Genepop Documentation - Calcul & Bio-Informatique ISE-M

Copy and paste this link to your website, so they can see this document directly without any plugins.


Genepop, file, test, with, each, this, distance, option, input, allele, This, from, that, tests, will, data, setting, haploid, should, gene, only, number, Option, isolation, some, estimates, line, through, alleles, genetic


Genepop 4.6
for Windows/Linux/Mac OS X
This documentation: November 29, 2016
F. Rousset
This is a documentation for the Genepop software. Genepop implements a
mixture of traditional methods and some more focused developments:
It computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci;
It computes estimates of F -statistics, null allele frequencies, allele size-based
statistics for microsatellites, etc., and of number of immigrants by Barton &
Slatkin’s 1986 private allele method;
It performs analyses of isolation by distance from pairwise comparisons of
individuals or population samples, including confidence intervals for “neighborhood size”.
A formal reference for the current version of Genepop is Rousset (2008).
Future developments may include a more systematic implementation of bootstrap confidence intervals. Likelihood methods based on coalescent algorithms are
being developed in a companion software, Migraine (Rousset & Leblois, 2007,
2012; Leblois et al., 2014).
Recent versions since 4.0 have routinely been checked under both Windows and
Linux, and should be easy to compile on Unix-alike operating systems, including
Mac OS X.
Genepop converts data from the Genepop input format to formats of some
softwares that were around in Genepop’s youth (Raymond & Rousset, 1995b);
there was little need to update this option as many more recent softwares for
population genetic analyses read input files in the Genepop format.
What is new to Genepop 4.+?
Version 4.6
A bootstrap analysis of mean differentiation has been introduced, in particular
to allow comparison of the mean differentiation observed over a given range of
geographical distances, in intra vs. inter-ecotypic analyses. In can be called by the
setting meanDifferentiationTest.
The Mantel test based on regression slope (not the one on ranks) was not handling appropriately cases where some pairwise data had to be excluded. This is
corrected. Such cases concern in particular pairs of samples in the same location
(e.g., pairs of individuals), when geographical distance is log-transformed, because
the pairwise differentiation between such individuals cannot be used for the computation of the regression. The bootstrap analyses ere already handling correctly
this case.
Version 4.5
A new keyword inter_all_pairs for setting ”popTypeSelection” allows one to
perform spatial regressions (but not Mantel tests) between all pairs of individuals
or populations belonging to different types (e.g., individuals belonging to different
patches, excluding pairwise statistics for pairs of individuals within patches).
Version 4.4
Mantel tests are by default no longer based on rank correlation. The older rank
tests can be performed using the new MantelRankTest setting. In addition, a
MaximalDistance setting has been added, affecting the computation of spatial
Version 4.3
Two new “miscellaneous” conversion options have been added: option 8.5 converts
population data to individual data (as 8.4) but keeps the individual names (hence
the geographic location of each individual); and option 8.6 randomly samples haploid data at diploid loci.
Version 4.2
One can now perform all isolation-by-distance analyses with a user-provided distance matrix instead of the geographic distance matrix computed from the coordinates of the samples (geoDistFile setting).
Version 4.1
It is possible to test trends in gene diversity among samples.
Analyses of isolation by distance have been strengthened in several ways. Variants of previously described estimators have been implemented for both haploid
and diploid data. 0ne can select subsets of the data for analyses of isolation by
distance within and between these subsets. Further, analysis of isolation by distance from several one-locus genetic distance matrices is now possible through the
MultiMigFile option. In contrast to IsolationFile, this allows the construction
of bootstrap confidence intervals. Finally, it is possible to test specific values of
the slope of the spatial regression, using the testPoint setting.
The input file reading procedure is better protected against nonstandard file
formats (in particular those produced by some Microsoft software under Mac OS
The new sub-option 8.4 has been added to convert population-based data to
individual-based data (each individual in its own Pop).
Version 4.0
Version 4.0 was a complete rewrite of the fossil version 3.4, with the following
Use of the G (log likelihood ratio) statistic has been generalized to all contingency tables (though previous probability tests implemented in Genepop are still
available). Genepop now provides bootstrap confidence intervals for strength
of isolation by distance between groups of individuals, an alternative estimator
for analyses of “differentiation between individuals”, and facilities to evaluate the
performance of these methods. The genetic distance matrix produced by these
options can also be exported in Phylip (Felsenstein, 2005) format. The option for
null allele estimation implements additional estimators with confidence intervals,
and its output is better organized.
Some additional facilities have been implemented for better ease of use.
Earlier versions of Genepop required from the user some effort to deal with either
3-digits-coded alleles or with haploid data. Genepop is more practical, in that
haploid and diploid genotypes in both 2- or 3-digits allele codings are automatically
recognized as such by the program and all these different types of data can be mixed
in the same input file. The input format is otherwise unchanged so that input
files prepared for earlier versions of Genepop are still read by Genepop
(backward compatibility).
In addition, Genepop’s behaviour can be controlled using an option file and by
inline arguments in a console command line. This allows batch calls to Genepop
and repetitive use of Genepop on simulated data. However, those familiar with
the old Genepop menus can also use Genepop in an almost unchanged way.
Previous Genepop distributions included two small utilities, hw.bat and
struc.bat, for testing of single data matrices using a fast ad hoc data input.
These facilities are available in Genepop 4.0 through the HWfile and StrucFile
options. Previous Genepop distributions also included the Isolde program for
analysis of isolation by distance between groups of individuals, from one genetic
distance and one geographic distance matrices. All such analyses can now be performed through the unique Genepop executable (other facilities that were unique
to Isolde are now accessible through the IsolationFile setting).
Other minor, and often trivial, differences with earlier versions of Genepop
will be pointed out in footnotes.
The remainder of this documentation is as follows:
1 Installing Genepop and session examples 6
1.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2 Example sessions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.1 Example 1: basic session . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.2 Example 2: using the settings file . . . . . . . . . . . . . . . . . . . 7
1.2.3 Example 3: Batch processing . . . . . . . . . . . . . . . . . . . . . 7
2 The input file 8
3 The settings file and command line arguments 11
4 All menu options 14
4.1 Option 1: Hardy-Weinberg (HW) exact tests . . . . . . . . . . . . . . . . 14
4.1.1 Sub-options 1–3: Tests for each locus in each population . . . . . . 15
4.1.2 Sub-options 4,5: Global tests across loci or across samples . . . . . 16
4.1.3 Analyzing a single genotypic matrix . . . . . . . . . . . . . . . . . 17
4.2 Option 2: Tests and tables for linkage disequilibrium . . . . . . . . . . . . 18
4.2.1 Sub-option 1: Tests . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2.2 Sub-option 2: create tables . . . . . . . . . . . . . . . . . . . . . . 20
4.3 Option 3: population differentiation . . . . . . . . . . . . . . . . . . . . . 20
4.3.1 Sub-options 1 or 2 (genic differentiation) . . . . . . . . . . . . . . . 20
4.3.2 Sub-options 3 or 4 (genotypic differentiation) . . . . . . . . . . . . 21
4.3.3 Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.3.4 Gene diversity as a test statistic . . . . . . . . . . . . . . . . . . . 22
4.3.5 Analyzing a single contingency table . . . . . . . . . . . . . . . . . 23
4.4 Option 4: private alleles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.5 Option 5: Basic information, FIS, and gene diversities . . . . . . . . . . . 24
4.5.1 Sub-option 1: Allele and genotype frequencies . . . . . . . . . . . . 24
4.5.2 Sub-option 2: Identity-based gene diversities and FIS . . . . . . . . 25
4.5.3 Sub-option 3: Allele size-based gene diversities and ρIS . . . . . . . 25
4.6 Option 6: Fst and other correlations, isolation by distance . . . . . . . . . 25
4.6.1 Sub-options 1–4: F -statistics and ρ-statistics . . . . . . . . . . . . 26
4.6.2 Sub-option 5: isolation by distance between individuals . . . . . . 27
4.6.3 Sub-option 6: isolation by distance between groups . . . . . . . . . 31
4.6.4 Former sub-option 5 of Genepop: analysis of isolation by distance
from a genetic distance matrix . . . . . . . . . . . . . . . . . . . . 31
4.6.5 User-provided geographic distance matrices . . . . . . . . . . . . . 33
4.6.6 Analysis of isolation by distance from multiple genetic distance
matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.6.7 Analysis of mean differentiation . . . . . . . . . . . . . . . . . . . . 34
4.7 Data selection for analyses of isolation by distance . . . . . . . . . . . . . 34
4.7.1 Selecting a subset of samples . . . . . . . . . . . . . . . . . . . . . 34
4.8 Option 7: File conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.9 Option 8: Null alleles and some input file utilities . . . . . . . . . . . . . . 36
4.9.1 Sub-option 1: null alleles . . . . . . . . . . . . . . . . . . . . . . . 36
4.9.2 Sub-option 2: Diploidisation of haploid data . . . . . . . . . . . . . 38
4.9.3 Sub-option 3: Relabeling alleles names . . . . . . . . . . . . . . . . 38
4.9.4 Sub-options 4 and 5: Conversion of population data to individual
data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.9.5 Sub-option 6: Random sampling of haploid genotypes from diploid
ones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5 Evaluating the performance of inferences for Isolation by distance 39
6 Methods 40
6.1 Null alleles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
6.2 Exact tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
6.3 Algorithms for exact tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
6.4 Accuracy of P values estimated by the Markov chain algorithms . . . . . 41
6.5 Test statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
6.6 Estimating F -statistics and related quantities . . . . . . . . . . . . . . . . 42
6.6.1 ANOVA estimators: single- and multilocus definitions . . . . . . . 43
6.6.2 Microsatellite allele sizes, RST, and ρST . . . . . . . . . . . . . . . 44
6.6.3 Robertson and Hill’s estimator of FIS . . . . . . . . . . . . . . . . 45
6.7 Bootstraps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
6.8 Mantel test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
6.8.1 Misuse 1: tests of correlation at different distance . . . . . . . . . . 45
6.8.2 Misuse 2: partial Mantel tests . . . . . . . . . . . . . . . . . . . . . 46
7 Code history, compilation, credits, contact, etc. 47
7.1 Contact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
8 Copyright 48
Bibliography 48
Index 53
1 Installing Genepop and session examples
1.1 Installation
Under Microsoft Windows, one only needs to unzip/copy the executable on hard disk.
Both 32- and 64-bit versions of the executables are now distributed. Under Linux, copy
all sources and compile by the simple line
g++ -DNO_MODULES -o Genepop GenepopS.cpp -O3.
(O in -O3 is the letter O, not zero). This should also work under Mac OSX (even with
recent distributions which by default use the clang compiler even if one types g++ on
the command line).
The data files do not need to be in the same directory as the executable1; however,
users might find that specifying path names under Windows is not as easy at it should.
You may wish to install the examples, documentation, and source code. All files are
available on the Genepop distribution page.
Linkdos, a program described by Garnier-Géré & Dillmann (1992), is distributed
with Genepop (but is not part of Genepop). It is originally a DOS program, but the
source file distributed with Genepop can be recompiled under Linux using the Free
Pascal compiler.
1.2 Example sessions
To reproduce the examples of this session one should download the example package
from the Genepop distribution page.
1.2.1 Example 1: basic session
Open a console window in the directory where Genepop has been installed and just
If Genepop has never been run before, it will ask for an input file. Otherwise, the main
menu should appear, in which case you should use the C option to load this input file.
For this sample session, the file name to be given is sample.txt. Genepop will display
some information about the file read, then display the main menu:
-------> Change Data ................... C
Testing :
Hardy-Weinberg exact tests (several options) ...................... 1
Exact tests for genotypic disequilibrium (several options) ........ 2 contrast to earlier versions of Genepop.
Exact tests for population differentiation (several options) ...... 3
Nm estimates (private allele method) .............................. 4
Allele frequencies, various Fis and gene diversities .............. 5
Fst & other correlations, isolation by distance (several options).. 6
Ecumenicism and various utilities:
Ecumenicism: file conversion (several options) .................... 7
Null alleles and miscellaneous input file utilities ............... 8
QUIT Genepop .......................................................... 9
Your choice? :
Each option will be described later. Let us see some tests for heterozygote deficiency.
Reply 1, next 1, next y(es). As indicated, the results of the analysis are stored in the
file sample.txt.D.
That was simple enough, even simpler than a first contact with previous versions
of Genepop. Now exit Genepop (press (Return), next 9) and discover a new facet of
1.2.2 Example 2: using the settings file
Genepop settingsFile=SampleSettings.txt
Do not add spaces in the arguments. Capitalisation matters for file names (here SampleSettings.txt) if it matters for the operating system (i.e. for Linux).
