Package 'hierfstat'

Description

Calculates corrected Assignment Index as described in Goudet etal. (2002)

Usage

AIc(dat)

Arguments

Value

aic The corrected assignment index of each individual

Author(s)

Jerome Goudet [email protected]

References

Goudet J, Perrin N, Waser P (2002) Tests for sex-biased dispersal using bi-parentally inherited genetic markers 11, 1103:1114

Description

Counts the number of copies of the different alleles at each locus and population

Usage

allele.count(data,diploid=TRUE)

Arguments

Value

A list of tables, –each with np (number of populations) columns and nl (number of loci) rows– of the count of each allele

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
allele.count(gtrunchier[,-2])

Description

Estimates allelic richness, the rarefied allelic counts, per locus and population

Usage

allelic.richness(data,min.n=NULL,diploid=TRUE)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

References

El Mousadik A. and Petit R.J. (1996) High level of genetic differentiation for allelic richness among populations of the argan tree argania spinosa skeels endemic to Morocco. Theoretical and Applied Genetics, 92:832-839

Hurlbert S.H. (1971) The nonconcept of species diversity: a critique and alternative parameters. Ecology, 52:577-586

Petit R.J., El Mousadik A. and Pons O. (1998) Identifying populations for conservation on the basis of genetic markers. Conservation Biology, 12:844-855

Examples

data(gtrunchier)
allelic.richness(gtrunchier[,-1])

Description

Estimates individual counts, allelic frequencies, observed heterozygosities and genetic diversities per locus and population. Also Estimates mean observed heterozygosities, mean gene diversities within population Hs, Gene diversities overall Ht and corrected Htp, and Dst, Dstp. Finally, estimates Fst and Fstp as well as Fis following Nei (1987) per locus and overall loci

Usage

basic.stats(data,diploid=TRUE,digits=4)

## S3 method for class 'basic.stats'
print(x,...)

Hs(data,...)

Ho(data,...)

Arguments

Value

Note

For the perloc and overall tables (see value section), the following statistics, defined in eq.7.38– 7.43 pp.164–5 of Nei (1987) are estimated:

The observed heterozygosity

$Ho= 1-\sum_k \sum_i Pkii/np,$

where $Pkii$ represents the proportion of homozygote $i$ in sample $k$ and $np$ the number of samples.

The within population gene diversity (sometimes misleadingly called expected heterozygosity):

$Hs=\tilde{n}/(\tilde{n}-1)[1-\sum_i\bar{p_i^2}-Ho/2\tilde{n}],$

where $\tilde{n}=np/\sum_k 1/n_k$ and $\bar{p_i^2}=\sum_k p_{ki}^2/np$

The overall gene diversity

$Ht= 1-\sum_i\bar{p_i}^2+Hs/(\tilde{n} np)-Ho/(2\tilde{n} np),$

where $\bar{p_i}=\sum_kp_{ki}/np$.

The amount of gene diversity among samples $Dst=Ht-Hs$

$Dst'=np/(np-1)Dst$

$Ht'=Hs+Dst'$

$Fst=Dst/Ht$.(This is not the same as Nei's $Gst$, Nei's $Gst$ is an estimator of $Fst$ based on allele frequencies only)

$Fst'=Dst'/Ht'$

$Fis=1-Ho/Hs$

Last, $Dest=np/(np-1) (Ht'-Hs)/(1-Hs)$ a measure of population differentiation as defined by Jost (2008) is also given

Here, the $p_{ki}$ are unweighted by sample size. These statistics are estimated for each locus and an overall loci estimates is also given, as the unweighted average of the per locus estimates. In this way, monomorphic loci are accounted for (with estimated value of 0) in the overall estimates.

Note that the equations used here all rely on genotypic rather than allelic number and are corrected for heterozygosity.

Author(s)

Jerome Goudet [email protected]

References

Nei M. (1987) Molecular Evolutionary Genetics. Columbia University Press

Jost L (2008) GST and its relatives do not measure differentiation. Molecular Ecology, 17, 4015-4026.

Nei M, Chesser R (1983) Estimation of fixation indexes and gene diversities. Annals of Human Genetics, 47, 253-259.

See Also

ind.count,pop.freq.

Examples

data(gtrunchier)
basic.stats(gtrunchier[,-1])
Hs(gtrunchier[,-2])
Ho(gtrunchier[,-2])

Description

Estimates pairwise kinships (coancestries) and individual inbreeding coefficient using Weir and Goudet (2017) beta estimator.

Usage

beta.dosage(dos,inb=TRUE,Mb=FALSE,matching=FALSE)

kas.dosage(dos, inb = TRUE, Mb = FALSE, matching = FALSE)

Arguments

Details

This function is written for dosage data, i.e., how many doses of an allele (0, 1 or 2) an individual carries. It should be use for bi-allelic markers only (e.g. SNPs), although you might "force" a k multiallelic locus to k biallelic loci (see fstat2dos).

Matching proportions can be obtained by the following equation: $M=\beta*(1-Mb)+Mb$

By default (inb=TRUE) the inbreeding coefficient is returned on the main diagonal. With inb=FALSE, self coancestries are reported.

Twice the betas with self-coancestries on the diagonal gives the Genomic Relationship Matrix (GRM)

Following a suggestion from Olivier Hardy, missing data are removed from the estimation procedure, rather than imputed (this is taken care off automatically)

Value

if Mb=FALSE, a matrix of pairwise kinships and inbreeding coefficients (if inb=TRUE) or self-coancestries (inb=FALSE); if Mb=TRUE, a list with elements inb (whether inbreeding coefficients rather than kinships should be returned on the main diagonal), MB (the average off-diagonal matching) and betas the kinships or inbreeding coefficients.

Author(s)

Jerome Goudet [email protected]

References

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

Goudet, J., Kay, T. and Weir BS. 2018 How to estimate kinship. Molecular Ecology 27:4121.

Examples

## Not run: 
 dos<-matrix(sample(0:2,size=10000,replace=TRUE),ncol=100)
 beta.dosage(dos,inb=TRUE)
 
 #matrix of kinship/inbreeding coeff
 data(gtrunchier)
 beta.dosage(fstat2dos(gtrunchier[,-c(1:2)]))
 
 #individual inbreeding coefficients
 dat<-sim.genot(size=100,nbloc=100,nbal=20,mig=0.01,f=c(0,0.3,0.7))
 hist(diag(beta.dosage(fstat2dos(dat[,-1]))),breaks=-10:100/100,main="",xlab="",ylab="")
 abline(v=c(0.0,0.3,0.7),col="red")
 #only 20 loci
 hist(diag(beta.dosage(fstat2dos(dat[,2:21]))),breaks=-5:20/20,main="",xlab="",ylab="")
 abline(v=c(0.0,0.3,0.7),col="red")
 

## End(Not run)

Description

Estimate populations (Population specific FST) or individual coancestries and a bootstrap confidence interval, assuming random mating

Usage

betas(dat,nboot=0,lim=c(0.025,0.975),diploid=TRUE,betaijT=FALSE)

## S3 method for class 'betas'
print(x, digits = 4, ...)

Arguments

Details

If betaijT=TRUE, and the first column contains a unique identifier for each individual, the function returns the matrix of individual coancestries/kinships. Individual inbreeding coefficients can be obtained by multiplying by 2 the diagonal and substracting 1.

