4.8 Article

Inferring Epistasis from Genetic Time-series Data

Journal

MOLECULAR BIOLOGY AND EVOLUTION
Volume 39, Issue 10, Pages -

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/molbev/msac199

Keywords

Bayesian inference; selection; epistasis; linkage; path integral; diffusion; time-series data; longitudinal data

Funding

  1. Hong Kong Research Grants Council [16204121, 16201620]
  2. National Institute of General Medical Sciences of the National Institutes of Health [R35GM138233]
  3. Australian Research Council Future Fellowship [FT200100928]
  4. Australian Government
  5. Australian Research Council [FT200100928] Funding Source: Australian Research Council

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This article introduces a method for inferring epistasis and the fitness effects of individual mutations from observed evolutionary histories. Simulations show that this method can accurately infer pairwise epistatic interactions when there is sufficient genetic diversity in the data.
Epistasis refers to fitness or functional effects of mutations that depend on the sequence background in which these mutations arise. Epistasis is prevalent in nature, including populations of viruses, bacteria, and cancers, and can contribute to the evolution of drug resistance and immune escape. However, it is difficult to directly estimate epistatic effects from sampled observations of a population. At present, there are very few methods that can disentangle the effects of selection (including epistasis), mutation, recombination, genetic drift, and genetic linkage in evolving populations. Here we develop a method to infer epistasis, along with the fitness effects of individual mutations, from observed evolutionary histories. Simulations show that we can accurately infer pairwise epistatic interactions provided that there is sufficient genetic diversity in the data. Our method also allows us to identify which fitness parameters can be reliably inferred from a particular data set and which ones are unidentifiable. Our approach therefore allows for the inference of more complex models of selection from time-series genetic data, while also quantifying uncertainty in the inferred parameters.

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