Package 'dartR'

Description

indexing dartR objects correctly...

Usage

## S4 method for signature 'dartR,ANY,ANY,ANY'
x[i, j, ..., pop = NULL, treatOther = TRUE, quiet = TRUE, drop = FALSE]

Arguments

Description

This a test data set to test the validity of functions within dartR and is based on a DArT SNP data set of simulated bandicoots across Australia. It contains 96 individuals and 1000 SNPs.

Usage

bandicoot.gl

Format

genlight object

Author(s)

Bernd Gruber (bugs? Post to https://groups.google.com/d/forum/dartr

Description

cbind is a bit lazy and does not take care for the metadata (so data in the other slot is lost). You can get most of the loci metadata back using gl.compliance.check.

Usage

## S3 method for class 'dartR'
cbind(...)

Arguments

Value

A genlight object

Examples

t1 <- platypus.gl
class(t1) <- "dartR"
t2 <- cbind(t1[,1:10],t1[,11:20])

Description

Converts a genind object into a genlight object

Usage

gi2gl(gi, parallel = FALSE, verbose = NULL)

Arguments

Details

Be aware due to ambiguity which one is the reference allele a combination of gi2gl(gl2gi(gl)) does not return an identical object (but in terms of analysis this conversions are equivalent)

Value

A genlight object, with all slots filled.

Author(s)

Bernd Gruber (Post to https://groups.google.com/d/forum/dartr)

Description

Calculates allele frequency of the first and second allele for each loci A very simple function to report allele frequencies

Usage

gl.alf(x)

Arguments

Value

A simple data.frame with alf1, alf2.

Author(s)

Bernd Gruber (bugs? Post to https://groups.google.com/d/forum/dartr)

Examples

#for the first 10 loci only
gl.alf(possums.gl[,1:10])
barplot(t(as.matrix(gl.alf(possums.gl[,1:10]))))

Description

This script performs an AMOVA based on the genetic distance matrix from stamppNeisD() [package StAMPP] using the amova() function from the package PEGAS for exploring within and between population variation. For detailed information use their help pages: ?pegas::amova, ?StAMPP::stamppAmova. Be aware due to a conflict of the amova functions from various packages I had to 'hack' StAMPP::stamppAmova to avoid a namespace conflict.

Usage

gl.amova(x, distance = NULL, permutations = 100, verbose = NULL)

Arguments

Value

An object of class 'amova' which is a list with a table of sums of square deviations (SSD), mean square deviations (MSD), and the number of degrees of freedom, and a vector of variance components.

Author(s)

Bernd Gruber (bugs? Post to https://groups.google.com/d/forum/dartr)

Examples

#permutations should be higher, here set to 1 because of speed
out <- gl.amova(bandicoot.gl, permutations=1)

Description

This function takes one individual and estimates their probability of coming from individual populations from multilocus genotype frequencies.

Usage

gl.assign.grm(x, unknown, verbose = NULL)

Arguments

Details

This function is a re-implementation of the function multilocus_assignment from package gstudio. Description of the method used in this function can be found at: https://dyerlab.github.io/applied_population_genetics/population-assignment.html

Value

A data.frame consisting of assignment probabilities for each population.

Author(s)

Custodian: Luis Mijangos – Post to https://groups.google.com/d/forum/dartr

Examples

require("dartR.data")
if ((requireNamespace("rrBLUP", quietly = TRUE)) &(requireNamespace("gplots", quietly = TRUE)) ) {
res <- gl.assign.grm(platypus.gl,unknown="T27")
}

Description

This script assigns an individual of unknown provenance to one or more target populations based on the unknown individual's proximity to population centroids; proximity is estimated using Mahalanobis Distance.

The following process is followed:

1. An ordination is undertaken on the populations to again yield a series of orthogonal (independent) axes.

2. A workable subset of dimensions is chosen, that specified, or equal to the number of dimensions with substantive eigenvalues, whichever is the smaller.

3. The Mahalobalis Distance is calculated for the unknown against each population and probability of membership of each population is calculated. The assignment probabilities are listed in support of a decision.

Usage

gl.assign.mahalanobis(
  x,
  dim.limit = 2,
  plevel = 0.999,
  plot.out = TRUE,
  unknown,
  verbose = NULL
)

Arguments

Details

There are three considerations to assignment. First, consider only those populations for which the unknown has no private alleles. Private alleles are an indication that the unknown does not belong to a target population (provided that the sample size is adequate, say >=10). This can be evaluated with gl.assign.pa().

A next step is to consider the PCoA plot for populations where no private alleles have been detected. The position of the unknown in relation to the confidence ellipses is plotted by this script as a basis for narrowing down the list of putative source populations. This can be evaluated with gl.assign.pca().

The third step (delivered by this script) is to consider the assignment probabilities based on the squared Generalised Linear Distance (Mahalanobis distance) of the unknown from the centroid for each population, then to consider the probability associated with its quantile using the Chisquare approximation. In effect, this index takes into account position of the unknown in relation to the confidence envelope in all selected dimensions of the ordination. The larger the assignment probability, the greater the confidence in the assignment.

If dim.limit is set to 2, to correspond with the dimensions used in gl.assign.pa(), then the output provides a ranking of the final set of putative source populations.

If dim.limit is set to be > 2, then this script provides a basis for further narrowing the set of putative populations.If the unknown individual is an extreme outlier, say at less than 0.001 probability of population membership (0.999 confidence envelope), then the associated population can be eliminated from further consideration.

Warning: gl.assign.mahal() treats each specified dimension equally, without regard to the percentage variation explained after ordination. If the unknown is an outlier in a lower dimension with an explanatory variance of, say, 0.1 dimensions from the ordination.

Each of these above approaches provides evidence, none are 100 They need to be interpreted cautiously.

In deciding the assignment, the script considers an individual to be an outlier with respect to a particular population at alpha = 0.001 as default

Value

A data frame with the results of the assignment analysis.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

Examples

## Not run: 
#Test run with a focal individual from the Macleay River (EmmacMaclGeor) 
test <- gl.assign.pa(testset.gl, unknown='UC_01044', nmin=10, threshold=1,
verbose=3) 
test_2  <- gl.assign.pca(test, unknown='UC_01044', plevel=0.95, verbose=3)
df <- gl.assign.mahalanobis(test_2, unknown='UC_01044', verbose=3)

## End(Not run)

Description

This script eliminates from consideration as putative source populations, those populations for which the individual has too many private alleles. The populations that remain are putative source populations, subject to further consideration.

The algorithm identifies those target populations for which the individual has no private alleles or for which the number of private alleles does not exceed a user specified threshold.

An excessive count of private alleles is an indication that the unknown does not belong to a target population (provided that the sample size is adequate, say >=10).

Usage

gl.assign.pa(
  x,
  unknown,
  nmin = 10,
  threshold = 0,
  n.best = NULL,
  verbose = NULL
)

Arguments

Value

A genlight object containing the focal individual (assigned to population 'unknown') and populations for which the focal individual is not distinctive (number of loci with private alleles less than or equal to the threshold). If no such populations, the genlight object contains only data for the unknown individual.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.assign.pca

Examples

# Test run with a focal individual from the Macleay River (EmmacMaclGeor)
  test <- gl.assign.pa(testset.gl, unknown='UC_00146', nmin=10, threshold=1,
  verbose=3)

Description

This script assigns an individual of unknown provenance to one or more target populations based on its proximity to each population defined by a confidence ellipse in ordinated space of two dimensions.

The following process is followed:

1. The space defined by the loci is ordinated to yield a series of orthogonal axes (independent), and the top two dimensions are considered. Populations for which the unknown lies outside the specified confidence limits are no longer removed from the dataset.

Usage

gl.assign.pca(x, unknown, plevel = 0.999, plot.out = TRUE, verbose = NULL)

Arguments

Details

There are three considerations to assignment. First, consider only those populations for which the unknown has no private alleles. Private alleles are an indication that the unknown does not belong to a target population (provided that the sample size is adequate, say >=10). This can be evaluated with gl.assign.pa().

A next step is to consider the PCoA plot for populations where no private alleles have been detected and the position of the unknown in relation to the confidence ellipses as is plotted by this script. Note, this plot is considering only the top two dimensions of the ordination, and so an unknown lying outside the confidence ellipse can be unambiguously interpreted as it lying outside the confidence envelope. However, if the unknown lies inside the confidence ellipse in two dimensions, then it may still lie outside the confidence envelope in deeper dimensions. This second step is good for eliminating populations from consideration, but does not provide confidence in assignment.

The third step is to consider the assignment probabilities, using the script gl.assign.mahalanobis(). This approach calculates the squared Generalised Linear Distance (Mahalanobis distance) of the unknown from the centroid for each population, and calculates the probability associated with its quantile under the zero truncated normal distribution. This index takes into account position of the unknown in relation to the confidence envelope in all selected dimensions of the ordination.

Each of these approaches provides evidence, none are 100 need to be interpreted cautiously. They are best applied sequentially.

In deciding the assignment, the script considers an individual to be an outlier with respect to a particular population at alpha = 0.001 as default.

Value

A genlight object containing only those populations that are putative source populations for the unknown individual.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

Examples

## Not run: 
#Test run with a focal individual from the Macleay River (EmmacMaclGeor) 
test <- gl.assign.pa(testset.gl, unknown='UC_00146', nmin=10, threshold=1,
verbose=3) 
test_2 <- gl.assign.pca(test, unknown='UC_00146', plevel=0.95, verbose=3)

## End(Not run)

Description

Based on function basic.stats. Check ?basic.stats for help.

Usage

gl.basic.stats(x, digits = 4, verbose = NULL)

Arguments

Value

Several tables and lists with all basic stats. basic.stats for details.

Author(s)

Bernd Gruber (bugs? Post to https://groups.google.com/d/forum/dartr)

Examples

if (!(requireNamespace("hierfstat", quietly = TRUE))) {
out <- gl.basic.stats(possums.gl[1:10,1:100])
}

Description

Basic Local Alignment Search Tool (BLAST; Altschul et al., 1990 & 1997) is a sequence comparison algorithm optimized for speed used to search sequence databases for optimal local alignments to a query. This function creates fasta files, creates databases to run BLAST, runs blastn and filters these results to obtain the best hit per sequence.

This function can be used to run BLAST alignment of short-read (DArTseq data) and long-read sequences (Illumina, PacBio... etc). You can use reference genomes from NCBI, genomes from your private collection, contigs, scaffolds or any other genetic sequence that you would like to use as reference.

Usage

gl.blast(
  x,
  ref_genome,
  task = "megablast",
  Percentage_identity = 70,
  Percentage_overlap = 0.8,
  bitscore = 50,
  number_of_threads = 2,
  verbose = NULL
)

Arguments

Details

Installing BLAST

You can download the BLAST installs from: https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/

It is important to install BLAST in a path that does not contain spaces for this function to work.

Running BLAST

Four different tasks are supported:

• “megablast”, for very similar sequences (e.g, sequencing errors)

• “dc-megablast”, typically used for inter-species comparisons

• “blastn”, the traditional program used for inter-species comparisons

• “blastn-short”, optimized for sequences less than 30 nucleotides

If you are running a BLAST alignment of similar sequences, for example Turtle Genome Vs Turtle Sequences, the recommended parameters are: task = “megablast”, Percentage_identity = 70, Percentage_overlap = 0.8 and bitscore = 50.

If you are running a BLAST alignment of highly dissimilar sequences because you are probably looking for sex linked hits in a distantly related species, and you are aligning for example sequences of Chicken Genome Vs Bassiana, the recommended parameters are: task = “dc-megablast”, Percentage_identity = 50, Percentage_overlap = 0.01 and bitscore = 30.

Be aware that running BLAST might take a long time (i.e. days) depending of the size of your query, the size of your database and the number of threads selected for your computer.