Value

Hi Within population gene diversities (complement to 1 of matching probabilities)

Hb Between populations gene diversities

betaiovl Average $\hat{\beta_{WT}^i}$ over loci (Population specific FSTs), Table 3 of Weir and Goudet, 2017 (Genetics)

betaW Average of the betaiovl $\hat{\beta_{WT}}$ over loci (overall population FST)

ci The bootstrap confidence interval of population specific FSTs (only if more than 100 bootstraps requested AND if more than 10 loci are present)

if betaijT=TRUE, return the matrix of pairwise kinships only.

Methods (by generic)

• print(betas): print function for betas class

Author(s)

Jerome Goudet [email protected]

References

Weir and Goudet, 2017 (Genetics) A unified characterization of population structure and relatedness.

See Also

fs.dosage, beta.dosage for Fst estimates (not assuming Random Mating) and kinship estimates from dosage data, respectively

Examples

## Not run: 
#3 different population sizes lead to 3 different betais
dat<-sim.genot(size=40,N=c(50,200,1000),nbloc=50,nbal=10)
betas(dat,nboot=100)
 
#individual coancestries from the smallest population are large
ind.coan<-betas(cbind(1:120,dat[,-1]),betaij=T)
diag(ind.coan$betaij)<-NA
graphics::image(1:120,1:120,ind.coan$betaij,xlab="Inds",ylab="Inds")

## End(Not run)

Description

Converts bi-allelic SNPs hierfstat format to dosage format, the number of alternate allele copies at a locus for an individual, i.e. 11 -> 0; 12 or 21 >1 and 22 ->2

Usage

biall2dos(dat,diploid=TRUE)

Arguments

Value

a matrix containing allelic dosages

Examples

## Not run: 
biall2dos(sim.genot(nbal=2,nbloc=10)[,-1])  # a 10 column matrix

## End(Not run)

Description

Estimates bootstrap confidence intervals for pairwise betas FST estimates.

Usage

boot.ppbetas(dat=dat,nboot=100,quant=c(0.025,0.975),diploid=TRUE,digits=4)

Arguments

Value

a matrix with upper limit of the bootstrap CI above the diagonal and lower limit below the diagonal

See Also

betas pairwise.betas

Examples

## Not run: 
data(gtrunchier)
boot.ppbetas(gtrunchier[,-2])

## End(Not run)

Description

Performs bootstrapping over loci of population's Fis

Usage

boot.ppfis(dat=dat,nboot=100,quant=c(0.025,0.975),diploid=TRUE,dig=4,...)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

Examples

dat<-sim.genot(nbpop=4,nbloc=20,nbal=10,f=c(0,0.2,0.4,0.6))
boot.ppfis(dat)

Description

Performs bootstrapping over loci of pairwise Fst using Weir and Cockerham (1984) estimator of Fst

Usage

boot.ppfst(dat=dat,nboot=100,quant=c(0.025,0.975),diploid=TRUE,...)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
x<-boot.ppfst(gtrunchier[,-2])
x$ll
x$ul

Description

Provides a bootstrap confidence interval (over loci) for sums of the different variance components (equivalent to gene diversity estimates at the different levels), and the derived F-statistics, as suggested by Weir and Cockerham (1984). Will not run with less than 5 loci. Raymond and Rousset (199X) points out shortcomings of this method.

Usage

boot.vc(levels=levels,loci=loci,diploid=TRUE,nboot=1000,quant=c(0.025,0.5,0.975))

Arguments

Value

References

Raymond M and Rousset F, 1995. An exact test for population differentiation. Evolution. 49:1280-1283

Weir, B.S. (1996) Genetic Data Analysis II. Sinauer Associates.

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

See Also

varcomp.glob.

Examples

#load data set
data(gtrunchier)
boot.vc(gtrunchier[,c(1:2)],gtrunchier[,-c(1:2)],nboot=100)

Description

A simple diploid dataset, with allele encoded as one digit number. Up to 4 alleles per locus

Usage

data(cont.isl)

Format

A data frame with 150 rows and 6 columns:

Pop

Population identifier, from 1 to 3

loc.1

genotype at loc.1

loc.2

genotype at loc.2

loc.3

genotype at loc.3

loc.4

genotype at loc.4

loc.5

genotype at loc.5

...

Source

generated with function sim.genot()

Examples

data(cont.isl)
allele.count(cont.isl)

Description

A simple diploid dataset, with alleles encoded as two digits numbers. Up to 99 alleles per locus

Usage

data(cont.isl99)

Format

A data frame with 150 rows and 6 columns:

Pop

Population identifier, from 1 to 3

loc.1

genotype at loc.1

loc.2

genotype at loc.2

loc.3

genotype at loc.3

loc.4

genotype at loc.4

loc.5

genotype at loc.5

...

Source

generated with function sim.genot(nbal=99)

Examples

data(cont.isl99)
allele.count(cont.isl99)

Description

A dataset containing microsatellite genotypes, population and sex of 140 Crocidura russula individuals

Usage

data(crocrussula)

References

Favre et al. (1997) Female-biased dispersal in the monogamous mammal Crocidura russula: evidence from field data and microsatellite patterns. Proceedings of the Royal Society, B (264): 127-132

Goudet J, Perrin N, Waser P (2002) Tests for sex-biased dispersal using bi-parentally inherited genetic markers 11, 1103:1114

Examples

data(crocrussula)
aic<-AIc(crocrussula$genot)
boxplot(aic~crocrussula$sex)
sexbias.test(crocrussula$genot,crocrussula$sex)

Description

A simple diploid dataset, with allele encoded as one digit number

Usage

data(diploid)

Format

A data frame with 44 rows and 6 columns:

Pop

Population identifier, from 1 to 6

loc-1

genotype at loc-1 (only allele 4 present)

loc-2

genotype at loc-1 (alleles 3 and 4)

loc-3

genotype at loc-1 (alleles 2, 3 and 4)

loc-4

genotype at loc-1 (alleles 1, 2, 3 and 4)

loc-5

genotype at loc-1 (only allele 4)

...

Source

Given in Weir, B.S. Genetic Data Analysis. Sinauer

Examples

data(diploid)
basic.stats(diploid)

Description

Example data set with 4 levels, one diploid and one haploid locus

Usage

data(exhier)

Value

Examples

data(exhier)
varcomp(exhier[,1:5])
varcomp(exhier[,c(1:4,6)],diploid=FALSE)

Description

Reports individual inbreeding coefficients, Population specific and pairwise Fsts, and Fiss from dosage data

Usage

fs.dosage(dos, pop, matching = FALSE)

## S3 method for class 'fs.dosage'
plot(x, ...)

## S3 method for class 'fs.dosage'
print(x, digits = 4, ...)

fst.dosage(dos, pop, matching = FALSE)

fis.dosage(dos, pop, matching = FALSE)

pairwise.fst.dosage(dos, pop, matching = FALSE)

Arguments

Value

Fi list of individual inbreeding coefficients, estimated with the reference being the population to which the individual belongs.