You can see that the previous and additional analyses are performed, and that you
just need to hit Return each time Genepop stops and waits for feedback. Finally, you
are brought back to the main menu. Simple instructions for performing the analyses are
contained in the SampleSettings.txt file, which you may edit. Section 3 will explain
how to use this file. By default, Genepop seeks and eventually reads instructions in
a Genepop.txt file. You can see that one such file is present and was thus read when
performing Example 1.
1.2.3 Example 3: Batch processing
Execute the same command as in the previous example but with one more statement:
Genepop settingsFile=SampleSettings.txt Mode=Batch
Genepop should perform the same computations as in the previous example but it
will not stop and wait for feedback, and will exit after completion of the computations. Note again that spaces are not allowed within each of the arguments settingsFile=SampleSettings.txt and Mode=Batch, nor more generally in arguments specified
on the command line.
The batch mode makes it easy to analyze multiple files. However, note that concurrent Genepop processes should be run in distinct directories. Otherwise, the temporary
files of each process might conflict with each other.
2 The input file
As illustrated by the following examples, the input format requested by Genepop is:
First line: anything Use this line to store information about your data.
Locus names They may be given one per line, or on the same line but separated by
Pop sample indicator (Capitalization does not matter)2. Each sample from a different
geographical original is declared by a line with a pop statement.
Information for first individual. An example is:
ind#001 fem ,0101 0202 0000 0410
Here ind#001 fem is an identifier for your personal use. You can use any character
(except a comma!). You may leave it blank (at least one space) if you wish. The
last identifier of every sub-population is used by Genepop as the sample name
in output files. The comma between the identifier and the list of genotypes is
required. 0101 indicates that this individual is homozygous for the 01 allele at
the first locus. The same individual is homozygous for the 02 allele at the second
locus (0202). Data are missing at the third locus (0000). At the fourth locus, the
genotype is 0410, which indicates the presence of alleles 04 and 10.
More individuals Each individual information starts on a new line, but may extend
over several lines (do not start a new line in the middle of a one-locus genotype!).
More samples each declared by a pop statement on a new line
Blank lines at the end of the file are removed by Genepop.
An example of a short input file is given below:
Title line: "Grape populations in southern France"
ADH Locus 1
ADH #2
2Earlier versions of Genepop only accepted Pop, POP and pop...
ADH three
Grange des Peres , 0201 003003 0102 0302 1011 01
Grange des Peres , 0202 003001 0102 0303 1111 01
Grange des Peres , 0102 004001 0202 0102 1010 01
Grange des Peres , 0103 002002 0101 0202 1011 01
Grange des Peres , 0203 002004 0101 0102 1010 01
Tertre Roteboeuf , 0102 002002 0201 0405 0807 01
Tertre Roteboeuf , 0102 002001 0201 0405 0307 01
Tertre Roteboeuf , 0201 002003 0101 0505 0402 01
Tertre Roteboeuf , 0201 003003 0301 0303 0603 01
Tertre Roteboeuf , 0101 002001 0301 0505 0807 01
Bonneau 01 , 0101 002002 0304 0805 0304 01
Bonneau 02 , 0201 002002 0404 0505 0304 01
Bonneau 03 , 0101 002100 0304 0505 0101 01
Bonneau 04 , 0101 100100 0204 0805 0304 01
Bonneau 05 , 0101 100002 0104 0808 0304 01
, 0000 002001 0202 0402 0007 01
, 0200 002001 0202 0205 0707 01
, 0010 002001 0101
0105 0807 01
last pop, 0101 002001 0101 0401 0807 02
This example shows some useful features of the input file:
• There is no constraint on the number of blanks separating the various fields.
• The individual identifier has a free format.
• Alleles are numbered from 01 to 99 or 001 to 999 if needed. In 3-digits coding,
(say) homozygotes for the 90 allele are noted 090090, not 9090 as in the 2-digits
format. 2-digits and 3-digits coding of alleles can be intermixed (among loci, not
within loci!).3
• To designate alleles, consecutive numbers are not required.
• haploid and diploid data can be intermixed.4 6-digits genotypes are recognized
as 3-digits diploid genotypes; 4-digits genotypes are recognized as 2-digits diploid
3New to Genepop 4.0
4Also new to Genepop 4.0
genotypes; 2- and 3-digits genotypes are recognized as haploid genotypes. The
same coding should be used consistently within each locus. See the EstimationPloidy
setting for more information about analyzing haploid data. For haplo-diploid data
at a given locus, the haploid genotypes should be coded as diploid genotypes with
one unknown allele; note however that the information from haploid genotypes at
haplo-diploid loci will be used only for genic contingency table tests, and will be
ignored in estimation of genetic structure.
• Genotypes can extend on more than one line (see penultimate individual)
• To group various samples, just remove each relevant Pop separator.
It is possible to write all the locus names on one line, provided that a comma is used as
separator. This could be useful to clearly label each column. Thus the above input file
could have started as
Title line: "Grape populations in southern France"
Loc1,Loc2, ADH3,ADH4,ADH5,mtDNA
Grange des Peres , 0201 003003 0102 0302 1011 01
Note the absence of comma after the last locus name.
There are however constraints to be obeyed
• Missing data should be indicated with 00 (or 000 for 3-digits coding) and not with
blanks. The first locus in the last sample illustrates the various possibilities of
missing data: no information (first individual coded 0000) or partial information
(only one allele is determined: allele 02 for the second individual coded 0200 and
allele 10 for the third individual coded 0010).
• The number of locus names should correspond to the number of genotypes in each
individual. If you remove one or several loci from your input file, you should
remove both their names and the corresponding genotypes.
• No empty line should be present in the data file.
• Genepop accepts input file names either with the extension .txt5 or without any
• Genepop input files are ASCII text files.
5New to Genepop 4.0
The last point implies that under Windows, you should avoid using Microsoft Word to
edit input files (and settings files as well). Rather use a text editor such as Notepad++.a
It has also appeared that certain Microsoft products under Mac OS X still produced
files formatted according to the older Mac format. Genepop now catches and corrects
this miserable feature.
aThis documentation previously warned that the Windows basic text editor may not show all
end-of-line characters correctly, which may cause trouble; and for this reason it recommended
the MFC Wordpad program.
One can also find some conversion tools (e.g. from EXCEL) on the web.
If the input file is correctly read, the name of the larger allele number is indicated
for each locus. The number of distinct alleles for each locus is provided upon request. If
alleles have been labeled with consecutive numbers from 01 onwards, then the name of
the larger allele will correspond to the number of distinct alleles for each locus.
There are some limits to the number of samples and individuals imposed by the
compiler. These values, and a few other ones, are shown by running “Genepop Maxima=”
(see the Maxima setting). However, these built-in maxima are so large6 as to be practically
infinite even in the era of whole-genome sequencing. Computer memory, or user patience,
are more likely limits.
3 The settings file and command line arguments
The settings file allows finer control of Genepop and/or batch processing. Further
control is possible by using optional arguments when launching Genepop through the
operating system command line, following the general syntax explained below for the
settings file, e.g.
Genepop EstimationPloidy=Haploid DifferentiationTest=Proba
Indeed, command line arguments are written in the file cmdline.txt, then this file is
read much as the settings file.7
Henceforth, menu options are called options and batch file/command line options
are called settings.
Running Genepop help will display the help information, which so far is no more
than a list of available settings, loosely grouped semantically. A file showing all possible
settings is the following:
6in constrast to earlier versions of Genepop
7Long command lines: under some old versions of Windows, the command line had a fairly
limited maximum length, so it should have been used with moderation. This should no longer
be a problem with recent versions of Windows, but who knows with Microsoft... one may try to
find more information about command-line string limitation on
// sample Genepop settings file, showing all options.
/*********** Syntax of this file:
lines without ’equal’ symbol are ignored (hence this one is).
Lines beginning with a ’/’, /a ’#’ or a ’%’ are also ignored,
even if they contain ’=’ (hence this one is).
/*********** General options ***********
/*** allele sizes stuff
/*** selecting menu options
/********** Option 1 (HW tests) ***********
/ Emulating HW.BAT
/********** Option 2 ("linkage" disequilibrium) ***********
// old Genepop behaviour
/********** Option 3 (differentiation) ***********
// old Genepop behaviour
/ Emulating STRUC.BAT
/********** Option 4 (private alleles) ***********
//no specific setting, but may be affected
//by the estimationPloidy setting
/** Option 5 (basic information, Fis, gene diversities... )
//no specific setting, but may be affected
// by the AlleleSizes setting
/***** Option 6 (F-statistics, isolation by distance) *****
/PopTypes= 1 2 1 2 3
/PopTypeSelection= all
/ Emulating ISOLDE
/ Extending ISOLDE to multiple matrices
/ Isolation by distance with user-provided geographic distances
/********** Option 7 (file conversions) ***********
//no specific setting
/********** Option 8 (Various utilities) ***********
/******** Testing performance of some options *********
// Option 6.x: options as above plus
/********* Checking some limits of Genepop ***********
Each setting is specified following a Keyword=value syntax. Capitalisation is not
important (it is here only to ease reading) except for file names if the operating system
cares about it (as Linux does).
By default, Genepop seeks settings in the file Genepop.txt, but one can specify
another settings file through the command line, as was shown in the session examples:
Genepop settingsFile=SampleSettings.txt
The SettingsFile setting must be the first argument on the command line.
Settings specific to each menu option will be explained along with the description of
each option. Settings affecting several menu options are the following:
GenepopInputFile (or simply InputFile )
which is the name of the input file in Genepop format
Dememorisation, BatchLength and BatchNumber
which are Markov Chain parameters, which meaning is explained in Section 6.3:
the dememorisation number The default is 10000;8 values below 100 are not allowed.
the number of batches The default is 20 for sub-options 1.4 and 1.5 (multisample
HW tests), and 100 otherwise; values below 10 are not allowed.
the number of iterations per batch The default is 5000;9 values below 400 are not
The maximum allowed values will depend on the compiler, being the much-more-thanneeded 2,147,483,647 for all three parameters for the distributed Windows executable
(see the setting Maxima if you really need more information about this).
In multilocus estimates only diploid data are taken into account, unless the setting
EstimationPloidy=Haploid is given, in which case only haploid data are taken into
account. This setting applies to options 4 (private allele method), 5.2 and 5.3 (for
multilocus estimates of gene diversities), and 6 (F -statistics and isolation by distance).
Genepop has three modes: Mode=Ask will ask for some feedback even in cases where
the answer has been prespecified (e.g. through some setting; this may be useful when
one wishes to chance some settings in the course of a Genepop session). For example it
will ask for confirmation of the MC parameters. Mode=Batch will not wait for feedback:
8increased from Genepop 3.4’s default
9increased from Genepop 3.4’s default
execution of Genepop should complete without any user intervention. The third mode,
Mode=Default (which in most cases does not need to be explicitly specified) will ask
for unspecified settings but not request confirmation of prespecified ones, and will also
pause and wait for feedback when some notable information is displayed.
This tells Genepop to run the analyses as given through the menus: MenuOptions=1.1
will run option 1 sub-option 1 (test for heterozygote deficit), MenuOptions=1.1,2.2 will
run option 1.1 then 2.2, and so on.
AllelicDistance=Size (or =AlleleSize)
This tells Genepop to use allele size-based statistics (where meaningful). Allele sizes
are allele names unless specified by the next setting:
In the above example, the first such line AlleleSizes=1:5,2:10,3:15,10:50 says that
at the first locus, allele 1 has size 5, allele 2 has size 10... 0 cannot be given a size since
it means missing information. Any unlisted allele retain its name as its size. The second
line specifies allele size at the second locus. The third line AlleleSizes= implies that
at the third locus, all alleles retain their name as their size (don’t forget the ‘=’). It
is needed only so that the next line AlleleSizes=1:5,2:10,3:15,10:50 refers to the
fourth locus. As there are four AlleleSizes declarations, alleles retain their name as
their size for any locus beyond the fourth one.
RandomSeed and MantelSeed
One may change the seed of the pseudo-random number generator by the setting RandomSeed=value, except for the Mantel test for which the seed is given by the setting
MantelSeed=value. The default value for both seeds is 67144630.
With this setting, Genepop will only display some maximal values, including the maximum int and long int values for the compiler (the Markov chain dememorization and
batch length are long int and the number of batches is int).
4 All menu options
4.1 Option 1: Hardy-Weinberg (HW) exact tests
The following menu appears:
Hardy Weinberg tests:
HW test for each locus in each population:
H1 = Heterozygote deficiency.......1
H1 = Heterozygote excess...........2
Probability test...................3
Global test:
H1 = Heterozygote deficiency.......4
H1 = Heterozygote excess...........5
Main menu.............................6
4.1.1 Sub-options 1–3: Tests for each locus in each population
Three distinct tests are available, all concerned with the same null hypothesis (random
union of gametes). The difference between them is the construction of the rejection
zone. For the Probability test (sub-option 3), the probability of the observed sample
