Unknown

Dataset Information

0

Efficient Recycled Algorithms for Quantitative Trait Models on Phylogenies.


ABSTRACT: We present an efficient and flexible method for computing likelihoods for phenotypic traits on a phylogeny. The method does not resort to Monte Carlo computation but instead blends Felsenstein's discrete character pruning algorithm with methods for numerical quadrature. It is not limited to Gaussian models and adapts readily to model uncertainty in the observed trait values. We demonstrate the framework by developing efficient algorithms for likelihood calculation and ancestral state reconstruction under Wright's threshold model, applying our methods to a data set of trait data for extrafloral nectaries across a phylogeny of 839 Fabales species.

SUBMITTER: Hiscott G 

PROVIDER: S-EPMC4898791 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

altmetric image

Publications

Efficient Recycled Algorithms for Quantitative Trait Models on Phylogenies.

Hiscott Gordon G   Fox Colin C   Parry Matthew M   Bryant David D  

Genome biology and evolution 20160512 5


We present an efficient and flexible method for computing likelihoods for phenotypic traits on a phylogeny. The method does not resort to Monte Carlo computation but instead blends Felsenstein's discrete character pruning algorithm with methods for numerical quadrature. It is not limited to Gaussian models and adapts readily to model uncertainty in the observed trait values. We demonstrate the framework by developing efficient algorithms for likelihood calculation and ancestral state reconstruct  ...[more]

Similar Datasets

| S-EPMC3249367 | biostudies-literature
| S-EPMC1821103 | biostudies-other
| S-EPMC5646422 | biostudies-literature
| S-EPMC10781664 | biostudies-literature
| S-EPMC3375403 | biostudies-literature
| S-EPMC3532851 | biostudies-literature
| S-EPMC4829193 | biostudies-literature
| S-EPMC3984176 | biostudies-literature
| S-EPMC7612194 | biostudies-literature
2010-06-25 | E-GEOD-7628 | biostudies-arrayexpress