FsM matrix containing population specific FSTs on the diagonal. The off diagonal elements contains the average of the kinships for pairs of individuals, one from each population, relative to the mean kinship for pairs of individuals between populations.

Fst2x2 matrix containing pairwise FSTs

Fs The first row contains population specific and overall Fis, the second row population specific (average $\hat{\beta_{ST}^i}$ over loci) FSTs and overall Fst $\hat{\beta_{ST}}$ (see Table 3 of Weir and Goudet, 2017 (Genetics))

Methods (by generic)

• plot(fs.dosage): Plot function for fs.dosage class

• print(fs.dosage): Print function for fs.dosage class

Author(s)

Jerome Goudet [email protected]

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

See Also

betas

Examples

## Not run: 
 dos<-matrix(sample(0:2,size=10000,replace=TRUE),ncol=100)
 fs.dosage(dos,pop=rep(1:5,each=20))
 plot(fs.dosage(dos,pop=rep(1:5,each=20)))

## End(Not run)

Description

Converts a hierfstat genetic data frame to dosage. For each allele at each locus, allelic dosage (number of copies of the allele) is reported. The column name is the allele identifier

Usage

fstat2dos(dat,diploid=TRUE)

Arguments

Value

a matrix with $\sum_l n_l^a$ columns (where $n_l^a$ is the number of alleles at locus l), as many rows as individuals, and containing the number of copies (dosage) of the corresponding allele

Examples

## Not run: 
dat<-sim.genot(nbal=5,nbloc=10)
dos<-fstat2dos(dat[,-1])
dim(dos) 
wc(dat)
fst.dosage(dos,pop=dat[,1])


## End(Not run)

Description

Calculates the likelihood ratio G-statistic on a contingency table of alleles at one locus X sampling unit. The sampling unit could be any hierarchical level

Usage

g.stats(data,diploid=TRUE)

Arguments

Value

Author(s)

Jerome Goudet, DEE, UNIL, CH-1015 Lausanne Switzerland

[email protected]

References

Goudet J., Raymond, M., DeMeeus, T. and Rousset F. (1996) Testing differentiation in diploid populations. Genetics. 144: 1933-1940

Goudet J. (2005). Hierfstat, a package for R to compute and test variance components and F-statistics. Molecular Ecology Notes. 5:184-186

Petit E., Balloux F. and Goudet J.(2001) Sex-biased dispersal in a migratory bat: A characterization using sex-specific demographic parameters. Evolution 55: 635-640.

See Also

g.stats.glob.

Examples

data(gtrunchier)
attach(gtrunchier)
g.stats(data.frame(Patch,L21.V))

Description

Calculates the likelihood ratio G-statistic on a contingency table of alleles at one locus X sampling unit, and sums this statistic over the loci provided. The sampling unit could be any hierarchical level (patch, locality, region,...). By default, diploid data are assumed

Usage

g.stats.glob(data,diploid=TRUE)

Arguments

Value

Author(s)

Jerome Goudet, DEE, UNIL, CH-1015 Lausanne Switzerland

[email protected]

References

Goudet J. (2005). Hierfstat, a package for R to compute and test variance components and F-statistics. Molecular Ecology Notes. 5:184-186

Goudet J., Raymond, M., DeMeeus, T. and Rousset F. (1996) Testing differentiation in diploid populations. Genetics. 144: 1933-1940

Petit E., Balloux F. and Goudet J.(2001) Sex-biased dispersal in a migratory bat: A characterization using sex-specific demographic parameters. Evolution 55: 635-640.

See Also

g.stats, samp.within,samp.between.

Examples

## Not run: 
data(gtrunchier)
attach(gtrunchier)
nperm<-99
nobs<-length(Patch)
gglobs.o<-vector(length=(nperm+1))
gglobs.p<-vector(length=(nperm+1))
gglobs.l<-vector(length=(nperm+1))

gglobs.o[nperm+1]<-g.stats.glob(data.frame(Patch,gtrunchier[,-c(1,2)]))$g.stats
gglobs.p[nperm+1]<-g.stats.glob(data.frame(Patch,gtrunchier[,-c(1,2)]))$g.stats
gglobs.l[nperm+1]<-g.stats.glob(data.frame(Locality,gtrunchier[,-c(1,2)]))$g.stats

for (i in 1:nperm) #careful, might take a while
{
  gglobs.o[i]<-g.stats.glob(data.frame(Patch,gtrunchier[sample(Patch),-c(1,2)]))$g.stats
  gglobs.p[i]<-g.stats.glob(data.frame(Patch,gtrunchier[samp.within(Locality),-c(1,2)]))$g.stats
  gglobs.l[i]<-g.stats.glob(data.frame(Locality,gtrunchier[samp.between(Patch),-c(1,2)]))$g.stats
}
#p-value of first test (among patches)
p.globs.o<-sum(gglobs.o>=gglobs.o[nperm+1])/(nperm+1) 

#p-value of second test (among patches within localities)
p.globs.p<-sum(gglobs.p>=gglobs.p[nperm+1])/(nperm+1) 

#p-value of third test (among localities)
p.globs.l<-sum(gglobs.l>=gglobs.l[nperm+1])/(nperm+1) 


#Are alleles associated at random among patches
p.globs.o 

#Are alleles associated at random among patches within localities?
#Tests differentiation among patches within localities
p.globs.p 

#Are alleles associated at random among localities, keeping patches as one unit?
#Tests differentiation among localities
p.globs.l 

## End(Not run)

Description

Estimates one of several genetic distances among all pairs of populations.

Usage

genet.dist(dat,diploid=TRUE,method="Dch")

Arguments

Details

the method argument specify which genetic distance to use, among eight, all briefly described in Takezaki and Nei (1996)

“Dch” By default, Cavalli-Sforza and Edwards Chord distance (eqn 6 in the reference) is returned. This distance is used as default since Takezaki & Nei (1996) found that it was the best to retrieve the relation among samples.

