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Efficient epistasis inference via higher-order covariance matrix factorization.

Kai S Shimagaki1,2, John P Barton1,2

  • 1Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.

Genetics
|June 20, 2025
PubMed
Summary
This summary is machine-generated.

We developed a new computational method to efficiently detect epistasis, which is gene interaction affecting evolution. This method revealed negative epistasis between beneficial mutations in HIV-1 evolution, particularly those aiding immune escape.

Keywords:
Bayesian inferencediffusionepistasishigher-order statisticslinkageselectionviral evolution

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Genetics

Background:

  • Epistasis, the interaction between genes, significantly impacts evolutionary trajectories.
  • Temporal genetic data offers insights into epistasis but is challenging to analyze.
  • Current methods for detecting epistasis are computationally intensive, limiting their use.

Purpose of the Study:

  • To develop a computationally efficient and accurate method for inferring epistatic interactions from sequence data.
  • To apply this novel method to understand the role of epistasis in the evolution of Human Immunodeficiency Virus type 1 (HIV-1).

Main Methods:

  • Developed a novel computational approach to infer epistasis with reduced computational cost.
  • Validated the method's accuracy using simulations.
  • Applied the method to longitudinal HIV-1 sequence data from 16 individuals over multiple years.

Main Results:

  • The new method significantly reduces computational demands for epistasis detection.
  • Analysis of HIV-1 evolution revealed a notable excess of negative epistatic interactions.
  • These interactions were particularly prevalent between beneficial mutations associated with immune escape.

Conclusions:

  • The developed method provides an accurate and efficient way to study epistasis in large genetic datasets.
  • Negative epistasis between immune escape mutations plays a crucial role in HIV-1 evolution.
  • This generalizable method can be applied to diverse evolutionary studies across various organisms.