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Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles
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Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles
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Journal Article
Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles
2023
Overview
An important challenge in genetics, evolution and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploids, mutations in a gene can combine on the same chromosome or on different chromosomes as a “heteroallelic combination”. However, a direct comparison of the extent, sign, and stability of the genetic interactions between variants within and between alleles is lacking. Here we use thermodynamic models of protein folding and ligand-binding to show that interactions between mutations within and between alleles are expected in even very simple biophysical systems. Protein folding alone generates within-allele interactions and a single molecular interaction is sufficient to cause between-allele interactions and dominance. These interactions change differently, quantitatively and qualitatively as a system becomes more complex. Altering the concentration of a ligand can, for example, switch alleles from dominant to recessive. Our results show that intra-molecular epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of both intra-molecular epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning.
The authors examine how mutations combine to alter phenotypes in biophysical models of proteins and conclude that non-additive interactions (epistasis and dominance) are frequent, context-dependent and so challenging to predict in even the simplest of biological systems.