“Da” This is Nei's et al genetic distance (eqn 7), performing nearly as well as “Dch”

“Ds” Nei's standard genetic distance (eqn 1). Increases linearly with diverence time but has larger variance

“Fst” Latter's and also approximately Reynolds et al Genetic distance (eqn 3)

“Dm” Nei's minimum distance (eqn 2)

“Dr” Rogers's distance (eqn 4)

“Cp” Prevosti et al's distance (eqn 5)

“X2” Sanghvi's distance (eqn 8)

“Nei87” see pairwise.neifst

“WC84” see pairwise.WCfst

Value

A matrix of pairwise genetic distance

Author(s)

Jerome Goudet [email protected]

References

Takezaki & Nei (1996) Genetic distances and reconstruction of Phylogenetic trees from microsatellite DNA. Genetics 144:389-399

Nei, M. (1987) Molecular Evolutionary Genetics. Columbia University Press

Weir B.S. and Cockerham C.C. (1984) Estimating F-Statistics for the Analysis of Population Structure. Evolution 38:1358

See Also

pairwise.WCfst pairwise.neifst

Examples

data(gtrunchier)
genet.dist(gtrunchier[,-1])
genet.dist(gtrunchier[,-1],method="Dr")

Description

Converts genind objects from adegenet into a hierfstat data frame

Usage

genind2hierfstat(dat,pop=NULL)

Arguments

Value

a data frame with nloci+1 columns and ninds rows. The first column contains the population identifier, the following the genotypes at each locus

Examples

## Not run: 
library(adegenet)
data(nancycats)
genind2hierfstat(nancycats)
basic.stats(nancycats)
genet.dist(nancycats)
data(H3N2)
basic.stats(genind2hierfstat(H3N2,pop=rep(1,dim(H3N2@tab)[1])),diploid=FALSE)

## End(Not run)

Description

Separates the input vector of diploid genotypes in two vectors each containing one allele, and returns a vector of length 2*length(y) with the second part being the second allele

Usage

genot2al(y)

Arguments

Value

returns a vector of length 2*length(y), with the second half of the vector containing the second alleles

Author(s)

Jerome Goudet, DEE, UNIL, CH-1015 Lausanne Switzerland

[email protected]

References

Goudet J. (2004). A library for R to compute and test variance components and F-statistics. In Prep

See Also

varcomp.

Examples

data(gtrunchier)
genot2al(gtrunchier[,4])

Description

Converts diploid genotypic data into allelic data

Usage

getal(data)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
getal(data.frame(gtrunchier[,-2]))

Description

Converts a data frame of genotypic diploid data with as many lines as individuals (ni) and as many columns as loci (nl) into an array [ni,nl,2] of allelic data

Usage

getal.b(data)

Arguments

Value

an array [ni,nl,2] of alleles. The two alleles are stored in the third dimension of the array

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
#multilocus diploid genotype of the first individual
gtrunchier[1,-c(1:2)]
#the diploid genotype splitted in its two constituent alleles
getal.b(gtrunchier[,-c(1:2)])[1,,]

Description

Converts a Genetic Relationship Matrix (GRM) to a kinship matrix

Usage

grm2kinship(x)

Arguments

Details

$k[ii]=x[ii]-1; k[ij]=x[ij]/2$

Value

a kinship matrix

Author(s)

Jerome Goudet [email protected]

Description

Data set consisting of the microsatellite genotypes of 370 Galba truncatula, a tiny freshwater snail, collecting from different localities and several patches within localities in Western Switzerland.

Usage

data(gtrunchier)

Value

gtrunchier\$L21.V[1]

References

Trouve S., L. Degen et al. (2000) Microsatellites in the hermaphroditic snail, Lymnaea truncatula, intermediate host of the liver fluke, Fasciola hepatica.Molecular Ecology 9: 1662-1664.

Trouve S., Degen L. and Goudet J. (2005) Ecological components and evolution of selfing in the freshwater snail Galba truncatula. Journal of Evolutionary Biology. 18, 358-370

Description

This package contains functions to estimate hierarchical F-statistics for any number of hierarchical levels using the method described in Yang (1998). It also contains functions allowing to test the significance of population differentiation at any given level using the likelihood ratio G-statistic, showed previoulsly to be the most powerful statistic to test for differnetiation (Goudet et al., 1996) . The difficulty in a hierarchical design is to identify which units should be permutted. Functions samp.within and samp.between give permutations of a sequence that allows reordering of the observations in the original data frame. An exemple of application is given in the help page for function g.stats.glob.

Hierfstat includes now all the capabilities of Fstat, and many others. A new serie of functions implementing the statistics described in Weir and Goudet (2017) and Goudet et al. (2018) (beta.dosage, fs.dosage) have been written to deal with large genomic data sets and take as input a matrix of allelic dosages, the number of alternate alleles an individual carries at a locus.

Several functions have been written to simulate genetic data, or to import them from existing sofwares such as quantiNemo or Hudson's ms

Hierfstat links easily with the gaston, SNPRelate and adegenet packages, among others.

Author(s)

Jerome Goudet [email protected]

References

Goudet J. (2005) Hierfstat, a package for R to compute and test variance components and F-statistics. Molecular Ecology Notes. 5:184-186

Goudet J., Raymond, M., DeMeeus, T. and Rousset F. (1996) Testing differentiation in diploid populations. Genetics. 144: 1933-1940

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

Goudet J., Kay T. and Weir B.S. (2018) How to estimate kinship. Molecular Ecology. 27: 4121:4135

Weir, B.S. (1996) Genetic Data Analysis II. Sinauer Associates.

Yang, R.C. (1998) Estimating hierarchical F-statistics. Evolution 52(4):950-956

Description

Counts the number of individual genotyped per locus and population

Usage

ind.count(data)

Arguments

Value

A table –with np (number of populations) columns and nl (number of loci) rows– of genotype counts

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
ind.count(gtrunchier[,-2])

Description

Carry out a PCA on the centered, unscaled matrix of individual's allele frequencies.

Usage

indpca(dat,ind.labels=NULL,scale=FALSE)

## S3 method for class 'indpca'
print(x,...)
## S3 method for class 'indpca'
plot(x,eigen=FALSE,ax1=1,ax2=2,...)

Arguments

Value

An object of class indpca with components

Author(s)

Jerome Goudet [email protected]

Examples

##not run
data(gtrunchier)
x<-indpca(gtrunchier[,-2],ind.labels=gtrunchier[,2])
plot(x,col=gtrunchier[,1],cex=0.7)

Description

Converts a kinship matrix to a distance matrix

Usage

kinship2dist(x)

Arguments

Details

$D_{ii}=0, D_{ij}=\frac{1-(x-min(x))}{(1-min(x))}$

Value

A distance matrix

Author(s)

Jerome Goudet [email protected]

Description

Converts a kinship matrix to a Genetic Relation Matrix (GRM)

Usage

kinship2grm(x)

Arguments

Details

for off-diagonal elements, $GRM=2 \times x_{ij}$; for diagonal elements, $GRM=1+ x_{ii}$

Value

a GRM matrix

Author(s)

Jerome Goudet [email protected]

Examples

## Not run: 
dos<-matrix(sample(0:2,replace=TRUE,size=1000),nrow=10) #dosage matrix for 10 inds at 100 loci
ks<-beta.dosage(dos) # kinship matrix
kinship2grm(ks)

## End(Not run)

Description

Shifts a kinship matrix

Usage

kinshipShift(x,shift=NULL)

Arguments

Details

The kinship matrix produced by beta.dosage is relative to the average kinship of the set of individuals analysed ($1/(n(n-1)/2) \sum_i \sum_{j>i} x_{ij}=0$). Another reference point might be useful, for instance to avoid negative kinship values, one might want to shift the matrix by $min(x_{ij}), i \neq j$.