BLAST output

The BLAST output is formatted as a table using output format 6, with columns defined in the following order:

• qseqid - Query Seq-id

• sacc - Subject accession

• stitle - Subject Title

• qseq - Aligned part of query sequence

• sseq - Aligned part of subject sequence

• nident - Number of identical matches

• mismatch - Number of mismatches

• pident - Percentage of identical matches

• length - Alignment length

• evalue - Expect value

• bitscore - Bit score

• qstart - Start of alignment in query

• qend - End of alignment in query

• sstart - Start of alignment in subject

• send - End of alignment in subject

• gapopen - Number of gap openings

• gaps - Total number of gaps

• qlen - Query sequence length

• slen - Subject sequence length

• PercentageOverlap - length / min(qlen,slen)

Databases containing unfiltered aligned sequences, filtered aligned sequences and one hit per sequence are saved to the temporal directory (tempdir) and can be accessed with the function gl.print.reports and listed with the function gl.list.reports. Note that they can be accessed only in the current R session because tempdir is cleared each time that the R session is closed.

BLAST filtering

BLAST output is filtered by ordering the hits of each sequence first by the highest percentage identity, then the highest percentage overlap and then the highest bitscore. Only one hit per sequence is kept based on these selection criteria.

Value

If the input is a genlight object: returns a genlight object with one hit per sequence merged to the slot $other$loc.metrics. If the input is a fasta file: returns a dataframe with one hit per sequence.

Author(s)

Berenice Talamantes Becerra & Luis Mijangos (Post to https://groups.google.com/d/forum/dartr)

References

• Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of molecular biology, 215(3), 403-410.

• Altschul, S. F., Madden, T. L., Schäffer, A. A., Zhang, J., Zhang, Z., Miller, W., & Lipman, D. J. (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids research, 25(17), 3389-3402.

• Pearson, W. R. (2013). An introduction to sequence similarity (“homology”) searching. Current protocols in bioinformatics, 42(1), 3-1.

See Also

gl.print.history

Examples

## Not run: 
res <- gl.blast(x= testset.gl,ref_genome = 'sequence.fasta')
# display of reports saved in the temporal directory
gl.list.reports()
# open the reports saved in the temporal directory
blast_databases <- gl.print.reports(1)

## End(Not run)

Description

The verbosity can be set in one of two ways – (a) explicitly by the user by passing a value using the parameter verbose in a function, or (b) by setting the verbosity globally as part of the r environment (gl.set.verbosity).

Usage

gl.check.verbosity(x = NULL)

Arguments

Value

The verbosity, in variable verbose

Author(s)

Bernd Gruber (Post to https://groups.google.com/d/forum/dartr)

Examples

gl.check.verbosity()

Description

This script takes a file generated by gl.fixed.diff and amalgamates populations with distance less than or equal to a specified threshold. The distance matrix is generated by gl.fixed.diff().

The script then applies the new population assignments to the genlight object and recalculates the distance and associated matrices.

Usage

gl.collapse(fd, tpop = 0, tloc = 0, pb = FALSE, verbose = NULL)

Arguments

Value

A list containing the gl object x and the following square matrices:

1. $gl – the new genlight object with populations collapsed;

2. $fd – raw fixed differences;

3. $pcfd – percent fixed differences;

4. $nobs – mean no. of individuals used in each comparison;

5. $nloc – total number of loci used in each comparison;

6. $expfpos – NA's, populated by gl.fixed.diff [by simulation]

7. $expfpos – NA's, populated by gl.fixed.diff [by simulation]

8. $prob – NA's, populated by gl.fixed.diff [by simulation]

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

Examples

fd <- gl.fixed.diff(testset.gl,tloc=0.05)
fd
fd2 <- gl.collapse(fd,tpop=1)
fd2
fd3 <- gl.collapse(fd2,tpop=1)
fd3

 fd <- gl.fixed.diff(testset.gl,tloc=0.05)
 fd2 <- gl.collapse(fd)

Description

This function will check to see that the genlight object conforms to expectation in regard to dartR requirements (see details), and if it does not, will rectify it.

Usage

gl.compliance.check(x, verbose = NULL)

Arguments

Details

A genlight object used by dartR has a number of requirements that allow functions within the package to operate correctly. The genlight object comprises:

1. The SNP genotypes or Tag Presence/Absence data (SilicoDArT);

2. An associated dataframe (gl@other$loc.metrics) containing the locus metrics (e.g. Call Rate, Repeatability, etc);

3. An associated dataframe (gl@other$ind.metrics) containing the individual/sample metrics (e.g. sex, latitude (=lat), longitude(=lon), etc);

4. A specimen identity field (indNames(gl)) with the unique labels applied to each individual/sample;

5. A population assignment (popNames) for each individual/specimen;

6. Flags that indicate whether or not calculable locus metrics have been updated.

Value

A genlight object that conforms to the expectations of dartR

Author(s)

Custodian: Luis Mijangos – Post to https://groups.google.com/d/forum/dartr

Examples

x <- gl.compliance.check(testset.gl)
x <- gl.compliance.check(testset.gs)

Description

Calculates a cost distance matrix, to be used with run.popgensim.

Usage

gl.costdistances(landscape, locs, method, NN, verbose = NULL)

Arguments

Value

A costdistance matrix between all pairs of locs.

Examples

## Not run: 
data(possums.gl)
library(raster)  #needed for that example
landscape.sim <- readRDS(system.file('extdata','landscape.sim.rdata', 
package='dartR'))
#calculate mean centers of individuals per population
xy <- apply(possums.gl@other$xy, 2, function(x) tapply(x, pop(possums.gl),
 mean))
cd <- gl.costdistances(landscape.sim, xy, method='leastcost', NN=8)
round(cd,3)

## End(Not run)

Description

The script reassigns existing individuals to a new population and removes their existing population assignment.

The script returns a genlight object with the new population assignment.

Usage

gl.define.pop(x, ind.list, new, verbose = NULL)

Arguments

Value

A genlight object with the redefined population structure.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

Examples

popNames(testset.gl)
gl <- gl.define.pop(testset.gl, ind.list=c('AA019073','AA004859'), 
new='newguys')
popNames(gl)
indNames(gl)[pop(gl)=='newguys']

Description

Different causes may be responsible for lack of Hardy-Weinberg proportions. This function helps diagnose potential problems.

Usage

gl.diagnostics.hwe(
  x,
  alpha_val = 0.05,
  bins = 20,
  stdErr = TRUE,
  colors_hist = two_colors,
  colors_barplot = two_colors_contrast,
  plot_theme = theme_dartR(),
  save2tmp = FALSE,
  n.cores = "auto",
  verbose = NULL
)

Arguments

Details

This function initially runs gl.report.hwe and reports the ternary plots. The remaining outputs follow the recommendations from Waples (2015) paper and De Meeûs 2018. These include:

1. A histogram with the distribution of p-values of the HWE tests. The distribution should be roughly uniform across equal-sized bins.

2. A bar plot with observed and expected (null expectation) number of significant HWE tests for the same locus in multiple populations (that is, the x-axis shows whether a locus results significant in 1, 2, ..., n populations. The y axis is the count of these occurrences. The zero value on x-axis shows the number of non-significant tests). If HWE tests are significant by chance alone, observed and expected number of HWE tests should have roughly a similar distribution.

3. A scatter plot with a linear regression between Fst and Fis, averaged across subpopulations. De Meeûs 2018 suggests that in the case of Null alleles, a strong positive relationship is expected (together with the Fis standard error much larger than the Fst standard error, see below). Note, this is not the scatter plot that Waples 2015 presents in his paper. In the lower right corner of the plot, the Pearson correlation coefficient is reported.

4. The Fis and Fst (averaged over loci and subpopulations) standard errors are also printed on screen and reported in the returned list (if stdErr=TRUE). These are computed with the Jackknife method over loci (See De Meeûs 2007 for details on how this is computed) and it may take some time for these computations to complete. De Meeûs 2018 suggests that under a global significant heterozygosity deficit:

   - if the correlation between Fis and Fst is strongly positive, and StdErrFis >> StdErrFst, Null alleles are likely to be the cause.

   - if the correlation between Fis and Fst is ~0 or mildly positive, and StdErrFis > StdErrFst, Wahlund may be the cause.

   - if the correlation between Fis and Fst is ~0, and StdErrFis ~ StdErrFst, selfing or sib mating could to be the cause.

   It is important to realise that these statistics only suggest a pattern (pointers). Their absence is not conclusive evidence of the absence of the problem, as their presence does not confirm the cause of the problem.

5. A table where the number of observed and expected significant HWE tests are reported by each population, indicating whether these are due to heterozygosity excess or deficiency. These can be used to have a clue of potential problems (e.g. deficiency might be due to a Wahlund effect, presence of null alleles or non-random sampling; excess might be due to sex linkage or different selection between sexes, demographic changes or small Ne. See Table 1 in Wapples 2015). The last two columns of the table generated by this function report chisquare values and their associated p-values. Chisquare is computed following Fisher's procedure for a global test (Fisher 1970). This basically tests whether there is at least one test that is truly significant in the series of tests conducted (De Meeûs et al 2009).

Value

A list with the table with the summary of the HWE tests and (if stdErr=TRUE) a named vector with the StdErrFis and StdErrFst.

Author(s)

Custodian: Carlo Pacioni – Post to https://groups.google.com/d/forum/dartr

References

• de Meeûs, T., McCoy, K.D., Prugnolle, F., Chevillon, C., Durand, P., Hurtrez-Boussès, S., Renaud, F., 2007. Population genetics and molecular epidemiology or how to “débusquer la bête”. Infection, Genetics and Evolution 7, 308-332.

• De Meeûs, T., Guégan, J.-F., Teriokhin, A.T., 2009. MultiTest V.1.2, a program to binomially combine independent tests and performance comparison with other related methods on proportional data. BMC Bioinformatics 10, 443-443.

• De Meeûs, T., 2018. Revisiting FIS, FST, Wahlund Effects, and Null Alleles. Journal of Heredity 109, 446-456.

• Fisher, R., 1970. Statistical methods for research workers Edinburgh: Oliver and Boyd.

• Waples, R. S. (2015). Testing for Hardy–Weinberg proportions: have we lost the plot?. Journal of heredity, 106(1), 1-19.

See Also

gl.report.hwe

Examples

## Not run: 
require("dartR.data")
res <- gl.diagnostics.hwe(x = gl.filter.allna(platypus.gl[,1:50]), 
stdErr=FALSE, n.cores=1)

## End(Not run)

Description

Comparing simulations against theoretical expectations

Usage

gl.diagnostics.sim(
  x,
  Ne,
  iteration = 1,
  pop_he = 1,
  pops_fst = c(1, 2),
  plot_theme = theme_dartR(),
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Details

Two plots are presented comparing the simulations against theoretical expectations:

1. Expected heterozygosity under neutrality (Crow & Kimura, 1970, p. 329) is calculated as:

   Het = He0(1-(1/2Ne))^t,

   where Ne is effective population size, He0 is heterozygosity at generation 0 and t is the number of generations.

2. Expected FST under neutrality (Takahata, 1983) is calculated as:

   FST=1/(4Nem(n/(n-1))^2+1),

   where Ne is effective populations size of each individual subpopulation, m is dispersal rate and n the number of subpopulations (always 2).

Value

Returns plots comparing simulations against theoretical expectations

Author(s)

Custodian: Luis Mijangos – Post to https://groups.google.com/d/forum/dartr

References

• Crow JF, Kimura M. An introduction to population genetics theory. An introduction to population genetics theory. 1970.

• Takahata N. Gene identity and genetic differentiation of populations in the finite island model. Genetics. 1983;104(3):497-512.

