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Related Concept Videos

Epistasis Analysis01:09

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Inferring Epistasis from Genetic Time-series Data.

Muhammad Saqib Sohail1, Raymond H Y Louie2, Zhenchen Hong3

  • 1Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, People's Republic of China.

Molecular Biology and Evolution
|September 21, 2022
PubMed
Summary

We developed a new method to infer epistasis, the interaction effect of mutations, from evolutionary data. This approach accurately estimates pairwise epistatic interactions and quantifies uncertainty in complex selection models.

Keywords:
Bayesian inferencediffusionepistasislinkagelongitudinal datapath integralselectiontime-series data

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Area of Science:

  • Evolutionary Biology
  • Genetics
  • Computational Biology

Background:

  • Epistasis describes how mutation effects depend on genetic background.
  • It is common in viruses, bacteria, and cancer, influencing drug resistance and immune escape.
  • Estimating epistasis from population data is challenging due to confounding evolutionary factors.

Purpose of the Study:

  • To develop a novel method for inferring epistatic interactions from evolutionary histories.
  • To disentangle selection, mutation, recombination, and drift effects.
  • To quantify the reliability of inferred fitness parameters.

Main Methods:

  • Developed a computational method to infer epistasis and individual mutation fitness effects.
  • Utilized observed evolutionary histories as input data.
  • Employed simulations to validate the method's accuracy and parameter identifiability.

Main Results:

  • Accurate inference of pairwise epistatic interactions was achieved with sufficient genetic diversity.
  • The method can identify reliably inferable fitness parameters and unidentifiable ones.
  • Enabled inference of more complex selection models from time-series genetic data.

Conclusions:

  • The new method provides a robust way to estimate epistasis from evolutionary data.
  • It enhances our understanding of molecular evolution and the genetic basis of adaptation.
  • Quantifies uncertainty, allowing for more reliable modeling of evolutionary processes.