is used to define the rejection zone, and the P -value of the test corresponds to the
sum of the probabilities of all tables (with the same allelic counts) with the same or
lower probability. This is the “exact HW test” of Haldane (1954), Weir (1996), Guo &
Thompson (1992) and others. When the alternative hypothesis of interest is heterozygote
excess or deficiency, more powerful tests than the probability test can be used (Rousset
& Raymond, 1995). One of them, the score test or U test, is available here, either
for heterozygote deficiency (sub-option 1) or heterozygote excess (sub-option 2). The
multi-samples versions of these two tests are accessible through sub-options 4 or 5.
Two distinct algorithms are available: first, the complete enumeration method, as
described by Louis & Dempster (1987). This algorithm works for less than five alleles. As an exact P -value is calculated by complete enumeration, no standard error is
computed. Second, a Markov chain (MC) algorithm to estimate without bias the exact
P -value of this test (Guo & Thompson, 1992), and three parameters are needed to control this algorithm (see Section 6.3). These different values may be provided either at
Genepop’s request, or through the Dememorisation, BatchLength and BatchNumber
settings. Two results are provided for each test by the MC algorithm: the estimated
P -value associated with the null hypothesis of HW equilibrium, and the standard error
(S.E.) of this estimate.
For all tests concerned with sub-options 1-3, there are three possible cases. The
number of distinct alleles at each locus in each sample is
no more than 4: Genepop will give you the choice between the complete enumeration
and the MC method. If you have less than 1000 individuals per sample, the
complete enumeration is recommended. Otherwise, the MC method could be
much faster. But there are no general rules, results are highly variable, depending
also on allele frequencies.
always 5 or more: Genepop will automatically perform only the MC method.
sometimes higher than 4, sometimes not: For cases where the number of alleles
is 4 or lower, Genepop will give you the choice between both methods. For
the other situations (5 alleles or more in some samples), the MC method will be
automatically performed.
Whether one wants enumeration or MC methods to be performed can be specified at
runtime, or otherwise by the HWtests setting, with options HWtests=enumeration and
HWtests=MCMC. The default in the batch mode is enumeration.
Results are stored in a file named as follows
sub-option Extension
1 yourdata.D
2 yourdata.E
3 yourdata.P
4 yourdata.DG
5 yourdata.EG
where yourdata is (throughout this document) the name of the input file.
For each test, several values are indicated on the same line: (i) the P -value of the
test (or “-” is no data were available, or only one allele was present, or two alleles were
detected but one was represented by only one copy); (ii) the standard error (only if a
MC method was used); (iii) two estimates of FIS, Weir & Cockerham’s (1984) estimate
(W&C), and Robertson & Hill’s (1984) estimate (R&H). The latter has a lower variance
under the null hypothesis. Finally, the number of “steps” is given: for the complete
enumeration algorithm this is the number of different genotypic matrices considered, and
for the Markov chain algorithm the number of switches (change of genotypic matrice)
4.1.2 Sub-options 4,5: Global tests across loci or across samples
For sub-option 3, a global test across loci or across sample is constructed using Fisher’s
method. This method (sometimes conservative because discrete probabilities are analyzed), is only performed for convenience and its relevance should be first established
(e.g. statistical independence of loci).
General statistical theory shows that there is no uniformly better way to combine
P -values of different tests. When an alternative model is specified, it is possible to
find a better way of combining results from different data sets than Fisher’s method,
and usually not by combining P -values. In the present context one such method is the
10New to Genepop 4.0.
multisample score test of Rousset & Raymond (1995), which defines a global test across
loci and/or across samples generalizing the tests of sub-options 1 and 2. The global tests
are performed by sub-options 4 and 5, only by the MC algorithm. Independence of loci
is also assumed for these global tests.
The output file reports global P value estimates and standard errors per population,
per locus, and over all loci and populations. For each global P value, the average
number of switches per test combined is also reported. Since it is tempting to reduce the
chain length parameters in this option, special care is needed in checking this accuracy
diagnostic (see p. 41).11
This option generates several large temporary files. The space used temporarily
by Genepop can be estimated as: (# of Loci+# of pop+1)*batches*(iterations per
batch)*8 octets. For example it will require about 240 Mo of temporary hard disk space
if you have 10 loci, 50 samples and if you use a chain of 500,000 steps (100 batches of
5000 iterations).
4.1.3 Analyzing a single genotypic matrix
It is possible to perform a single HW test independently of the Genepop input file. This
option is not presented in the Genepop menu. You should have an input file with a
genotypic matrix (which can be taken from the output file of option 5 and edited), and
use the HWfile setting.12 When Genepop is launched in this way, the following menu
will appear:
HW test for each locus in each population:
H1 = Heterozygote deficiency .................1
H1 = Heterozygote excess .....................2
Probability test .............................3
Allele frequencies, expected genotypes, Fis .... 4
Quit ........................................... 5
All HW tests corresponding to options 1.1–3 of “regular”Genepop are available through
options 1–3, and basic information similar to that given by regular option 5.1 is available
through the present option 4. Results are stored at the end of your input file. The exact
format of the input file is:
First line: anything. Use this line to store information about your data.
Second line: The number of alleles n.
Line three through n+ 2: the genotypic matrix (see example).
11Again new to Genepop 4.0.
12In earlier versions of Genepop, this analysis was done through the HW.BAT batch file.
Beyond line n+ 2: anything (this is not read by the program).
An example with four alleles is:
Human Monoamine Oxidase (MOAO) Data
12 24
30 34 54
22 21 20 10
If this file is named MOAO, you can analyze it by setting HWfile=MOAO in the settings;
you can also set HWfileOptions=1 to run option 1 without making your way through
the menus. All this can be done through the console command line. For example
Genepop HWFile=MOAO HWfileOptions=1,2,3,4
will perform all four analyses available through the above menu. General settings Dememorisation, BatchLength, BatchNumber, and Mode all affect these analyses in the
same way as they affect analyses of regular input files.
Code checks
Code for HW tests has a now venerable history of testing. Early versions of Genepop
were compared with the Exactp step in Biosys (Swofford & Selander, 1989) for two
allele cases, and with data published in Louis & Dempster (1987) and Guo & Thompson
(1992) for more alleles. The sample files LouisD87.txt and GuoT92.txt contain two
such test samples, in single-matrix format.
4.2 Option 2: Tests and tables for linkage disequilibrium
The following menu appears:13
Pairwise associations (haploid and genotypic disequilibrium):
Test for each pair of loci in each population ......... 1
Only create genotypic contingency tables .............. 2
Menu ....................................................... 3
13The distinct option 2.3 of Genepop 3.4 is no longer necessary as option 2.1 of Genepop
4.0 more gracefully handles haploid data.
4.2.1 Sub-option 1: Tests
For this option the null hypothesis is: “Genotypes at one locus are independent from
genotypes at the other locus”. For a pair of diploid loci, no assumption is made about
the gametic phase in double heterozygotes. In particular, it is not inferred assuming onelocus HW equilibrium, as such equilibrium is not assumed anywhere in the formulation
of the test. The test is thus one of association between diploid genotypes at both loci,
sometimes described as a test of the composite linkage disequilibrium (Weir, 1996, p.
126–128). For a haploid locus and a diploid one, a test of association between the haploid
and diploid genotypes is computed (there is no concern about gametic phase in this case).
This makes it easy to test for cyto-nuclear disequilibria. For a pair of loci with haploid
information, a straightforward test of association of alleles at the two loci is computed.
The default test statistic is now the log likelihood ratio statistic (G-test). However
one can still perform probability tests (as implemented in earlier versions of Genepop)
by using the GameticDiseqTest=Proba setting.
For a given pair of loci within one sample, the relevant information is represented by
a contingency table looking e.g. like
1.1 1.3 3.3 1.7 3.7
EST _________________________
1.1 1 1 0 0 1 3
1.2 16 6 1 3 2 28
17 7 1 3 3 31
for two diploid loci (1.1, etc., are the diploid genotypes at each locus). Contingency
tables are created for all pairs of loci in each sample, then a G test or a probability test
for each table is computed for each table using the Markov chain algorithm of Raymond
& Rousset (1995a). The number of switches of the algorithm is given for each table
Results are stored in the file yourdata.DIS. Three intractable situations are indicated:
empty tables (“No data”), table with one row or one column only (“No contingency
table”), and tables for which all rows or all columns marginal sums are 1 (“No information”). For each locus pair within each sample, the unbiased estimate of the P-value is
indicated, as well as the standard error. Next, a global test (Fisher’s method) for each
pair of loci is performed across samples.
See also the next section for analysis of a single table.
14This was not the case in earlier versions of Genepop
4.2.2 Sub-option 2: create tables
Suboption 2 only generates the above contingency tables and stores them in the file
Code checks
See code checks for Option 3.
4.3 Option 3: population differentiation
The following menu appears:
Testing population differentiation :
Genic differentiation:
for all populations ........................ 1
for all pairs of populations ............... 2
Genotypic differentiation:
for all populations ........................ 3
for all pairs of populations ............... 4
Main menu ...................................... 5
All tests are based on Markov chain algorithms. The Markov chain parameters are
controlled exactly as in option 1.
4.3.1 Sub-options 1 or 2 (genic differentiation)
They are concerned with the distribution of alleles is the various samples. The null
hypothesis tested is “alleles are drawn from the same distribution in all populations”.
For each locus, the test is performed on a contingency table like this one:
Sub-Pop. Alleles
1 2 Total
1 14 46 60
2 6 76 82
3 10 74 84
4 4 58 62
Total 34 254 288
For each locus, an unbiased estimate of the P-value is computed. The test statistic is
either the probability of the sample conditional on marginal values, the G log likelihood
ratio, or the level of gene diversity. In the first case, the test is Fisher’s exact probability
test, and the algorithm is described in Raymond & Rousset (1995a). A simple modification of this algorithm is used for the exact G test.15 Genepop’s default is the G
test. You can revert to Fisher’s test by using the DifferentiationTest=Proba setting.
Finally, the level of gene diversity can be used as a test statistic when coupled with the
GeneDivRanks setting (this was new to version 4.1; see Section 4.3.4).
For sub-option 2, the tests are the same, but they are performed for all pairs of
samples for all loci.
4.3.2 Sub-options 3 or 4 (genotypic differentiation)
are concerned with the distribution of diploid genotypes in the various populations.
The null hypothesis tested is “genotypes are drawn from the same distribution in all
populations”. For each locus, the test is performed on a contingency table like this one:
1 1 2 1 2 3
Pop: 1 2 2 3 3 3 All
Pop1 142 27 0 13 1 0 183
Pop2 149 20 0 11 0 4 184
Pop3 131 12 0 9 0 1 153
Pop4 119 22 1 10 0 0 152
Pop5 120 17 1 10 1 0 149
Pop6 134 18 2 15 0 0 169
Pop7 116 15 1 10 1 1 144
Pop8 214 41 3 14 2 1 275
Pop9 84 17 0 7 2 0 110
Pop10 107 18 0 15 3 0 143
Pop11 134 32 1 21 4 0 192
Pop12 105 26 1 11 1 4 148
Pop13 97 19 2 23 4 0 145
Pop14 95 28 3 19 3 1 149
All: 1747 312 15 188 22 12 2296
An unbiased estimate of the P-value of a log-likelihood ratio (G) based exact test is
performed. For this test, the statistics defining the rejection zone is theG value computed
15Up to version 3.4, Genepop only computed Fisher’s exact test in these sub-options.
on the genic table derived from the genotypic one (see Goudet et al., 1996 for the choice
of this statistic), so that the rejection zone is defined as the sum of the probabilities of
all tables (with the same marginal genotypic values as the observed one) having a G
value computed on the derived genic table higher than or equal to the observed G value.
For sub-option 4, the test is the same but is performed for all pairs of samples for all
4.3.3 Output
For the four sub-options, results are stored in a file named as follows:16
sub-option test output file name
1 Probability test yourdata.PR
1 G yourdata.GE
2 Probability test yourdata.PR2
2 G yourdata.GE2
3 G yourdata.G
4 G yourdata.2G2
All contingency tables are saved in the output file. Two intractable situations are indicated: empty tables or tables with one row or one column only (“No table”), and tables
for which all rows or all columns marginal sums are 1 (“No information”). Estimates of
P-values are given, as well as (for sub-options 1 and 3) a combination of all test results
(Fisher’s method), which assumes a statistical independence across loci. For sub-options
2 and 4, this combination of all tests across loci (Fisher’s method) is performed for
each sample pair. The result Highly sign.[ificant] is reported when at least one of the
individual tests being combined yielded a zero P -value estimate.
4.3.4 Gene diversity as a test statistic
DifferentiationTest=GeneDiv makes Genepop use gene diversity as test statistic in
tests of genetic differentiation (option 3). The test will look for a decrease in gene
diversity from populations ranked first (value 1 in GeneDivRanks) to populations ranked
last. This should work for both genic and genotypic tables, and for pairwise comparisons
as well as for all populations, i.e. for all sub-options 3.1 to 3.4. The test statistic is∑
all subsamples i