See Also

gl.filter.callrate

Examples

## Not run: 
ref_table <- gl.sim.WF.table(file_var=system.file('extdata', 
'ref_variables.csv', package = 'dartR'),interactive_vars = FALSE)
res_sim <- gl.sim.WF.run(file_var = system.file('extdata', 
'sim_variables.csv', package ='dartR'),ref_table=ref_table,
interactive_vars = FALSE,number_pops_phase2=2,population_size_phase2="50 50")
res <- gl.diagnostics.sim(x=res_sim,Ne=50)

## End(Not run)

Description

This script calculates various distances between individuals based on allele frequencies or presence-absence data

Usage

gl.dist.ind(
  x,
  method = NULL,
  scale = FALSE,
  swap = FALSE,
  output = "dist",
  plot.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors = two_colors,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Details

The distance measure for SNP genotypes can be one of:

• Euclidean Distance [method = "Euclidean"]

• Scaled Euclidean Distance [method='Euclidean", scale=TRUE]

• Simple Mismatch Distance [method="Simple"]

• Absolute Mismatch Distance [method="Absolute"]

• Czekanowski (Manhattan) Distance [method="Manhattan"]

The distance measure for Sequence Tag Presence/Absence data (binary) can be one of:

• Euclidean Distance [method = "Euclidean"]

• Scaled Euclidean Distance [method='Euclidean", scale=TRUE]

• Simple Matching Distance [method="Simple"]

• Jaccard Distance [method="Jaccard"]

• Bray-Curtis Distance [method="Bray-Curtis"]

Refer to the dartR Technical Note on Distances in Genetics.

Value

An object of class 'matrix' or dist' giving distances between individuals

Author(s)

Author(s): Arthur Georges. Custodian: Arthur Georges – Post to #' https://groups.google.com/d/forum/dartr

Examples

D <- gl.dist.ind(testset.gl[1:20,], method='manhattan')
D <- gl.dist.ind(testset.gs[1:20,], method='Jaccard',swap=TRUE)

D <- gl.dist.ind(testset.gl[1:20,], method='euclidean',scale=TRUE)

Description

This script calculates various distances between populations based on allele frequencies (SNP genotypes) or frequency of presences in presence-absence data (Euclidean and Fixed-diff distances only).

Usage

gl.dist.pop(
  x,
  method = "euclidean",
  plot.out = TRUE,
  scale = FALSE,
  output = "dist",
  plot_theme = theme_dartR(),
  plot_colors = two_colors,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Details

The distance measure can be one of 'euclidean', 'fixed-diff', 'reynolds', 'nei' and 'chord'. Refer to the documentation of functions described in the the dartR Distance Analysis tutorial for algorithms and definitions.

Value

An object of class 'dist' giving distances between populations

Author(s)

author(s): Arthur Georges. Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

Examples

## Not run: 
# SNP genotypes
D <- gl.dist.pop(possums.gl[1:90,1:100], method='euclidean')
D <- gl.dist.pop(possums.gl[1:90,1:100], method='euclidean',scale=TRUE)
#D <- gl.dist.pop(possums.gl, method='nei')
#D <- gl.dist.pop(possums.gl, method='reynolds')
#D <- gl.dist.pop(possums.gl, method='chord')
#D <- gl.dist.pop(possums.gl, method='fixed-diff')
#Presence-Absence data [only 10 individuals due to speed]
D <- gl.dist.pop(testset.gs[1:10,], method='euclidean')

## End(Not run)
res <- gl.dist.pop(platypus.gl)

Description

The script, having deleted individuals, optionally identifies resultant monomorphic loci or loci with all values missing and deletes them (using gl.filter.monomorphs.r). The script also optionally recalculates statistics made redundant by the deletion of individuals from the dataset.

The script returns a genlight object with the individuals deleted and, optionally, the recalculated locus metadata.

#' See more about data manipulation in the [tutorial](http://georges.biomatix.org/storage/app/media/uploaded-files/tutorial4dartrdatamanipulation22-dec-21-3.pdf).

Usage

gl.drop.ind(x, ind.list, recalc = FALSE, mono.rm = FALSE, verbose = NULL)

Arguments

Value

A genlight object with the reduced data

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.keep.ind to keep rather than drop specified individuals

Examples

# SNP data
   gl2 <- gl.drop.ind(testset.gl,
   ind.list=c('AA019073','AA004859'))
   gl2 <- gl.drop.ind(testset.gl,
   ind.list=c('AA019073','AA004859'))
 # Tag P/A data
   gs2 <- gl.drop.ind(testset.gs,
   ind.list=c('AA020656','AA19077','AA004859'))
   gs2 <- gl.drop.ind(testset.gs, ind.list=c('AA020656'
   ,'AA19077','AA004859'),mono.rm=TRUE, recalc=TRUE)

Description

The script returns a genlight object with specified loci deleted.

#' See more about data manipulation in the [tutorial](http://georges.biomatix.org/storage/app/media/uploaded-files/tutorial4dartrdatamanipulation22-dec-21-3.pdf).

Usage

gl.drop.loc(x, loc.list = NULL, first = NULL, last = NULL, verbose = NULL)

Arguments

Value

A genlight object with the reduced data.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.keep.loc to keep rather than drop specified loci

Examples

# SNP data
  gl2 <- gl.drop.loc(testset.gl, loc.list=c('100051468|42-A/T', '100049816-51-A/G'),verbose=3)
# Tag P/A data
  gs2 <- gl.drop.loc(testset.gs, loc.list=c('20134188','19249144'),verbose=3)

Description

Individuals are assigned to populations based on the specimen metadata file (csv) used with gl.read.dart. The script, having deleted populations, optionally identifies resultant monomorphic loci or loci with all values missing and deletes them (using gl.filter.monomorphs.r). The script also optionally recalculates statistics made redundant by the deletion of individuals from the dataset.

The script returns a genlight object with the new population assignments and the recalculated locus metadata.

#' See more about data manipulation in the [tutorial](http://georges.biomatix.org/storage/app/media/uploaded-files/tutorial4dartrdatamanipulation22-dec-21-3.pdf).

Usage

gl.drop.pop(
  x,
  pop.list,
  as.pop = NULL,
  recalc = FALSE,
  mono.rm = FALSE,
  verbose = NULL
)

Arguments

Value

A genlight object with the reduced data

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.keep.pop to keep rather than drop specified populations

Examples

# SNP data
   gl2 <- gl.drop.pop(testset.gl,
   pop.list=c('EmsubRopeMata','EmvicVictJasp'),verbose=3)
   gl2 <- gl.drop.pop(testset.gl, pop.list=c('EmsubRopeMata','EmvicVictJasp'),
   mono.rm=TRUE,recalc=TRUE)
   gl2 <- gl.drop.pop(testset.gl, pop.list=c('Male','Unknown'),as.pop='sex',verbose=3)
 # Tag P/A data
   gs2 <- gl.drop.pop(testset.gs, pop.list=c('EmsubRopeMata','EmvicVictJasp'))

Description

A script to edit individual names in a genlight object, or to create a reassignment table taking the individual labels from a genlight object, or to edit existing individual labels in an existing recode_ind file.

Usage

gl.edit.recode.ind(
  x,
  out.recode.file = NULL,
  outpath = tempdir(),
  recalc = FALSE,
  mono.rm = FALSE,
  verbose = NULL
)

Arguments

Details

Renaming individuals may be required when there have been errors in labelling arising in the process from sample to DArT files. There may be occasions where renaming individuals is required for preparation of figures. Caution needs to be exercised because of the potential for breaking the 'chain of evidence' between the samples themselves and the analyses. Recoding individuals can also be done with a recode table (csv).

This script will input an existing recode table for editing and optionally save it as a new table, or if the name of an input table is not supplied, will generate a table using the individual labels in the parent genlight object.

The script, having deleted individuals, optionally identifies resultant monomorphic loci or loci with all values missing and deletes them (using gl.filter.monomorphs.r). The script also optionally recalculates statistics made redundant by the deletion of individuals from the dataset.

Use outpath=getwd() or outpath='.' when calling this function to direct output files to your working directory.

The script returns a genlight object with the new individual labels and the recalculated locus metadata.

Value

An object of class ('genlight') with the revised individual labels.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.recode.ind, gl.drop.ind, gl.keep.ind

Examples

## Not run: 
gl <- gl.edit.recode.ind(testset.gl)
gl <- gl.edit.recode.ind(testset.gl, out.recode.file='ind.recode.table.csv')

## End(Not run)

Description

A script to edit population assignments in a genlight object, or to create a reassignment table taking the population assignments from a genlight object, or to edit existing population assignments in a pop.recode.table.

Usage

gl.edit.recode.pop(
  x,
  pop.recode = NULL,
  out.recode.file = NULL,
  outpath = tempdir(),
  recalc = FALSE,
  mono.rm = FALSE,
  verbose = NULL
)

Arguments

Details

Genlight objects assign specimens to populations based on information in the ind.metadata file provided when the genlight object is first generated. Often one wishes to subset the data by deleting populations or to amalgamate populations. This can be done with a pop.recode table with two columns. The first column is the population assignment in the genlight object, the second column provides the new assignment.

This script will input an existing reassignment table for editing and optionally save it as a new table, or if the name of an input table is not supplied, will generate a table using the population assignments in the parent genlight object.

The script, having deleted populations, optionally identifies resultant monomorphic loci or loci with all values missing and deletes them (using gl.filter.monomorphs.r). The script also optionally recalculates statistics made redundant by the deletion of individuals from the dataset.

Use outpath=getwd() or outpath='.' when calling this function to direct output files to your working directory.

The script returns a genlight object with the new population assignments and the recalculated locus metadata.

Value

An object of class ('genlight') with the revised population assignments

Author(s)

Custodian: Arthur Georges –Post to https://groups.google.com/d/forum/dartr

See Also

gl.recode.pop, gl.drop.pop, gl.keep.pop, gl.merge.pop, gl.reassign.pop

Examples

## Not run: 
gl <- gl.edit.recode.pop(testset.gl)
gs <- gl.edit.recode.pop(testset.gs)

## End(Not run)

Description

This function takes a genlight object and runs a STRUCTURE analysis based on functions from strataG

Usage

gl.evanno(sr, plot.out = TRUE)

Arguments

Details

The function is basically a convenient wrapper around the beautiful strataG function evanno (Archer et al. 2016). For a detailed description please refer to this package (see references below).

Value

An Evanno plot is created and a list of all four plots is returned.

Author(s)

Bernd Gruber (Post to https://groups.google.com/d/forum/dartr)

References

• Pritchard, J.K., Stephens, M., Donnelly, P. (2000) Inference of population structure using multilocus genotype data. Genetics 155, 945-959.

• Archer, F. I., Adams, P. E. and Schneiders, B. B. (2016) strataG: An R package for manipulating, summarizing and analysing population genetic data. Mol Ecol Resour. doi:10.1111/1755-0998.12559

• Evanno, G., Regnaut, S., and J. Goudet. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14:2611-2620.

See Also

gl.run.structure, clumpp,

Examples

## Not run: 
#CLUMPP and STRUCTURE need to be installed to be able to run the example
#bc <- bandicoot.gl[,1:100]
#sr <- gl.run.structure(bc, k.range = 2:5, num.k.rep = 3, exec = './structure.exe')
#ev <- gl.evanno(sr)
#ev
#qmat <- gl.plot.structure(sr, k=3, CLUMPP='d:/structure/')
#head(qmat)
#gl.map.structure(qmat, bc, scalex=1, scaley=0.5)

## End(Not run)

Description

This function takes two populations and generates allele frequency profiles for them. It then samples an allele frequency for each, at random, and estimates a sampling distribution for those two allele frequencies. Drawing two samples from those sampling distributions, it calculates whether or not they represent a fixed difference. This is applied to all loci, and the number of fixed differences so generated are counted, as an expectation. The script distinguished between true fixed differences (with a tolerance of delta), and false positives. The simulation is repeated a given number of times (default=1000) to provide an expectation of the number of false positives, given the observed allele frequency profiles and the sample sizes. The probability of the observed count of fixed differences is greater than the expected number of false positives is calculated.

