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The relationship between dN/dS and scaled selection coefficients.

Stephanie J Spielman¹, Claus O Wilke²

¹Department of Integrative Biology, Center for Computational Biology and Bioinformatics, and Institute of Cellular and Molecular Biology, The University of Texas at Austin stephanie.spielman@gmail.com.

Molecular Biology and Evolution

|January 11, 2015

Summary

This summary is machine-generated.

This study reveals that mutation-selection (MutSel) models and dN/dS models have complex relationships. Standard dN/dS models can be biased, and model fit doesn't guarantee accurate estimates of natural selection.

Keywords:

Markov models of sequence evolution dN/dS mutation-selection models protein evolution scaled selection coefficients

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Area of Science:

Evolutionary biology
Computational biology
Bioinformatics

Background:

Phylogenetic modeling frameworks like dN/dS and mutation-selection (MutSel) models are used to study natural selection in protein-coding sequences.
The relationship and comparative limitations of these distinct modeling frameworks remain largely uncharacterized.

Purpose of the Study:

To elucidate the mathematical relationship between dN/dS and MutSel models.
To gain deeper insights into the behaviors, limitations, and applicabilities of these two frameworks.
To propose a new benchmarking strategy for dN/dS models using MutSel simulations.

Main Methods:

Derivation of a mathematical relationship between dN/dS and scaled selection coefficients.
Benchmarking dN/dS model inferences against MutSel simulations.
Evaluation of model fit using AIC and BIC scores.

Main Results:

Established a mathematical link showing MutSel models correspond to dN/dS ≤ 1 if synonymous changes are neutral, but dN/dS can be arbitrarily high if synonymous codons differ in fitness.
Demonstrated that Goldman-Yang-style dN/dS models yield biased estimates, while Muse-Gaut-style models show less bias.
Found that models with the best fit (AIC/BIC) do not necessarily yield the least biased or most precise dN/dS estimates.

Conclusions:

The mathematical relationship highlights limitations: MutSel models may not accommodate positive selection, and dN/dS models cannot distinguish between purifying selection on synonymous codons and positive selection on amino acids.
Selecting models based solely on goodness-of-fit can lead to poor parameter estimates if the model doesn't match the data-generating mechanism.
Establishing mathematical links between modeling frameworks is a powerful strategy for identifying model strengths and weaknesses.