Coalescent algorithms for maximum likelihood analysis of population genetic data
Current version

All changes since first version are described here.


Migraine implements coalescent algorithms for maximum likelihood analysis of population genetic data. The data currently handled are allelic counts (e.g. microsatellite data for all models and SNPs for structured population models) and sequences. Both type of data can be combined in a single analysis. The code is designed to be compiled with the GNU C++ compiler under Windows and Linux (including its Mac OSX incarnation).

The version available through this page includes implementations of one- and two-dimensional isolation by distance models (including the island model as a subcase, Rousset & Leblois 2007, Rousset & Leblois 2012), as well as the two-populations model described in de Iorio et al. (2005) and the elementary one-population model (with 2Nmu as unique parameter). Two models of a single population with past size variations are also implemented and can be used to detect and characterize past bottlenecks and/or expansions from microsatellite or sequence data (Leblois et al. 2014, Rousset et al. 2018). Typical analyses can be performed in seconds (in the one-parameter case) or overnight (in some three-parameter cases), and longer analyses can easily been split among different cores/computers.

Developed by

François Rousset, Raphaël Leblois and Champak Reddy Beeravolu


If you have any question, please feel free to contact me. However, I strongly recommend you read the manual first.

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Rousset F, Beeravolu CR & Leblois R, 2018. Likelihood computation and inference of demographic and mutational parameters from population genetic data under coalescent approximations . Journal de la Société Française de Statistique, 159(3): 142-166. (https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/708/748)

Leblois R, Pudlo P, Bertaux F, Neron J, Beeravolu CR, Vitalis R & Rousset F, 2014. Maximum likelihood inference of population size contractions from microsatellite data. Molecular Biology and Evolution 31: 2805-2823. (https://doi.org/10.1093/molbev/msu212)

Rousset F & Leblois R, 2012. Likelihood-based inferences under a coalescent model of isolation by distance: two-dimensional habitats and confidence intervals. Molecular Biology and Evolution 29: 957-973. (https://doi.org/10.1093/molbev/msr262)

Rousset F. & Leblois R, 2007. Likelihood and approximate likelihood analyses of genetic structure in a linear habitat: performance and robustness to model mis-specification. Molecular Biology and Evolution 24: 2730-2745. (https://doi.org/10.1093/molbev/msm206)

De Iorio M, Griffiths R, Leblois R & Rousset F, 2005. Stepwise mutation likelihood computation by sequential importance sampling in subdivided population models. Theoretical Population Biology 68: 41-53. (https://doi.org/10.1016/j.tpb.2005.02.001)


Migraine is freeware (i.e. you don’t need to pay). It is free software covered by the CeCILL licence (GPL compatible), i.e. it can be used, copied, studied, modified, and redistributed in other free software (i.e. also covered by a GPL-compatible licence, with freely available source code, even if commercial software) provided the Migraine source is acknowledged.

Migraine © François Rousset, Raphael Leblois and Champak Reddy Beeravolu 2007-present and copyright © 2007 – 2018 INRAE.

Last updated

By Raphael Leblois on 2020-10-01