Statistical methods


This R package to infer quantitative genetic parameters from Generalised Linear Mixed Models (GLMM), e.g. for non Gaussian phenotypic traits. It allows for the computation of quantitative genetic parameters such as additive genetic variance, heritabilities, intra-class correlation coefficients, G-matrices (for multivariate analyses), but also for evolutionary predictions. The package is available on CRAN. The source code is available on GitHub. Please report issues here.


The new environmental version of BayeScEnv can be found on GitHub. This genome scan method allows to detect high population differentiation (i.e. high FST) due to a high environmental differentiation (local adaptation scenario). It is also less error-prone than BayeScan.


Estimating heritability using MCMCglmm (version 2)

This tutorial is intended for students or researchers in the domain of evolutionary ecology, interested in using the animal model to estimate the heritability of biological traits in a wild population. It aims at bringing theoretical and practical help on three main issues: (i) understanding what heritability is, what it quantifies and how the animal model works; (ii) learning by practice how to implement animal models using the MCMCglmm R package; and (iii) introducing Bayesian statistics (priors, Markov Chain Monte Carlo, etc.).

This is a new version published during September 2021.

Online Practice Material for “General Quantitative Genetic Methods for Comparative Biology”

Below are the PDF for the OPM of our chapter “General Quantitative Genetic Methods for Comparative Biology” by myself and Shinichi Nakagawa (part of the book “Modern Phylogenetic Comparative Methods”, ed. L.Z. Garamszegi). It shows how to use Phylogenetic Generalised Linear Mixed Models to perform various comparative analyses (including accounting for intra-specific variance, missing data and non-Gaussian traits).