The Arrival of the Frequent: How Bias in Genotype-Phenotype Maps Can Steer Populations to Local Optima

@article{Schaper2014TheAO,
  title={The Arrival of the Frequent: How Bias in Genotype-Phenotype Maps Can Steer Populations to Local Optima},
  author={Steffen Schaper and Ard A. Louis},
  journal={PLoS ONE},
  year={2014},
  volume={9}
}
Genotype-phenotype (GP) maps specify how the random mutations that change genotypes generate variation by altering phenotypes, which, in turn, can trigger selection. Many GP maps share the following general properties: 1) The total number of genotypes is much larger than the number of selectable phenotypes; 2) Neutral exploration changes the variation that is accessible to the population; 3) The distribution of phenotype frequencies , with the number of genotypes mapping onto phenotype , is… 

Figures from this paper

Genetic Correlations Greatly Increase Mutational Robustness and Can Both Reduce and Enhance Evolvability
TLDR
This work compares three GP maps to a simple random null model that maintains the number of genotypes mapping to each phenotype, but assigns genotypes randomly, and studies non-neutral correlations, which 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.
Phenotypes can be robust and evolvable if mutations have non-local effects on sequence constraints
TLDR
This work introduces two analytically tractable GP map models that follow the principles of real biological GP maps, and finds that a positive correlation between phenotype robustness and evolvability only emerges if mutations at one sequence position can have non-local effects on the sequence constraints at another position.
A network perspective on genotype–phenotype mapping in genetic programming
TLDR
This study numerically study the redundant genotype–phenotype mapping of a simple Boolean linear genetic programming system and quantify the mutational connections among phenotypes using tools of complex network analysis, which yields several interesting statistics of the phenotype network.
The structure of the genotype–phenotype map strongly constrains the evolution of non-coding RNA
TLDR
It is shown that the genotype–phenotype map for RNA secondary structure (SS) for systems up to length L = 126 nucleotides strongly constrains the evolution of non-coding RNA (ncRNA), and this strikingly close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces.
Structural properties of genotype–phenotype maps
  • S. Ahnert
  • Biology
    Journal of The Royal Society Interface
  • 2017
TLDR
An overview of the study of GP maps is given, with particular emphasis on structural properties of the distribution of phenotypes across the point-mutation network of genotypes, and a model that attempts to explain why these properties arise is discussed.
The structure of genotype-phenotype maps makes fitness landscapes navigable
TLDR
This work investigates how common structural properties of high-dimensional genotype-phenotype maps, such as the presence of neutral networks, affect the navigability of fitness landscapes and shows that accessible paths are also likely to be utilised under evolutionary dynamics.
Mutation bias interacts with composition bias to influence adaptive evolution
TLDR
The results reveal the way in which composition bias interacts with biases in the mutation process under different population genetic conditions, and how such interaction impacts fundamental properties of adaptive evolution, such as its predictability, as well as the evolution of genetic diversity and mutational robustness.
Non-deterministic genotype-phenotype maps of biological self-assembly (a)
TLDR
By redefining deterministic and nondeterministic Polyomino self-assembly phenotypes in terms of the pattern of possible interactions rather than the final structure, this work is able to calculate GP map properties for the non-deterministic part of the map, and finds that they match those found in deterministic maps.
Survival of the frequent at finite population size and mutation rate: filing the gap between quasispecies and monomorphic regimes
TLDR
Wright’s well-known stationary distribution of genotypes under selection and mutation is transformed to give the probability distribution of phenotypes, assuming a general genotype-phenotype map, which shows that the degeneracies of each phenotype appear by weighting the mutation term.
Adding levels of complexity enhances robustness and evolvability in a multilevel genotype–phenotype map
TLDR
The results suggest that adding levels of complexity to the mapping of genotypes to phenotypes and increasing genome size enhances both robustness and evolvability, as well as the number of genes in a genotype.
...
...

References

SHOWING 1-10 OF 80 REFERENCES
Epistasis can lead to fragmented neutral spaces and contingency in evolution
TLDR
This work uses the folding of RNA sequences into secondary structures as a model genotype–phenotype map and explores the neutral spaces corresponding to networks of genotypes with the same phenotype, finding that it is not possible to connect all genotypes to one another by single point mutations.
An End to Endless Forms: Epistasis, Phenotype Distribution Bias, and Nonuniform Evolution
TLDR
It is suggested that early and late evolution have a different character that is classified into micro- and macroevolutionary configurations, and finds that species become more alike through time, whereas higher-level grades have a tendency to diverge.
From genes to phenotype: dynamical systems and evolvability
TLDR
The mathematical properties of the genotype-phenotype mapping function are reviewed, its emerging properties are explored and they are related to the issue of opportunity and constraint in morphological evolution.
The Ascent of the Abundant: How Mutational Networks Constrain Evolution
TLDR
It is found that phenotype abundance—the number of genotypes producing a particular phenotype—varies in a predictable manner and critically influences evolutionary dynamics, which supports an “ascent of the abundant” hypothesis, in which evolution yields abundant phenotypes even when they are not the most fit.
Neutral evolution of mutational robustness.
TLDR
The results quantify the extent to which populations evolve mutational robustness-the insensitivity of the phenotype to mutations-and thus reduce genetic load.
Evolution of digital organisms at high mutation rates leads to survival of the flattest
TLDR
According to quasi-species theory, selection favours the cloud of genotypes, interconnected by mutation, whose average replication rate is highest, and this prediction is confirmed using digital organisms that self-replicate, mutate and evolve.
Free fitness that always increases in evolution.
  • Y. Iwasa
  • Biology
    Journal of theoretical biology
  • 1988
On the application of statistical physics to evolutionary biology.
A comparison of genotype-phenotype maps for RNA and proteins.
Neutral network sizes of biological RNA molecules can be computed and are not atypically small
TLDR
The biological RNA structures examined are more abundant than random structures, which indicates that their robustness and their ability to produce new phenotypic variants may also be high, and a generalized Monte Carlo approach is introduced that can measure neutral set sizes in larger spaces.
...
...