gl.fixed.diff: Generates a matrix of fixed differences and associated...

In dartR: Importing and Analysing 'SNP' and 'Silicodart' Data Generated by Genome-Wide Restriction Fragment Analysis

Generates a matrix of fixed differences and associated statistics for populations taken pairwise

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

x	Name of the genlight object containing SNP genotypes or tag P/A data (SilicoDArT) or an object of class 'fd' [required].
tloc	Threshold defining a fixed difference (e.g. 0.05 implies 95:5 vs 5:95 is fixed) [default 0].
test	If TRUE, calculate p values for the observed fixed differences [default FALSE].
delta	Threshold value for the true population minor allele frequency (MAF) from which resultant sample fixed differences are considered true positives [default 0.02].
alpha	Level of significance used to display non-significant differences between populations as they are compared pairwise [default 0.05].
reps	Number of replications to undertake in the simulation to estimate probability of false positives [default 1000].
mono.rm	If TRUE, loci that are monomorphic across all individuals are removed before beginning computations [default TRUE].
pb	If TRUE, show a progress bar on time consuming loops [default FALSE].
verbose	Verbosity: 0, silent or fatal errors; 1, begin and end; 2, progress log; 3, progress and results summary; 5, full report [default 2 or as specified using gl.set.verbosity].

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 )

dartR documentation built on June 8, 2023, 6:48 a.m.