A formal perturbation equation between genotype and phenotype determines the Evolutionary Action of protein-coding variations on fitness

By Panagiotis Katsonis, Olivier Lichtarge. 2014

140 citations recorded by the source corpus.

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.

Subfields: Genetics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

Citations in the corpus

Quantitative missense variant effect prediction using large-scale mutagenesis data (2017, positive): “Thus, variant effect predictors such as PolyPhen2 (Adzhubei et al., 2010), SIFT (Sim et al., 2012), SNAP2 (Hecht et al., 2015), Evolutionary Action (Katsonis and Lichtarge, 2014), CADD (Kircher et al., 2014), and a host of others (Tang and Thomas, 2016) will continue to be widely used to predict…”
Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma (2017, unknown): “We identified for each pathway the set of genes that maximized bias toward high evolutionary action (EA) mutation scores (a measure of relative evolutionary conservation) compared to the cohort background (q < 0.05; Figures S7A– S7H) (Katsonis and Lichtarge, 2014).”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, methodology): “For some algorithms such as Eigen [22], hEAt [23], GERP [24], etc., cut-offs defining pathogenic or benign assertion are either not recommended or inferred arbitrarily.”
PANTHER-PSEP: predicting disease-causing genetic variants using position-specific evolutionary preservation (2016, methodology): “…tree in addition to a multiple sequence alignment, pioneered by the Evolutionary Trace method (Lichtarge et al. , 1996) and later applied to variant pathogenicity prediction in the PANTHER-PSECv2 (Thomas and Kejariwal, 2004) and Evolutionary Action (Katsonis and Lichtarge, 2014) methods.”
DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment (2015, background): “Multiple sequence alignment (MSA) is a ubiquitous task in biology, and has a wide variety of applications including homology detection [1], predicting residue couplings [2], finding evolutionarily important sites [3], oligonucleotide design [4], and phylogenetics.”