Genetic Correlations Greatly Increase Mutational Robustness and Can Both Reduce and Enhance Evolvability

Area of Science:

• Evolutionary biology
• Computational biology
• Genetics

Background:

• Genotype-phenotype (GP) maps describe how genetic mutations influence observable traits.
• Understanding the structure of GP maps is crucial for predicting evolutionary trajectories.
• Previous assumptions suggest mutational neighborhoods likely share traits, impacting evolutionary dynamics.

Purpose of the Study:

• To quantify the non-random correlations within genotype-phenotype maps.
• To compare real biological GP maps against a random null model.
• To assess the impact of these correlations on evolvability.

Main Methods:

• Comparison of three biological GP maps (RNA secondary structure, HP protein tertiary structure, Polyomino protein quaternary structure) with a random null model.
• Analysis of mutational neighborhoods and neutral correlations (robustness to mutations).
• Investigation of non-neutral correlations, including phenotype recurrence and neighborhood similarity.

Main Results:

• Biological GP maps exhibit significantly higher robustness to mutations than random models.
• Neutral correlations facilitate the formation of large neutral networks, enhancing exploration of phenotypic novelty.
• Non-neutral correlations show mixed effects: some may reduce evolvability, while others, particularly those preventing deleterious mutations, may increase it.

Conclusions:

• Neutral correlations in GP maps are a key driver of evolvability.
• The structure of GP maps, beyond random chance, significantly influences evolutionary potential.
• Both neutral and non-neutral correlations play complex roles in shaping evolutionary outcomes.