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Genotypic Complexity of Fisher's Geometric Model.

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Complex adaptations evolve in small steps due to pleiotropic constraints. Fisher

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Area of Science:

  • Evolutionary biology
  • Theoretical biology
  • Genetics

Background:

  • Fisher's geometric model explains adaptation via pleiotropic constraints.
  • Assumptions of additivity of mutational effects lead to genotypic epistasis.
  • Reciprocal sign epistasis is crucial for multipeaked genotypic fitness landscapes.

Purpose of the Study:

  • Investigate the probability of sign epistasis between mutations.
  • Analyze the emergence of genotypic epistasis from phenotypic mapping.
  • Quantify the complexity of genotypic fitness landscapes.

Main Methods:

  • Computed the probability of sign epistasis for random mutations.
  • Derived expressions for the mean number of fitness maxima.
  • Analyzed landscape complexity based on the number of mutations (L).

Main Results:

  • Sign epistasis probability decreases with increasing phenotypic dimension (n).
  • The number of fitness maxima increases exponentially with L.
  • Identified three distinct phases of genotypic fitness landscape structures.

Conclusions:

  • Phenotypic complexity does not always correlate with fitness landscape complexity.
  • Organisms with single phenotypic traits can exhibit complex fitness landscapes.
  • Results aid in interpreting experimental data and understanding empirical fitness landscapes.