp <- sqrt(diag(cov.fst.popsp))
cov2.fst.popsp <- apply(cov.fst.popsp, 2, function(x){x / sd.fst.popsp})
cor.fst.popsp <- apply(cov2.fst.popsp, 1, function(x){x / sd.fst.popsp})

# Pop-pairwise FST and population structure
fst.poppair <- pop_pairwiseFST(popdata)
fst.md <- cmdscale(fst.poppair)

# GLS analysis of FST and environmental factors
test.data <- data.frame(fst=fst.md[,1], herring$environment)
GLS(fst~., scale(test.data), omega=cor.fst.popsp)

Remove designated markers from a GENEPOP file.

Description

This function reads a GENEPOP file (Rousset 2008), remove designated markers, and write a GENEPOP file of clipped data. The user can directly designate the names of the markers to be removed. The user also can set the filtering threshold of major allele frequency.

Usage

clip.GENEPOP(infile, outfile, remove.list = NULL, major.af = NULL)

Arguments

infile

A character value specifying the name of the GENEPOP file to be clipped.

outfile

A character value specifying the name of the clipped GENEPOP file.

remove.list

A character value or vector specifying the names of the markers to be removed. The names must be included in the target GENEPOP file.

major.af

A numeric value specifying the threshold of major allele frequency for marker removal. Markers with major allele frequencies higher than this value will be removed. This value must be between 0 and 1.

Author(s)

Reiichiro Nakamichi

References

Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resources, 8, 103-106.

Examples

# Example of GENEPOP file
data(jsmackerel)
jsm.genepop.file <- tempfile()
cat(jsmackerel$MS.genepop, file=jsm.genepop.file, sep="\n")

# Remove markers designated by their names
clipped_by_name.jsm.genepop.file <- tempfile()
clip.GENEPOP(infile=jsm.genepop.file,
             outfile=clipped_by_name.jsm.genepop.file,
             remove.list=c("Sni21","Sni26"))

# Remove markers with high major allele frequencies (in this example, > 0.5)
clipped_by_af.jsm.genepop.file <- tempfile()
clip.GENEPOP(infile=jsm.genepop.file,
             outfile=clipped_by_af.jsm.genepop.file,
             major.af=0.5)

# Remove markers both by their names and by major allele frequencies
clipped_by_both.jsm.genepop.file <- tempfile()
clip.GENEPOP(infile=jsm.genepop.file,
             outfile=clipped_by_both.jsm.genepop.file,
             remove.list=c("Sni21","Sni26"), major.af=0.5)

# See four text files in temporary directory.
#  jsm.genepop.file                 : original data of five markers
#  clipped_by_name.jsm.genepop.file : clipped data by marker names
#  clipped_by_af.jsm.genepop.file   : clipped data by allele frequency
#  clipped_by_both.jsm.genepop.file : clipped data by both names and frequency

Genome-wide global FST (Weir & Cockerham 1984).

Description

This function estimates genom-wide global FST based on Weir and Cockerham's theta (Weir & Cockerham 1984) from a GENEPOP data object (Rousset 2008). Missing genotype values in the GENEPOP file ("0000" or "000000") are simply ignored.

Usage

globalFST(popdata)

Arguments

popdata

Population data object created by read.GENEPOP function from a GENEPOP file.

Value

fst

Estimated genome-wide global FST

se

Standard error of estimated FST

Author(s)

Reiichiro Nakamichi, Shuichi Kitada, Hirohisa Kishino

References

Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resources, 8, 103-106.

Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution, 38, 1358-1370.

See Also

read.GENEPOP

Examples

# Example of GENEPOP file
data(jsmackerel)
jsm.ms.genepop.file <- tempfile()
jsm.popname.file <- tempfile()
cat(jsmackerel$MS.genepop, file=jsm.ms.genepop.file, sep="\n")
cat(jsmackerel$popname, file=jsm.popname.file, sep=" ")

# Data load
# Prepare your GENEPOP file and population name file in the working directory.
# Replace "jsm.ms.genepop.file" and "jsm.popname.file" by your file names.
popdata <- read.GENEPOP(genepop=jsm.ms.genepop.file, popname=jsm.popname.file)

# theta estimation
result.globalFST <- globalFST(popdata)
print(result.globalFST)

An example dataset of Atlantic herring.

Description

An example of a genetic data for Atlantic herring population (Limborg et al. 2012). It contains genotypic information of 281 SNPs from 18 subpopulations of 607 individuals. GENEPOP format (Rousset 2008) text file is available. Subpopulation names, environmental factors (longitude, latitude, temperature and salinity) at each subpopulation are attached.

Usage

data("herring")

Format

$ genepop : Genotypic information of 281 SNPs in GENEPOP format text data.
$ popname : Names of subpopulations.
$ environment : Table of temperature and salinity at each subpopulation.

References

Limborg MT, Helyar SJ, de Bruyn M et al. (2012) Environmental selection on transcriptome-derived SNPs in a high gene flow marine fish, the Atlantic herring (Clupea harengus). Molecular Ecology, 21, 3686-3703.

Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resources, 8, 103-106.

See Also

read.GENEPOP, GLS

Examples

data(herring)
ah.genepop.file <- tempfile()
ah.popname.file <- tempfile()
cat(herring$genepop, file=ah.genepop.file, sep="\n")
cat(herring$popname, file=ah.popname.file, sep=" ")

# See two text files in temporary directory.
#  ah.genepop.file  : GENEPOP format file of 281SNPs in 18 subpopulations
#  ah.popname.file  : plain text file of subpopulation names

print(herring$environment)

An example dataset of Japanese Spanich mackerel in GENEPOP and frequency format.

Description

An example of a genetic data for a Japanese Spanish mackerel population (Nakajima et al. 2014). It contains genotypic information of 5 microsatellite markers from 8 subpopulations of 715 individuals. GENEPOP format (Rousset 2008) text files are available. Name list of subpopulations also is attached.

Usage

data("jsmackerel")

Format

$ MS.genepop: Genotypic information of 5 microsatellites in GENEPOP format text data.
$ popname: Names of subpopulations.

References

Nakajima K et al. (2014) Genetic effects of marine stock enhancement: a case study based on the highly piscivorous Japanese Spanish mackerel. Canadian Journal of Fisheries and Aquatic Sciences, 71, 301-314.

Kitada S, Kitakado T, Kishino H (2007) Empirical Bayes inference of pairwise FST and its distribution in the genome. Genetics, 177, 861-873.

Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resources, 8, 103-106.

See Also

read.GENEPOP

Examples

data(jsmackerel)
jsm.ms.genepop.file <- tempfile()
jsm.popname.file <- tempfile()
cat(jsmackerel$MS.genepop, file=jsm.ms.genepop.file, sep="\n")
cat(jsmackerel$popname, file=jsm.popname.file, sep=" ")

# See two text files in temporary directory.
#  jsm.ms.genepop.file  : GENEPOP format file of microsatellite data
#  jsm.popname.file     : plain text file of subpopulation names

Locus-specific global FST (Kitada et al. 2007, 2017).

Description

This function estimates locus-specific global FST among subpopulations using empirical Bayes method (Kitada et al. 2007, 2017) from a GENEPOP data object (Rousset 2008). Missing genotype values in the GENEPOP file ("0000" or "000000") are simply ignored.

Usage

locus_specificFST(popdata)

Arguments

popdata

Population data object created by read.GENEPOP function from a GENEPOP file.

Value

Estimated locus-specific global FST.

Author(s)

Reiichiro Nakamichi, Shuichi Kitada, Hirohisa Kishino

References

Kitada S, Kitakado T, Kishino H (2007) Empirical Bayes inference of pairwise FST and its distribution in the genome. Genetics, 177, 861-873.

Kitada S, Nakamichi R, Kishino H (2017) The empirical Bayes estimators of fine-scale population structure in high gene flow species. Mol. Ecol. Resources, DOI: 10.1111/1755-0998.12663

Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resources, 8, 103-106.

See Also

read.GENEPOP, read.frequency

Examples

# Example of GENEPOP file
data(jsmackerel)
jsm.ms.genepop.file <- tempfile()
jsm.popname.file <- tempfile()
cat(jsmackerel$MS.genepop, file=jsm.ms.genepop.file, sep="\n")
cat(jsmackerel$popname, file=jsm.popname.file, sep=" ")

# Data load
# Prepare your GENEPOP file and population name file in the working directory.
# Replace "jsm.ms.genepop.file" and "jsm.popname.file" by your file names.
popdata <- read.GENEPOP(genepop=jsm.ms.genepop.file, popname=jsm.popname.file)

# FST estimation
locspFST <- locus_specificFST(popdata)
print(locspFST)

Genome-wide poppulation-paiwise FST (Nei & Chesser 1983).

Description

This function estimates genome-wide poppulation-paiwise FST among subpopulations based on Nei and Chesser's corrected GST (Nei&Chesser 1983) from a GENEPOP data object (Rousset 2008). Missing genotype values in the GENEPOP file ("0000" or "000000") are simply ignored.

Usage

pop_pairwiseFST(popdata)

Arguments

popdata

Population data object created by read.GENEPOP function from a GENEPOP file.

Value

Estimated genome-wide population-pairwise FST.

Author(s)

Reiichiro Nakamichi, Shuichi Kitada, Hirohisa Kishino

References

Nei M, Chesser RK (1983) Estimation of fixation indices and gene diversity. Annals of Human Genetics, 47, 253-259.

Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resources, 8, 103-106.

