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Fitness inference tested by in silico population genetics
Hong-Li Zeng1, Yu-Han Huang1, John Barton2
1School of Science, Nanjing University of Posts and Telecommunications, National Laboratory of Solid State Microstructures, Nanjing University, Nanjing 210093, CHINA; Key Laboratory of Radio and Micro-Nano Electronics of Jiangsu Province, Nanjing, 210023, CHINA.
Arxiv
|November 24, 2025
Summary
This study explores inferring genetic fitness from population data. It identifies conditions under which fitness parameters can be reliably determined from whole-genome, time-stratified population data.
Area of Science:
- Evolutionary biology
- Population genetics
- Genomics
Background:
- Understanding evolutionary dynamics requires knowledge of fitness.
- Inferring fitness from genomic data is crucial for evolutionary studies.
Purpose of the Study:
- To determine if fitness parameters and genotype fitness order can be inferred from population-wide genomic data.
- To establish the feasibility of fitness inference under various evolutionary conditions.
Main Methods:
- Simulated populations undergoing natural selection, mutation, and recombination.
- Analysis of whole-genome, time-stratified data from simulated individuals.
Main Results:
- Delineation of parameter ranges where fitness inference is possible.
- Identification of parameter ranges where fitness inference is not feasible.
- Development of a framework for assessing fitness inference feasibility.
Conclusions:
- Fitness inference from population-wide genomic data is feasible within specific parameter ranges.
- The developed framework aids in identifying suitable biological systems for fitness inference.
- Highlights limitations and possibilities for inferring evolutionary parameters from genomic data.
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