Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation
Grace Avecilla1,2, Julie N Chuong1,2, Fangfei Li3
1Department of Biology, New York University, New York, New York, United States of America.
Plos Biology
|May 27, 2022
Summary
We developed a new method using neural networks to estimate the rate and effects of beneficial copy number variants (CNVs) in yeast evolution. This approach reveals CNVs are a major driver of rapid adaptation.
Area of Science:
- Evolutionary biology
- Genetics
- Computational biology
Background:
- Adaptive evolution is driven by beneficial mutations, but their rates and fitness effects are hard to measure.
- Copy number variants (CNVs) are a significant source of variation that can accelerate adaptation.
- Previous work established a fluorescent reporter to track CNV dynamics in yeast chemostats.
Purpose of the Study:
- To estimate the rate of beneficial CNV formation and their fitness effects using adaptation dynamics.
- To evaluate the performance of simulation-based likelihood-free inference methods for evolutionary studies.
- To compare the utility of different evolutionary models and inference algorithms, including Neural Posterior Estimation (NPE).
Main Methods:
- Utilized CNV adaptation dynamics in yeast populations grown in chemostats.
- Applied simulation-based likelihood-free inference, comparing Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) and Neural Posterior Estimation (NPE).
- Tested the suitability of Wright-Fisher and chemostat evolutionary models.
- Experimentally validated estimates using barcode lineage tracking and pairwise fitness assays.
Main Results:
- Neural Posterior Estimation (NPE) showed advantages over ABC-SMC for inference.
- A Wright-Fisher model was sufficient for most scenarios.
- Estimated CNV formation rate at the GAP1 locus in yeast: 10^-4.7 to 10^-4 per cell division.
- Estimated fitness coefficient for GAP1 CNVs: 0.04 to 0.1 per generation.
- Beneficial CNV supply rate is ~10-fold higher than beneficial single-nucleotide mutations.
Conclusions:
- The study demonstrates the power of NPE for inferring evolutionary parameters from empirical data.
- CNVs play a crucial role in rapid adaptive evolution, contributing significantly more than previously thought.
- The developed framework has broad applications for studying evolutionary processes in various systems, including tumors and viruses.
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