PGA-cmdr is an interactive Shiny application for exploratory and descriptive population genetics analyses from individual-based multilocus datasets (microsatellites, codominant markers). All computations run locally in R/C++ (Rcpp, OpenMP) – no data is sent to any server.
It covers the full analytical workflow:
Every module follows the same pattern: set parameters, click
Run, and a single .txt (Allele Freq,
Local/Global Panmixia, Subdivision, Diversities, LD) or a
.zip of several files (General Stats, Null Alleles, IBD,
Mantel) downloads automatically with everything you need – there is
nothing to preview or export separately first. The exact output file
name(s) are always shown just above the Run button before you click
it.
PGA-cmdr is distributed through R-universe as ready-to-use binaries for Windows, macOS and Linux (no compiler needed). Install it once, or run the same command again to update to the latest version:
Every time you want to use the application:
The application opens in your default browser.
PGA-cmdr accepts CSV or tabulation-delimited TXT files. The separator (comma, semicolon, or tab) is detected automatically, and the file loads as soon as it is chosen – no extra click needed. The file must contain a header row, one population column, and one or more genetic marker columns; other columns (individual ID, latitude, longitude, etc.) are simply ignored. Two genotype encodings are supported, but a single file must use only one of them throughout.
Each locus occupies one column; alleles are separated by a character
(/, -, _, etc.):
Population B12 C07 D12
Pop1 192/194 145/192 92/112
Pop1 200/200 179/179 108/112
Pop2 145/145 92/92 92/100Each locus occupies two adjacent columns, one per allele.
The first allele column carries the locus name (e.g. B12);
the second must immediately follow it and be named B12_1
(or B12.1):
Population B12 B12_1 C07 C07_1
Pop1 192 194 145 192
Pop1 200 200 179 179
Pop2 145 145 92 92Missing genotypes are coded as 0 (or 0/0)
by default and are customisable on import.
Once the file is loaded, a numbered Column reference table appears at the bottom of the Import Data page, listing every column name next to its position – use it to check exactly which number corresponds to which column.
Fill in:
The app shows the resulting column range live (e.g. “6 loci x 2 columns/locus = columns 9 to 20”) before you click Apply.
The package ships a demonstration dataset from Thierry de Meeus’
teaching material (inst/extdata/default_dataset.csv): adult
Boophilus ticks sampled from cattle across New Caledonia,
genotyped at 6 microsatellite loci (B12, C07, D12, D10, A12, C03) across
8 sampling localities.
The steps below follow the app’s recommended workflow (see the in-app Help tab for the full reference list).
Click “Load Default Data” in the sidebar of the Import Data tab, or follow the manual steps above for your own file. The application imports the data via DuckDB, builds the internal genotype table (individual x locus, packed integers), and is ready for every other module immediately.
Set an output name on the Allele Freq tab (or leave it
auto-filled) and click Run. A single .txt
downloads with per-locus, per-population allele frequencies plus a
population-size-weighted global column – every population and marker is
included automatically.
The General Stats tab reports, per locus: Ho, Hs, Ht, and FIS/FST/FIT (Weir & Cockerham 1984, in that order), plus any of the optional indices you tick (Fst-max, Fst’, GST, GST’’). The same Run also produces a per-population summary, the observed heterozygosity of every population at every locus, and per-allele F-statistics – all in one zip.
In the per-population summary, Ho and Hs are averaged over loci weighted by the number of individuals typed at each locus (loci with at least two typed individuals), which is the convention of Genepop; an unweighted mean differs from it by up to about 0.005 when loci have different amounts of missing data. The Weir & Cockerham FIS of a population is the multilocus ratio of sums.
If a locus’s Ho/Hs ratio looks unusually low, check the Null Alleles tab: null allele frequencies are estimated per locus x sub-sample by the FreeNA EM algorithm (Chapuis & Estoup 2007), with both raw and ENA-corrected FST/DCSE reported, and a full pairwise table.
The Local Panmixia tab writes one .txt
file with FIS (Weir & Cockerham 1984) by
locus and by population; both are always
computed. A header states the number of permutations, the type of every
bootstrap and its number of replicates:
_indiv: bootstrap over individuals (resampled within
each sample);_subs: bootstrap over sub-samples (populations
resampled as blocks), by locus;_loci: bootstrap over loci, for the Overall row and for
each population.P-values are two-sided permutation p-values. Fewer than 5 sub-samples
or individuals gives NA rather than an unreliable interval.
