Migraine

coalescent algorithms for maximum likelihood analysis of population genetic data
Overview

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 2Nμ 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). 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.



Citations
Leblois R., C. R. Beeravolu, F. Rousset. 2017. Likelihood analysis of population genetic data under coalescent models: computational and inferential aspects. Journal de la Société Française de Statistique Submitted

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

Rousset F., R. Leblois. 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 - doi:10.1093/molbev/msr262

Rousset, F., R. Leblois. 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

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