Evidence map›Paper›PMID 41628884›Full record

ArticleGenetics2026

Predicting the topography of fitness landscapes from the structure of genotype-phenotype maps.

Malvika Srivastava, Ard A Louis, Nora S Martin

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Article in Genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Malvika SrivastavaInstitute of Integrative Biology, ETH Zurich, Zurich 8092, Switzerland.ORCID 0000-0003-0757-4990
Ard A LouisRudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, United Kingdom.
Nora S MartinCRG (Barcelona Collaboratorium for Modelling and Predictive Biology), Dr. Aiguader 88, Barcelona 08003, Spain.ORCID 0000-0003-4026-0985

Funding

European Union NextGenerationEU/PRTRSwiss National Science Foundation JDC2022-049526-ISwiss National Science Foundation PP00P3_170604
6 · The paper itself

Abstract

Ruggedness-the prevalence of fitness peaks-and navigability-the existence of fitness-increasing paths to a target-are key factors affecting evolution on fitness landscapes. Here, we analyze these properties in landscapes that inherit biophysically grounded genotype-phenotype (GP) maps. By assuming a random phenotype-fitness assignment as a baseline, the structure of the GP maps is included without imposing further fitness correlations. We show analytically that the expected ruggedness can be predicted from two quantities: the sizes of neutral components (NCs)-mutationally connected genotype sets with the same phenotype-and their evolvabilities, defined as the number of distinct phenotypes among the NC's mutational neighbors. Other features-such as robustness-influence ruggedness only indirectly via correlations with evolvability. Numerical results across diverse GP maps confirm that NC size and evolvability alone suffice to predict both the mean prevalence and heights of peaks. These calculations also provide new insights: Under random phenotype-fitness assignment, peaks arising from high-evolvability NCs have higher expected fitness than those from low-evolvability NCs. Thus, when evolvability correlates positively with NC size, the formation of large low-fitness peaks is impeded. We further derive an approximate scaling law for the minimal average evolvability required for navigability. Our framework applies broadly across GP maps, providing general insight into when and why fitness landscapes are expected to be rugged or navigable.

Indexed as

Evolution, MolecularGenetic FitnessGenotypeModels, GeneticMutationPhenotypeSelection, Geneticaccessible pathsfitness landscapegenotype–phenotype map

Identifiers

PMID41628884
PMCPMC13050206

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.