Genetic correlations greatly increase mutational robustness and can both reduce and enhance evolvability

Abstract

Mutational neighbourhoods in genotype-phenotype (GP) maps are widely believed to be more likely to share characteristics than expected from random chance. Such genetic correlations should, as John Maynard Smith famously pointed out, strongly influence evolutionary dynamics. We explore and quantify these intuitions by comparing three GP maps - RNA SS, HP for tertiary, Polyominoes for protein quaternary structure - to a simple random null model that maintains the number of genotypes mapping to each phenotype, but assigns genotypes randomly. The mutational neighbourhood of a genotype in these GP maps is much more likely to contain (mutationally neutral) genotypes mapping to the same phenotype than in the random null model. These neutral correlations can increase the robustness to mutations by orders of magnitude over that of the null model, raising robustness above the critical threshold for the formation of large neutral networks that enhance the capacity for neutral exploration. We also study {\em non-neutral correlations}: Compared to the null model, i) If a particular (non-neutral) phenotype is found once in the 1-mutation neighbourhood of a genotype, then the chance of finding that phenotype multiple times in this neighbourhood is larger than expected; ii) If two genotypes are connected by a single neutral mutation, then their respective non-neutral 1-mutation neighbourhoods are more likely to be similar; iii) If a genotype maps to a folding or self-assembling phenotype, then its non-neutral neighbours are less likely to be a potentially deleterious non-folding or non-assembling phenotype. Non-neutral correlations of type i) and ii) reduce the rate at which new phenotypes can be found by neutral exploration, and so may diminish evolvability, while non-neutral correlations of type iii) may instead facilitate evolutionary exploration and so increase evolvability.

corrected manuscript

References in corpus (3)

• The arrival of the frequent: how bias in genotype-phenotype maps can steer populations to local optima
• Population size effects in evolutionary dynamics on neutral networks and toy landscapes
• Genotype networks in metabolic reaction spaces

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