Mount Fuji's stubby peak: the genotypic density of additive landscapes near maximal fitness

Kinney, Justin B (May 2026) Mount Fuji's stubby peak: the genotypic density of additive landscapes near maximal fitness. bioRxiv. ISSN 2692-8205 (Submitted)

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Abstract

Additive fitness landscapes-also called Mount Fuji landscapes-are the simplest and most widely used models of sequence-function relationships. As such, they play essential roles across multiple areas of biology, including evolutionary theory, quantitative genetics, gene regulation, and protein science. One of the most basic properties of any fitness landscape is its genotypic density-the number of sequences near a given fitness value. Understanding this density is especially important near fitness peaks, as it quantifies the supply of high-fitness genotypes. Here I study the genotypic density of additive landscapes near fitness peaks. Although this density is well known to be approximately Gaussian near the middle of the fitness range, its behavior near maximal fitness has not been reported. I begin by deriving a saddle-point approximation that accurately describes the genotypic density of additive landscapes over virtually the entire fitness range. I then show that the log density follows a power law near maximal fitness, with the exponent determined by how much the best allele at each position outperforms its nearest competitor. This power-law behavior holds over a substantial fraction of fitness values, besting the Gaussian approximation on both simulated and empirical landscapes across roughly a quarter to a third of the fitness range. Under certain conditions this behavior also extends to globally epistatic landscapes (defined as nonlinear functions of one or more additive traits), though with a reduced range of validity. These findings advance our understanding of one of the most fundamental models of sequence-function relationships. In particular, they reveal that the uppermost reaches of Mount Fuji landscapes, rather than being sharply peaked, are actually quite stubby.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > quantitative biology
CSHL Authors:	Kinney, Justin B.
Communities:	CSHL labs > Kinney lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	8 May 2026
Date Deposited:	30 Jun 2026 19:06
Last Modified:	30 Jun 2026 19:06
PMCID:	PMC13081852
Related URLs:	Publisher
URI:	https://repository.cshl.edu/id/eprint/42245

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