A New Comparative Framework for Estimating Selection on Synonymous Substitutions

TL;DR

This paper introduces a new method to study how natural selection affects synonymous DNA substitutions across different species.

Contribution

The paper introduces and validates multiclass synonymous substitution (MSS) models to estimate selection on codon usage.

Findings

MSS models reveal significant heterogeneity in synonymous substitution rates across 12 Drosophila species.

MSS rates correlate with population polymorphism and tRNA abundance, indicating detection of weak selection signals.

MSS models are robust to misspecification and can be applied to diverse taxa without prior assumptions.

Abstract

Selection on synonymous codon usage is a well-known and widespread phenomenon, yet existing models often do not account for it or its effect on synonymous substitution rates. In this article, we develop and expand the capabilities of multiclass synonymous substitution (MSS) models, which account for such selection by partitioning synonymous substitutions into 2 or more classes and estimating a relative substitution rate for each class, while accounting for important confounders like mutation bias. We identify extensive heterogeneity among relative synonymous substitution rates in an empirical dataset of ∼12,000 gene alignments from 12 Drosophila species. We validate model performance using data simulated under a forward population genetic simulation, demonstrating that MSS models are robust to model misspecification. MSS rates are significantly correlated with other covariates of selection on codon usage (population-level polymorphism data and tRNA abundance data), suggesting that models can detect weak signatures of selection on codon usage. With the MSS model, we can now study selection on synonymous substitutions in diverse taxa, independent of any a priori assumptions about the forces driving that selection.

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