Value

the shifted kinship matrix $\frac{x-shift}{1-shift}$

Author(s)

Jerome Goudet [email protected]

Description

creates a vector from a matrix

Usage

mat2vec(mat,upper=FALSE)

Arguments

Value

a vector

Examples

{

 mat2vec(matrix(1:16,nrow=4))
 mat2vec(matrix(1:16,nrow=4),upper=TRUE)
}

Description

Estimates matching (or Allele Sharing) between pairs of individuals (for each locus, gives 1 if the two individuals are homozygous for the same allele, 0 if they are homozygous for a different allele, and 1/2 if at least one individual is heterozygous. Matching is the average of these 0, 1/2 and 1s)

Usage

matching(dos)

AlleleSharing(dos)

Arguments

Details

This function is written for dosage data, i.e., how many doses of an allele (0, 1 or 2) an individual carries. It should be use for bi-allelic markers only (e.g. SNPs), although you might "force" a k multiallelic locus to k biallelic loci (see fstat2dos).

Value

a matrix of pairwise matching / Allele Sharing

Description

Import the output of the ms program into a BED object, as defined in the gaston package

Usage

ms2bed(fname,chrom=NULL)

Arguments

Details

ms2bed relies on ms2dos. The population identifier is in bed@ped$famid

Value

a bed object

Description

Import the output of the ms program into suitable format for further manipulations

Usage

ms2dos(fname,chrom=NULL)

Arguments

Value

alldat a matrix with as many row as (haploid) individuals and as many columns as SNPs

bim a data frame with two components chr contains the chromosome (replicate) id; pos contains the SNPs positions on the chromosome

Description

Counts the number of different alleles at each locus and population

Usage

nb.alleles(data,diploid=TRUE)

Arguments

Value

A table, –with np (number of populations) columns and nl (number of loci) rows– of the number of different alleles

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
nb.alleles(gtrunchier[,-2])

Description

Estimates pairwise betas according to Weir and Goudet (2017)

Usage

pairwise.betas(dat,diploid=TRUE)

Arguments

Value

a matrix of pairwise betas

Author(s)

Jerome Goudet [email protected]

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

Examples

data(gtrunchier)
pairwise.betas(gtrunchier[,-2],diploid=TRUE)

Description

Estimate pairwise FSTs according to Nei (1987)

Usage

pairwise.neifst(dat,diploid=TRUE)

Arguments

Details

FST are calculated using Nei (87) equations for FST', as described in the note section of basic.stats

Value

A matrix of pairwise FSTs

Author(s)

Jerome Goudet [email protected]

References

Nei, M. (1987) Molecular Evolutionary Genetics. Columbia University Press

See Also

pairwise.WCfst genet.dist basic.stats

Examples

data(gtrunchier)
pairwise.neifst(gtrunchier[,-2],diploid=TRUE)

Description

Estimates pairwise FSTs according to Weir and Cockerham (1984)

Usage

pairwise.WCfst(dat,diploid=TRUE)

Arguments

Details

FST are calculated using Weir & Cockerham (1984) equations for FST', as described in the note section of wc

Value

A matrix of pairwise FSTs

Author(s)

Jerome Goudet [email protected]

References

Weir, B.S. (1996) Genetic Data Analysis II. Sinauer Associates.

Weir B.S. and Cockerham C.C. (1984) Estimating F-Statistics for the Analysis of Population Structure. Evolution 38:1358

See Also

pairwise.neifst genet.dist

Examples

data(gtrunchier)
pairwise.WCfst(gtrunchier[,-2],diploid=TRUE)

Description

principal coordinates analysis as described in Legendre & Legendre Numerical Ecology

Usage

pcoa(mat,plotit=TRUE,...)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
colo<-c("black","red","blue","yellow","orange","green")
pcoa(as.matrix(genet.dist(gtrunchier[,-1])),col=rep(colo,c(5,5,4,5,5,5)))

Description

Estimates nucleotide diversity $\pi= \sum_l 2 p_ l(1-p_l) 2n/(2n-1)$ from a dosage matrix

Usage

pi.dosage(dos,L=NULL)

Arguments

Value

if L=NULL (default), returns the sum over SNPs of nucleotide diversity; otherwise return the average nucleotide diversity per nucleotide given the length L of the sequence

Description

Estimates allelic frequencies for each population and locus

Usage

pop.freq(dat,diploid=TRUE)

Arguments

Value

A list containing allele frequencies. Each element of the list is one locus. For each locus, Populations are in columns and alleles in rows

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
pop.freq(gtrunchier[,-2])

Description

fst per pair following Weir and Cockerham (1984)

Usage

pp.fst(dat=dat,diploid=TRUE,...)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

References

Weir B.S. and Cockerham C.C. (1984) Estimating F-Statistics for the Analysis of Population Structure. Evolution 38:1358

Weir, B.S. (1996) Genetic Data Analysis II. Sinauer Associates.

Description

wrapper to return per locus variance components between pairs of samples x & y

Usage

pp.sigma.loc(x,y,dat=dat,diploid=TRUE,...)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

Description

print function for pp.fst

Usage

## S3 method for class 'pp.fst'
print(x,...)

Arguments

Author(s)

Jerome Goudet [email protected]

Read QuantiNemo extended format for genotype files Read QuantiNemo (http://www2.unil.ch/popgen/softwares/quantinemo/) genotype files extended format (option 2)

Description

Read QuantiNemo extended format for genotype files

Read QuantiNemo (http://www2.unil.ch/popgen/softwares/quantinemo/) genotype files extended format (option 2)

Usage

qn2.read.fstat(fname, na.s = c("NA","NaN"))

Arguments

Value

dat a data frame with nloc+1 columns, the first being the population to which the individual belongs and the next being the genotypes, one column per locus; and ninds rows

sex the sex of the individuals

Author(s)

Jerome Goudet [email protected]

References

Neuenschwander S, Michaud F, Goudet J (2019) QuantiNemo 2: a Swiss knife to simulate complex demographic and genetic scenarios, forward and backward in time. Bioinformatics 35:886

Neuenschwander S, Hospital F, Guillaume F, Goudet J (2008) quantiNEMO: an individual-based program to simulate quantitative traits with explicit genetic architecture in a dynamic metapopulation. Bioinformatics 24:1552

See Also

read.fstat

Examples

dat<-qn2.read.fstat(system.file("extdata","qn2_sex.dat",package="hierfstat"))
  sexbias.test(dat[[1]],sex=dat[[2]])

Description

Imports a FSTAT data file into R. The data frame created is made of nl+1 columns, nl being the number of loci. The first column corresponds to the Population identifier, the following columns contains the genotypes of the individuals.