(Qj −Qi)(Rj −Ri) (1)
where Qi is gene identity in subsample i and Ri is the GeneDivRanks value for this
16slightly modified in comparison to earlier versions of Genepop
This option also works on input files in contingency table format (strucfile setting).
In that case each row of the table is interpreted as a new population.
4.3.5 Analyzing a single contingency table
It is possible to analyse any contingency table independently of the Genepop input file.
You should have an input file with a contingency table, and use the strucFile setting.17
This option is not presented in the Genepop menu. Both the G and probability tests
are available and performed as in option 3.1. Results are stored at the end of your input
file. An example of input file is:
Dull example
6 5
1 2 5 10 11
2 0 8 11 15
0 0 1 5 6
10 15 20 51 55
0 0 0 2 1
4 5 6 11 10
If this file is named structest, you can analyze it by writing StrucFile=structest in
the settings file, or by the console command line
Genepop StrucFile=structest
The exact format of the input file is:
First line: anything. Use this line to store information about your data.
Second line: The numbers of rows (n) and columns.
Line three through n+ 2: the contingency table (see example).
Beyond line n+ 2: anything (this is not read by the program).
The default is to perform a G test, but as in options 3.1 and 3.2 you can revert to Fisher’s
exact test by the setting DifferentiationTest=Proba.
Code checks
Code for contingency tables also has a venerable history of testing. Early versions of
Genepop were tested by comparison with published data (e.g. Mehta & Patel, 1983)
or by hand calculations. The example file MehtaP83.txt contains one such test sample.
17In previous versions of Genepop, this analysis was done by the Struc program called
through the Struc.BAT batch file.
4.4 Option 4: private alleles
This option provides a multilocus estimate of the effective number of migrants (Nm).
Three estimates of Nm are provided, using the three regression lines published in Barton
& Slatkin (1986), and a corrected estimate is provided using the values from the closest
regression line (see Barton & Slatkin, 1986). Results are stored in the file yourdata.PRI.
4.5 Option 5: Basic information, FIS, and gene diversities
The following menu appears:
Allele and genotype frequencies per locus and per sample .. 1
Gene diversities & Fis :
Using allele identity ......... 2
Using allele size ............. 3
Main menu ................................................. 4
4.5.1 Sub-option 1: Allele and genotype frequencies
This option provides basic information on the data set. The output file is saved in the
file yourdata.INF. For each locus in each sample, several variables are calculated:
• allele frequencies.
• observed and expected genotype proportions.
• FIS estimates for each allele following Weir & Cockerham (1984).
• global estimate of FIS over alleles according to Weir & Cockerham (1984) (W&C)
and Robertson & Hill (1984) (R&H).
• observed and “expected” number of homozygotes and heterozygotes. “Expected”
here means the expected numbers, conditional on observed allelic counts, under
HW equilibrium; the difference from naive products of observed allele frequencies
is sometimes called Levene’s correction, after Levene (1949).
• the genotypic matrix.
A table of allele frequencies for each locus and for each sample is also computed.
4.5.2 Sub-option 2: Identity-based gene diversities and FIS
This option takes the observed frequencies of identical pairs of genes as estimates (Q̂) of
corresponding probabilities of identity (Q) and then simply computes diversities as 1−Q̂:
gene diversity within individuals (1-Qintra), and among individuals within samples (1Qinter), per locus per sample, and averaged over samples or over loci. One-locus FIS
estimates are also computed in a way consistent with Weir & Cockerham (1984). No
estimate is given when no information is available (e.g. no estimate of diversity between
individuals within a sample when only one individual has been genotyped).
For haploid data, only the gene diversity among individuals is computed. Multilocus estimates ignore haploid loci, or on the contrary ignore diploid loci if the setting
EstimationPloidy=Haploid is used. Single-locus estimates are computed for both haploid and diploid loci irrespective of this setting.
The output is saved in the file yourdata.DIV.
4.5.3 Sub-option 3: Allele size-based gene diversities and ρIS
Option 5.3 is analogous to option 5.2. It computes measures of diversity based on allele
size, namely mean squared allele size differences within individuals (MSDintra), and
among individuals within samples (MSDinter), per locus per sample, and averaged over
samples or over loci. Corresponding ρIS (the FIS analogue, see Section 6.6.2) estimates
are also computed. Allele size is the allele name unless it has been given through the
AlleleSizes setting.
For haploid data, only the mean squared difference MSDinter among individuals is
computed. Multilocus estimates ignore haploid loci, or on the contrary ignore diploid loci
if the setting EstimationPloidy=Haploid is used. Single-locus estimates are computed
for both haploid and diploid loci irrespective of this setting.
The output is saved in the file yourdata.MSD.
4.6 Option 6: Fst and other correlations, isolation by distance
The following menu appears:
Estimating spatial structure:
The information considered is :
--> Allele identity (F-statistics)
For all populations ............ 1
For all population pairs ....... 2
--> Allele size (Rho-statistics)
For all populations ............ 3
Data ploidy pop=individual? isolationStatistic
Estimator used
Diploid Yes (option 6.5) =a â
Diploid Yes (option 6.5) =e ê
Diploid No (option 6.6) none (default) FST/(1− FST)
Diploid No (option 6.6) =singleGeneDiv F/(1−F ) variant with
denominator common
to all pairs
Haploid Yes (option 6.5) none (default) â-like statistic with
stand-in for withindeme gene diversity
Haploid No (option 6.6) none (default) FST/(1− FST)
Haploid No (option 6.6) =singleGeneDiv F/(1−F ) variant with
denominator common
to all pairs
Table 1: Genetic distance statistics available in options 6.5 and 6.6
For all population pairs ....... 4
Isolation by distance
between individuals ............ 5
between groups.................. 6
Main menu ................................. 7
Suboptions 5 and 6 provide a variety of analyses of isolation by distance patterns,
including bootstrap confidence intervals of the slope of spatial regression (or equivalently,
for “neighborhood” size estimates). Starting with version 4.1, it is even possible to test
given values of the slope, through the testPoint setting; and additional estimators
(merely minor variation on a common logic) have been implemented, in particular for
haploid data. Table 1 summarizes the choice of methods, each of which will now be
4.6.1 Sub-options 1–4: F -statistics and ρ-statistics
These options compute estimates of FIS, FIT and FST or analogous correlations for allele
size, either for each pair of population (sub-options 2 and 4) or a single measure for all
populations (sub-options 1 and 3). FST is estimated by a “weighted” analysis of variance
Cockerham (1973); Weir & Cockerham (1984), and the analogous measure of correlation
in allele size (ρST) is estimated by the same technique (see Section 6.6.2). Multilocus
estimates are computed as detailed in Section 6.6). For haploid data, remember to use
the EstimationPloidy=Haploid setting.
In sub-option 1, the output is saved in the file yourdata.FST. Beyond FIS, FIT and FST
estimates, estimation of within-individual gene diversity and within-population amongindividual gene diversity are reported as in option 5.2.
In sub-option 2 (pairs of populations), single locus and multilocus estimates are
written in the yourdata.ST2 file and multilocus estimates are also written in the yourdata.MIG file in a format suitable for analysis of isolation by distance (see option 6.6 for
further details).
Sub-option 3 is analogous to sub-option 1, but for allele-size based estimates. the
output is saved in the file yourdata.RHO. Beyond ρIS, ρIT and ρST estimates, estimation of
within-individual gene diversity and within-population among-individual gene diversity
are reported as in option 5.3.
Sub-option 4 is analogous to sub-option 2, but for allele-size based estimates. Output
file names are as in sub-option 2.
4.6.2 Sub-option 5: isolation by distance between individuals
This option allows analysis of isolation by distance between pairs of individuals. It
provides estimates of “neighborhood size”, or more precisely of Dσ2, the product of population density and axial mean square parent-offspring distance, derived from the slope of
the regression of pairwise genetic statistics against geographical distance or log(distance)
in linear or two-dimensional habitats, respectively. More details are described in Rousset
(2000) (â statistic), Leblois et al. (2003) (bootstrap confidence intervals) and Watts et al.
(2007) (ê statistic). For haploid data, a proxy for the â statistic has been introduced in
version 4.1.
The position of individuals must be specified as two coordinates standing for their
name (i.e. before the comma on the line for each individual), and since each individual
is considered as a sample, it must be separated by a Pop. An example of such input file
is given below: The first individual is located at the point x = 0.0, y = 15.0 (showing
that the decimal separator is a period), the second at the point x = 0, y = 30, etc. This
example also shows that individual identifiers can be added after these coordinates.
Title line: A really too small data set
ADH Locus 1
ADH #2
ADH three
0.0 15.0, 0201 0303 0102 0302 1011
0 30 Second indiv, 0202 0301 0102 0303 1111
0 45, 0102 0401 0202 0102 1010
0 60, 0103 0202 0101 0202 1011
0 75, 0203 0204 0101 0102 1010
15 15, 0102 0202 0201 0405 0807
15 30, 0102 0201 0201 0405 0307
15 45, 0201 0203 0101 0505 0402
15 60, 0201 0303 0301 0303 0603
15 75, 0101 0201 0301 0505 0807
Missing information arises when there is no genetic estimate (if a pair of individuals has no genotypes for the same locus, for example), or when geographic distance is
zero and log(distance) is used. Genepop will correctly handle such missing information
until it comes to the point where regression cannot be computed or there are not several
loci to bootstrap over.
Options to be described within option 6.5 are: â or ê pairwise statistics (for diploid
data); log transformation for geographic distances; minimal geographic distance; coverage probability of confidence interval; testing a given value of the slope; Mantel test
settings; conversion to genetic distance matrix in Phylip format. Allele-size based analogues of â or ê can be defined, but they should perform very poorly (Leblois et al., 2003;
Rousset, 2007), so such an analysis has been purposely disabled.
Pairwise statistics for diploid data: They are selected by the setting IsolationStatistic=a or =e, or at runtime (in batch mode, the default is â). The ê statistic
is asymptotically biased in contrast to â, but has lower variance. The bias of the ê-based
slope is higher the more limited dispersal is, so it performs less well in the lower range of
observed dispersal among various species. Confidence intervals are also biased (Leblois
et al., 2003; Watts et al., 2007), being too short in the direction of low Dσ2 values, and
on the contrary conservative in the direction of low Dσ2 values. Based on the simulation results of Watts et al. (2007), a provisional advice is to run analyses with both
statistics, and to derive an upper bound for the Dσ2 confidence interval (CI), hence the
lower bound for the regression slope, from ê (which has CI shorter than â, though still
conservative) and the other Dσ2 bound, hence the upper bound for the regression slope,
from â (which has too short CI, but less biased than the ê CI). When the ê-based Dσ2
estimate is below 2500 (linear habitat) or 4 (two-dimensional habitat) it is suggested to
derive both bounds from â.
Note that ê is essentially Loiselle’s statistic (Loiselle et al., 1995), which use in this
context has previously been advocated by e.g. Vekemans & Hardy (2004).
For haploid data (i.e. EstimationPloidy=Haploid) the denominators of the â and
ê statistics cannot be computed. Ideally the denominator should be the gene diversity
among individuals that would compete for the same position, as could be estimated from
“group” data. As a reasonable first substitute, Genepop uses a single estimate of gene
diversity (from the total sample and for each locus) to compute the denominators for all
pairs of individuals. This amount to assume that overall differentiation in the population
is weak.
Log transformation for geographic distances: This transformation is required
for estimation of Dσ2 when dispersal occurs over a surface rather than over a linear
habitat. It is the default option in batch mode. It can be turned on and off by the
setting GeographicScale=Log or =Linear or equivalently by Geometry=2D or =1D
Coverage probability of confidence interval This is the target probability that
the confidence interval contains the parameter value. The usage is to compute intervals
with 95% coverage and equal 2.5% tails, and this is the default coverage in Genepop.
This can be changed by the setting CIcoverage, e.g. CIcoverage=0.99 will compute
interval with target probabilities 0.5% that either the confidence interval is too low or
too high (an unrealistically large number of loci may be necessary to achieve the latter
Minimal and maximal geographic distances: As discussed in Rousset (1997),
samples at small geographic distances are not expected to follow the simple theory of
the regression method, so the program asks for a minimum geographical distance. Only
pairwise comparisons of samples at larger distances are used to estimate the regression
coefficient (all pairs are used for the Mantel test). The minimal distance may be specified
by the setting MinimalDistance=value or at runtime. This being said, it is wise to
include all pairs in the estimation as no substantial bias is expected, and this avoids
uncontrolled hacking the data. Thus, the suggested minimal distance here is any distance
large enough to exclude only pairs at zero geographical distance. Only non-negative
values are accepted, and the default in batch mode is 0.0001.
There is also a setting MaximalDistance=value. This should not be abused, and is New to
version 4.4(therefore) available only through the settings file, not as a runtime option.
Testing a given value of the slope The setting testPoint=0.00123 (say) returns
the unidirectional P-value for a specific value of the slope, using the ABC bootstrap
method. This is the reciprocal of a confidence interval computation: confidence intervals evaluate parameter values corresponding to given error levels, say the 0.025 and
0.975 unidirectional levels for a 95% bidirectional CI, while this option evaluates the
unidirectional P-value associated with a given parameter value.
Mantel test: The Mantel test is implemented. See Section 6.8 for some comments
on uses of this test. In the present context this is an exact test of the null hypothesis
that there in no spatial correlation between genetic samples.
Up to version 4.3 Genepop implemented only a Mantel test based on the rank
correlation. It now also implements, and performs by default, Mantel tests based on
the regression coefficient for the “genetic distance” statistic used to quantify isolation New to
version 4.4by distance. The latter tests should generally be more congruent with the confidence
intervals based on the same distances than the rank-based tests are. The rank test can
