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From Genes to Traits: A Combined Approach for Detecting Sequence Errors and Convergent Evolution
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2025-08
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Biology
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https://doi.org/10.34944/a603-7444
Abstract
Understanding the molecular basis of phenotypic convergence is a key challenge in evolutionary biology. Comparative genomics provides a powerful framework for identifying genes involved in adaptation by leveraging natural replicates of trait evolution across diverse lineages. However, detecting convergently evolving genes is complicated by methodological limitations, including sensitivity to alignment errors and the difficulty of distinguishing adaptive evolution from other causes of elevated evolutionary rates. This dissertation introduces and applies codon and rate-based models designed to address these limitations and improve the reliability and interpretability of positive selection analyses. The first contribution is MoleRate, a likelihood-based method for detecting shifts in evolutionary rate associated with phenotypic traits. Unlike previous rate-based methods, MoleRate directly integrates rate estimation and hypothesis testing, accounting for uncertainty and reducing the influence of outlier lineages. The second is BUSTED-E, an extension of the BUSTED codon model that incorporates a filtering mechanism to identify and mitigate the impact of misaligned regions in sequence alignments. The third method, BUSTED-PH, is a phylogenetically informed codon model that detects positive selection associated with binary phenotypes while controlling for background selection pressures across the tree. These methods are applied to investigate the molecular basis of endothermy in ray-finned fish, a rare trait that has evolved independently in multiple lineages. By scanning thousands of genes across over 200 fish species and comparing distinct types of endothermy, the analyses identify candidate genes associated with different heat-generating mechanisms, offering new insights into this extreme physiological adaptation. Together, these models enhance the ability to identify adaptively evolving genes with greater accuracy and biological relevance. This work contributes to a broader understanding of how complex traits repeatedly evolve and demonstrates the power of integrative phylogenomic models in evolutionary genomics.
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