Dataset Information

Gene Tree Affects Inference of Sites Under Selection by the Branch-Site Test of Positive Selection.

ABSTRACT: The branch-site test of positive selection is a standard approach to detect past episodic positive selection in a priori-specified branches of a gene phylogeny. Here, we ask if differences in the topology of the gene tree have any influence on the ability to infer positively selected sites. Using simulated sequences, we compare the results obtained for true and rearranged topologies. We find a strong relationship between "conflicting branch length," which occurs when the set of sequences that experiences selection for a given topology and foreground is changed, and the ability to predict positively selected sites. Moreover, by reanalyzing a previously published data set, we show that the choice of a gene tree also affects the results obtained for real-world sequences. This is the first study to demonstrate that tree topology has a clear effect on the inference of positive selection. We conclude that the choice of a gene tree is an important factor for the branch-site analysis of positive selection.

SUBMITTER: Diekmann Y

PROVIDER: S-EPMC4718152 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Gene Tree Affects Inference of Sites Under Selection by the Branch-Site Test of Positive Selection.

Diekmann Yoan Y Pereira-Leal José B JB

Evolutionary bioinformatics online 20150101 Suppl 2

The branch-site test of positive selection is a standard approach to detect past episodic positive selection in a priori-specified branches of a gene phylogeny. Here, we ask if differences in the topology of the gene tree have any influence on the ability to infer positively selected sites. Using simulated sequences, we compare the results obtained for true and rearranged topologies. We find a strong relationship between "conflicting branch length," which occurs when the set of sequences that ex ...[more]

PMID: 26819542

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Project description:BACKGROUND:An excess of nonsynonymous substitutions, over neutrality, is considered evidence of positive Darwinian selection. Inference for proteins often relies on estimation of the nonsynonymous to synonymous ratio (? = dN/dS) within a codon model. However, to ease computational difficulties, ? is typically estimated assuming an idealized substitution process where (i) all nonsynonymous substitutions have the same rate (regardless of impact on organism fitness) and (ii) instantaneous double and triple (DT) nucleotide mutations have zero probability (despite evidence that they can occur). It follows that estimates of ? represent an imperfect summary of the intensity of selection, and that tests based on the ? >?1 threshold could be negatively impacted. RESULTS:We developed a general-purpose parametric (GPP) modelling framework for codons. This novel approach allows specification of all possible instantaneous codon substitutions, including multiple nonsynonymous rates (MNRs) and instantaneous DT nucleotide changes. Existing codon models are specified as special cases of the GPP model. We use GPP models to implement likelihood ratio tests for ? >?1 that accommodate MNRs and DT mutations. Through both simulation and real data analysis, we find that failure to model MNRs and DT mutations reduces power in some cases and inflates false positives in others. False positives under traditional M2a and M8 models were very sensitive to DT changes. This was exacerbated by the choice of frequency parameterization (GY vs. MG), with rates sometimes >?90% under MG. By including MNRs and DT mutations, accuracy and power was greatly improved under the GPP framework. However, we also find that over-parameterized models can perform less well, and this can contribute to degraded performance of LRTs. CONCLUSIONS:We suggest GPP models should be used alongside traditional codon models. Further, all codon models should be deployed within an experimental design that includes (i) assessing robustness to model assumptions, and (ii) investigation of non-standard behaviour of MLEs. As the goal of every analysis is to avoid false conclusions, more work is needed on model selection methods that consider both the increase in fit engendered by a model parameter and the degree to which that parameter is affected by un-modelled evolutionary processes.

Dataset Information

Gene Tree Affects Inference of Sites Under Selection by the Branch-Site Test of Positive Selection.

Publications

Gene Tree Affects Inference of Sites Under Selection by the Branch-Site Test of Positive Selection.

Similar Datasets

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