now be performed by using the setting MantelRankTest= (no value needed)..
Ideally the confidence interval for the slope should contain zero if and only if the
Mantel test is non-significant. Some exceptions may occur as the bootstrap method
is only approximate, but such exceptions appear to be rare. Exceptions may more
commonly occur when the bootstrap is based on the regression of genetic “distance” and
geographic distance over a selected range of the latter.
The number of permutations may be specified by the setting MantelPermutations=value,
or else at runtime. In batch mode, if no such value has been given the default behaviour
is not to perform the test.
Export genetic distance matrix in Phylip format. This option is activated by
the setting PhylipMatrix= (no value needed). It may be useful if you wish to use Phylip
to draw a tree based on genetic distances. A constant is added to all values if necessary
so that all resulting distances are positive. Output is written in the file yourdata.PMA.
No further estimation or testing is done, so the name of the groups/individuals does not
need to be their spatial coordinates.
Except for this export option, output files are:
• the yourdata.ISO output file, containing (i) a genetic distance (â or ê) half-matrix
and a geographic (log-)distance half-matrix; missing information is reported as
‘-’; (ii) regression estimates and bootstrap confidence intervals; (iii) the result of
testing a slope value (using testPoint); (iv) results of a Mantel test for evidence
of isolation by distance, if requested. The order of elements in the half-matrices
1 2 3
2 x
3 x x
4 x x x
• a yourdata.MIG output file, containing the same genetic and geographic distances
as in the ISO file, but with more digits, and without estimation or test results.
This file was formerly useful as input for the Isolde program (see “Former option
5 of Genepop”, below), and is a bit redundant now.
• a yourdata.GRA output file, where again the genetic and geographic distances are
reported, now as (x, y) coordinates for each pair of individuals (one per line). This
is useful e.g. for importing the output into programs with good graphics. Pairs
with missing values (either x or y) are not reported in this file.
4.6.3 Sub-option 6: isolation by distance between groups
This option is analogous to the previous one, but derives Dσ2 estimates from a regression
of FST/(1 − FST) estimates to geographic distance in a linear habitat, or log(distance)
in a two-dimensional habitat (Rousset, 1997).
Both diploid and haploid data (through EstimationPloidy=Haploid) are handled.
Missing information is handled as in option 6.5. Input format is the same, except that
some samples must contain several individuals. The coordinates of each sample are still
contained in the name of each sample, that is in the name of the last individual in each
In addition some allele-size based analyses are possible (by the setting AllelicDistance=Size) but again they are not advised in general. Further options within option
6.6 are: isolationStatistic; SingleGeneDiv; minimal geographic distance; log transformation for geographic distances; testing a given value of the slope; Mantel test settings; conversion to genetic distance matrix in Phylip format. They operate as described
above for analyses between individuals, the only difference being the genetic distance
used (see Table 1). In particular, a minor variant of the F/(1 − F ) estimator is introduced in version 4.1, by analogy to the “between individuals” estimators. Recall that
F/(1 − F ) = (Qr −Q0)/(1 −Q0) where 1 −Q0 is the within-deme gene diversity. The
F/(1−F ) method uses per-pair estimates of this within-deme gene diversity, which may
not be best. With IsolationStatistic=SingleGeneDiv a single estimate is used for
all pairwise statistics. In principe this should be better when small per-group samples
are considered, but the generic F/(1−F ) method is still available as the default method.
Limited testing so far suggests little effect of the choice of the statistic on inferences from
samples with 10 haploid individuals per group and high overall diversity.
Output is written in three files yourdata.ISO, yourdata.MIG, and yourdata.GRA with
the same contents as in option 6.5, except for the nature of the genetic distances.
4.6.4 Former sub-option 5 of Genepop: analysis of isolation by distance from a genetic distance matrix
That option (using the Isolde program) allowed to perform the analyses of sub-options
5 and 6 from a file with two semi-matrices, one for genetic “distances” (FST or whatever),
the other for Euclidian distances. These analyses are now available through the IsolationFile setting. Most choices within options 6.5 and 6.6 are available through this
option, and missing data are handled18 (see example below). However, it is not possible
to compute nonparametric confidence intervals for the regression slope since per-locus
information is not provided (remarkably, some software pretends to compute nonparametric intervals in this case). This option may serve as a general purpose program for
Mantel tests. Of course, some settings (minimal geographic distance, the F/(1 − F )
transformation, and the interpretation of one one-tailed P value as a test of isolation by
distance) make sense in the narrower inference context of options 6.5 and 6.6.
The option is called by IsolationFile=input file name where the input file follows
the format of the yourdata.MIG file written by options 6.5 and 6.6, which may be used
as models. An example is
Lousy data <------anything (comments)
8 (an example) <---# of samples (comments ignored)
Fst estimates: <---anything (comments)
0.18 0.107
0.19 0.068 0.011
0.20 0.664 0.665 0.009
0.21 0.098 - 0.673 0.675
0.22 0.048 0.682 0.683 0.017 0.001
0.23 0.715 0.721 0.666 0.666 0.037 0.006
distances: <---anything (comments)
158.0 1215.0
158.1 1213.0 2300.0
158.2 2300.0 2.0 1057.0
158.3 1055.0 2525.0 2525.0 1000.0
158.4 1057.0 1055.0 2525.0 2525.0 1000.0
- 3582.0 3582.0 3582.0 3582.0 1.0 2.222
Anything after the second half matrix <----as it says
is ignored
The order of elements in the half-matrices is again
1 2 3
2 x
3 x x
4 x x x
18more extensively than in earlier versions of Genepop.
Again as in options 6.5 and 6.6, both missing genetic and geographic information (‘-’)
are handled.
Output is written at the end of the input file, and as in options 6.5 and 6.6, (x, y)
data points are also written in the file yourdata.GRA.
Genepop IsolationFile=input file name MantelRankTest= will further replicate
the rank test of the old Isolde program.
4.6.5 User-provided geographic distance matrices
The setting geoDistFile=file name19 can be used to provide a geographic distance
matrix. Its format is that of other geographic distances matrices, with one required line
of comment:
Geographic distances: <---anything (comments)
31 32
41 42 43
The number of samples does not need to be given.
4.6.6 Analysis of isolation by distance from multiple genetic distance
If another program as generated FST or FST/(1 − FST) matrices for a number of loci,
the computation of bootstrap confidence intervals is possible. Analysis of such data sets
is allowed by the MultiMigFile=input file name setting. The format of the input file is
the same as for a single genetic matrix, except that it contains multiple matrices and
that the number of genetic matrices must be given (third line of input):
More lousy data
16 loci (for example) <---# of samples (comments ignored)
locus 1: <---anything (comments)
... <-half matrix (not shown here)
locus 2: <---anything (comments)
... <-more loci and half matrices (not shown here)
locus 16: <---anything (comments)
19New to Genepop 4.2
Geographic distances: <---anything (comments)
158.0 1215.0
158.1 1213.0 2300.0
158.2 2300.0 2.0 1057.0
158.3 1055.0 2525.0 2525.0 1000.0
158.4 1057.0 1055.0 2525.0 2525.0 1000.0
- 3582.0 3582.0 3582.0 3582.0 1.0 2.222
Anything after the second half matrix <----as it says
is ignored
The main use of this option is to allow analyses based on genetic distances not considered in Genepop. If the same estimates are input as would be computed by Genepop,
the results should be similar to those from options 6.5 and 6.6, but not identical in general, because Genepop’s bootstrap estimates are computed as ratio of weighted average
numerators and denominators of genetic estimates, while MultiMigFile can only use
weighted averages of the ratios, i.e. of the input genetic values.
4.6.7 Analysis of mean differentiation
It is possible to perform a bootstrap analysis of the mean pairwise differentiation. Although not an analysis of isolation by distance, it takes into account selection of data by
both PopTypes and range of geographical distances, and should be accessible through all
menu options that lead to bootstrap analyses of isolation by distance, when additionally
using the setting MeanDifferentiationTest=TRUE.
4.7 Data selection for analyses of isolation by distance
4.7.1 Selecting a subset of samples
The settings PopTypes and PopTypeSelection have been developed to facilitate comparison of differentiation patterns within and among different ecotypes or host races.
They are used as follows:
PopTypes= 1 1 2 1 2 1 1 2 3 4
PopTypeSelection=only 1
// PopTypeSelection=inter 1 2
// PopTypeSelection=all
PopTypes allows to distinguish different types of samples (e.g. different ecotypes) by
integer indices. The number of indices must match the number of samples in the data
PopTypeSelection allows performing analyses (genetic distance regressions, confidence intervals, Mantel tests) only on pairs of populations belonging to the types specified. That is, the genetic differentiation statistic among excluded pairs is not used in
any of these analyses. The different choices are shown above: all excludes no pairs (this
is the default value); inter a b will exclude all pairs that do not involve both types a
and b (only two types can be specified); and only a will exclude all pairs that involve a
type different from a (only one type can be specified). For the latter two choices, permutations are made only among samples from a given type. inter_all_pairs excludes
all pairs within types; no Mantel test is performed in that case.
You have to perform the “only” and “inter” analyses in distinct Genepop runs if
you wish to compare their results. Rousset (1999) explains how inferences can be made
from such comparisons. Note that in this perspective, some comparison of the intercept
may be useful and that Genepop also provides confidence intervals on the intercept at
zero distance [or log(distance)].
The inter-type Mantel test may be misleading. The null hypothesis implied by the
permutation procedure is that there is no isolation by distance among populations within
each type, rather than the often more relevant hypothesis that spatial processes within
each type of populations are independent from each other. For this reason, a more
appropriate test of the latter hypothesis is whether the bootstrap confidence interval for
the inter-types regression slope includes zero or not.
4.8 Option 7: File conversions
This option allows the conversion of the Genepop input file toward other formats required by some other programs (the “ecumenical” function of Genepop). Given the
limited interest in some of these conversions, little effort has been made to update them.
In particular, data including haploid loci or in three-digits format may not be converted
into valid input for the other programs.
The following menu appears:
File conversion (diploid data, 2-digits coding only):
GENEPOP --> FSTAT (F statistics) ........................ 1
GENEPOP --> BIOSYS (letter code) ........................ 2
GENEPOP --> BIOSYS (number code) ........................ 3
GENEPOP --> LINKDOS (D statistics) ...................... 4
Main menu .............................................. 5
Sub-option 1 converts the Genepop input file into the format required by the Fstat
program of Goudet (1995). The new format is saved in the file yourdata.DAT.
Sub-options 2 and 3 converts the Genepop input file into the format required by
Biosys (Swofford & Selander, 1989), either the letter or the number code. The new
format is saved in the file yourdata.BIO. You should add the STEP procedures at the
end of this new file before running Biosys. Refer to the Biosys manual for details.
Sub-option 4 converts the Genepop input file into the format required by Linkdos, a program described by Garnier-Géré & Dillmann (1992) and based on Black &
Krafsur (1985). This program performs pairwise linkage disequilibria analyses in subdivided populations and Ohta’s (1982) D statistics. The new format is saved in the
file yourdata.LKD. The source Linkdos program (LINKDOS.PAS) and an executable
(LINKDOS.EXE) have been distributed with previous versions of Genepop with permission of their authors, and are still available on the Genepop distribution page. The
executable distributed with Genepop has been compiled for 40 samples, 20 loci and 99
alleles per locus. It may be wise to relabel alleles (option 8.3) before the conversion.
Garnier-Géré & Dillmann (1992) should be cited whenever this program is used.
4.9 Option 8: Null alleles and some input file utilities
The following menu appears20
Miscellaneous :
Null allele: estimates of allele frequencies .......... 1
Diploidisation of haploid data ........................ 2
Relabeling alleles .................................... 3
Conversion to individual data with population names ... 4
Conversion to individual data with individual names ... 5
Random sampling of haploid genotypes from diploid ones 6
Main Menu ........................................... 7
4.9.1 Sub-option 1: null alleles
This sub-option allows estimation of gene frequencies when a null allele is present. Different methods are available: maximum likelihood, maximum likelihood with genotyping failure, and Brookfield’s (1996) estimator, which differences are explained in Section 6.1.21
Genepop takes the allele with the highest number for a given locus across all
populations as the null allele.22 For example, if you have 4 alleles plus a null allele, a
20Former sub-option 3 (erasing all temporary files) has been discarded.
21The last two methods are new to Genepop 4.0.
22This is a notable difference from Genepop3.4, where the allele with the highest number
in each population was taken as the null allele in this population. Consequently, null allele
estimation is now meaningful even if no null homozygote is observed in a given population. The
null homozygote individual should be indicated as e.g. 0505 or 9999 in the input file.
The default estimation method is maximum likelihood, using the EM algorithm of
Dempster et al. (1977). Apparent null genotypes may also be due to nonspecific genotyping failures. Joint maximum likelihood estimation of such failure rate (“β”) and of allele
frequencies is available through the setting NullAlleleMethod=ApparentNulls. Finally,
the estimator of Brookfield (1996) is also available through the setting NullAlleleMethod=B96..
Confidence intervals for null allele frequencies are computed for each locus in each population. Their coverage probability can be modified by the same setting CIcoverage as
in options 6.5 and 6.6.
The output file is saved in the file yourdata.NUL. This file may contain
• For the maximum likelihood methods, estimated allelic frequencies and predicted
numbers of homozygotes and of heterozygotes with a null allele. For example, in
an output such as
Allele EM freq. Homoz. Null Heter.
1 0.2762 2.7046 4.2954
2 0.2576 1.8500 3.1500
3 0.2251 1.3567 2.6433
4 0.0217 0.0000 0.0000
Null 0.2193
of the seven (2.7046+4.2954) apparent homozygotes for allele 1, it is predicted that