Usage

read.fstat(fname, na.s = c("0","00","000","0000","00000","000000","NA"))

Arguments

The first line is immediately followed by nl lines, each containing the name of a locus, in the order they will appear in the rest of the file.

On line nl+2, a series of numbers as follow:

1     0102   0103   0101  0203          0      0303

The first number identifies the sample to which the individual belongs, the second is the genotype of the individual at the first locus, coded with a 2 digits number for each allele, the third is the genotype at the second locus, until locus nl is entered (in the example above, nl=6). Missing genotypes are encoded with 0, 00, 0000, 000000 or NA. Note that 0001 or 0100 are not a valid format, as both alleles at a locus have to be known, otherwise, the genotype is considered as missing. No empty lines are needed between samples.

Value

a data frame containing the desired data, in a format adequate to pass to varcomp

References

Goudet J. (1995). FSTAT (Version 1.2): A computer program to calculate F- statistics. Journal of Heredity 86:485-486

Goudet J. (2005). Hierfstat, a package for R to compute and test variance components and F-statistics. Molecular Ecology Notes. 5:184-186

Examples

read.fstat(paste(path.package("hierfstat"),"/extdata/diploid.dat",sep="",collapse=""))

Description

Imports a FSTAT data file into R. The data frame created is made of nl+1 columns, nl being the number of loci. The first column corresponds to the Population identifier, the following columns contains the genotypes of the individuals.

Usage

read.fstat.data(fname, na.s = c("0","00","000","0000","00000","000000","NA"))

Arguments

The first line is immediately followed by nl lines, each containing the name of a locus, in the order they will appear in the rest of the file.

On line nl+2, a series of numbers as follow:

1     0102   0103   0101  0203          0      0303

The first number identifies the sample to which the individual belongs, the second is the genotype of the individual at the first locus, coded with a 2 digits number for each allele, the third is the genotype at the second locus, until locus nl is entered (in the example above, nl=6). Missing genotypes are encoded with 0, 00, 0000, 000000 or NA. Note that 0001 or 0100 are not a valid format, as both alleles at a locus have to be known, otherwise, the genotype is considered as missing. No empty lines are needed between samples.

Value

a data frame containing the desired data, in a format adequate to pass to varcomp

References

Goudet J. (1995). FSTAT (Version 1.2): A computer program to calculate F- statistics. Journal of Heredity 86:485-486

Goudet J. (2005). Hierfstat, a package for R to compute and test variance components and F-statistics. Molecular Ecology Notes. 5:184-186

Examples

read.fstat.data(paste(path.package("hierfstat"),"/extdata/diploid.dat",sep="",collapse=""))

Read data generated by Hudson ms program Read data generated by Hudson ms program, either as Haplotypes or as SNPs.

Description

With argument what="SNP", each site is read as a SNP, with the ancestral allele encoded as 0 and the alternate allele encoded as 1. If the ms output file contains several replicates, the different replicates will be collated together. Hence, the number of loci is the sum of all sites from all replicates.

Usage

read.ms(fname,what=c("SNP","Haplotype"))

Arguments

Details

With argument what="Haplotype", each different sequence from a replicate is read as a haplotype, by converting it first to a factor, and then to an integer. There will be as many loci as there are replicates, and the number of alleles per locus will be the number of different haplotypes in the corresponding replicate.

Value

alldat a data frame with nloc+1 columns, the first being the population to which the individual belongs and the next being the genotypes, one column per locus; and one row per (haploid) individual.

Author(s)

Jerome Goudet [email protected]

References

Hudson, R. R. (2002) Generating samples under a Wright-Fisher neutral model of genetic variation. Bioinformatics 18 : 337-338.

Examples

## Not run: 
  datH<-read.ms(system.file("extdata","2pops_asspop.txt",package="hierfstat"),what="Haplotype")
  dim(datH)
  head(datH[,1:10]
  datS<-read.ms(system.file("extdata","2pops_asspop.txt",package="hierfstat"),what="SNP")
  dim(datS)
  head(datS[,1:10])
  
## End(Not run)

Description

Reads a https://samtools.github.io/hts-specs/Variant Call Format (VCF) file into a BED object, retaining bi-allelic SNPs only

Usage

read.VCF(fname,BiAllelic=TRUE,...)

Arguments

Value

A bed.matrix-class object

See Also

read.vcf

Examples

filepath <-system.file("extdata", "LCT.vcf.gz", package="gaston")
 x1 <- read.VCF( filepath )
 x1

Description

Used to generate a permutation of a sequence 1:length(lev). blocks of observations are permutted, according to the vector lev passed to the function.

Usage

samp.between(lev)

Arguments

Value

a vector 1:length(lev) (with blocks defined by data) randomly permuted. Usually, one passes the result to reorder observations in a data set in order to carry out permutation-based tests

Author(s)

Jerome Goudet, DEE, UNIL, CH-1015 Lausanne Switzerland

[email protected]

References

Goudet J. (2005). Hierfstat, a package for R to compute and test variance components and F-statistics. Molecular Ecology Notes. 5:184-186

See Also

samp.within, g.stats.glob.

Examples

samp.between(rep(1:4,each=4))
#for an application see example in g.stats.glob

Description

Used to generate a permutation of a sequence 1:length(inner.lev). blocks of observations defined by inner.lev are permutted within blocks defined by outer.lev

Usage

samp.between.within(inner.lev, outer.lev)

Arguments

Value

a vector 1:length(lev) (with blocks defined by data) randomly permuted. Usually, one passes the result to reorder observations in a data set in order to carry out permutation-based tests

See Also

test.between.within.

Description

Used to generate a permutation of a sequence 1:length(lev). observations are permutted within blocks, according to the vector lev passed to the function.

Usage

samp.within(lev)

Arguments

Value

a vector 1:length(lev) (with blocks defined by

lev

) randomly permuted. Usually, one passes the result to reorder observations in a data set in order to carry out permutation-based tests.

Author(s)

Jerome Goudet, DEE, UNIL, CH-1015 Lausanne Switzerland

[email protected]

References

Goudet J. (2005). Hierfstat, a package for R to compute and test variance components and F-statistics. Molecular Ecology Notes. 5:184-186

See Also

samp.between,g.stats.glob.

Examples

samp.within(rep(1:4,each=4))
#for an application see example in g.stats.glob

Description

Test whether one sex disperses more than the other using the method described in Goudet etal. (2002)

Usage

sexbias.test(dat,sex,nperm=NULL,test="mAIc",alternative="two.sided")

Arguments

Value

call the function call

res the observation for each sex

statistic the observed statistic for the chosen test

p.value the p-value of the hypothesis

Author(s)

Jerome Goudet [email protected]

References

Goudet J, Perrin N, Waser P (2002) Tests for sex-biased dispersal using bi-parentally inherited genetic markers 11, 1103:1114

Examples

data(crocrussula)
  sexbias.test(crocrussula$genot,crocrussula$sex)
  dat<-qn2.read.fstat(system.file("extdata","qn2_sex.dat",package="hierfstat"))
  sexbias.test(dat[[1]],sex=dat[[2]])
  ## Not run: 
  sexbias.test(crocrussula$genot,crocrussula$sex,nperm=1000)
  sexbias.test(dat[[1]],sex=dat[[2]],nperm=100,test="FST",alternative="greater")
  
## End(Not run)

Description

Simulates frequencies, for internal use only

Description

Simulates genotypes from several individuals in several populations at several loci in an island model at equilibrium. The islands may differ in size and inbreeding coeeficients.