A second output, FIS per locus and population, gives the one-sided
heterozygote deficit and excess p-values in the way FSTAT reports
them.
The Global Panmixia tab writes one .txt file
with the multilocus FIT across all populations: per-locus confidence
intervals from the bootstrap over sub-samples, and two Overall rows,
Overall (BS/Ss) (sub-samples) and
Overall (BS/Loci) (loci), with two-sided permutation
p-values.
The Subdivision tab writes one .txt
file: (1) FST per locus (Weir & Cockerham 1984) with the
bootstrap confidence interval over sub-samples and a one-sided
permutation p-value based on the G statistic (as in FSTAT), followed by
two Overall rows (bootstrap over sub-samples, bootstrap over loci); (2)
FIS, FST, FIT, HS and HT at the multilocus level with confidence
intervals from the bootstrap over loci and over sub-samples; (3) the
genotype-reassignment G-test of FSTAT (Goudet et al. 1996), with its two
p-value columns (p_>=: permuted G >= observed G;
p_>: permuted G > observed G) and the number of
complete multilocus genotypes per sample. The number of permutations of
the tab is used for both tests. An option appends the bootstrap
replicate values at the end of the file so that their distributions can
be inspected.
The Diversities tab writes one file,
Diversities_confidence_intervals_<date>.txt, with HS
(within-population) and HT (total) gene diversity – using the
unbiased small-sample estimator of Nei & Chesser
(1983), not the simpler biased form: HS per locus (bootstrap
over individuals and over sub-samples), HS per population (over
individuals and over loci), HT per locus, and the multilocus estimators
(over loci and over sub-samples).
Resampling individuals makes every replicate slightly less diverse than the sample (the bootstrap mean is about HS/(2n) below the observed HS), so a plain percentile interval hugs the observed value. The intervals from the bootstrap over individuals are therefore shifted by (observed HS - bootstrap mean); the bootstrap over sub-samples shows no such bias and is left as is.
The LD tab writes one .txt file. It tests every
locus pair, per population and combined (“All”), using a
permutation-based log-likelihood-ratio (G) test. One locus’s genotypes
are permuted among individuals within each population (10,000
permutations by default) and compared to the observed G statistic. Loci,
locus pairs and populations are listed in the order of the data
file.
The IBD tab offers two independent analyses:
All F-statistics follow Weir & Cockerham (1984):
Weir, B.S. & Cockerham, C.C. (1984). Estimating F-statistics for the analysis of population structure. Evolution, 38, 1358–1370.
Gene diversity (HS, HT) follows the unbiased small-sample estimator of Nei & Chesser (1983):
Nei, M. & Chesser, R.K. (1983). Estimation of fixation indices and gene diversities. Annals of Human Genetics, 47, 253–259.
Null allele frequencies and the ENA correction follow Chapuis & Estoup (2007) (the FreeNA method):
Chapuis, M.P. & Estoup, A. (2007). Microsatellite null alleles and estimation of population differentiation. Molecular Biology and Evolution, 24, 621–631.
Isolation by distance follows Rousset (1997):
Rousset, F. (1997). Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics, 145, 1219–1228.
The genotype-reassignment G-test for subdivision follows FSTAT:
Goudet, J., Raymond, M., de Meeus, T. & Rousset, F. (1996). Testing differentiation in diploid populations. Genetics, 144, 1933–1940.
Fst-max and Fst’ follow Meirmans (2006) and Hedrick (2005); GST’’ follows Meirmans & Hedrick (2011):
Meirmans, P.G. & Hedrick, P.W. (2011). Assessing population structure: FST and related measures. Molecular Ecology Resources, 11, 5–18.
Permutation p-values use the formula
p = (b + 1) / (m + 1), where b is the number
of permuted statistics at least as extreme as the observed value and
m is the number of permutations, following:
Phipson, B. & Smyth, G.K. (2010). Permutation p-values should never be zero: calculating exact p-values when permutations are randomly drawn. Statistical Applications in Genetics and Molecular Biology, 9, 39.
Heavy computations (bootstrap, permutation loops) are parallelised with OpenMP via C++17 (Rcpp).
Most modules are fully self-contained: they read the imported dataset and compute everything themselves. The one exception is Isolation by Distance, whose Rousset regression reuses the Null Alleles module’s pairwise table instead of recomputing it. If you go directly to IBD without having run Null Alleles first, the app will tell you so and ask you to run it (or upload an external pairwise file instead). Keep this in mind if you change your imported data or parameters between the two – re-run Null Alleles before trusting IBD’s regression output.