4.2954 are actually heterozygotes for allele 1 and for the null allele. This predicted
value is the expected, or average, number of such heterozygotes over different
samples with the same number of apparent genotypes, under the assumptions of
the model.
• a summary locus-by-population table of estimates of null allele frequencies.
• a summary locus-by-population table of estimates of genotyping failure frequencies
(“beta”), if applicable.
• A table of confidence intervals for estimates of null allele frequencies.
Note that there may be insufficient information to compute estimates and/or confidence
intervals: not enough alleles in the sample, for example. These are indicated by the
message No information. Sometimes the point estimate can formally be computed but
the computed CI is not meaningful. This happens for example in case of heterozygote
excess, and generates a (No info for CI) warning (if all pseudo-samples generated by
output format has also been improved, compared to earlier versions of Genepop, with a more
logical ordering of results (samples within loci) and a final locus by population table of estimated
null allele frequencies.
some resampling technique show an heterozygote excess, all pseudo-estimates of null
allele frequency will be zero and there is no information to construct a non-null CI from
this distribution).
4.9.2 Sub-option 2: Diploidisation of haploid data
This sub-option “diploidizes” haploid loci. For example, the line
popul 1, 01 02 10 00
of an haploid dataset with 4 loci, will become
popul 1, 0101 0202 1010 0000.
Only haploid data are thus modified in a mixed haploid/diploid data file. The new file
is named Dyourdata.23
Note that there may no longer be any need for this option for further analyses
with Genepop (except perhaps as a preliminary to file conversions, option 7), since
Genepop 4.0 now perform analyses on haploid data without such prior “diploidization”
(don’t forget the EstimationPloidy=Haploid setting).
4.9.3 Sub-option 3: Relabeling alleles names
This sub-option relabels all alleles starting from 1 up to x, x being the true number of
distinct alleles for each locus. The new file is named Nyourdata. The correspondence
between the old and the new numbering is indicated in the file new file name.NUM. This
option was originally introduced in Genepop because for some options, the memory
space required depends on the highest allele number. I don’t expect this to be a cause
of concern now.
4.9.4 Sub-options 4 and 5: Conversion of population data to individual
These sub-options convert “population” data (with several individuals per Pop to “individual” data where each individual is put in a distinct Pop. This is useful for individualbased analyses of isolation by distance and, in this perspective, the name of each individual is replaced by what should be its coordinates, that is, either the name of the last
individual in the original population (sub-option 4), or the name of each individual if
their locations are distinguished (sub-option 5)24.
23No longer truncated to 8 letters as it was in earlier versions of Genepop
24New to Genepop 4.3
4.9.5 Sub-option 6: Random sampling of haploid genotypes from diploid
This sub-option randomly samples haploid genotypes at diploid loci.25 This may be
useful for external analyses that require haploid data or that would be biased by HardyWeinberg disequilibria.
5 Evaluating the performance of inferences for
Isolation by distance
Genepop can analyze multiple files, using the settings settings
GenepopRootFile=file <-- or GenepopRootFileName...
This will perform analysis of data in files file1 to file100. Default values of these
three settings are GP, 1, and 1. Users need to assemble results from the multiple output
files. A more integrated output is provided for analyses of isolation by distance. For
the regression estimators of Dσ2 (menu options 6.5 and 6.6), the result.CI file will
contain a Table of point estimates, bootstrap confidence intervals, and (if requested
using the testPoint setting) the bootstrap P-value for a given tested neighborhood
value. including the performance of the bootstrap confidence intervals.
The Performance=value setting provides a convenient (if somewhat ad hoc) shortcut
for selecting the following analyses:
analysis value
â, 1-dim. aLinear or equivalently a1D
ê, 2-dim. aPlanar or a2D
â, 1-dim. eLinear or e1D
ê, 2-dim. ePlanar or e2D
F/(1− F ), 1-dim. FLinear or F2D
F/(1− F ), 2-dim. FPlanar or F2D
Performance sets Genepop in batch mode. Then, the GenepopRootFile, JobMin,
and JobMax values must be given in the settings file. Alternatively, these values can be
given interactively if the Ask or Default mode has been specified after the Performance
setting, in which case Genepop will carry all further computations in Default mode.
25New to Genepop 4.3
6 Methods
This section is only intended as a quick reference guide. The primary literature should
be consulted for further information about the methods implemented in Genepop.
6.1 Null alleles
When apparent null homozygotes are observed, one may wonder whether these are truly
null homozygotes, or whether some technical failure independent of genotype has occurred. Maximum likelihood estimates of null allele frequency, or of this frequency
jointly with the failure rate, can be obtained by the EM algorithm (Dempster et al.,
1977; Hartl & Clark, 1989; Kalinowski & Taper, 2006), which is one of the methods
implemented in Genepop (menu option 8.1).
Also implemented is a simpler estimator defined by Brookfield (1996) for the case
where apparent null homozygotes are true null homozygotes. He also described this as a
maximum likelihood estimator, but there are some (often small) differences with the ML
estimates derived by the EM algorithm as implemented in this and previous versions of
Genepop, which may to be due to the fact that Brookfield wrote a likelihood formula for
the number of apparent homozygotes and heterozygotes, while the EM implementation
is based on a likelihood formula where apparent homozygotes and heterozygotes for
different alleles are distinguished.
For the case where one is unsure whether apparent null homozygotes are true null
homozygotes, Chakraborty et al. (1992) described a method to estimate the null allele
frequency from the other data, excluding any apparent null homozygote. The estimator
is not implemented in Genepop because, beyond its relatively low efficiency, its behavior
is sometimes puzzling (for example, where there is no obvious heterozygote in a sample,
the estimated null allele frequency is always 1, whatever the number of alleles obviously
present and even if only non-null genotypes are present). Actually, even if apparent null
homozygotes are not true null homozygotes, their number bring some information, and it
is more logical to estimate the null allele frequency jointly with the nonspecific genotyping
failure rate by maximum likelihood (Kalinowski & Taper, 2006). This analysis is possible
when at least three alleles are obviously present.
6.2 Exact tests
The probability of a sample of genotypes depends on allele frequencies at one or more
loci. In the tests of Hardy Weinberg equilibrium, population differentiation and pairwise
independence between loci (“linkage equilibrium”) implemented in Genepop, one is not
interested in the allele frequencies themselves and, given they are unknown, the aim is
to derive valid conclusions whatever their values. In these different cases, this can be
achieved by considering only the probability of samples conditional on observed allelic
(e.g. for HW tests) or genotypic counts (e.g. for tests of population differentiation not
assuming HW equilibrium). Because exact probabilities are computed, these conditional
tests are also known as exact tests. See Cox & Hinkley (1974) and Lehmann (1994) for
the underlying theory; a much more elementary introduction to the tests implemented
in Genepop is Rousset & Raymond (1997).
6.3 Algorithms for exact tests
Conditional tests require in principle the complete enumeration of all possible samples
satisfying the given condition. In many cases this is not practical, and the P -value may
be computed by simple permutation algorithms or by more elaborate Markov chain algorithms, in particular the Metropolis-Hastings algorithm (Hastings, 1970). The latter
algorithm explores the universe of samples satisfying the given condition in a “random
walk” fashion. For HW testing Guo & Thompson (1992) found a Metropolis-Hastings
algorithm to be efficient compared to permutations. A slight modification of their algorithm is implemented in Genepop. Guo and Thompson also considered tests for
contingency tables (Technical report No. 187, Department of Statistics, University of
Washington, Seattle, USA, 1989) and again a slightly modified algorithm is implemented
in Genepop (Raymond & Rousset, 1995a). A run of the Markov chain (MC) algorithms
starts with a dememorization step; if this step is long enough, the state of the chain at
the end of the dememorization is independent of the initial state. Then, further simulation of the MC is divided in batches. In each batch a P-value estimate is derived by
counting the proportion of time the MC spends visiting sample configurations more extreme (according to the given test statistic) than the observed sample. If the batches are
long enough, the P-value estimates from successive batches are essentially independent
from each other and a standard error for the P-value can be derived from the variance
of per-batch P-values (Hastings, 1970). As could be expected, the longer the runs, the
lower the standard error.
6.4 Accuracy of P values estimated by the Markov chain
For most data sets the MC “mixes well” so that the default values of the dememorization
length and batch length implemented in Genepop appear quite sufficient (in many other
applications of MC algorithms, things are not so simple; e.g. Brooks & Gelman, 1998).
Nevertheless, inaccurate P-values can be detected when the standard error is large or,
else if the number of switches (the number of times the sample configuration changes
in the MC run) is low (this may occur when the P-value estimate is close to 0 or 1).
Therefore, it is wise to increase the number of batches if the standard error is too large,
in particular if it is of the order of P (the P-value) for small P or of the order of 1− P
for large P , or else if the number of switches is low (< 1000).
6.5 Test statistics
The Markov chain algorithms were first implemented for probability tests, i.e. tests where
the rejection zone is defined out of the least likely samples under the null hypothesis. Such
tests also had Fisher’s preference (e.g. Fisher, 1935); in particular the probability test for
independence in contingency tables is known as Fisher’s exact test. However, probability
tests are not necessarily the most powerful. Depending on the alternative hypothesis of
importance, other test statistics are often preferable (see again Cox & Hinkley, 1974 or
Lehmann, 1994 for textbook accounts). Efficient tests for detecting heterozygote excesses
and deficits (Rousset & Raymond, 1995) were introduced in Genepop from the start (see
option 1), and log likelihood ratio (G) tests were introduced with the implementation
of the genotypic tests for population differentiation (Goudet et al., 1996). The allelic
weighting implicit in the G statistic is indeed optimal for detecting differentiation under
an island model (Rousset, 2007) and use of the G statistic has been generalized to all
contingency table tests in Genepop 4.0, though probability tests performed in earlier
versions of Genepop are still available.
Global tests are performed either using methods tuned to specific alternative hypotheses (for heterozygote excess or deficiency) or using Fisher’s combination of probabilities technique. While the latter has been criticized (Whitlock, 2005), the recommended
alternative can fail spectacularly on discrete data.
6.6 Estimating F -statistics and related quantities
The definition of F -statistics used here is
Q1 −Q2
Q2 −Q3
Q1 −Q3
where the Q are probabilities of identity in state, Q1 among genes (gametes) within
individuals, Q2 among genes in different individuals within groups (populations), and
Q3 among groups (populations). Such formulas appear in Cockerham & Weir (1987);
see Rousset (2002a) for an account of most implications of such definitions, except estimation.
The commonly held idea that it is more difficult to estimate F -statistics when there
are more alleles is generally incorrect; actually many inferences may be more accurate
when more alleles are present (e.g. Leblois et al., 2003, at least as long as gene diversity
is less than 0.8). The issue is not to estimate the frequencies of all alleles, but only to
estimate the above ratios. Any expression of the form (Qi−Qj)/(1−Qj) can be estimated
as (Q̂i− Q̂j)/(1− Q̂j) where any Q̂k is the observed frequency of identical pairs of genes
in the sample, among pairs satisfying the condition designated by the k index. This is
only slightly different (see Rousset, 2007) from what the following estimators achieve.
6.6.1 ANOVA estimators: single- and multilocus definitions
Well-known work by Cockerham (e.g. Cockerham, 1973; Weir & Cockerham, 1984) has
used the formalism of analysis of variance (ANOVA) to define estimators of F -statistics.
These estimators may be expressed in terms of the mean sums of squares MSG, MSI,
MSP (for Gametes, Individuals, and Populations) computed by an analysis of variance
(see e.g. Weir, 1996). Equivalently, they can be expressed in terms of “components
of variances” σ̂2G, σ̂
I , σ̂
P which are unbiased estimates of the corresponding parametric
“components of variances” σ2G, σ
I , σ
P in an ANOVA model. The snag is, in general
(and in some notable applications), these parametric “components of variance” are not
variances but rather differences between variances and can be negative. The σ2 notation
is misleading in this respect; this is a lasting source of confusion, explained in Rousset
(2007). Of course, the σ̂2 estimators can be negative even if the σ2 parameters are
positive, but this is a distinct issue.
The mean squares can themselves be interpreted in terms of observed frequencies
Q̂ of identical pairs of genes in the sample. For balanced samples, the relationships are
simple: 1−Q̂1 = MSG ≡ σ̂2G, Q̂1−Q̂2 = (MSI−MSG)/2 ≡ σ̂2I and Q̂2−Q̂3 = (MSP−
MSI)/(2n) ≡ σ̂2P where n is group size. Hence the single-group (single-population) FIS
estimator is
Q̂1 − Q̂2
1− Q̂2
σ̂2I + σ̂
. (5)
For unbalanced groups (“populations” of unequal size), estimates over several groups
are complex weighted averages of observed frequencies of identical pairs of genes within
groups, not detailed here (see Rousset, 2007). However, ANOVA expressions still satisfy
MSG ≡ σ̂2G and (MSI −MSG)/2 ≡ σ̂2I , and (MSP −MSI)/(2nc) ≡ σ̂2P where nc is
a function of the size of each group (nc ≡ [S1 − S2/S1]/(n − 1), where S1 is the total
sample size, S2 is the sum of squared group sizes, and n is the number of non-empty
groups). Then
F̂IS =
σ̂2I + σ̂
, (6)
F̂ST =
MSP + (nc − 1)MSI + ncMSG
σ̂2P + σ̂
I + σ̂
, (7)
F̂IT =
MSP + (nc − 1)MSI − ncMSG
MSP + (nc − 1)MSI + ncMSG
σ̂2P + σ̂
σ̂2P + σ̂
I + σ̂
. (8)
With several loci, such an analysis is performed for each locus i and the multilocus
estimate is the ratio of a weighted sum of the above locus-specific numerators over
locus-specific denominators. However, there is no single consistent way to compute the
weighted sums. Weir & Cockerham’s (1984) multilocus estimators are defined from
sums of intermediate statistics a, b, and c for each locus, which appear to be the σ̂2’s.
The numerator of the multilocus estimator of FST is thus

