LAMARC - Likelihood Analysis with Metropolis Algorithm using Random Coalescence

This documentation covers use of the program LAMARC, which can estimate population sizes, migration rates, growth rates, and recombination rate using a Metropolis-Hastings Monte Carlo Markov Chain algorithm. LAMARC's 'sister program' is MIGRATE, with which it shares history, algorithms, and some code base, but which is now being maintained exclusively by Peter Beerli at Florida State University.

General:

• Overview
• Changes in Version 2.1.9
• Plans for the Future
• Frequently Asked Questions
• Error and Warning Messages
• Limitations of LAMARC
• Glossary of Terms

Tutorials:

• What kind of data to use with LAMARC
• How to design a LAMARC analysis
• How to design a Bayesian LAMARC analysis
• Interpreting the results
• Combining multiple computers to complete a LAMARC run
• Using LAMARC for fine-scale mapping

Specific Topics:

• Compiling LAMARC
• File conversion utilities
• Modeling Linkage Properties and Relative Mutation Rates
• Migration Matrix
• Divergence
• Panel Corrections
• The Converter Command File
• XML data input format
• Using the LAMARC interactive menu
• Dividing your data into regions and segments
• Modelling the mutational process
• Handling variation in mutation rate (several types, including gamma-distributed rates)
• Evolutionary forces
• What do the parameters mean?
• Search strategies
• Interpreting the output
• Bayesian analysis
• Using Tracer to examine LAMARC results
• Genotypic (phase-unknown) data
• Specific issues with virus data

References:

Kuhner, M. K. and L. P. Smith, 2007 "Comparing Likelihood and Bayesian Coalescent Estimation of Population Parameters" Genetics 175: 155-165.

Kuhner, M. K., 2006 "LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters." Bioinformatics 22(6): 768-770.