Fitness inference tested by in silico population genetics

TL;DR

This paper investigates the feasibility of inferring organism fitness parameters from population genetic data, identifying conditions under which such inference is possible or not, using simulations and a theoretical framework.

Contribution

It introduces a framework for assessing when fitness inference from population data is feasible and delineates parameter ranges for successful inference.

Findings

Identifies parameter ranges where fitness inference is possible.

Provides a framework applicable to biological organisms and pathogens.

Highlights limitations in certain evolutionary scenarios.

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 if 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.