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

On macroevolutionary timescales, extensive mutations and phylogenetic
uncertainty mask the signals of genotype-phenotype associations underlying
convergent evolution. To overcome this problem, we extended the widely
used framework of nonsynonymous-to-synonymous substitution rate ratios and
developed the novel metric ωC, which measures the error-corrected
convergence rate of protein evolution. While ωC distinguishes natural
selection from genetic noise and phylogenetic errors in simulation and
real examples, its accuracy allows an exploratory genome-wide search of
adaptive molecular convergence without phenotypic hypothesis or candidate
genes. Using gene expression data, we explored over 20 million branch
combinations in vertebrate genes and identified the joint convergence of
expression patterns and protein sequences with amino acid substitutions in
functionally important sites, providing hypotheses on undiscovered
phenotypes. We further extended our method with a heuristic algorithm to
detect highly repetitive convergence among computationally nontrivial
higher-order phylogenetic combinations. Our approach allows bidirectional
searches for genotype-phenotype associations, even in lineages that
diverged for hundreds of millions of years.