| gl.report.parent.offspring {dartR.captive} | R Documentation |
Identifies putative parent offspring within a population
Description
This script 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.report.parent.offspring(
x,
min.rdepth = 12,
min.reproducibility = 1,
range = 1.5,
plot_theme = theme_dartR(),
plot_colors = gl.colors(2),
plot.dir = NULL,
plot.file = NULL,
verbose = NULL
)
Arguments
x |
Name of the genlight object containing the SNP genotypes [required]. |
min.rdepth |
Minimum read depth to include in analysis [default 12]. |
min.reproducibility |
Minimum reproducibility to include in analysis [default 1]. |
range |
Specifies the range to extend beyond the interquartile range for delimiting outliers [default 1.5 interquartile ranges]. |
plot_theme |
Theme for the plot. See Details for options [default theme_dartR()]. |
plot_colors |
List of two color names for the borders and fill of the plots [default gl.colors(2)]. |
plot.dir |
Directory to save the plot RDS files [default as specified by the global working directory or tempdir()] |
plot.file |
Name for the RDS binary file to save (base name only, exclude extension) [default NULL] Creates a plot that shows the sex linked markers. |
verbose |
Verbosity: 0, silent or fatal errors; 1, begin and end; 2, progress log; 3, progress and results summary; 5, full report [default 2, unless specified using gl.set.verbosity]. |
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.
The function gl.filter.parent.offspring will filter out those
individuals in a parent offspring relationship.
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.
Examples of other themes that can be used can be consulted in
Value
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.report.rdepth ,gl.report.reproducibility,
gl.filter.parent.offspring
Examples
out <- gl.report.parent.offspring(testset.gl[1:10, 1:100])