Usage

sim.genot(size=50,nbal=4,nbloc=5,nbpop=3,N=1000,mig=0.001,mut=0.0001,f=0)

Arguments

Value

a data frame with nbpop*size lines and nbloc+1 columns. Individuals are in rows and genotypes in columns, the first column being the population identifier

Author(s)

Jerome Goudet [email protected]

Examples

## Not run: 
dat<-sim.genot(nbpop=4,nbal=20,nbloc=10,mig=0.001,mut=0.0001,N=c(100,100,1000,1000),f=0)
betas(dat)$betaiovl

## End(Not run)

Description

This function allows to simulate genetic data from a metapopulation model, where each population can have a different size and a different inbreeding coefficient, and migration between each population is given in a migration matrix.

This function simulates genetic data under a migration matrix model. Each population $i$ sends a proportion of migrant alleles $m_{ij}$ to population $j$ and receives a proportion of migrant alleles $m_{ji}$ from population $j$.

Usage

sim.genot.metapop.t(size=50,nbal=4,nbloc=5,nbpop=3,N=1000,
mig=diag(3),mut=0.0001,f=0,t=100)

Arguments

Details

In this model, $\theta_t$ can be written as a function of population size $N_i$, migration rate $m_{ij}$, mutation rate $\mu$ and $\theta_{(t-1)}$.

The rational is as follows:

With probability $\frac{1}{N_i}$, 2 alleles from 2 different individuals in the current generation are sampled from the same individual of the previous generation:

-Half the time, the same allele is drawn from the parent;

-The other half, two different alleles are drawn, but they are identical in proportion $\theta_{(t-1)}$.

-With probability $1-\frac{1}{N_i}$, the 2 alleles are drawn from different individuals in the previous generation, in which case they are identical in proportion $\theta_{(t-1)}$.

This holds providing that neither alleles have mutated or migrated. This is the case with probability $m_{ii}^2 \times (1-\mu)^2$. If an allele is a mutant, then its coancestry with another allele is 0.

Note also that the mutation scheme assumed is the infinite allele (or site) model. If the number of alleles is finite (as will be the case in what follows), the corresponding mutation model is the K-allele model and the mutation rate has to be adjusted to $\mu'=\frac{K-1}{K}\mu$.

Continue derivation

Value

A data frame with size*nbpop rows and nbloc+1 columns. Each row is an individual, the first column contains the identifier of the population to which the individual belongs, the following nbloc columns contain the genotype for each locus.

Author(s)

Jerome Goudet [email protected]

Examples

#2 populations
psize<-c(10,1000)
mig.mat<-matrix(c(0.99,0.01,0.1,0.9),nrow=2,byrow=TRUE)
dat<-sim.genot.metapop.t(nbal=10,nbloc=100,nbpop=2,N=psize,mig=mig.mat,mut=0.00001,t=100)
betas(dat)$betaiovl # Population specific estimator of FST

#1D stepping stone
## Not run: 
np<-10
m<-0.2
mig.mat<-diag(np)*(1-m)
diag(mig.mat[-1,-np])<-m/2
diag(mig.mat[-np,-1])<-m/2
mig.mat[1,1:2]<-c(1-m/2,m/2)
mig.mat[np,(np-1):np]<-c(m/2,1-m/2)
dat<-sim.genot.metapop.t(nbal=10,nbloc=50,nbpop=np,mig=mig.mat,t=400)
pcoa(as.matrix(genet.dist(dat))) # principal coordinates plot

## End(Not run)

Description

This function allows to simulate genetic data from a non-equilibrium continent-island model, where each island can have a different size and a different inbreeding coefficient.

This function simulates genetic data under the continent-islands model (IIM=TRUE) or the finite island model (IIM=FALSE). In the IIM, a continent of infinite size sends migrants to islands of finite sizes $N_i$ at a rate $m$. Alleles can also mutate to a new state at a rate $\mu$. Under this model, the expected $F_{STi}, \theta_i$, can be calculated and compared to empirical estimates.

Usage

sim.genot.t(size=50,nbal=4,nbloc=5,nbpop=3,N=1000,
mig=0.001,mut=0.0001,f=0,t=100,IIM=TRUE)

Arguments

Details

In this model, $\theta_t$ can be written as a function of population size $N_i$, migration rate $m$, mutation rate $\mu$ and $\theta_{(t-1)}$.

The rational is as follows:

With probability $\frac{1}{N}$, 2 alleles from 2 different individuals in the current generation are sampled from the same individual of the previous generation:

-Half the time, the same allele is drawn from the parent;

-The other half, two different alleles are drawn, but they are identical in proportion $\theta_{(t-1)}$.

-With probability $1-\frac{1}{N}$, the 2 alleles are drawn from different individuals in the previous generation, in which case they are identical in proportion $\theta_{(t-1)}$.

This holds providing that neither alleles have mutated or migrated. This is the case with probability $(1-m)^2 \times (1-\mu)^2$. If an allele is a mutant or a migrant, then its coancestry with another allele is 0 in the infinite continent-islands model (it is not the case in the finite island model).

Note also that the mutation scheme assumed is the infinite allele (or site) model. If the number of alleles is finite (as will be the case in what follows), the corresponding mutation model is the K-allele model and the mutation rate has to be adjusted to $\mu'=\frac{K-1}{K}\mu$.

Lets substitute $\alpha$ for $(1-m)^2 (1-\mu)^2$ and $x$ for $\frac{1}{2N}$.

The expectation of $F_{ST}$, $\theta$ can be written as:

$\theta_t=(\alpha (1-x))^t \theta_0 + \frac{x}{1-x}\sum_{i=1}^t (\alpha (1-x))^i$

which reduces to $\theta_t=\frac{x}{1-x}\sum_{i=1}^t (\alpha (1-x))^i$ if $\theta_0=0$.

Transition equations for $theta$ in the migrant-pool island model (IIM=FALSE) are given in Rouseet (1996). Currently, the migrant pool is made of equal contribution from each island, irrespective of their size.

Value

A data frame with size*nbpop rows and nbloc+1 columns. Each row is an individual, the first column contains the island to which the individual belongs, the following nbloc columns contain the genotype for each locus.

