Functional bottlenecks can emerge from non-epistatic underlying traits
Protein fitness landscapes frequently exhibit epistasis, where the effect of a mutation depends on the genetic context in which it occurs, \textit{i.e.}, the rest of the protein sequence. Epistasis increases landscape complexity, often resulting in multiple fitness peaks. In its simplest form, known as global epistasis, fitness is modeled as a non-linear function of an underlying additive trait. In contrast, more complex epistasis arises from a network of (pairwise or many-body) interactions between residues, which cannot be removed by a single non-linear transformation. Recent studies have explored how global and network epistasis contribute to the emergence of functional bottlenecks - fitness landscape topologies where two broad high-fitness basins, representing distinct phenotypes, are separated by a bottleneck that can only be crossed via one or a few mutational paths. Here, we introduce and analyze a simple model of global epistasis with an additive underlying trait. We demonstrate that functional bottlenecks arise with high probability if the model is properly calibrated. Our results underscore the necessity of sufficient heterogeneity in the mutational effects selected by evolution for the emergence of functional bottlenecks. Moreover, we show that the model agrees with experimental findings, at least in small enough combinatorial mutational spaces.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Genomic Prediction of Quantitative Traits using Sparse and Locally Epistatic Models
In plant and animal breeding studies a distinction is made between the genetic value (additive + epistatic genetic effects) and the breeding value (additive genetic effects) of an individual since it is expected that som…
Locally Epistatic Models for Genome-wide Prediction and Association by Importance Sampling
In statistical genetics an important task involves building predictive models for the genotype-phenotype relationships and thus attribute a proportion of the total phenotypic variance to the variation in genotypes. Numer…
AttributeInferring genotype-phenotype maps using attention models
Predicting phenotype from genotype is a central challenge in genetics. Traditional approaches in quantitative genetics typically analyze this problem using methods based on linear regression. These methods generally assu…
Transfer LearningGPA-Tree: Statistical Approach for Functional-Annotation-Tree-Guided Prioritization of GWAS Results
Motivation: In spite of great success of genome-wide association studies (GWAS), multiple challenges still remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), each with sm…
Cancer initiation with epistatic interactions between driver and passenger mutations
We investigate the dynamics of cancer initiation in a mathematical model with one driver mutation and several passenger mutations. Our analysis is based on a multi type branching process: We model individual cells which …