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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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Published on: February 3, 2023

Population genetics inference for longitudinally-sampled mutants under strong selection.

Miguel Lacerda¹, Cathal Seoighe²

¹Department of Statistical Sciences, University of Cape Town, Rondebosch 7701, South Africa miguel.lacerda@uct.ac.za.

|September 13, 2014

Summary

This summary is machine-generated.

This study introduces a new computational method for analyzing mutant frequencies in large populations. It accurately estimates selection strength, even under strong evolutionary pressure, outperforming traditional models.

Keywords:

Wright–Fisher model allele frequencies diffusion approximation population genetics selection coefficient

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Area of Science:

Population Genetics
Evolutionary Biology
Computational Biology

Background:

Longitudinal allele frequency data are crucial for understanding the evolution of mutant variants.
Current methods often approximate the Wright-Fisher model with diffusion processes, assuming weak selection and mutation, which is not always biologically realistic.
Strong selection, such as drug resistance in pathogens, challenges these approximations.

Purpose of the Study:

To develop a computationally efficient approximation for mutant frequency distribution.
To create a method that does not assume weak selection or mutation.
To accurately estimate population genetics parameters, particularly the selection coefficient, under strong selection.

Main Methods:

Developed a novel approximation to the mutant-frequency distribution.
Compared its performance against the Wright-Fisher model and Gaussian diffusion approximations using simulation studies.
Applied the method to real-world mutant frequency data from bacteriophage evolution experiments.

Main Results:

The new method provides a superior approximation to mutant frequency distribution in large populations under strong selection compared to diffusion models.
All tested methods performed comparably when selection was weak.
Maximum-likelihood estimates of the selection coefficient were significantly attenuated under diffusion models with strong selection, but not with the proposed method.

Conclusions:

The novel approximation method is recommended for estimating selection coefficients in large populations where the Wright-Fisher model is computationally intractable.
This method overcomes the limitations of diffusion approximations by not requiring assumptions about the strength of selection or mutation.
It offers a more accurate and reliable approach for analyzing evolutionary dynamics in diverse biological systems.