Detecting macroevolutionary genotype-phenotype associations using error-corrected rates of protein convergence

Area of Science:

• Evolutionary Biology
• Molecular Evolution
• Genomics

Background:

• Macroevolutionary studies often face challenges in detecting genotype-phenotype associations due to mutations and phylogenetic uncertainty.
• Convergent evolution signals can be obscured by genetic noise and phylogenetic errors over long evolutionary timescales.

Purpose of the Study:

• To develop a novel metric, omega_C (ωC), for accurately measuring the rate of protein evolution convergence.
• To enable genome-wide searches for adaptive molecular convergence without prior phenotypic hypotheses.
• To facilitate bidirectional searches for genotype-phenotype associations across deep evolutionary divergences.

Main Methods:

• Extended the framework of non-synonymous to synonymous substitution rate ratios.
• Developed and applied the omega_C (ωC) metric for error-corrected convergence rate calculation.
• Utilized gene expression data and a heuristic algorithm to analyze millions of vertebrate gene branch combinations and higher-order phylogenetic combinations.

Main Results:

• The omega_C (ωC) metric successfully distinguishes natural selection from genetic noise and phylogenetic errors in simulations and real-world examples.
• Identified joint convergence of gene expression patterns and protein sequences, pinpointing amino acid substitutions in functionally important sites.
• Generated hypotheses for undiscovered phenotypes based on molecular convergence patterns.

Conclusions:

• The developed omega_C (ωC) metric provides an accurate and robust approach for studying adaptive molecular convergence.
• The method allows for exploratory, genome-wide identification of convergent evolution across vast evolutionary distances.
• This approach opens new avenues for discovering genotype-phenotype associations and understanding evolutionary adaptations.