Usage

gl.fdsim(
  x,
  poppair,
  obs = NULL,
  sympatric = FALSE,
  reps = 1000,
  delta = 0.02,
  verbose = NULL
)

Arguments

Value

A list containing the following square matrices [[1]] observed fixed differences; [[2]] mean expected number of false positives for each comparison; [[3]] standard deviation of the no. of false positives for each comparison; [[4]] probability the observed fixed differences arose by chance for each comparison.

Author(s)

Custodian: Arthur Georges (Post to https://groups.google.com/d/forum/dartr)

Examples

fd <- gl.fdsim(testset.gl[,1:100],poppair=c('EmsubRopeMata','EmmacBurnBara'),
sympatric=TRUE,verbose=3)

Description

This script deletes deletes loci or individuals with all calls missing (NA), from a genlight object

A DArT dataset will not have loci for which the calls are scored all as missing (NA) for a particular individual, but such loci can arise rarely when populations or individuals are deleted. Similarly, a DArT dataset will not have individuals for which the calls are scored all as missing (NA) across all loci, but such individuals may sneak in to the dataset when loci are deleted. Retaining individual or loci with all NAs can cause issues for several functions.

Also, on occasion an analysis will require that there are some loci scored in each population. Setting by.pop=TRUE will result in removal of loci when they are all missing in any one population.

Note that loci that are missing for all individuals in a population are not imputed with method 'frequency' or 'HW'. Consider using the function gl.filter.allna with by.pop=TRUE.

Usage

gl.filter.allna(x, by.pop = FALSE, recalc = FALSE, verbose = NULL)

Arguments

Value

A genlight object having removed individuals that are scored NA across all loci, or loci that are scored NA across all individuals.

Author(s)

Author(s): Arthur Georges. Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

Other filter functions: gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

# SNP data
  result <- gl.filter.allna(testset.gl, verbose=3)
# Tag P/A data
  result <- gl.filter.allna(testset.gs, verbose=3)

Description

SNP datasets generated by DArT have missing values primarily arising from failure to call a SNP because of a mutation at one or both of the restriction enzyme recognition sites. The script gl.filter.callrate() will filter out the loci with call rates below a specified threshold.

Tag Presence/Absence datasets (SilicoDArT) have missing values where it is not possible to determine reliably if there the sequence tag can be called at a particular locus.

Usage

gl.filter.callrate(
  x,
  method = "loc",
  threshold = 0.95,
  mono.rm = FALSE,
  recalc = FALSE,
  recursive = FALSE,
  plot.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors = two_colors,
  bins = 25,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Details

Because this filter operates on call rate, this function recalculates Call Rate, if necessary, before filtering. If individuals are removed using method='ind', then the call rate stored in the genlight object is, optionally, recalculated after filtering.

Note that when filtering individuals on call rate, the initial call rate is calculated and compared against the threshold. After filtering, if mono.rm=TRUE, the removal of monomorphic loci will alter the call rates. Some individuals with a call rate initially greater than the nominated threshold, and so retained, may come to have a call rate lower than the threshold. If this is a problem, repeated iterations of this function will resolve the issue. This is done by setting mono.rm=TRUE and recursive=TRUE, or it can be done manually.

Callrate is summarized by locus or by individual to allow sensible decisions on thresholds for filtering taking into consideration consequential loss of data. The summary is in the form of a tabulation and plots.

Plot themes can be obtained from

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Resultant ggplot(s) and the tabulation(s) are saved to the session's temporary directory.

Value

The reduced genlight or genind object, plus a summary

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.report.callrate

Other filter functions: gl.filter.allna(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

# SNP data
  result <- gl.filter.callrate(testset.gl[1:10], method='loc', threshold=0.8,
   verbose=3)
  result <- gl.filter.callrate(testset.gl[1:10], method='ind', threshold=0.8,
   verbose=3)
  result <- gl.filter.callrate(testset.gl[1:10], method='pop', threshold=0.8,
   verbose=3)
# Tag P/A data
  result <- gl.filter.callrate(testset.gs[1:10], method='loc', 
  threshold=0.95, verbose=3)
  result <- gl.filter.callrate(testset.gs[1:10], method='ind', 
  threshold=0.8, verbose=3)
  result <- gl.filter.callrate(testset.gs[1:10], method='pop', 
  threshold=0.8, verbose=3)
  
  res <- gl.filter.callrate(platypus.gl)

Description

Hamming distance is calculated as the number of base differences between two sequences which can be expressed as a count or a proportion. Typically, it is calculated between two sequences of equal length. In the context of DArT trimmed sequences, which differ in length but which are anchored to the left by the restriction enzyme recognition sequence, it is sensible to compare the two trimmed sequences starting from immediately after the common recognition sequence and terminating at the last base of the shorter sequence.

Usage

gl.filter.hamming(
  x,
  threshold = 0.2,
  rs = 5,
  taglength = 69,
  plot.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors = two_colors,
  pb = FALSE,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Details

Hamming distance can be computed by exploiting the fact that the dot product of two binary vectors x and (1-y) counts the corresponding elements that are different between x and y. This approach can also be used for vectors that contain more than two possible values at each position (e.g. A, C, T or G).

If a pair of DNA sequences are of differing length, the longer is truncated.

The algorithm is that of Johann de Jong https://johanndejong.wordpress.com/2015/10/02/faster-hamming-distance-in-r-2/ as implemented in utils.hamming.

Only one of two loci are retained if their Hamming distance is less that a specified percentage. 5 base differences out of 100 bases is a 20

Value

A genlight object filtered on Hamming distance.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

Examples

# SNP data
test <- platypus.gl
test <- gl.subsample.loci(platypus.gl,n=50)
result <- gl.filter.hamming(test, threshold=0.25, verbose=3)

Description

Calculates the observed heterozygosity for each individual in a genlight object and filters individuals based on specified threshold values. Use gl.report.heterozygosity to determine the appropriate thresholds.

Usage

gl.filter.heterozygosity(x, t.upper = 0.7, t.lower = 0, verbose = NULL)

Arguments

Value

The filtered genlight object.

Author(s)

Custodian: Luis Mijangos – Post to https://groups.google.com/d/forum/dartr

See Also

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

result <- gl.filter.heterozygosity(testset.gl,t.upper=0.06,verbose=3)
 tmp <- gl.report.heterozygosity(result,method='ind')

Description

This function filters out loci showing significant departure from H-W proportions based on observed frequencies of reference homozygotes, heterozygotes and alternate homozygotes.

Loci are filtered out if they show HWE departure either in any one population (n.pop.threshold =1) or in at least X number of populations (n.pop.threshold > 1).

Usage

gl.filter.hwe(
  x,
  subset = "each",
  n.pop.threshold = 1,
  method_sig = "Exact",
  multi_comp = FALSE,
  multi_comp_method = "BY",
  alpha_val = 0.05,
  pvalue_type = "midp",
  cc_val = 0.5,
  min_sample_size = 5,
  verbose = NULL
)

Arguments

Details

There are several factors that can cause deviations from Hardy-Weinberg proportions including: mutation, finite population size, selection, population structure, age structure, assortative mating, sex linkage, nonrandom sampling and genotyping errors. Therefore, testing for Hardy-Weinberg proportions should be a process that involves a careful evaluation of the results, a good place to start is Waples (2015).

Note that tests for H-W proportions are only valid if there is no population substructure (assuming random mating) and have sufficient power only when there is sufficient sample size (n individuals > 15).

Populations can be defined in three ways:

• Merging all populations in the dataset using subset = 'all'.

• Within each population separately using: subset = 'each'.

• Within selected populations using for example: subset = c('pop1','pop2').

Two different statistical methods to test for deviations from Hardy Weinberg proportions:

• The classical chi-square test (method_sig='ChiSquare') based on the function HWChisq of the R package HardyWeinberg. By default a continuity correction is applied (cc_val=0.5). The continuity correction can be turned off (by specifying cc_val=0), for example in cases of extreme allele frequencies in which the continuity correction can lead to excessive type 1 error rates.

• The exact test (method_sig='Exact') based on the exact calculations contained in the function HWExactStats of the R package HardyWeinberg, and described in Wigginton et al. (2005). The exact test is recommended in most cases (Wigginton et al., 2005). Three different methods to estimate p-values (pvalue_type) in the Exact test can be used:

  • 'dost' p-value is computed as twice the tail area of a one-sided test.

  • 'selome' p-value is computed as the sum of the probabilities of all samples less or equally likely as the current sample.

  • 'midp', p-value is computed as half the probability of the current sample + the probabilities of all samples that are more extreme.

  The standard exact p-value is overly conservative, in particular for small minor allele frequencies. The mid p-value ameliorates this problem by bringing the rejection rate closer to the nominal level, at the price of occasionally exceeding the nominal level (Graffelman & Moreno, 2013).

Correction for multiple tests can be applied using the following methods based on the function p.adjust:

• 'holm' is also known as the sequential Bonferroni technique (Rice, 1989). This method has a greater statistical power than the standard Bonferroni test, however this method becomes very stringent when many tests are performed and many real deviations from the null hypothesis can go undetected (Waples, 2015).

• 'hochberg' based on Hochberg, 1988.

• 'hommel' based on Hommel, 1988. This method is more powerful than Hochberg's, but the difference is usually small.

• 'bonferroni' in which p-values are multiplied by the number of tests. This method is very stringent and therefore has reduced power to detect multiple departures from the null hypothesis.

• 'BH' based on Benjamini & Hochberg, 1995.

• 'BY' based on Benjamini & Yekutieli, 2001.

The first four methods are designed to give strong control of the family-wise error rate. The last two methods control the false discovery rate (FDR), the expected proportion of false discoveries among the rejected hypotheses. The false discovery rate is a less stringent condition than the family-wise error rate, so these methods are more powerful than the others, especially when number of tests is large. The number of tests on which the adjustment for multiple comparisons is the number of populations times the number of loci.

From v2.1 gl.filter.hwe takes the argument n.pop.threshold. if n.pop.threshold > 1 loci will be removed only if they are concurrently significant (after adjustment if applied) out of hwe in >= n.pop.threshold > 1.

Value

A genlight object with the loci departing significantly from H-W proportions removed.

Author(s)

Custodian: Luis Mijangos – Post to https://groups.google.com/d/forum/dartr

References

• Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 1165–1188.

• Graffelman, J. (2015). Exploring Diallelic Genetic Markers: The Hardy Weinberg Package. Journal of Statistical Software 64:1-23.

• Graffelman, J. & Morales-Camarena, J. (2008). Graphical tests for Hardy-Weinberg equilibrium based on the ternary plot. Human Heredity 65:77-84.

• Graffelman, J., & Moreno, V. (2013). The mid p-value in exact tests for Hardy-Weinberg equilibrium. Statistical applications in genetics and molecular biology, 12(4), 433-448.

• Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75, 800–803.

• Hommel, G. (1988). A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika, 75, 383–386.

• Rice, W. R. (1989). Analyzing tables of statistical tests. Evolution, 43(1), 223-225.

• Waples, R. S. (2015). Testing for Hardy–Weinberg proportions: have we lost the plot?. Journal of heredity, 106(1), 1-19.

• Wigginton, J.E., Cutler, D.J., & Abecasis, G.R. (2005). A Note on Exact Tests of Hardy-Weinberg Equilibrium. American Journal of Human Genetics 76:887-893.

See Also

gl.report.hwe

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

result <- gl.filter.hwe(x = bandicoot.gl)

Description

This function uses the statistic set in the parameter stat_keep from function gl.report.ld.map to choose the SNP to keep when two SNPs are in LD. When a SNP is selected to be filtered out in each pairwise comparison, the function stores its name in a list. In subsequent pairwise comparisons, if the SNP is already in the list, the other SNP will be kept.

