Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics
In 1988, Felsenstein described a framework for assessing the likelihood of a genetic data set in which all of the possible genealogical histories of the data are considered, each in proportion to their probability. Although not analytically solvable, several approaches, including Markov chain Monte...
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| Veröffentlicht in: | Proceedings of the National Academy of Sciences - PNAS 2007-02, Vol.104 (8), p.2785-2790 |
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| container_title | Proceedings of the National Academy of Sciences - PNAS |
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| creator | Hey, Jody Nielsen, Rasmus |
| description | In 1988, Felsenstein described a framework for assessing the likelihood of a genetic data set in which all of the possible genealogical histories of the data are considered, each in proportion to their probability. Although not analytically solvable, several approaches, including Markov chain Monte Carlo methods, have been developed to find approximate solutions. Here, we describe an approach in which Markov chain Monte Carlo simulations are used to integrate over the space of genealogies, whereas other parameters are integrated out analytically. The result is an approximation to the full joint posterior density of the model parameters. For many purposes, this function can be treated as a likelihood, thereby permitting likelihood-based analyses, including likelihood ratio tests of nested models. Several examples, including an application to the divergence of chimpanzee subspecies, are provided. |
| doi_str_mv | 10.1073/pnas.0611164104 |
| format | Article |
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| subjects | Animals Bayes Theorem Biological Sciences Datasets Density estimation Evolution Genealogy Genealogy and Heraldry Genetics, Population - statistics & numerical data Markov Chains Mathematical analysis Mathematical independent variables Models, Genetic Monte Carlo Method Monte Carlo simulation Pan troglodytes Pan troglodytes - genetics Parametric models Population genetics Population migration Population parameters Population size Simulations |
| title | Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics |
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