Resources for Software and Statistical Methods for Population Genetics 2025 (SSMPG 2025) (Aussois, September 8-12 2025)
This public repository contains resources and instructions for the sixth edition of the Summer School on Software and Statistical Methods for Population Genetics (SSMPG 2025). The SSMPG 2025 Summer School will provide a comprehensive overview of software and statistical methods for genotype-environment association analyses to detect genetic loci involved in local adaptation, as well as methods for predicting population maladaptation to future climate conditions.
The dataset contains allele frequencies measured in 100 populations of the snow slug (Limax nivalis), an emblematic species in Olivier's computer that survived an abrupt climatic change. Sampled individuals were genotyped at L=2333 loci. Sixteen bioclimatic variables—comprising eight precipitation and eight temperature variables—were recorded both before and after the abrupt change in environmental conditions.
The objective of the practical session is to apply genotype-environment association methods to detect loci associated with fitness traits in the 100 snow slug populations. These loci will then be used to compute predictions of genetic offset for each population. The ultimate goal is to approximate as closely as possible the ground-truth measure of fitness loss observed in the populations.
The woolly marmot is an emblematic rodent species that lives in Thibaut's computer and recently disappeared from three directories due to poor coding decisions. Thibaut would like to repopulate these three sites but wants to ensure that the reintroduced individuals are optimally adapted to local environmental conditions.
The goal of the practical session is to select source populations that minimize maladaptation at the three reintroduction sites using genetic offset measures. The data consist of a genotype matrix for 610 individuals from 61 populations, genotyped at L=1000 loci (in 0/1/2 genotype format). Ten environmental variables have been measured for each source and reintroduction directory.
The associated datasets can be found in the Woolly marmot folder.
During the data analysis sessions, participants are encouraged to form teams. Ideally, each team should consist of 4–5 participants. Teams will collaboratively present a synthesis of their analysis in a public session on the final day and submit three files to the organizers for each data analysis challenge. The submitted files should include:
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A list of candidate loci detected using their preferred GEA method, or a combination of methods, for both challenges
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A list of genetic offset values obtained using their preferred offset method, or a combination of methods, for each snow slug population
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A list of optimal source populations for the three Woolly marmot reintroduction sites
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A short README file ("Materials and Methods") explaining all the methodological choices made during the analysis
Each team will be asked to present 2–3 slides for each dataset in the public session on the final day.
Thibaut and Olivier will reveal the truth about woolly marmots and snow slug, and the limits of inference from the genotypes and environmental data Don't be worried. Everyone wins!
To participate in the practical sessions, bring your own laptop and install R and RStudio, an integrated development environment (IDE) for R.
To install R packages for the data analyses, copy and paste the following pieces of code in the R session
#Install R packages for SSMPG 2025
#Package LEA (latest version)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("LEA")
#Package gradientForest
install.packages("gradientForest", repos="http://R-Forge.R-project.org")
#Package vegan for RDA
install.packages("vegan")
#Package qvalue for controlling FDRs
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("qvalue")
Download the archive for the latest stable version (2.3) from http://www1.montpellier.inra.fr/CBGP/software/baypass/ or directly via the following command run on a terminal:
wget http://www1.montpellier.inra.fr/CBGP/software/baypass/files/baypass_2.3.tar.gz
Extract the archive, e.g., from a terminal:
tar -zxvf baypass_2.3.tar.gz
The source files are to be found in the src subdirectory. BayPass is coded in Fortran90 and can therefore be compiled for any system supporting a Fortran90 compiler using the provided Makefile. This Makefile is designed to work with either the free compiler gfortran (if not already installed in your system, binaries are available at https://gcc.gnu.org/wiki/GFortranBinaries and are easy to install for most Windows, Mac and Linux OS versions) or the commercial ifort intel Fortran compiler that is now freely available (for non commercial use) as part of the Intel oneAPI Toolkit (see installation instruction for Windows, MacOS and Linux system). BayPass also uses OpenMP (http://openmp.org/wp/) to implement multithreading, which allows parallel calculation on computer systems that have multiple CPUs or CPUs with multiple cores. Users thus have to make sure that the corresponding libraries are installed (which is usually the case, on Linux OS or following compiler installation previously described). The following instructions run within the src subdirectory allows to compile the code and to produce a binary:
- using the gfortran free compiler (the command should automatically produce an executable called g_baypass):
make clean all FC=gfortran
- using the ifort intel Fortran compiler (the command should automatically produce an executable called i_baypass):
make clean all FC=ifort
Note: Under Linux (or MacOS), before the first use, make sure to give appropriate execution rights to the program. For instance you may run:
chmod +x baypass