See Also

read.GENEPOP, as.dist, as.dendrogram, hclust, cmdscale

Examples

# Example of GENEPOP file
data(jsmackerel)
jsm.ms.genepop.file <- tempfile()
jsm.popname.file <- tempfile()
cat(jsmackerel$MS.genepop, file=jsm.ms.genepop.file, sep="\n")
cat(jsmackerel$popname, file=jsm.popname.file, sep=" ")

# Data load
# Prepare your GENEPOP file and population name file in the working directory.
# Replace "jsm.ms.genepop.file" and "jsm.popname.file" by your file names.
popdata <- read.GENEPOP(genepop=jsm.ms.genepop.file, popname=jsm.popname.file)

# GST estimation
result.poppairFST <- pop_pairwiseFST(popdata)
poppairFST.d <- as.dist(result.poppairFST)
print(poppairFST.d)

# dendrogram
poppairFST.hc <- hclust(poppairFST.d,method="average")
plot(as.dendrogram(poppairFST.hc), xlab="",ylab="",main="", las=1)

# MDS plot
mds <- cmdscale(poppairFST.d)
plot(mds, type="n", xlab="",ylab="")
text(mds[,1],mds[,2], popdata$pop_names)

Genome-wide population-specific FST (Weir & Goudet 2017).

Description

This function estimates genome-wide poppulation-specific FST based on Weir and Goudet's Method (Weir&Goudet 2017) from a GENEPOP data object (Rousset 2008). Missing genotype values in the GENEPOP file ("0000" or "000000") are simply ignored.

Usage

pop_specificFST(popdata, cov = FALSE)

Arguments

popdata

Population data object created by read.GENEPOP function from a GENEPOP file.

cov

A Logical argument indicating whether variance-covariance matrix of estimated FST should be calculated.

Value

fst

Estimated genome-wide population-specific FST and standard error.

cov

Variance-covariance matrix of estimated FST

Author(s)

Reiichiro Nakamichi, Shuichi Kitada, Hirohisa Kishino

References

Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resources, 8, 103-106.

Weir BS, Goudet J (2017) A Unified Characterization of Population Structure and Relatedness. Genetics, 206, 2085-2103.

See Also

read.GENEPOP

Examples

# Example of GENEPOP file
data(jsmackerel)
jsm.ms.genepop.file <- tempfile()
jsm.popname.file <- tempfile()
cat(jsmackerel$MS.genepop, file=jsm.ms.genepop.file, sep="\n")
cat(jsmackerel$popname, file=jsm.popname.file, sep=" ")

# Data load
# Prepare your GENEPOP file and population name file in the working directory.
# Replace "jsm.ms.genepop.file" and "jsm.popname.file" by your file names.
popdata <- read.GENEPOP(genepop=jsm.ms.genepop.file, popname=jsm.popname.file)

# FST estimation
result.popspFST <- pop_specificFST(popdata)
print(result.popspFST$fst)

Create a genotype data object of populations from a GENEPOP format file.

Description

This function reads a GENEPOP format file (Rousset 2008) and parse it into an R data object. This data object provides a summary of genotype/haplotype of each sample, allele frequency in each population, and marker status. This data object is used in downstream analysis of this package.

Usage

read.GENEPOP(genepop, popname = NULL)

Arguments

genepop

A character value specifying the name of the GENEPOP file to be analyzed.

popname

A character value specifying the name of the plain text file containing the names of subpopulations to be analyzed. This text file must not contain other than subpopulation names. The names must be separated by spaces, tabs or line breaks. If this argument is omitted, serial numbers will be assigned as subpopulation names.

Value

num_pop

Number of subpopulations.

pop_sizes

Number of samples in each subpopulation.

pop_names

Names of subpopulations.

ind_names

Names of samples in each subpopulation.

num_loci

Number of loci.

loci_names

Names of loci.

num_allele

Number of alleles at each locus.

allele_list

A list of alleles at each locus.

ind_count

Observed count of genotyped samples in each subpopulation at each locus.

allele_count

Observed count of genotyped alleles in each subpopulation at each locus.

allele_freq

Observed allele frequencies in each subpopulation at each locus.

genotype

Genotypes of each sample at each locus in haploid designation.

call_rate_loci

Call rate of each locus (rate of genotyped samples at each locus).

call_rate_ind

Call rate of each sample (rate of genotyped markers for each sample).

He

Expected heterozigosity in each subpopulation.

Ho

Observed heterozigosity in each subpopulation.

Author(s)

Reiichiro Nakamichi

References

Rousset F (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resources, 8, 103-106.

Examples

# Example of GENEPOP file
data(jsmackerel)
jsm.ms.genepop.file <- tempfile()
jsm.popname.file <- tempfile()
cat(jsmackerel$MS.genepop, file=jsm.ms.genepop.file, sep="\n")
cat(jsmackerel$popname, file=jsm.popname.file, sep=" ")

# Read GENEPOP file with subpopulation names.
# Prepare your GENEPOP file and population name file in the working directory.
# Replace "jsm.ms.genepop.file" and "jsm.popname.file" by your file names.
popdata <- read.GENEPOP(genepop=jsm.ms.genepop.file, popname=jsm.popname.file)

# Read GENEPOP file without subpopulation names.
popdata.noname <- read.GENEPOP(genepop=jsm.ms.genepop.file)