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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Multi-locus analysis of genomic time series data from experimental evolution.

Jonathan Terhorst¹, Christian Schlötterer², Yun S Song³

¹Department of Statistics, University of California, Berkeley, Berkeley, California, United States of America.

|April 8, 2015

Summary

New computational methods analyze genomic time series data from experimental evolution. This approach accurately detects and estimates fitness of selected alleles, improving evolutionary studies.

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Area of Science:

Evolutionary biology
Population genetics
Genomics

Background:

Evolve-and-resequence (E&R) experiments generate valuable genomic time series data for studying evolution.
Standard population genetic methods are insufficient for time-series data, necessitating new analytical approaches.

Purpose of the Study:

To develop a novel computational method for analyzing genomic time series data from E&R experiments.
To accurately infer evolutionary parameters, including allele fitness and population dynamics, from time-course genomic data.

Main Methods:

Developed a Gaussian process approximation to the multi-locus Wright-Fisher process.
Incorporated selection and linkage effects across time points and genomic locations.
Validated the method using simulated data to assess detection, localization, and fitness estimation of selected alleles.

Main Results:

The method successfully detects, locates, and estimates the fitness of selected alleles from simulated genomic time series data.
Demonstrated the method's power across various parameters like selection strength, population size, and sampling frequency.
Applied the method to real genome-wide data from D. melanogaster adaptation experiments.

Conclusions:

The Gaussian process approximation provides a principled framework for analyzing E&R time series data.
This method enhances the ability to study evolutionary adaptation and can inform experimental design.
The approach facilitates inference of key population genetic parameters from experimental evolution data.