loci i ai =

i[(MSP −
MSI)/(2nc)]i. On the other hand Weir’s (1996) multilocus estimators are defined from
distinct intermediate statistics S1, S2, and S3 for each locus, where for locus i, S1i =
[(MSP −MSI)]i/(2n̄) for an average sample size across loci n̄, and the numerator of
the multilocus estimate is

loci i Si =

i[anc]i/n̄. Hence the 1984 and 1996 estimators
slightly differ.
However, both give the same weight to the estimates of the Q’s for a locus typed
at 5 individuals in each subpopulation as for a locus typed at 50 individuals in each
subpopulation. Genepop follows another logic. The multilocus estimator of FST has

i[nc(MSP−MSI)]i, which will give 10 time more weight to the Q estimates
for the more intensively typed locus. ‘Explicit’ formulas for the estimators are:
F̂IS =

i[nc(MSI −MSG)]i∑
i[nc(MSI +MSG)]i

I ]i∑
I + ncσ̂
, (9)
F̂ST =

i[MSP −MSI]i∑
i[MSP + (nc − 1)MSI + ncMSG]i

P ]i∑
P + ncσ̂
I + ncσ̂
, (10)
F̂IT =

i[MSP + (nc − 1)MSI − ncMSG]i∑
i[MSP + (nc − 1)MSI + ncMSG]i

P + ncσ̂
I ]i∑
P + ncσ̂
I + ncσ̂
. (11)
Data from the example file Fmulti.txt (3 samples, 3 loci) illustrate the difference between results obtained by the different methods:
Estimate FIS FST FIT
locus 1 -0.0483 0.5712 0.5505
locus 2 -0.1161 0.8560 0.8393
locus 3 0.0051 -0.0023 0.0028
Multilocus (1984 a,b,c method) -0.0286 0.5606 0.5480
Multilocus (1996 S1,S2,S3 method) -0.0286 0.5633 0.5508
Multilocus (Genepop v3.3 and later) -0.0275 0.5436 0.5310
Most of the time the different estimators yield close values; I expect the Genepop
method to provide better FST estimates under weak differentiation.
6.6.2 Microsatellite allele sizes, RST, and ρST
Following Slatkin (1995), statistics based on allele size have been widely used. The
parameters ρIS, ρST and ρIT and their estimators are defined by replacing any 1−Qk by
the expected square difference in allele size between the genes compared (Rousset, 1996)
in all formulas above, and any 1 − Q̂k by the observed mean square difference (more
formulas are given in Michalakis & Excoffier, 1996). Then the estimators become plain
ANOVA estimators of intraclass correlation for allele size; if there are only two alleles,
ρ̂ST = F̂ST, but Slatkin’s RST 6= F̂ST.
6.6.3 Robertson and Hill’s estimator of FIS
This estimator, reported in options 1 and 5, was designed to have lower variance than
the ANOVA estimator and no small-sample bias when FIS is low, assuming a probability
model for sample probabilities (Robertson & Hill, 1984). The score test computed in
heterozygote excess and deficiency sub-options of option 1 is equivalent to this estimator
for testing purposes.
6.7 Bootstraps
Option 6 constructs approximate bootstrap confidence (ABC) intervals (DiCiccio &
Efron, 1996), assuming that each locus is an independent realization of genealogical
and mutation processes. The bootstrap is a general methodology with different incarnations. The ABC methods were chosen because they balance moderate computation needs
with good accuracy compared to alternatives. Bootstrap methods are approximate, and
simulation tests of their performance (a too rare deed in statistical population genetics)
for the present application are reported in Leblois et al. (2003) and Watts et al. (2007).
The ABC method is also applied over individuals in option 8 to compute confidence
intervals for null allele frequency estimates.
6.8 Mantel test
The principle of the Mantel permutation procedure is to permute samples between geographical locations, so it generates a distribution conditional on having n given sets
of genotypic data in n different samples. The permutations provide the distribution of
any statistic under the null hypothesis of independence between the two variables (here,
genotype counts and geographic location).
Mantel (1967) considered a particular statistics and approximations for its distribution. Instead, Genepop uses no such approximation. Isolation by distance will generate
positive correlations between geographic distance and genetic distance estimates, and
this is best tested using one-tailed P-values. The program provides both one-tailed Pvalues. The probability of observing the sample correlation is the sum of these two
P-values minus 1.
6.8.1 Misuse 1: tests of correlation at different distance
Genetic processes of isolation by distance generate asymptotically decreasing variation
in genetic differentiation with increasing geographic distances, and there is some temp45
tation to use the Mantel test to test for the presence of correlation at specific distances.
However, Genepop prevents this as this is logically unsound, and the more quantitative
methods it provides are better suited to address variation of patterns with distance.
As soon as a process generates data with an expected non-zero correlation at some
distance, it contradicts the null hypothesis under which the Mantel test is an exact test.
Thus it may not make sense to use a Mantel test for testing correlation at some distance
if there is correlation at another distance.
One can still wonder whether a permutation-based test could have some approximate
validity for testing absence of correlation at some distance. However, the bootstrap procedure already addresses this case. Alternative procedures would require further definition on an ad-hoc basis to be operational (e.g., the idea of eliminating samples that form
pairs below or above a given distance may not unambiguously define a sample selection
procedure that will retain power) and would be likely to generate some confusion.
For these reasons, in the present implementation the Mantel tests are always based
on all pairs, ignoring all selection of data according to distance.
6.8.2 Misuse 2: partial Mantel tests
Partial Mantel tests have been used to test for effects of a variable Y on a response
variable Z, while supposedly removing spatial autocorrelation effects on Z. Both standard
theory of exact tests (as used by Raufaste & Rousset, 2001) and simulation (Oden &
Sokal, 1992; Raufaste & Rousset, 2001; Rousset, 2002b; Guillot & Rousset, 2013) show
that the permutation procedure of the Mantel test is not appropriate for the partial
Mantel test when the Y variable itself presents spatial gradients. Asymptotic arguments
have also been proposed to support the use of such permutation tests (e.g. Anderson,
2001) but they fail in the same conditions. As shown by Raufaste & Rousset (2001),
the problem is inherent to the permutation procedure, not to a specific test statistic.
Unfortunately, some papers maintain confusion about these different aspects of “partial
Mantel tests”. Legendre & Fortin (2010) argued how miserable the papers by Raufaste
& Rousset (2001) and Rousset (2002b) were, and claimed that some versions of the
tests should be preferred because they used pivotal statistics (without evidence that the
statistics were indeed pivotal, a property that depends on the statistical model). Guillot
& Rousset (2013) reviewed old and more recent literature demonstrating issues with the
partial Mantel test, provided new simulations showing that the different tests discussed
by Legendre & Fortin (2010) failed, and criticized their verbal arguments. Despite
this, Legendre et al. (2015) criticized this more recent paper again for ignoring the old
literature, and repeated the same kind of verbal explanations that have previously failed
(but still did not show anything about pivotal statistics).
7 Code history, compilation, credits, contact, etc.
Version 4.0 of Genepop was a C++ rewrite of Genepop 3.4 (Raymond & Rousset,
1995b) by F.R., using draft C translations of many Genepop modules by O. Guillaume,
N. Benhamou and A. André, and some draft C++ classes by R. Leblois. Genepop
uses R. J. Wagner’s implementation of the Mersenne Twister random number generator
(Matsumoto & Nishimura, 1998). The Genepop Windows executable has been compiled
with version 4.6.3 of GNU’s C++ compiler (Windows version from the Rtools).
Beyond M. Raymond and F.R., credit for previous Genepop code is as follows. The
complete enumeration procedure for HW tests was derived from Fortran code provided
by E. J. Louis (Inst. Mol. Med., Oxford, UK). Some of the procedures for isolation by
distance “between individuals” were first written by R. Leblois with help from S. Piry
(INRA-CBGP, Montpellier). P. David, É. Imbert and S. Samadi wrote some early code
in 1993.
B. Anderson, M.A. Beaumont, A. Becher, T.J.C. Beebee, S. Bellman, L. Bernatchez,
D. Bourguet, J. Britton-Davidian, E. Bucheli, J. Carlier, G. Carmody, R. Castilho, F.
Catzeflis, C. Chevillon, J. Clayton, J. Dallas, P. David, P. Dias, B. Dodd, R. Eritja, A.
Estoup, A.-B. Failloux, E. Fjerdingstad, R.C. Fleischer, A.J. Gharrett, S. T. Glenn, S.(?)
Goodman, J. Goudet, L. Henke, D. Innes, P. Jarne, L. Jermiin, J. Kelso, N. KhromovBorissov, J. Lagnel, M. Lascoux, L.S. Magnussen, J. Mallet, D., (?) McDonald, C.
Moran, F. Nicholas, I. Olivieri, M. van Oppen, N. Pasteur, R. Paxton, F. Renaud, H.
Rosa, L., P. W. Shaw, Shapiro, J. Shykoff, D. Sicard, J. Slate, M. Slatkin, M. Small, T.
Staedler, F. Thomas, F. Viard, P. Waldmann, K. J. Wetherall, (?) Winker, Z. Xu, made
suggestions or tests on the various states of Genepop until version 3.4.
T. Antão, E. Archer, R.I. Bailey, J.S.F. Barker, D. Bourguet, T. Devitt, É. Imbert, R.
Leblois, T. de Meeus, P. Morin, S. Ponsard, V. Ravigné, E. Taschen, and Y. Zimmermann
have pointed issues or have stimulated additional developments of more recent versions.
7.1 Contact
If you think you have found a bug, you can contact me. Requests which do not meet the
following requirements are likely to meet poor response. Please provide a minimal input
file illustrating the suspected problem, whenever relevant. Please use the latest version
of Genepop taken from a web page I maintain. Note that I do not maintain the
“Genepop on the web” port of Genepop: any question related to this port
should be addressed to Eleanor Morgan. Please specify the version of Genepop
you are using. Please do not ask whether Genepop is commercial software. Please read
this documentation.
I may answer queries about methods implemented in Genepop, and the more so
when they are specific to Genepop. But in most cases there are published references
describing the methods, cited in this documentation. Please read this documentation.
Bug fixes since release of Genepop version 3.4 in May 2003 until first
release of Genepop 4.0:
The sign of the lower confidence interval bound for regression slope in Isolde did not
appear on output file when it was negative.
For computation of allele size-based statistics (Option 6.2 and 6.4) with the option
“allele name = allele size”, the allele ‘99’ was interpreted as having size zero.
See the distribution page for more recent bug fixes.
8 Copyright
The sources of earlier versions of Genepop were distributed as “public domain”. The
Genepop 4.+ code is c© F. Rousset, and distributed under the GPL-compatible CeCill
licence (see The Mersenne Twister code is
c© R. J. Wagner, and open source code under the BSD Licence.
Anderson, M. J., 2001. Permutation tests for univariate or multivariate analysis of
variance and regression. Can. J. Fish. Aquatic Sci. 58: 626–639.
Barton, N. H. & Slatkin, M., 1986. A quasi-equilibrium theory of the distribution of rare
alleles in a subdivided population. Heredity 56: 409–415.
Black, IV, W. C. & Krafsur, E. S., 1985. A FORTRAN program for the calculation
and analysis of two-locus linkage disequilibrium coefficients. Theor. Appl. Genet. 70:
Brookfield, J. F. Y., 1996. A simple new method for estimating null allele frequency
from heterozygote deficiency. Mol. Ecol. 5: 453–455.
Brooks, S. & Gelman, A., 1998. General methods for monitoring convergence of iterative
simulations. J. Computational Graphical Stat. 7: 434–455.
Chakraborty, R., de Andrade, M., Daiger, S. & Budowle, B., 1992. Apparent heterozygote deficiencies observed in DNA typing data and their implications in forensic
applications. Ann. Hum. Genetics 56: 45–57.
Cockerham, C. C., 1973. Analyses of gene frequencies. Genetics 74: 679–700.
Cockerham, C. C. & Weir, B. S., 1987. Correlations, descent measures: drift with
