期刊
MOLECULAR BIOLOGY AND EVOLUTION
卷 30, 期 3, 页码 669-688出版社
OXFORD UNIV PRESS
DOI: 10.1093/molbev/mss258
关键词
across-site rate variation; Dirichlet process mixture model; Bayesian model selection
资金
- Marsden Fund [UOA0809]
- Rutherford Discovery Fellowship
- University of Auckland Doctoral Scholarship
- [NIH R01 GM086887]
- [R01 HG006139.]
Probabilistic inference of a phylogenetic tree from molecular sequence data is predicated on a substitution model describing the relative rates of change between character states along the tree for each site in the multiple sequence alignment. Commonly, one assumes that the substitution model is homogeneous across sites within large partitions of the alignment, assigns these partitions a priori, and then fixes their underlying substitution model to the best-fitting model from a hierarchy of named models. Here, we introduce an automatic model selection and model averaging approach within a Bayesian framework that simultaneously estimates the number of partitions, the assignment of sites to partitions, the substitution model for each partition, and the uncertainty in these selections. This new approach is implemented as an add-on to the BEAST 2 software platform. We find that this approach dramatically improves the fit of the nucleotide substitution model compared with existing approaches, and we show, using a number of example data sets, that as many as nine partitions are required to explain the heterogeneity in nucleotide substitution process across sites in a single gene analysis. In some instances, this improved modeling of the substitution process can have a measurable effect on downstream inference, including the estimated phylogeny, relative divergence times, and effective population size histories.
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