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Inferring Fitness Effects from Time-Resolved Sequence Data with a Delay-Deterministic Model.

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Summary

Inferring genetic selection is challenging due to genetic drift. This study introduces a new delay-deterministic model to correct misleading inferences from standard deterministic models, improving selection quantification in evolutionary systems.

Keywords:
delay-deterministic modelinference of fitness landscapestime-resolved sequence dataviral adaptation

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Area of Science:

  • Evolutionary biology
  • Population genetics

Background:

  • Inferring selection on genetic variants is crucial for understanding evolution.
  • Genetic drift often confounds selection inference, necessitating specialized methods.
  • Deterministic models show promise for selection inference, even with complex demography.

Purpose of the Study:

  • To identify limitations of deterministic models in evolutionary inference.
  • To propose a novel 'delay-deterministic' model to correct for mutation's nondeterministic effects.
  • To demonstrate the improved performance of the new model in quantifying selection.

Main Methods:

  • Development of the delay-deterministic model.
  • Application to a simple evolutionary system for performance evaluation.
  • Testing on sequence data from an evolutionary experiment.

Main Results:

  • Deterministic models can yield misleading inferences due to mutation's stochasticity in finite populations.
  • The proposed delay-deterministic model corrects these errors.
  • The model accurately quantifies selection in tested evolutionary systems.

Conclusions:

  • The delay-deterministic model offers improved accuracy for selection inference.
  • This approach is particularly beneficial in scenarios where standard deterministic models falter.
  • Identifying situations requiring the new model is possible using regular deterministic models.