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    Fitness inference tested by in silico population genetics

    Hong-Li Zeng* and Yu-Han Huang

    Erik Aurell†

    John Barton‡

    • Department of Computational Science and Technology, AlbaNova University Center, SE-106 91 Stockholm, Sweden

    • Department of Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213, USA

    • *Contact author: hlzeng@njupt.edu.cn
    • †Contact author: eaurell@kth.se
    • ‡Contact author: jpbarton@pitt.edu

    Phys. Rev. E 113, 044415 – Published 22 April, 2026

    DOI: https://doi.org/10.1103/4ljm-gy5p

    Abstract

    We consider populations evolving according to natural selection, mutation, and recombination and assume that the genomes of all or a representative selection of individuals are known. We pose the problem of whether it is possible to infer fitness parameters and genotype fitness order from such data. We tested this hypothesis in simulated populations. We delineate parameter ranges where this is possible and other ranges where it is not. Our work provides a framework for determining when fitness inference is feasible from population-wide, whole-genome, time-stratified data and highlights settings where it is not. We give a brief survey of biological model organisms and human pathogens that fit into this framework.

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