Usage

gl.filter.ld(
  x,
  ld_report,
  threshold = 0.2,
  pop.limit = ceiling(nPop(x)/2),
  verbose = NULL
)

Arguments

Value

The reduced genlight object.

Author(s)

Custodian: Luis Mijangos – Post to https://groups.google.com/d/forum/dartr

See Also

gl.report.ld.map

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

## Not run: 
test <- bandicoot.gl
test <- gl.filter.callrate(test,threshold = 1)
res <- gl.report.ld.map(test)
res_2 <- gl.filter.ld(x=test,ld_report = res)
res_3 <- gl.report.ld.map(res_2)

## End(Not run)
if ((requireNamespace("snpStats", quietly = TRUE)) & (requireNamespace("fields", quietly = TRUE))) {
test <- gl.filter.callrate(platypus.gl, threshold = 1)
test <- gl.filter.monomorphs(test)
test <- test[,1:20]
report <- gl.report.ld.map(test)
res <- gl.filter.ld(x=test,ld_report = report)
}

Description

This script uses any field with numeric values stored in $other$loc.metrics to filter loci. The loci to keep can be within the upper and lower thresholds ('within') or outside of the upper and lower thresholds ('outside').

Usage

gl.filter.locmetric(x, metric, upper, lower, keep = "within", verbose = NULL)

Arguments

Details

The fields that are included in dartR, and a short description, are found below. Optionally, the user can also set his/her own filter by adding a vector into $other$loc.metrics as shown in the example.

1. SnpPosition - position (zero is position 1) in the sequence tag of the defined SNP variant base.

2. CallRate - proportion of samples for which the genotype call is non-missing (that is, not '-' ).

3. OneRatioRef - proportion of samples for which the genotype score is 0.

4. OneRatioSnp - proportion of samples for which the genotype score is 2.

5. FreqHomRef - proportion of samples homozygous for the Reference allele.

6. FreqHomSnp - proportion of samples homozygous for the Alternate (SNP) allele.

7. FreqHets - proportion of samples which score as heterozygous, that is, scored as 1.

8. PICRef - polymorphism information content (PIC) for the Reference allele.

9. PICSnp - polymorphism information content (PIC) for the SNP.

10. AvgPIC - average of the polymorphism information content (PIC) of the Reference and SNP alleles.

11. AvgCountRef - sum of the tag read counts for all samples, divided by the number of samples with non-zero tag read counts, for the Reference allele row.

12. AvgCountSnp - sum of the tag read counts for all samples, divided by the number of samples with non-zero tag read counts, for the Alternate (SNP) allele row.

13. RepAvg - proportion of technical replicate assay pairs for which the marker score is consistent.

Value

The reduced genlight dataset.

Author(s)

Luis Mijangos – Post to https://groups.google.com/d/forum/dartr

See Also

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

# adding dummy data
test <- testset.gl
test$other$loc.metrics$test <- 1:nLoc(test)
result <- gl.filter.locmetric(x=test, metric= 'test', upper=255,
lower=200, keep= 'within', verbose=3)

Description

This script calculates the minor allele frequency for each locus and updates the locus metadata for FreqHomRef, FreqHomSnp, FreqHets and MAF (if it exists). It then uses the updated metadata for MAF to filter loci.

Usage

gl.filter.maf(
  x,
  threshold = 0.01,
  by.pop = FALSE,
  pop.limit = ceiling(nPop(x)/2),
  ind.limit = 10,
  recalc = FALSE,
  plot.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors_pop = discrete_palette,
  plot_colors_all = two_colors,
  bins = 25,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Details

Careful consideration needs to be given to the settings to be used for this fucntion. When the filter is applied globally (i.e. by.pop=FALSE) but the data include multiple population, there is the risk to remove markers because the allele frequencies is low (at global level) but the allele frequencies for the same markers may be high within some of the populations (especially if the per-population sample size is small). Similarly, not always it is a sensible choice to run this function using by.pop=TRUE because allele that are rare in a population may be very common in other, but the (possible) allele frequencies will depend on the sample size within each population. Where the purpose of filtering for MAF is to remove possible spurious alleles (i.e. sequencing errors), it is perhaps better to filter based on the number of times an allele is observed (MAC, Minimum Allele Count), under the assumption that if an allele is observed >MAC, it is fairly rare to be an error. From v2.1 The threshold can take values > 1. In this case, these are interpreted as a threshold for MAC.

Value

The reduced genlight dataset

Author(s)

Custodian: Luis Mijangos – Post to https://groups.google.com/d/forum/dartr

See Also

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

result <- gl.filter.monomorphs(testset.gl)
result <- gl.filter.maf(result, threshold=0.05, verbose=3)

Description

This script deletes monomorphic loci from a genlight {adegenet} object

A DArT dataset will not have monomorphic loci, but they can arise, along with loci that are scored all NA, when populations or individuals are deleted.

Retaining monomorphic loci unnecessarily increases the size of the dataset and will affect some calculations.

Note that for SNP data, NAs likely represent null alleles; in tag presence/absence data, NAs represent missing values (presence/absence could not be reliably scored)

Usage

gl.filter.monomorphs(x, verbose = NULL)

Arguments

Value

A genlight object with monomorphic (and all NA) loci removed.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

# SNP data
  result <- gl.filter.monomorphs(testset.gl, verbose=3)
# Tag P/A data
  result <- gl.filter.monomorphs(testset.gs, verbose=3)

Description

This function checks the position of the SNP within the trimmed sequence tag and identifies those for which the SNP position is outside the trimmed sequence tag. This can happen, rarely, when the sequence containing the SNP resembles the adaptor.

The SNP genotype can still be used in most analyses, but functions like gl2fasta() will present challenges if the SNP has been trimmed from the sequence tag.

Not fatal, but should apply this filter before gl.filter.secondaries, for obvious reasons.

Usage

gl.filter.overshoot(x, save2tmp = FALSE, verbose = NULL)

Arguments

Value

A new genlight object with the recalcitrant loci deleted

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

result <- gl.filter.overshoot(testset.gl, verbose=3)

Description

This script is meant to be used prior to gl.nhybrids to maximise the information content of the SNPs used to identify hybrids (currently newhybrids does allow only 200 SNPs). The idea is to use first all loci that have fixed alleles between the potential source populations and then 'fill up' to 200 loci using loci that have private alleles between those. The functions filters for those loci (if invers is set to TRUE, the opposite is returned (all loci that are not fixed and have no private alleles - not sure why yet, but maybe useful.)

Usage

gl.filter.pa(x, pop1, pop2, invers = FALSE, verbose = NULL)

Arguments

Value

The reduced genlight dataset, containing now only fixed and private alleles.

Author(s)

Authors: Bernd Gruber & Ella Kelly (University of Melbourne); Custodian: Luis Mijangos – Post to https://groups.google.com/d/forum/dartr

See Also

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

result <- gl.filter.pa(testset.gl, pop1=pop(testset.gl)[1], 
pop2=pop(testset.gl)[2],verbose=3)

Description

This script removes individuals suspected of being related as parent-offspring,using the output of the function gl.report.parent.offspring, which examines the frequency of pedigree inconsistent loci, that is, those loci that are homozygotes in the parent for the reference allele, and homozygous in the offspring for the alternate allele. This condition is not consistent with any pedigree, regardless of the (unknown) genotype of the other parent. The pedigree inconsistent loci are counted as an indication of whether or not it is reasonable to propose the two individuals are in a parent-offspring relationship.

Usage

gl.filter.parent.offspring(
  x,
  min.rdepth = 12,
  min.reproducibility = 1,
  range = 1.5,
  method = "best",
  rm.monomorphs = FALSE,
  plot.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors = two_colors,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Details

If two individuals are in a parent offspring relationship, the true number of pedigree inconsistent loci should be zero, but SNP calling is not infallible. Some loci will be miss-called. The problem thus becomes one of determining if the two focal individuals have a count of pedigree inconsistent loci less than would be expected of typical unrelated individuals. There are some quite sophisticated software packages available to formally apply likelihoods to the decision, but we use a simple outlier comparison.

To reduce the frequency of miss-calls, and so emphasize the difference between true parent-offspring pairs and unrelated pairs, the data can be filtered on read depth. Typically minimum read depth is set to 5x, but you can examine the distribution of read depths with the function gl.report.rdepth and push this up with an acceptable loss of loci. 12x might be a good minimum for this particular analysis. It is sensible also to push the minimum reproducibility up to 1, if that does not result in an unacceptable loss of loci. Reproducibility is stored in the slot @other$loc.metrics$RepAvg and is defined as the proportion of technical replicate assay pairs for which the marker score is consistent. You can examine the distribution of reproducibility with the function gl.report.reproducibility.

Note that the null expectation is not well defined, and the power reduced, if the population from which the putative parent-offspring pairs are drawn contains many sibs. Note also that if an individual has been genotyped twice in the dataset, the replicate pair will be assessed by this script as being in a parent-offspring relationship.

You should run gl.report.parent.offspring before filtering. Use this report to decide min.rdepth and min.reproducibility and assess impact on your dataset.

Note that if your dataset does not contain RepAvg or rdepth among the locus metrics, the filters for reproducibility and read depth are no used.

Function's output

Plots and table are saved to the temporal directory (tempdir) and can be accessed with the function gl.print.reports and listed with the function gl.list.reports. Note that they can be accessed only in the current R session because tempdir is cleared each time that the R session is closed.

Examples of other themes that can be used can be consulted in

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

the filtered genlight object without A set of individuals in parent-offspring relationship. NULL if no parent-offspring relationships were found.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.list.reports, gl.report.rdepth , gl.print.reports,gl.report.reproducibility, gl.report.parent.offspring

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

out <- gl.filter.parent.offspring(testset.gl[1:10,1:50])

Description

SNP datasets generated by DArT report AvgCountRef and AvgCountSnp as counts of sequence tags for the reference and alternate alleles respectively. These can be used to back calculate Read Depth. Fragment presence/absence datasets as provided by DArT (SilicoDArT) provide Average Read Depth and Standard Deviation of Read Depth as standard columns in their report.

Filtering on Read Depth using the companion script gl.filter.rdepth can be on the basis of loci with exceptionally low counts, or loci with exceptionally high counts.

Usage

gl.filter.rdepth(
  x,
  lower = 5,
  upper = 50,
  plot.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors = two_colors,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Details

For examples of themes, see:

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

Returns a genlight object retaining loci with a Read Depth in the range specified by the lower and upper threshold.

Author(s)

Custodian: Arthur Georges (Post to https://groups.google.com/d/forum/dartr)

See Also

gl.filter.rdepth

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

# SNP data
  gl.report.rdepth(testset.gl)
  result <- gl.filter.rdepth(testset.gl, lower=8, upper=50, verbose=3)
# Tag P/A data
  result <- gl.filter.rdepth(testset.gs, lower=8, upper=50, verbose=3)
  
  res <- gl.filter.rdepth(platypus.gl)

Description

SNP datasets generated by DArT have an index, RepAvg, generated by reproducing the data independently for 30 of alleles that give a repeatable result, averaged over both alleles for each locus.

SilicoDArT datasets generated by DArT have a similar index, Reproducibility. For these fragment presence/absence data, repeatability is the percentage of scores that are repeated in the technical replicate dataset.

