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Evolutionary constraints in fitness landscapes
Luca Ferretti1, Daniel Weinreich2, Fumio Tajima3
1The Pirbright Institute, Woking, UK. luca.ferretti@gmail.com.
Heredity
|July 12, 2018
Summary
Researchers developed new measures to analyze evolutionary constraints in genetic landscapes. These methods, focusing on mutation paths and obligatory mutations, offer insights into evolutionary predictability.
Area of Science:
- Evolutionary Biology
- Genetics
- Computational Biology
Background:
- Experimental determination of genotypic fitness landscapes has advanced significantly.
- Understanding general properties of real-world fitness landscapes requires structural characterization using simple measures related to evolutionary features.
Purpose of the Study:
- To introduce novel measures of evolutionary constraints.
- To analyze these measures in experimentally resolved fitness landscapes.
Main Methods:
- Developing measures based on the similarity between selectively accessible paths.
- Quantifying the abundance and characteristics of "chains" of obligatory mutations (paths through genotypes with a single fitter neighbor).
Main Results:
- Introduced measures with clear evolutionary interpretations.
- Demonstrated weak correlation between "chains" and classical epistasis measures.
- Observed non-monotonic relationship between constraint measures and epistasis, with maxima at intermediate values.
Conclusions:
- The new measures provide valuable insights into evolutionary constraints.
- These metrics enhance understanding of predictability in experimentally resolved fitness landscapes.
- Findings suggest epistasis's complex role in shaping evolutionary trajectories.
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