Exact Computation of Coalescent Likelihood for Panmictic and Subdivided Populations under the Infinite Sites Model
Issue No. 04 - October-December (2010 vol. 7)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.2
Coalescent likelihood is the probability of observing the given population sequences under the coalescent model. Computation of coalescent likelihood under the infinite sites model is a classic problem in coalescent theory. Existing methods are based on either importance sampling or Markov chain Monte Carlo and are inexact. In this paper, we develop a simple method that can compute the exact coalescent likelihood for many data sets of moderate size, including real biological data whose likelihood was previously thought to be difficult to compute exactly. Our method works for both panmictic and subdivided populations. Simulations demonstrate that the practical range of exact coalescent likelihood computation for panmictic populations is significantly larger than what was previously believed. We investigate the application of our method in estimating mutation rates by maximum likelihood. A main application of the exact method is comparing the accuracy of approximate methods. To demonstrate the usefulness of the exact method, we evaluate the accuracy of program Genetree in computing the likelihood for subdivided populations.
molecular biophysics, bioinformatics, biology computing, genetics,program Genetree, coalescent likelihood exact computation, panmictic population, subdivided population, infinite sites model, mutation rates, exact coalescent likelihood computation,Genetic mutations, Biology computing, Biological system modeling, Monte Carlo methods, Computational modeling, Maximum likelihood estimation, Stochastic processes, Data analysis, Throughput, Computer science,Population genetics, coalescent theory, algorithms, subdivided population.,
"Exact Computation of Coalescent Likelihood for Panmictic and Subdivided Populations under the Infinite Sites Model", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. , pp. 611-618, October-December 2010, doi:10.1109/TCBB.2010.2