Usage

gl.filter.reproducibility(
  x,
  threshold = 0.99,
  plot.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors = two_colors,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Value

Returns a genlight object retaining loci with repeatability (Repavg or Reproducibility) greater than the specified threshold.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.report.reproducibility

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.secondaries(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

# SNP data
  gl.report.reproducibility(testset.gl)
  result <- gl.filter.reproducibility(testset.gl, threshold=0.99, verbose=3)
# Tag P/A data
  gl.report.reproducibility(testset.gs)
  result <- gl.filter.reproducibility(testset.gs, threshold=0.99)
  
  
  test <- gl.subsample.loci(platypus.gl,n=100)
  res <- gl.filter.reproducibility(test)

Description

SNP datasets generated by DArT include fragments with more than one SNP and record them separately with the same CloneID (=AlleleID). These multiple SNP loci within a fragment (secondaries) are likely to be linked, and so you may wish to remove secondaries.

This script filters out all but the first sequence tag with the same CloneID after ordering the genlight object on based on repeatability, avgPIC in that order (method='best') or at random (method='random').

The filter has not been implemented for tag presence/absence data.

Usage

gl.filter.secondaries(x, method = "random", verbose = NULL)

Arguments

Value

The genlight object, with the secondary SNP loci removed.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.sexlinked(), gl.filter.taglength()

Examples

gl.report.secondaries(testset.gl)
result <- gl.filter.secondaries(testset.gl)

Description

Alleles unique to the Y or W chromosome and monomorphic on the X chromosomes will appear in the SNP dataset as genotypes that are heterozygotic in all individuals of the heterogametic sex and homozygous in all individuals of the homogametic sex. This function keeps or drops loci with alleles that behave in this way, as putative sex specific SNP markers.

Usage

gl.filter.sexlinked(
  x,
  sex = NULL,
  filter = NULL,
  read.depth = 0,
  t.het = 0.1,
  t.hom = 0.1,
  t.pres = 0.1,
  plot.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors = three_colors,
  verbose = NULL
)

Arguments

Details

Sex of the individuals for which sex is known with certainty can be provided via a factor (equal to the length of the number of individuals) or to be held in the variable x@other$ind.metrics$sex. Coding is: M for male, F for female, U or NA for unknown/missing. The script abbreviates the entries here to the first character. So, coding of 'Female' and 'Male' works as well. Character are also converted to upper cases.

' Function's output

This function creates also a plot that shows the heterozygosity of males and females at each loci for SNP data or percentage of present/absent in the case of SilicoDArT data.

Examples of other themes that can be used can be consulted in

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

The filtered genlight object (filter = 'keep': sex linked loci, filter='drop', everything except sex linked loci).

Author(s)

Arthur Georges, Bernd Gruber & Floriaan Devloo-Delva (Post to https://groups.google.com/d/forum/dartr)

See Also

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.taglength()

Examples

out <- gl.filter.sexlinked(testset.gl, filter='drop')

out <- gl.filter.sexlinked(testset.gs, filter='drop')

Description

SNP datasets generated by DArT typically have sequence tag lengths ranging from 20 to 69 base pairs.

Usage

gl.filter.taglength(x, lower = 20, upper = 69, verbose = NULL)

Arguments

Value

Returns a genlight object retaining loci with a sequence tag length in the range specified by the lower and upper threshold.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

Other filter functions: gl.filter.allna(), gl.filter.callrate(), gl.filter.heterozygosity(), gl.filter.hwe(), gl.filter.ld(), gl.filter.locmetric(), gl.filter.maf(), gl.filter.monomorphs(), gl.filter.overshoot(), gl.filter.pa(), gl.filter.parent.offspring(), gl.filter.rdepth(), gl.filter.reproducibility(), gl.filter.secondaries(), gl.filter.sexlinked()

Examples

# SNP data
  gl.report.taglength(testset.gl)
  result <- gl.filter.taglength(testset.gl,lower=60)
  gl.report.taglength(result)
# Tag P/A data
  gl.report.taglength(testset.gs)
  result <- gl.filter.taglength(testset.gs,lower=60)
  gl.report.taglength(result)
  
  test <- gl.subsample.loci(platypus.gl, n =100)
  res <- gl.report.taglength(test)

Description

This script takes SNP data or sequence tag P/A data grouped into populations in a genlight object (DArTSeq) and generates a matrix of fixed differences between populations taken pairwise

Usage

gl.fixed.diff(
  x,
  tloc = 0,
  test = FALSE,
  delta = 0.02,
  alpha = 0.05,
  reps = 1000,
  mono.rm = TRUE,
  pb = FALSE,
  verbose = NULL
)

Arguments

Details

A fixed difference at a locus occurs when two populations share no alleles or where all members of one population has a sequence tag scored, and all members of the other population has the sequence tag absent. The challenge with this approach is that when sample sizes are finite, fixed differences will occur through sampling error, compounded when many loci are examined. Simulations suggest that sample sizes of n1=5 and n2=5 are adequate to reduce the probability of [experiment-wide] type 1 error to negligible levels [ploidy=2]. A warning is issued if comparison between two populations involves sample sizes less than 5, taking into account allele drop-out.

Optionally, if test=TRUE, the script will test the fixed differences between final OTUs for statistical significance, using simulation, and then further amalgamate populations that for which there are no significant fixed differences at a specified level of significance (alpha). To avoid conflation of true fixed differences with false positives in the simulations, it is necessary to decide a threshold value (delta) for extreme true allele frequencies that will be considered fixed for practical purposes. That is, fixed differences in the sample set will be considered to be positives (not false positives) if they arise from true allele frequencies of less than 1-delta in one or both populations. The parameter delta is typically set to be small (e.g. delta = 0.02).

NOTE: The above test will only be calculated if tloc=0, that is, for analyses of absolute fixed differences. The test applies in comparisons of allopatric populations only. For sympatric populations, use gl.pval.sympatry().

An absolute fixed difference is as defined above. However, one might wish to score fixed differences at some lower level of allele frequency difference, say where percent allele frequencies are 95,5 and 5,95 rather than 100:0 and 0:100. This adjustment can be done with the tloc parameter. For example, tloc=0.05 means that SNP allele frequencies of 95,5 and 5,95 percent will be regarded as fixed when comparing two populations at a locus.

Value

A list of Class 'fd' containing the gl object and square matrices, as follows:

1. $gl – the output genlight object;

2. $fd – raw fixed differences;

3. $pcfd – percent fixed differences;

4. $nobs – mean no. of individuals used in each comparison;

5. $nloc – total number of loci used in each comparison;

6. $expfpos – if test=TRUE, the expected count of false positives for each comparison [by simulation];

7. $sdfpos – if test=TRUE, the standard deviation of the count of false positives for each comparison [by simulation];

8. $prob – if test=TRUE, the significance of the count of fixed differences [by simulation])

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

is.fixed

Examples

fd <- gl.fixed.diff(testset.gl, tloc=0, verbose=3 )
fd <- gl.fixed.diff(testset.gl, tloc=0, test=TRUE, delta=0.02, reps=100, verbose=3 )

Description

This script calculates pairwise Fst values based on the implementation in the StAMPP package (?stamppFst). It allows to run bootstrap to estimate probability of Fst values to be different from zero. For detailed information please check the help pages (?stamppFst).

Usage

gl.fst.pop(x, nboots = 1, percent = 95, nclusters = 1, verbose = NULL)

Arguments

Value

A matrix of distances between populations (class dist), if nboots =1, otherwise a list with Fsts (in a matrix), Pvalues (a matrix of pvalues), Bootstraps results (data frame of all runs). Hint: Use as.matrix(as.dist(fsts)) if you want to have a squared matrix with symmetric entries returned, instead of a dist object.

Author(s)

Bernd Gruber (bugs? Post to https://groups.google.com/d/forum/dartr)

Examples

test <- gl.filter.callrate(platypus.gl,threshold = 1)
test <- gl.filter.monomorphs(test)
out <- gl.fst.pop(test, nboots=1)

Description

This function calculates the pairwise distances (Euclidean, cost path distances and genetic distances) of populations using a friction matrix and a spatial genind object. The genind object needs to have coordinates in the same projected coordinate system as the friction matrix. The friction matrix can be either a single raster of a stack of several layers. If a stack is provided the specified cost distance is calculated for each layer in the stack. The output of this function can be used with the functions wassermann or lgrMMRR to test for the significance of a layer on the genetic structure.

Usage

gl.genleastcost(
  x,
  fric.raster,
  gen.distance,
  NN = NULL,
  pathtype = "leastcost",
  plotpath = TRUE,
  theta = 1,
  verbose = NULL
)

Arguments

Value

Returns a list that consists of four pairwise distance matrices (Euclidean, Cost, length of path and genetic) and the actual paths as spatial line objects.

Author(s)

Bernd Gruber (bugs? Post to https://groups.google.com/d/forum/dartr)

References

• Cushman, S., Wasserman, T., Landguth, E. and Shirk, A. (2013). Re-Evaluating Causal Modeling with Mantel Tests in Landscape Genetics. Diversity, 5(1), 51-72.

• Landguth, E. L., Cushman, S. A., Schwartz, M. K., McKelvey, K. S., Murphy, M. and Luikart, G. (2010). Quantifying the lag time to detect barriers in landscape genetics. Molecular ecology, 4179-4191.

• Wasserman, T. N., Cushman, S. A., Schwartz, M. K. and Wallin, D. O. (2010). Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landscape Ecology, 25(10), 1601-1612.

See Also

landgenreport, popgenreport, wassermann, lgrMMRR

Examples

## Not run: 
data(possums.gl)
library(raster)  #needed for that example
landscape.sim <- readRDS(system.file('extdata','landscape.sim.rdata', 
package='dartR'))
glc <- gl.genleastcost(x=possums.gl,fric.raster=landscape.sim ,
gen.distance = 'D', NN=8, pathtype = 'leastcost',plotpath = TRUE)
library(PopGenReport)
PopGenReport::wassermann(eucl.mat = glc$eucl.mat, cost.mat = glc$cost.mats, 
 gen.mat = glc$gen.mat)
lgrMMRR(gen.mat = glc$gen.mat, cost.mats = glc$cost.mats,  
eucl.mat = glc$eucl.mat)

## End(Not run)

Description

This function calculates the mean probability of identity by state (IBS) across loci that would result from all the possible crosses of the individuals analyzed. IBD is calculated by an additive relationship matrix approach developed by Endelman and Jannink (2012) as implemented in the function A.mat (package rrBLUP).

Usage

gl.grm(
  x,
  plotheatmap = TRUE,
  palette_discrete = discrete_palette,
  palette_convergent = convergent_palette,
  legendx = 0,
  legendy = 0.5,
  verbose = NULL,
  ...
)

Arguments

Details

Two or more alleles are identical by descent (IBD) if they are identical copies of the same ancestral allele in a base population. The additive relationship matrix is a theoretical framework for estimating a relationship matrix that is consistent with an approach to estimate the probability that the alleles at a random locus are identical in state (IBS).

This function also plots a heatmap, and a dendrogram, of IBD values where each diagonal element has a mean that equals 1+f, where f is the inbreeding coefficient (i.e. the probability that the two alleles at a randomly chosen locus are IBD from the base population). As this probability lies between 0 and 1, the diagonal elements range from 1 to 2. Because the inbreeding coefficients are expressed relative to the current population, the mean of the off-diagonal elements is -(1+f)/n, where n is the number of loci. Individual names are shown in the margins of the heatmap and colors represent different populations.

Value

An identity by descent matrix

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

References

• Endelman, J. B. (2011). Ridge regression and other kernels for genomic selection with r package rrblup. The Plant Genome 4, 250.

• Endelman, J. B. , Jannink, J.-L. (2012). Shrinkage estimation of the realized relationship matrix. G3: Genes, Genomics, Genetics 2, 1405.

See Also

gl.grm.network

Other inbreeding functions: gl.grm.network()

Examples

gl.grm(platypus.gl[1:10,1:100])

Description

This script takes a G matrix generated by gl.grm and represents the relationship among the specimens as a network diagram. In order to use this script, a decision is required on a threshold for relatedness to be represented as link in the network, and on the layout used to create the diagram.

