Bayesian inference of the evolution of a phenotype distribution on a phylogenetic tree
The distribution of a phenotype on a phylogenetic tree is often a quantity of interest. Many phenotypes have imperfect heritability, so that a measurement of the phenotype for an individual can be thought of as a single realisation from the phenotype distribution of that individual. If all individua...
| Egile Nagusiak: | , |
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| Formatua: | Journal article |
| Hizkuntza: | English |
| Argitaratua: |
Genetics Society of America
2016
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| _version_ | 1826267264717946880 |
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| author | Ansari, M Didelot, X |
| author_facet | Ansari, M Didelot, X |
| author_sort | Ansari, M |
| collection | OXFORD |
| description | The distribution of a phenotype on a phylogenetic tree is often a quantity of interest. Many phenotypes have imperfect heritability, so that a measurement of the phenotype for an individual can be thought of as a single realisation from the phenotype distribution of that individual. If all individuals in a phylogeny had the same phenotype distribution, measured phenotypes would be randomly distributed on the tree leaves. This is however often not the case, implying that the phenotype distribution evolves over time. Here we propose a new model based on this principle of evolving phenotype distribution on the branches of a phylogeny, which is different from ancestral state reconstruction where the phenotype itself is assumed to evolve. We develop an efficient Bayesian inference method to estimate the parameters of our model and to test the evidence for changes in the phenotype distribution. We use multiple simulated datasets to show that our algorithm has good sensitivity and specificity properties. Since our method identifies branches on the tree on which the phenotype distribution has changed, it is able to break down a tree into components for which this distribution is unique and constant. We present two applications of our method, one investigating the association between HIV genetic variation and human leukocyte antigen, and the other studying host range distribution in a lineage of Salmonella enterica, and we discuss many other potential applications. All the methods described in this paper are implemented in a software package called TreeBreaker which is freely available for download at https://github.com/ansariazim/TreeBreaker. |
| first_indexed | 2024-03-06T20:51:32Z |
| format | Journal article |
| id | oxford-uuid:37bfcd24-4bcd-436a-b7b4-642dc7e6616e |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:51:32Z |
| publishDate | 2016 |
| publisher | Genetics Society of America |
| record_format | dspace |
| spelling | oxford-uuid:37bfcd24-4bcd-436a-b7b4-642dc7e6616e2022-03-26T13:45:54ZBayesian inference of the evolution of a phenotype distribution on a phylogenetic treeJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:37bfcd24-4bcd-436a-b7b4-642dc7e6616eEnglishSymplectic Elements at OxfordGenetics Society of America2016Ansari, MDidelot, XThe distribution of a phenotype on a phylogenetic tree is often a quantity of interest. Many phenotypes have imperfect heritability, so that a measurement of the phenotype for an individual can be thought of as a single realisation from the phenotype distribution of that individual. If all individuals in a phylogeny had the same phenotype distribution, measured phenotypes would be randomly distributed on the tree leaves. This is however often not the case, implying that the phenotype distribution evolves over time. Here we propose a new model based on this principle of evolving phenotype distribution on the branches of a phylogeny, which is different from ancestral state reconstruction where the phenotype itself is assumed to evolve. We develop an efficient Bayesian inference method to estimate the parameters of our model and to test the evidence for changes in the phenotype distribution. We use multiple simulated datasets to show that our algorithm has good sensitivity and specificity properties. Since our method identifies branches on the tree on which the phenotype distribution has changed, it is able to break down a tree into components for which this distribution is unique and constant. We present two applications of our method, one investigating the association between HIV genetic variation and human leukocyte antigen, and the other studying host range distribution in a lineage of Salmonella enterica, and we discuss many other potential applications. All the methods described in this paper are implemented in a software package called TreeBreaker which is freely available for download at https://github.com/ansariazim/TreeBreaker. |
| spellingShingle | Ansari, M Didelot, X Bayesian inference of the evolution of a phenotype distribution on a phylogenetic tree |
| title | Bayesian inference of the evolution of a phenotype distribution on a phylogenetic tree |
| title_full | Bayesian inference of the evolution of a phenotype distribution on a phylogenetic tree |
| title_fullStr | Bayesian inference of the evolution of a phenotype distribution on a phylogenetic tree |
| title_full_unstemmed | Bayesian inference of the evolution of a phenotype distribution on a phylogenetic tree |
| title_short | Bayesian inference of the evolution of a phenotype distribution on a phylogenetic tree |
| title_sort | bayesian inference of the evolution of a phenotype distribution on a phylogenetic tree |
| work_keys_str_mv | AT ansarim bayesianinferenceoftheevolutionofaphenotypedistributiononaphylogenetictree AT didelotx bayesianinferenceoftheevolutionofaphenotypedistributiononaphylogenetictree |