Refining Convergent Rate Analysis with Topology in Mammalian Longevity and Marine Transitions

Abstract

The quest to map the genetic foundations of phenotypes has been empowered by the modern diversity, quality, and availability of genomic resources. Despite these expanding resources, the abundance of variation within lineages makes the association of genetic change to specific phenotypes improbable. Drawing such connections requires an a priori means of isolating the associated changes from background genomic variation. Evolution may provide these means via convergence; i.e., the shared variation that may result from replicate evolutionary experiments across independent trait occurrences. To leverage these opportunities, we developed TRACCER: Topologically Ranked Analysis of Convergence via Comparative Evolutionary Rates. As compared to current methods, this software empowers rate convergence analysis by factoring in topological relationships, because variation between phylogenetically proximate trait changes is more likely to be facilitating the trait. Pairwise comparisons are performed not with singular branches, but in reference to their most recent common ancestors. This ensures that comparisons represent identical genetic contexts and timeframes while obviating the problematic requirement of assigning ancestral states. We applied TRACCER to two case studies: marine mammal transitions, an unambiguous trait which has independently evolved three times, as well as the evolution of mammalian longevity, a less delineated trait but with more instances to compare. TRACCER, by factoring in topology, identifies highly significant, convergent genetic signals in these test cases, with important incongruities and statistical resolution when compared to existing convergence approaches. These improvements in sensitivity and specificity generate refined targets for downstream analysis of convergent evolution and identification of genotype-phenotype relationships.