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bioRxiv · 10.1101/2022.05.02.490345

Prediction of local convergent shifts in evolutionary rates with phyloConverge characterizes the phenotypic associations and modularity of regulatory elements

Saputra, E.·Mao, W.·Clark, N.·Chikina, M.
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Abstract

Physiological and morphological adaptations to extreme environments arise from the molecular evolution of protein-coding regions and regulatory elements (REs) that regulate gene expression. Comparative genomics methods can characterize genetic elements that underlie the organism-level adaptations, but convergence analyses of REs are often limited by their evolutionary properties. A RE can be modularly composed of multiple transcription factor binding sites (TFBS) that may each experience different evolutionary pressures. The modular composition and rapid turnover of TFBS also enables a compensatory mechanism among nearby TFBS that allows for weaker sequence conservation/divergence than intuitively expected. Here, we introduce phyloConverge, a comparative genomics method that can perform fast, fine-grained local convergence analysis of genetic elements. phyloConverge calibrates for local shifts in evolutionary rates using a combination of maximum likelihood-based estimation of nucleotide substitution rates and phylogenetic permutation tests. Using the classical convergence case of mammalian adaptation to subterranean environments, we validate that phyloConverge identifies rate-accelerated conserved non-coding elements (CNEs) that are strongly correlated with ocular tissues, with improved specificity compared to competing methods. We use phyloConverge to perform TFBS-scale and nucleotide-scale scoring to dissect each CNE into subregions with uneven convergence signals and demonstrate its utility for understanding the modularity and pleiotropy of REs. Subterranean-accelerated regions are also enriched for molecular pathways and TFBS motifs associated with neuronal phenotypes, suggesting that subterranean eye degeneration may coincide with a remodeling of the nervous system. phyloConverge offers a rapid and accurate approach for understanding the evolution and modularity of regulatory elements underlying phenotypic adaptation.

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Saputra, E., Mao, W., Clark, N., Chikina, M.. 2022-05-04. Prediction of local convergent shifts in evolutionary rates with phyloConverge characterizes the phenotypic associations and modularity of regulatory elements. https://doi.org/10.1101/2022.05.02.490345

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