migration and mutation. Proc. Natl. Acad. Sci. U. S. A. 84: 8512–8514.
Cox, D. R. & Hinkley, D. V., 1974. Theoretical statistics. Chapman & Hall, London.
Dempster, A. P., Laird, N. M. & Rubin, D. B., 1977. Maximum Likelihood from incomplete data via the EM algorithm (with discussion). J. R. Stat. Soc. B 39: 1–38.
DiCiccio, T. J. & Efron, B., 1996. Bootstrap confidence intervals (with discussion). Stat.
Sci. 11: 189–228.
Felsenstein, J., 2005. PHYLIP (Phylogeny Inference Package) version 3.6. Distributed
by the author. Department of Genome Sciences, University of Washington, Seattle.
Fisher, R. A., 1935. The logic of inductive inference (with discussion). J. R. Stat. Soc.
98: 39–82.
Garnier-Géré, P. & Dillmann, C., 1992. A computer program for testing pairwise linkage
disequilibria in subdivided populations. J. Hered. 83: 239.
Goudet, J., 1995. FSTAT (Version 1.2): A computer program to calculate F-statistics.
J. Hered. 86: 485–486.
Goudet, J., Raymond, M., de Meeüs, T. & Rousset, F., 1996. Testing differentiation in
diploid populations. Genetics 144: 1931–1938.
Guillot, G. & Rousset, F., 2013. Dismantling the Mantel tests. Methods Ecol. Evol. 4:
Guo, S. W. & Thompson, E. A., 1992. Performing the exact test of Hardy-Weinberg
proportion for multiple alleles. Biometrics 48: 361–372.
Haldane, J. B. S., 1954. An exact test for randomness of mating. Journal of Genetics
52: 631–635.
Hartl, D. L. & Clark, A. G., 1989. Principles of population genetics. Sinauer, Sunderland,
Mass., 2nd edn.
Hastings, W. K., 1970. Monte Carlo sampling methods using Markov chains and their
applications. Biometrika 57: 97–109.
Kalinowski, S. T. & Taper, M. L., 2006. Maximum likelihood estimation of the frequency
of null alleles at microsatellite loci. Conserv. Genetics 7: 991–995.
Leblois, R., Estoup, A. & Rousset, F., 2003. Influence of mutational and sampling
factors on the estimation of demographic parameters in a “continuous” population
under isolation by distance. Mol. Biol. Evol. 20: 491–502.
Leblois, R., Pudlo, P., Néron, J., Bertaux, F., Beeravolu, C. R., Vitalis, R. & Rousset, F.,
2014. Maximum likelihood inference of population size contractions from microsatellite
data. Mol. Biol. Evol. 31: 2805–2823.
Legendre, P. & Fortin, M.-J., 2010. Comparison of the Mantel test and alternative
approaches for detecting complex multivariate relationships in the spatial analysis of
genetic data. Mol. Ecol. Resources 10: 831–844.
Legendre, P., Fortin, M.-J. & Borcard, D., 2015. Should the Mantel test be used in
spatial analysis? Methods Ecol. Evol. 6: 1239–1247.
Lehmann, E. L., 1994. Testing statistical hypotheses. Chapman & Hall, New York, 2nd
Levene, H., 1949. On a matching problem arising in genetics. Annals of Mathematical
Statistics 20: 91–94.
Loiselle, B. A., Sork, V. L., Nason, J. & Graham, C., 1995. Spatial genetic structure
of a tropical understory shrub Psychotria officinalis (Rubiaceae). Am. J. Bot. 82:
Louis, E. J. & Dempster, E. R., 1987. An exact test for Hardy-Weinberg and multiple
alleles. Biometrics 43: 805–811.
Mantel, N., 1967. The detection of disease clustering and a generalized regression approach. Cancer Research 27: 209–220.
Matsumoto, M. & Nishimura, T., 1998. Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Trans. on Modeling and
Computer Simulation 8: 3–30.
Mehta, C. R. & Patel, N. R., 1983. A network algorithm for performing Fisher’s exact
test in r × c contingency tables. J. Am. Stat. Assoc. 78: 427–434.
Michalakis, Y. & Excoffier, L., 1996. A generic estimation of population subdivision
using distances between alleles with special interest to microsatellite loci. Genetics
142: 1061–1064.
Oden, N. L. & Sokal, R. R., 1992. An investigation of three-matrix permutation tests.
J. Classif. 9: 275–290.
Ohta, T., 1982. Linkage disequilibrium due to random genetic drift in finite subdivided
populations. Proc. Natl. Acad. Sci. U. S. A. 79: 1940–1944.
Raufaste, N. & Rousset, F., 2001. Are partial Mantel tests adequate? Evolution 55:
Raymond, M. & Rousset, F., 1995a. An exact test for population differentiation. Evolution 49: 1283–1286.
Raymond, M. & Rousset, F., 1995b. GENEPOP Version 1.2: population genetics software for exact tests and ecumenicism. J. Hered. 86: 248–249.
Robertson, A. & Hill, W. G., 1984. Deviations from Hardy-Weinberg proportions: sampling variances and use in estimation of inbreeding coefficients. Genetics 107: 703–
Rousset, F., 1996. Equilibrium values of measures of population subdivision for stepwise
mutation processes. Genetics 142: 1357–1362.
Rousset, F., 1997. Genetic differentiation and estimation of gene flow from F -statistics
under isolation by distance. Genetics 145: 1219–1228.
Rousset, F., 1999. Genetic differentiation within and between two habitats. Genetics
151: 397–407.
Rousset, F., 2000. Genetic differentiation between individuals. J. Evol. Biol. 13: 58–62.
Rousset, F., 2002a. Inbreeding and relatedness coefficients: what do they measure?
Heredity 88: 371–380.
Rousset, F., 2002b. Partial Mantel tests: reply to Castellano and Balletto. Evolution
56: 1874–1875.
Rousset, F., 2007. Inferences from spatial population genetics. In: Handbook of statistical genetics (D. J. Balding, M. Bishop & C. Cannings, eds.), pp. 945–979. Wiley,
Chichester, U.K., 3rd edn.
Rousset, F., 2008. Genepop’007: a complete reimplementation of the Genepop software
for Windows and Linux. Mol. Ecol. Resources 8: 103–106.
Rousset, F. & Leblois, R., 2007. Likelihood and approximate likelihood analyses of
genetic structure in a linear habitat: performance and robustness to model misspecification. Mol. Biol. Evol. 24: 2730–2745.
Rousset, F. & Leblois, R., 2012. Likelihood-based inferences under isolation by distance:
two-dimensional habitats and confidence intervals. Mol. Biol. Evol. 29: 957–973.
Rousset, F. & Raymond, M., 1995. Testing heterozygote excess and deficiency. Genetics
140: 1413–1419.
Rousset, F. & Raymond, M., 1997. Statistical analyses of population genetic data: old
tools, new concepts. Trends Ecol. Evol. 12: 313–317.
Slatkin, M., 1995. A measure of population subdivision based on microsatellite allele
frequencies. Genetics 139: 457–462.
Swofford, D. L. & Selander, R. B., 1989. BIOSYS-1. A Computer Program for the
Analysis of Allelic Variation in Population Genetics and Biochemical Systematics.
Release 1.7. Illinois Natural History Survey, Champaign.
Vekemans, X. & Hardy, O. J., 2004. New insights from fine-scale spatial genetic structure
analyses in plant populations. Mol. Ecol. 13: 921–934.
Watts, P. C., Rousset, F., Saccheri, I. J., Leblois, R., Kemp, S. J. & Thompson, D. J.,
2007. Compatible genetic and ecological estimates of dispersal rates in insect (Coenagrion mercuriale: Odonata: Zygoptera) populations: analysis of ‘neighbourhood size’
using a more precise estimator. Mol. Ecol. 16: 737–751.
Weir, B. S., 1996. Genetic Data Analysis II. Sinauer, Sunderland, Mass.
Weir, B. S. & Cockerham, C. C., 1984. Estimating F -statistics for the analysis of
population structure. Evolution 38: 1358–1370.
Whitlock, M. C., 2005. Combining probability from independent tests: the weighted
Z-method is superior to Fisher’s approach. J. Evol. Biol. 18: 1368–1373.
Allele coding, 9
2-digits, 10
3-digits, 3, 9
Allele size-based statistics, 14, Options 6.3
& 6.4
ρST, 44
RST, 44
AlleleSizes setting, 14
AllelicDistance setting, 14
Batch mode, 7, 39
BatchLength setting, 13
BatchNumber setting, 13
Biosys program, 36
Bootstrap, see Confidence intervals
Bug reports, 47
Bugs, 48
CIcoverage setting, 29, 37
Code checks, 18, 23
Combination of different tests, 16, 19, 42
Command line, 11
Concurrent processes, 8
Confidence intervals, 29
bootstrap, 45
Data selection
by ploidy, see estimationPloidy, 52
subset of samples, see popTypeSelection, 52
Dememorisation setting, 13
Differentiation, Option 3
gene diversity, 22
genic, 20
genic-genotypic test, 22
genotypic, 21
DifferentiationTest setting, 21, 22
Dσ2 estimation
â statistic, 28
ê statistic, 28
FST/(1− FST) statistic, 31
Loiselle’s statistic, 29
EstimationPloidy setting, 13
Exact tests, see also Differentiation; Linkage disequilibrium; Hardy-Weinberg
tests; Mantel test
conditional tests, 40
Fisher’s, 42
Metropolis-Hastings algorithm, 41
permutation algorithms, 41
probability test, 42
F -statistics, see also FIS
definition, 42
estimation formulas, 42
FST, Options 6.1 & 6.2
File conversions, Option 7
multisample multilocus, Option 6.1
multisample per locus, Options 5.2 &
per sample multilocus, Option 5.2
per sample per locus, Options 5.1 & 5.2
Robertson & Hill’s estimator of FIS, 45
Fstat program, 35
GameticDiseqTest setting, 19
Gene diversities, Options 5.2 & 5.3
GeneDivRanks setting, 22
Genepop, differences from previous versions, 1, see also footnotes throughout this document
GenepopInputFile setting, 13
GenepopRootFile setting, 39
geoDistFile setting, 33
GeographicScale setting, 29
Geometry setting, 29
Haplo-diploid genotypes, 10
Haploid data, 3, 9, 10, 13, 25, 27, 35
from diploid, 39
to haploid, 38
Hardy-Weinberg tests, 16, Option 1
multisample score test, 17, Options 1.4
& 1.5
score test, 15
help, 11
Heterozygosities, see Gene diversities
HW program, 3, 17
HWfile setting, 3, 17
HWfileOptions setting, 18
HWtests setting, 16
Individual data from population data, Option 8.4
Individual-based analysis
conversion of data for, 38
Input file, see GenepopInputFile
Input format, 8
for Mantel test, 32
for single contingency table, 23
for single HW test, 17
InputFile setting, 13
Isolation by distance
between groups, Option 6.6
between individuals, Option 6.5
IsolationFile setting, 32
IsolationStatistic setting, 28
Isolde program, 3, 31
JobMax, 39
JobMin, 39
Levene’s correction, 24
Linkage disequilibrium, Option 2
composite, 19
cyto-nuclear, 19
Ohta’s statistics, 36
Linkdos program, 6, 36
Linux, 6
installation on, 6
Mac OS X
file format issues, 11
installation on, 6
Mantel test, 14, 30, 45, Options 6.5 & 6.6
inter-type, 35
partial, 46
MantelPermutations setting, 30
MantelRankTestMantelRankTest setting, 30
MantelSeed setting, 14
Markov chain algorithms
accuracy, 41
parameters, 13
switches, 16, 19, 41
Maxima setting, 14
MaximalDistance setting, 29
Maximum sample size, see Maxima
MeanDifferentiationTest setting, 34
MenuOptions setting, 14
Microsoft Windows
file format issues, 11
installation on, 6
MinimalDistance setting, 29
Missing data, 10
Mode setting, 7, 13, 39
MultiMigFile setting, 33
Neighborhood size, see Dσ2 estimation
Null alleles, 36, 40, Option 8.1
NullAlleleMethod setting, 37
Performance setting, 39
Phylip package, see PhylipMatrix
PhylipMatrix setting, 30
PopTypes setting, 34
PopTypeSelection setting, 34
Population differentiation, see Differentiation
Population type selection, 34
Private allele method, Option 4
Pseudo-random numbers, 14, 47
RandomSeed setting, 14
Relabeling alleles, 38, Option 8.3
multisample multilocus, Option 6.3
multisample per locus, Options 5.3 &
per sample multilocus, Option 5.3
per sample per locus, Option 5.3
ρST, 44, Options 6.3 & 6.4
RST, see Allele size-based statistics
Sample size
limitations, 11
Selecting subset of samples, see Population
type selection
Settings file, 11
SettingsFile setting, 7, 13
Struc program, 3, 23
StrucFile setting, 3, 23
testPoint setting, 29

PDF Document reader online

This website is focused on providing document in readable format, online without need to install any type of software on your computer. If you are using thin client, or are not allowed to install document reader of particular type, this application may come in hand for you. Simply upload your document, and will transform it into readable format in a few seconds. Why choose

  1. Unlimited sharing - you can upload document of any size. If we are able to convert it into readable format, you have it here - saved for later or immediate reading
  2. Cross-platform - no compromised when reading your document. We support most of modern browers without the need of installing any of external plugins. If your device can oper a browser - then you can read any document on it
  3. Simple uploading - no need to register. Just enter your email, title of document and select the file, we do the rest. Once the document is ready for you, you will receive automatic email from us.

Previous 10

Next 10