Importance sampling on coalescent histories. II: Subdivided population models
De Iorio and Griffiths (2004) developed a new method of constructing sequential importance-sampling proposal distributions on coalescent histories of a sample of genes for computing the likelihood of a type configuration of genes in the sample by simulation. The method is based on approximating the...
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| Format: | Journal article |
| Language: | English |
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2004
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| _version_ | 1797101407027855360 |
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| author | De Iorio, M Griffiths, R |
| author_facet | De Iorio, M Griffiths, R |
| author_sort | De Iorio, M |
| collection | OXFORD |
| description | De Iorio and Griffiths (2004) developed a new method of constructing sequential importance-sampling proposal distributions on coalescent histories of a sample of genes for computing the likelihood of a type configuration of genes in the sample by simulation. The method is based on approximating the diffusion-process generator describing the distribution of population gene frequencies, leading to an approximate sample distribution and finally to importance-sampling proposal distributions. This paper applies that method to construct an importance-sampling algorithm for computing the likelihood of samples of genes in subdivided population models. The importance-sampling technique of Stephens and Donnelly (2000) is thus extended to models with a Markov chain mutation mechanism between gene types and migration of genes between subpopulations. An algorithm for computing the likelihood of a sample configuration of genes from a subdivided population in an infinitely-many-alleles model of mutation is derived, extending Ewens's (1972) sampling formula in a single population. Likelihood calculation and ancestral inference in gene trees constructed from DNA sequences under the infinitely-many-sites model are also studied. The Griffiths-Tavaré method of likelihood calculation in gene trees of Bahlo and Griffiths (2000) is improved for subdivided populations. © Applied Probability Trust 2004. |
| first_indexed | 2024-03-07T05:51:28Z |
| format | Journal article |
| id | oxford-uuid:e906bd0a-73f6-40ea-a3f9-8573f367b441 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:51:28Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | oxford-uuid:e906bd0a-73f6-40ea-a3f9-8573f367b4412022-03-27T10:51:25ZImportance sampling on coalescent histories. II: Subdivided population modelsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e906bd0a-73f6-40ea-a3f9-8573f367b441EnglishSymplectic Elements at Oxford2004De Iorio, MGriffiths, RDe Iorio and Griffiths (2004) developed a new method of constructing sequential importance-sampling proposal distributions on coalescent histories of a sample of genes for computing the likelihood of a type configuration of genes in the sample by simulation. The method is based on approximating the diffusion-process generator describing the distribution of population gene frequencies, leading to an approximate sample distribution and finally to importance-sampling proposal distributions. This paper applies that method to construct an importance-sampling algorithm for computing the likelihood of samples of genes in subdivided population models. The importance-sampling technique of Stephens and Donnelly (2000) is thus extended to models with a Markov chain mutation mechanism between gene types and migration of genes between subpopulations. An algorithm for computing the likelihood of a sample configuration of genes from a subdivided population in an infinitely-many-alleles model of mutation is derived, extending Ewens's (1972) sampling formula in a single population. Likelihood calculation and ancestral inference in gene trees constructed from DNA sequences under the infinitely-many-sites model are also studied. The Griffiths-Tavaré method of likelihood calculation in gene trees of Bahlo and Griffiths (2000) is improved for subdivided populations. © Applied Probability Trust 2004. |
| spellingShingle | De Iorio, M Griffiths, R Importance sampling on coalescent histories. II: Subdivided population models |
| title | Importance sampling on coalescent histories. II: Subdivided population models |
| title_full | Importance sampling on coalescent histories. II: Subdivided population models |
| title_fullStr | Importance sampling on coalescent histories. II: Subdivided population models |
| title_full_unstemmed | Importance sampling on coalescent histories. II: Subdivided population models |
| title_short | Importance sampling on coalescent histories. II: Subdivided population models |
| title_sort | importance sampling on coalescent histories ii subdivided population models |
| work_keys_str_mv | AT deioriom importancesamplingoncoalescenthistoriesiisubdividedpopulationmodels AT griffithsr importancesamplingoncoalescenthistoriesiisubdividedpopulationmodels |