Project Summary Linking genotype to phenotype is a unifying goal of genetics and is an important strategy for determining how biological systems operate in health and disease. This project develops new computational tools in the context of this FOA in computational genomics to link changes in genes and regulatory regions to the evolution of specific organismal phenotypes. It then applies those tools to multiple biological traits in mammals, birds, and fungi. The tools are present in our widely used comparative genomics package, RERconverge. RERconverge uses the power of convergent evolution, in which different species have repeatedly evolved the same phenotype or trait, to provide statistical repetition to locate genomic regions whose evolutionary rates responded to a particular convergent phenotype. The first goal of this project builds upon the RERconverge toolkit to analyze non-coding, potentially regulatory, regions of the genome. Regulatory regions evolve in a modular way that could be better analyzed with methods that operate at the scale of specific functional elements, such as transcription factor binding sites, and other methods that do not require aligned nucleotides between species, such as models derived from machine learning of regulatory elements. The aim then applies these methods to identify regulatory regions responsible for the evolution of large body size in mammals and birds. The second aim creates a new method to identify adaptive changes that led to a convergent phenotype, since adaptive change often leads to novel organismal traits. Currently, our convergent evolution methods and others in the field do not cleanly distinguish between regions experiencing adaptive evolution from those under reduced constraint. This aim will create specific codon model tests to address that deficit and applies them in primates and rodents to identify reproductive genes under positive selection in the context of sperm competition. The third aim is to speed up key functions of the RERconverge platform to allow more statistically robust analyses and to accommodate contemporary datasets of hundreds of species or more. The project will also benchmark and apply these new methods to locate genes, non-coding regions, and specific transcription factor sites responsible for convergent phenotypes of biomedical importance, such as body size, fertilization, eyesight, metabolism, and transposable element activity. The culmination of this research program will enable the rapid identification of genes and regulatory elements underlying countless morphological and physiological traits, thereby propelling experimental and medical genetics research with the power of evolutionary biology.