Usage

gl.grm.network(
  G,
  x,
  method = "fr",
  node.size = 8,
  node.label = TRUE,
  node.label.size = 2,
  node.label.color = "black",
  link.color = NULL,
  link.size = 2,
  relatedness_factor = 0.125,
  title = "Network based on a genomic relationship matrix",
  palette_discrete = NULL,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Details

The gl.grm.network function takes a genomic relationship matrix (GRM) generated by the gl.grm function to represent the relationship among individuals in the dataset as a network diagram. To generate the GRM, the function gl.grm uses the function A.mat from package rrBLUP, which implements the approach developed by Endelman and Jannink (2012).

The GRM is an estimate of the proportion of alleles that two individuals have in common. It is generated by estimating the covariance of the genotypes between two individuals, i.e. how much genotypes in the two individuals correspond with each other. This covariance depends on the probability that alleles at a random locus are identical by state (IBS). Two alleles are IBS if they represent the same allele. Two alleles are identical by descent (IBD) if one is a physical copy of the other or if they are both physical copies of the same ancestral allele. Note that IBD is complicated to determine. IBD implies IBS, but not conversely. However, as the number of SNPs in a dataset increases, the mean probability of IBS approaches the mean probability of IBD.

It follows that the off-diagonal elements of the GRM are two times the kinship coefficient, i.e. the probability that two alleles at a random locus drawn from two individuals are IBD. Additionally, the diagonal elements of the GRM are 1+f, where f is the inbreeding coefficient of each individual, i.e. the probability that the two alleles at a random locus are IBD.

Choosing a meaningful threshold to represent the relationship between individuals is tricky because IBD is not an absolute state but is relative to a reference population for which there is generally little information so that we can estimate the kinship of a pair of individuals only relative to some other quantity. To deal with this, we can use the average inbreeding coefficient of the diagonal elements as the reference value. For this, the function subtracts 1 from the mean of the diagonal elements of the GRM. In a second step, the off-diagonal elements are divided by 2, and finally, the mean of the diagonal elements is subtracted from each off-diagonal element after dividing them by 2. This approach is similar to the one used by Goudet et al. (2018).

Below is a table modified from Speed & Balding (2015) showing kinship values, and their confidence intervals (CI), for different relationships that could be used to guide the choosing of the relatedness threshold in the function.

|Relationship |Kinship | 95

|Identical twins/clones/same individual | 0.5 | - |

|Sibling/Parent-Offspring | 0.25 | (0.204, 0.296)|

|Half-sibling | 0.125 | (0.092, 0.158)|

|First cousin | 0.062 | (0.038, 0.089)|

|Half-cousin | 0.031 | (0.012, 0.055)|

|Second cousin | 0.016 | (0.004, 0.031)|

|Half-second cousin | 0.008 | (0.001, 0.020)|

|Third cousin | 0.004 | (0.000, 0.012)|

|Unrelated | 0 | - |

Four layout options are implemented in this function:

• 'fr' Fruchterman-Reingold layout layout_with_fr (package igraph)

• 'kk' Kamada-Kawai layout layout_with_kk (package igraph)

• 'gh' Graphopt layout layout_with_graphopt (package igraph)

• 'mds' Multidimensional scaling layout layout_with_mds (package igraph)

Value

A network plot showing relatedness between individuals

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

References

• Endelman, J. B. , Jannink, J.-L. (2012). Shrinkage estimation of the realized relationship matrix. G3: Genes, Genomics, Genetics 2, 1405.

• Goudet, J., Kay, T., & Weir, B. S. (2018). How to estimate kinship. Molecular Ecology, 27(20), 4121-4135.

• Speed, D., & Balding, D. J. (2015). Relatedness in the post-genomic era: is it still useful?. Nature Reviews Genetics, 16(1), 33-44.

See Also

gl.grm

Other inbreeding functions: gl.grm()

Examples

if (requireNamespace("igraph", quietly = TRUE) & requireNamespace("rrBLUP",
quietly = TRUE) & requireNamespace("fields", quietly=TRUE)) {
t1 <- possums.gl
# filtering on call rate 
t1 <- gl.filter.callrate(t1)
t1 <- gl.subsample.loci(t1,n = 100)
# relatedness matrix
res <- gl.grm(t1,plotheatmap = FALSE)
# relatedness network
res2 <- gl.grm.network(res,t1,relatedness_factor = 0.125)
}

Description

Estimates expected Heterozygosity

Usage

gl.He(gl)

Arguments

Value

A simple vector whit Ho for each loci

Author(s)

Bernd Gruber (Post to https://groups.google.com/d/forum/dartr)

Description

Estimates observed Heterozygosity

Usage

gl.Ho(gl)

Arguments

Value

A simple vector whit Ho for each loci

Author(s)

Bernd Gruber (bugs? Post to https://groups.google.com/d/forum/dartr)

Description

Hardy-Weinberg tests are performed for each loci in each of the populations as defined by the pop slot in a genlight object.

Usage

gl.hwe.pop(
  x,
  alpha_val = 0.05,
  plot.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors = c("gray90", "deeppink"),
  HWformat = FALSE,
  verbose = NULL
)

Arguments

Details

This function employs the HardyWeinberg package, which needs to be installed. The function that is used is HWExactStats, but there are several other great functions implemented in the package regarding HWE. Therefore, this function can return the data in the format expected by the HWE package expects, via HWformat=TRUE and then use this to run other functions of the package.

This functions performs a HWE test for every population (rows) and loci (columns) and returns a true false matrix. True is reported if the p-value of an HWE-test for a particular loci and population was below the specified threshold (alpha_val, default=0.05). The thinking behind this approach is that loci that are not in HWE in several populations have most likely to be treated (e.g. filtered if loci under selection are of interest). If plot=TRUE a barplot on the loci and the sum of deviation over all population is returned. Loci that deviate in the majority of populations can be identified via colSums on the resulting matrix.

Plot themes can be obtained from

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Resultant ggplots and the tabulation are saved to the session's temporary directory.

Value

The function returns a list with up to three components:

• 'HWE' is the matrix over loci and populations

• 'plot' is a plot (ggplot) which shows the significant results for population and loci (can be amended further using ggplot syntax)

• 'HWEformat=TRUE' the 'HWformat' entails SNP data for each population in 'HardyWeinberg'-format to be used with other functions of the package (e.g HWPerm or HWExactPrevious).

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

Examples

out <- gl.hwe.pop(bandicoot.gl[,1:33], alpha_val=0.05, plot.out=TRUE, HWformat=FALSE)

Description

This function performs an isolation by distance analysis based on a Mantel test and also produces an isolation by distance plot. If a genlight object with coordinates is provided, then an Euclidean and genetic distance matrices are calculated.'

Usage

gl.ibd(
  x = NULL,
  distance = "Fst",
  coordinates = "latlon",
  Dgen = NULL,
  Dgeo = NULL,
  Dgeo_trans = "Dgeo",
  Dgen_trans = "Dgen",
  permutations = 999,
  plot.out = TRUE,
  paircols = NULL,
  plot_theme = theme_dartR(),
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Details

Currently pairwise Fst and D between populations and 1-propShared and Euclidean distance between individuals are implemented. Coordinates are expected as lat long and converted to Google Earth Mercator projection. If coordinates are already projected, provide them at the x@other$xy slot.

You can provide also your own genetic and Euclidean distance matrices. The function is based on the code provided by the adegenet tutorial (http://adegenet.r-forge.r-project.org/files/tutorial-basics.pdf), using the functions mantel (package vegan), stamppFst, stamppNeisD (package StAMPP) and gl.propShared or gl.dist.ind. For transformation you need to have the dismo package installed. As a new feature you can plot pairwise relationship using double colored points (paircols=TRUE). Pairwise relationship can be visualised via populations or individuals, depending which distance is calculated. Please note: Often a problem arises, if an individual based distance is calculated (e.g. propShared) and some individuals have identical coordinates as this results in distances of zero between those pairs of individuals.

If the standard transformation [log(Dgeo)] is used, this results in an infinite value, because of trying to calculate'log(0)'. To avoid this, the easiest fix is to change the transformation from log(Dgeo) to log(Dgeo+1) or you could add some "noise" to the coordinates of the individuals (e.g. +- 1m, but be aware if you use lat lon then you rather want to add +0.00001 degrees or so).

Value

Returns a list of the following components: Dgen (the genetic distance matrix), Dgeo (the Euclidean distance matrix), Mantel (the statistics of the Mantel test).

Author(s)

Bernd Gruber (bugs? Post to https://groups.google.com/d/forum/dartr)

References

Rousset, F. (1997). Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics, 145(4), 1219-1228.

See Also

mantel, stamppFst

Examples

#because of speed only the first 100 loci
ibd <- gl.ibd(bandicoot.gl[,1:100], Dgeo_trans='log(Dgeo)' ,
Dgen_trans='Dgen/(1-Dgen)')
#because of speed only the first 10 individuals)
ibd <- gl.ibd(bandicoot.gl[1:10,], distance='euclidean', paircols='pop', 
Dgeo_trans='Dgeo')

#only first 100 loci
ibd <- gl.ibd(bandicoot.gl[,1:100])

Description

This function imputes genotypes on a population-by-population basis, where populations can be considered panmictic, or imputes the state for presence-absence data.

Usage

gl.impute(
  x,
  method = "neighbour",
  fill.residual = TRUE,
  parallel = FALSE,
  verbose = NULL
)

Arguments

Details

We recommend that imputation be performed on sampling locations, before any aggregation. Imputation is achieved by replacing missing values using either of two methods:

• If "frequency", genotypes scored as missing at a locus in an individual are imputed using the average allele frequencies at that locus in the population from which the individual was drawn.

• If "HW", genotypes scored as missing at a locus in an individual are imputed by sampling at random assuming Hardy-Weinberg equilibrium. Applies only to genotype data.

• If "neighbour", substitute the missing values for the focal individual with the values taken from the nearest neighbour. Repeat with next nearest and so on until all missing values are replaced.

• if "random", missing data are substituted by random values (0, 1 or 2).

The nearest neighbour is the one with the smallest Euclidean distance in all the dataset.

The advantage of this approach is that it works regardless of how many individuals are in the population to which the focal individual belongs, and the displacement of the individual is haphazard as opposed to:

(a) Drawing the individual toward the population centroid (HW and Frequency).

(b) Drawing the individual toward the global centroid (glPCA).

Note that loci that are missing for all individuals in a population are not imputed with method 'frequency' or 'HW'. Consider using the function gl.filter.allna with by.pop=TRUE to remove them first.

Value

A genlight object with the missing data imputed.

Author(s)

Custodian: Luis Mijangos (Post to https://groups.google.com/d/forum/dartr)

Examples

require("dartR.data")
# SNP genotype data
gl <- gl.filter.callrate(platypus.gl,threshold=0.95)
gl <- gl.filter.allna(gl)
gl <- gl.impute(gl,method="neighbour")
# Sequence Tag presence-absence data
gs <- gl.filter.callrate(testset.gs,threshold=0.95)
gl <- gl.filter.allna(gl)
gs <- gl.impute(gs, method="neighbour")

gs <- gl.impute(platypus.gl,method ="random")

Description

The function compares the installed packages with the the currently available ones on CRAN. Be aware this function only works if a version of dartR is already installed on your system. You can choose if you also want to have a specific version of dartR installed ('CRAN', 'master', 'beta' or 'dev' ). 'master' , 'beta' and 'dev' are installed from Github. Be aware that the dev version from github is not fully tested and most certainly will contain untested functions.

Usage

gl.install.vanilla.dartR(flavour = NULL, verbose = NULL)

Arguments

Value

Returns a message if the installation was successful/required.