Author(s)

Jerome Goudet [email protected]

References

Rousset, F. (1996) Equilibrium values of measures of population subdivision for stepwise mutation processes. Genetics 142:1357

Examples

psize<-c(100,1000,10000,100000,1000000)
dat<-sim.genot.t(nbal=4,nbloc=20,nbpop=5,N=psize,mig=0.001,mut=0.0001,t=100)
summary(wc(dat)) #Weir and cockerham overall estimators of FST & FIS
betas(dat) # Population specific estimator of FST

Description

Subsample a given number of individuals from a FSTAT data frame

Usage

subsampind(dat,sampsize = 10)

Arguments

Value

A data frame with population of origin as first column, and genotypes in following columns. Each population is made of at most sampsize individuals

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
subsampind(gtrunchier[,-1],6)  # check the warning

Description

Estimates Tajima's D from dosage data

Usage

TajimaD.dosage(dos)

Arguments

Value

Tajima's D (eqn 38 of Tajima, 1989)

References

Tajima F. 1989 Statistical Method for Testing the Neutral Mutation Hypothesis by DNA Polymorphism. Genetics 123:585-595.

Description

Tests the significance of the effect of test.lev on genetic differentiation

Usage

test.between(data, test.lev, rand.unit, nperm, ...)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
attach(gtrunchier)
#test whether the locality level has a significant effect on genetic structuring
test.between(gtrunchier[,-c(1,2)],test.lev=Locality,rand.unit=Patch)

Description

Tests, using permutations of rand.unit within units defined by the vector within the significance of the contingency tables allele X (levels of test.lev)

Usage

test.between.within(data, within, test.lev, rand.unit, nperm, ...)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

Examples

data(yangex)
attach(yangex)
#tests for the effect of spop on genetic structure
test.between.within(data.frame(genot),within=pop,test=spop,rand=sspop)

Description

Tests the significance of the effect of level on genetic differentiation

Usage

test.g(data = data, level, nperm = 100,...)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
attach(gtrunchier)
test.g(gtrunchier[,-c(1,2)],Locality)

Description

Tests the significance of the effect of inner.level on genetic differentiation within blocks defined by outer.level

Usage

test.within(data, within, test.lev, nperm, ...)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
attach(gtrunchier)
#tests whether the patch level has a significant effect on genetic structure
test.within(gtrunchier[,-c(1,2)],within=Locality,test.lev=Patch)

Description

Estimates $\theta_{Watterson}=S/a$, where $S$ is the number of segregating sites in a set of sequences and $a=1/\sum_i^{n-1} i$.

Usage

theta.Watt.dosage(dos,L=NULL)

Arguments

Value

if L=NULL (default), returns $\theta_{Watterson}$, else return $\theta_{Watterson}/L$

Description

Estimates variance components for each allele for a (fully) hierarchical random design defined by all but the last column of the data frame data, the last column containing the genetic data to analyse. Columns for the hierarchical design should be given from the outermost to the innermost before the individual (e.g. continent, region, population, patch,...)

Usage

varcomp(data,diploid=TRUE)

Arguments

Details

The format for genotypes is simply the code for the 2 alleles put one behind the other, without space in between. For instance if allele 1 at the locus has code 23 and allele 2 39, the genotype format is 2339.

Value

Author(s)

Jerome Goudet, DEE, UNIL, CH-1015 Lausanne Switzerland

[email protected]

http://www.unil.ch/popgen/people/jerome.htm

References

Goudet J. (2005). Hierfstat, a package for R to compute and test variance components and F-statistics. Molecular Ecology Notes. 5:184-186

Weir, B.S. (1996) Genetic Data Analysis II. Sinauer Associates.

Yang, R.C. (1998). Estimating hierarchical F-statistics. Evolution 52(4):950-956

See Also

varcomp.glob.

Examples

#load data set
data(gtrunchier)
attach(gtrunchier)
#
varcomp(data.frame(Locality,Patch,L21.V))

Description

Return multilocus estimators of variance components and F-statistics

Usage

varcomp.glob(levels=levels,loci=loci,diploid=TRUE)

Arguments

Value

Author(s)

Jerome Goudet DEE, UNIL, CH-1015 Lausanne Switzerland

[email protected]

References

Weir, B.S. (1996) Genetic Data Analysis II. Sinauer Associates.

Yang, R.C. (1998). Estimating hierarchical F-statistics. Evolution 52(4):950-956

Goudet J. (2005). Hierfstat, a package for R to compute and test variance components and F-statistics. Molecular Ecology Notes. 5:184-186

See Also

varcomp.

Examples

#load data set
data(gtrunchier)
attach(gtrunchier)
varcomp.glob(data.frame(Locality,Patch),gtrunchier[,-c(1,2)])

Description

Fills a triangular matrix from the inputed vector

Usage

vec2mat(x,diag=FALSE,upper=FALSE)

Arguments

Value

a matrix

Examples

{
 vec2mat(1:10)
 vec2mat(1:10,diag=TRUE)
 vec2mat(1:10,upper=TRUE)
}

Description

Computes Weir and Cockerham estimates of Fstatistics

Usage

wc(ndat,diploid=TRUE,pol=0.0)

## S3 method for class 'wc'
print(x,...)

Arguments

Value

Author(s)

Jerome Goudet [email protected]

Examples

data(gtrunchier)
wc(gtrunchier[,-1])

Description

write the genotypes in a format suitable for analysis with bayescan

Usage

write.bayescan(dat=dat,diploid=TRUE,fn="dat.bsc")

Arguments

Value

a text file fn is written in the current directory

Author(s)

Jerome Goudet [email protected]

References

Foll M and OE Gaggiotti (2008) Genetics 180: 977-993

http://cmpg.unibe.ch/software/BayeScan/

Description

Write a data frame to a text file in the fstat data format, see read.fstat

Usage

write.fstat(dat,fname="genotypes.dat")

Arguments

Value

None

Author(s)

Jerome Goudet

References

Goudet J. (1995). FSTAT (Version 1.2): A computer program to calculate F- statistics. Journal of Heredity 86:485-486

Examples

## Not run: data(gtrunchier)
write.fstat(gtrunchier[,-1],"galba.dat")

## End(Not run)

Description

write a ped and a map file suitable for analysis with PLINK

Usage

write.ped(dat, ilab = NULL, pop = NULL, 
        fname = "dat",na.str="0",f.id=NULL,m.id=NULL,loc.pos=NULL,sex=NULL)

Arguments

Value

a map file containing the loci positions

a ped file containing genotypes etc...

References

Chang et al. (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets

Description

Write a genotype data set to a file in the structure format

Usage

write.struct(dat,ilab=NULL,pop=NULL,MARKERNAMES=FALSE,MISSING=-9,fname="dat.str")

Arguments

Value

a text file in the structure format

Author(s)

Jerome Goudet [email protected]

References

Pritchard JK etal. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945-959

Description

Reproduce the example data set used in Yang's paper appendix. The genotype (column genot) is invented

Usage

data(exhier)

Value

References

Yang, R.C. (1998). Estimating hierarchical F-statistics. Evolution 52(4):950-956

Examples

data(yangex)
varcomp(yangex)
#the k matrix should be the same as matrix (A2) in Yang's appendix, p. 956