Migraine

Migraine performs maximum-likelihood inference of historical changes in population size from microsatellite allelic count data and currently focuses on allelic counts while planning future support for sequence data.

Key Features:

• Maximum-Likelihood Methodology: Employs a maximum-likelihood approach using importance sampling of gene genealogies to infer historical population dynamics, including past reductions in population size.
• Mutation Model Integration: Incorporates the generalized stepwise mutation model (GSM) to account for deviations from the single-step mutation assumption and reduce bias in contraction rate estimation.
• Performance and Robustness Testing: Evaluated via extensive simulations showing competitive estimation precision and confidence interval coverage relative to MSVAR and robustness to mutational model misspecification but sensitivity to unaccounted population structure.
• Computational Considerations: Analyses can incur increased computation time under strong disequilibrium and can be affected by forms of population structure that are not modeled.

Scientific Applications:

• Evolutionary biology: Inferring past demographic events and changes in population size from microsatellite allelic data.
• Molecular ecology: Reconstructing historical population sizes and dynamics to inform studies of population history using microsatellite allelic counts.

Methodology:

Maximum-likelihood estimation using importance sampling of gene genealogies; mutation modeling with the generalized stepwise mutation model (GSM); performance assessment via extensive simulations and comparison to MSVAR.