Author(s)

Custodian: Bernd Gruber (Post to https://groups.google.com/d/forum/dartr)

Description

This function combines two genlight objects and their associated metadata. The history associated with the two genlight objects is cleared from the new genlight object. The individuals/samples must be the same in each genlight object.

The function is typically used to combine datasets from the same service where the files have been split because of size limitations. The data is read in from multiple csv files, then the resultant genlight objects are combined.

Usage

gl.join(x1, x2, verbose = NULL)

Arguments

Value

A new genlight object

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

Examples

x1 <- testset.gl[,1:100]
x1@other$loc.metrics <-  testset.gl@other$loc.metrics[1:100,]
nLoc(x1)
x2 <- testset.gl[,101:150]
x2@other$loc.metrics <-  testset.gl@other$loc.metrics[101:150,]
nLoc(x2)
gl <- gl.join(x1, x2, verbose=2)
nLoc(gl)

Description

The script, having deleted individuals, optionally identifies resultant monomorphic loci or loci with all values missing and deletes them (using gl.filter.monomorphs.r). The script also optionally recalculates statistics made redundant by the deletion of individuals from the dataset.

The script returns a genlight object with the individuals deleted and, optionally, the recalculated locus metadata.

See more about data manipulation in the [tutorial](http://georges.biomatix.org/storage/app/media/uploaded-files/tutorial4dartrdatamanipulation22-dec-21-3.pdf).

Usage

gl.keep.ind(x, ind.list, recalc = FALSE, mono.rm = FALSE, verbose = NULL)

Arguments

Value

A genlight object with the reduced data

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.drop.ind to drop rather than keep specified individuals

Examples

# SNP data
    gl2 <- gl.keep.ind(testset.gl, ind.list=c('AA019073','AA004859'))
  # Tag P/A data
   gs2 <- gl.keep.ind(testset.gs, ind.list=c('AA020656','AA19077','AA004859'))

Description

The script returns a genlight object with the all but the specified loci deleted.

#' See more about data manipulation in the [tutorial](http://georges.biomatix.org/storage/app/media/uploaded-files/tutorial4dartrdatamanipulation22-dec-21-3.pdf).

Usage

gl.keep.loc(x, loc.list = NULL, first = NULL, last = NULL, verbose = NULL)

Arguments

Value

A genlight object with the reduced data

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.drop.loc to drop rather than keep specified loci

Examples

# SNP data
  gl2 <- gl.keep.loc(testset.gl, loc.list=c('100051468|42-A/T', '100049816-51-A/G'))
# Tag P/A data
  gs2 <- gl.keep.loc(testset.gs, loc.list=c('20134188','19249144'))

Description

Individuals are assigned to populations based on the specimen metadata data file (csv) used with gl.read.dart().

The script, having deleted the specified populations, optionally identifies resultant monomorphic loci or loci with all values missing and deletes them (using gl.filter.monomorphs.r). The script also optionally recalculates statistics made redundant by the deletion of individuals from the dataset.

The script returns a genlight object with the new population assignments and the recalculated locus metadata.

#' See more about data manipulation in the [tutorial](http://georges.biomatix.org/storage/app/media/uploaded-files/tutorial4dartrdatamanipulation22-dec-21-3.pdf).

Usage

gl.keep.pop(
  x,
  pop.list,
  as.pop = NULL,
  recalc = FALSE,
  mono.rm = FALSE,
  verbose = NULL
)

Arguments

Value

A genlight object with the reduced data

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.drop.pop to drop rather than keep specified populations

Examples

# SNP data
   gl2 <- gl.keep.pop(testset.gl, pop.list=c('EmsubRopeMata', 'EmvicVictJasp'))
   gl2 <- gl.keep.pop(testset.gl, pop.list=c('EmsubRopeMata', 'EmvicVictJasp'),
   mono.rm=TRUE,recalc=TRUE)
   gl2 <- gl.keep.pop(testset.gl, pop.list=c('Female'),as.pop='sex')
 # Tag P/A data
   gs2 <- gl.keep.pop(testset.gs, pop.list=c('EmsubRopeMata','EmvicVictJasp'))

Description

The function creates a plot showing the pairwise LD measure against distance in number of base pairs pooled over all the chromosomes and a red line representing the threshold (R.squared = 0.2) that is commonly used to imply that two loci are unlinked (Delourme et al., 2013; Li et al., 2014).

Usage

gl.ld.distance(
  ld_report,
  ld_resolution = 1e+05,
  pop_colors = NULL,
  plot_theme = NULL,
  plot.out = TRUE,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Value

A dataframe with information of LD against distance by population.

Author(s)

Custodian: Luis Mijangos – Post to https://groups.google.com/d/forum/dartr

References

• Delourme, R., Falentin, C., Fomeju, B. F., Boillot, M., Lassalle, G., André, I., . . . Marty, A. (2013). High-density SNP-based genetic map development and linkage disequilibrium assessment in Brassica napusL. BMC genomics, 14(1), 120.

• Li, X., Han, Y., Wei, Y., Acharya, A., Farmer, A. D., Ho, J., . . . Brummer, E. C. (2014). Development of an alfalfa SNP array and its use to evaluate patterns of population structure and linkage disequilibrium. PLoS One, 9(1), e84329.

See Also

Other ld functions: gl.ld.haplotype()

Examples

if ((requireNamespace("snpStats", quietly = TRUE)) & (requireNamespace("fields", quietly = TRUE))) {
require("dartR.data")
x <- platypus.gl
x <- gl.filter.callrate(x,threshold = 1)
x <- gl.filter.monomorphs(x)
x$position <- x$other$loc.metrics$ChromPos_Platypus_Chrom_NCBIv1
x$chromosome <- as.factor(x$other$loc.metrics$Chrom_Platypus_Chrom_NCBIv1)
ld_res <- gl.report.ld.map(x,ld_max_pairwise = 10000000)
ld_res_2 <- gl.ld.distance(ld_res,ld_resolution= 1000000)
}

Description

This function plots a Linkage disequilibrium (LD) heatmap, where the colour shading indicates the strength of LD. Chromosome positions (Mbp) are shown on the horizontal axis, and haplotypes appear as triangles and delimited by dark yellow vertical lines. Numbers identifying each haplotype are shown in the upper part of the plot.

The heatmap also shows heterozygosity for each SNP.

The function identifies haplotypes based on contiguous SNPs that are in linkage disequilibrium using as threshold ld_threshold_haplo and containing more than min_snps SNPs.

Usage

gl.ld.haplotype(
  x,
  pop_name = NULL,
  chrom_name = NULL,
  ld_max_pairwise = 1e+07,
  maf = 0.05,
  ld_stat = "R.squared",
  ind.limit = 10,
  min_snps = 10,
  ld_threshold_haplo = 0.5,
  coordinates = NULL,
  color_haplo = "viridis",
  color_het = "deeppink",
  plot.out = TRUE,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Details

The information for SNP's position should be stored in the genlight accessor "@position" and the SNP's chromosome name in the accessor "@chromosome" (see examples). The function will then calculate LD within each chromosome.

The output of the function includes a table with the haplotypes that were identified and their location.

Colors of the heatmap (color_haplo) are based on the function scale_fill_viridis from package viridis. Other color palettes options are "magma", "inferno", "plasma", "viridis", "cividis", "rocket", "mako" and "turbo".

Value

A table with the haplotypes that were identified.

Author(s)

Custodian: Luis Mijangos – Post to https://groups.google.com/d/forum/dartr

See Also

Other ld functions: gl.ld.distance()

Examples

require("dartR.data")
x <- platypus.gl
x <- gl.filter.callrate(x,threshold = 1)
x <- gl.keep.pop(x, pop.list = "TENTERFIELD")
x$chromosome <- as.factor(x$other$loc.metrics$Chrom_Platypus_Chrom_NCBIv1)
x$position <- x$other$loc.metrics$ChromPos_Platypus_Chrom_NCBIv1
ld_res <- gl.ld.haplotype(x,chrom_name = "NC_041728.1_chromosome_1",
                          ld_max_pairwise = 10000000 )

Description

This function is basically a convenience function that runs the LD Ne estimator using Neestimator2 (http://www.molecularfisherieslaboratory.com.au/neestimator-software/) within R using the provided genlight object. To be able to do so, the software has to be downloaded from their website and the appropriate executable Ne2-1 has to be copied into the path as specified in the function (see example below).

Usage

gl.LDNe(
  x,
  outfile = "genepopLD.txt",
  outpath = tempdir(),
  neest.path = getwd(),
  critical = 0,
  singleton.rm = TRUE,
  mating = "random",
  plot.out = TRUE,
  plot_theme = theme_dartR(),
  plot_colors_pop = discrete_palette,
  save2tmp = FALSE,
  verbose = NULL
)

Arguments

Value

Dataframe with the results as table

Author(s)

Custodian: Bernd Gruber (Post to https://groups.google.com/d/forum/dartr)

Examples

## Not run: 
# SNP data (use two populations and only the first 100 SNPs)
pops <- possums.gl[1:60,1:100]
nes <- gl.LDNe(pops, outfile="popsLD.txt", outpath=tempdir(),
neest.path = "./path_to Ne-21",
critical=c(0,0.05), singleton.rm=TRUE, mating='random')
nes

## End(Not run)

Description

Prints dartR reports saved in tempdir

Usage

gl.list.reports()

Value

Prints a table with all reports saved in tempdir. Currently the style cannot be changed.

Author(s)

Bernd Gruber & Luis Mijangos (bugs? Post to https://groups.google.com/d/forum/dartr)

See Also

gl.print.reports

Examples

## Not run: 
gl.report.callrate(testset.gl,save2tmp=TRUE)
gl.list.reports()

## End(Not run)

Description

This is a wrapper for readRDS()

Usage

gl.load(file, verbose = NULL)

Arguments

Details

The script loads the object from the current workspace and returns the gl object.

Value

The loaded object

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

See Also

gl.save

Examples

gl.save(testset.gl,file.path(tempdir(),'testset.rds'))
gl <- gl.load(file.path(tempdir(),'testset.rds'))

Description

Renaming individuals may be required when there have been errors in labelling arising in the process from sample to DArT files. There may be occasions where renaming individuals is required for preparation of figures. Caution needs to be exercised because of the potential for breaking the 'chain of evidence' between the samples themselves and the analyses. Recoding individuals can be done with a recode table (csv).

Usage

gl.make.recode.ind(
  x,
  out.recode.file = "default_recode_ind.csv",
  outpath = tempdir(),
  verbose = NULL
)

Arguments

Details

This script facilitates the construction of a recode table by producing a proforma file with current individual (=specimen) names in two identical columns. Edit the second column to reassign individual names. Use keyword Delete to delete an individual.

Apply the recoding using gl.recode.ind(). Deleting individuals can potentially generate monomorphic loci or loci with all values missing. Clean this up with gl.filter.monomorphic().

Value

A vector containing the new individual names.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

Examples

result <- gl.make.recode.ind(testset.gl, out.recode.file ='Emmac_recode_ind.csv',outpath=tempdir())

Description

Renaming populations may be required when there have been errors in assignment arising in the process from sample to DArT files or when one wishes to amalgamate populations, or delete populations. Recoding populations can also be done with a recode table (csv).

Usage

gl.make.recode.pop(
  x,
  out.recode.file = "recode_pop_table.csv",
  outpath = tempdir(),
  verbose = NULL
)

Arguments

Details

This script facilitates the construction of a recode table by producing a proforma file with current population names in two identical columns. Edit the second column to reassign populations. Use keyword Delete to delete a population.

Apply the recoding using gl.recode.pop().

Value

A vector containing the new population names.

Author(s)

Custodian: Arthur Georges – Post to https://groups.google.com/d/forum/dartr

Examples

result <- gl.make.recode.pop(testset.gl,out.recode.file='test.csv',outpath=tempdir(),verbose=2)