title: A formal perturbation equation between genotype and phenotype determines the Evolutionary Action of protein-coding variations on fitness.
abstract: The relationship between genotype mutations and phenotype variations determines health in the short term and evolution over the long term, and it hinges on the action of mutations on fitness. A fundamental difficulty in determining this action, however, is that it depends on the unique context of each mutation, which is complex and often cryptic. As a result, the effect of most genome variations on molecular function and overall fitness remains unknown and stands apart from population genetics theories linking fitness effect to polymorphism frequency. Here, we hypothesize that evolution is a continuous and differentiable physical process coupling genotype to phenotype. This leads to a formal equation for the action of coding mutations on fitness that can be interpreted as a product of the evolutionary importance of the mutated site with the difference in amino acid similarity. Approximations for these terms are readily computable from phylogenetic sequence analysis, and we show mutational, clinical, and population genetic evidence that this action equation predicts the effect of point mutations in vivo and in vitro in diverse proteins, correlates disease-causing gene mutations with morbidity, and determines the frequency of human coding polymorphisms, respectively. Thus, elementary calculus and phylogenetics can be integrated into a perturbation analysis of the evolutionary relationship between genotype and phenotype that quantitatively links point mutations to function and fitness and that opens a new analytic framework for equations of biology. In practice, this work explicitly bridges molecular evolution with population genetics with applications from protein redesign to the clinical assessment of human genetic variations.
authors.affiliation: Department of Molecular and Human Genetics.
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Katsonis
authors.affiliation: Department of Molecular and Human Genetics, Department of Biochemistry & Molecular Biology, Department of Pharmacology, Baylor College of Medicine, Houston, Texas 77030, USA; Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas 77030, USA lichtarge@bcm.edu.
authors.forename: Olivier
authors.initials: O
authors.lastname: Lichtarge
mesh_terms: D000465:Algorithms; D019143:Evolution, Molecular; D056726:Genetic Association Studies; D056084:Genetic Fitness; D014644:Genetic Variation; D005828:Genetics, Population; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D009017:Morbidity; D009154:Mutation; D016366:Open Reading Frames; D010641:Phenotype; D011110:Polymorphism, Genetic; D012372:ROC Curve