Back to Search
Start Over
An evolution-based high-fidelity method of epistasis measurement: Theory and application to influenza
- Source :
- PLoS Pathogens, PLoS Pathogens, Public Library of Science, 2021, 17 (6), pp.e1009669. ⟨10.1371/journal.ppat.1009669⟩, PLoS Pathogens, 2021, 17 (6), pp.e1009669. ⟨10.1371/journal.ppat.1009669⟩, PLoS Pathogens, Vol 17, Iss 6, p e1009669 (2021)
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- Linkage effects in a multi-locus population strongly influence its evolution. The models based on the traveling wave approach enable us to predict the average speed of evolution and the statistics of phylogeny. However, predicting statistically the evolution of specific sites and pairs of sites in the multi-locus context remains a mathematical challenge. In particular, the effects of epistasis, the interaction of gene regions contributing to phenotype, is difficult to predict theoretically and detect experimentally in sequence data. A large number of false-positive interactions arises from stochastic linkage effects and indirect interactions, which mask true epistatic interactions. Here we develop a proof-of-principle method to filter out false-positive interactions. We start by demonstrating that the averaging of haplotype frequencies over multiple independent populations is necessary but not sufficient for epistatic detection, because it still leaves high numbers of false-positive interactions. To compensate for the residual stochastic noise, we develop a three-way haplotype method isolating true interactions. The fidelity of the method is confirmed analytically and on simulated genetic sequences evolved with a known epistatic network. The method is then applied to a large sequence database of neurominidase protein of influenza A H1N1 obtained from various geographic locations to infer the epistatic network responsible for the difference between the pre-pandemic virus and the pandemic strain of 2009. These results present a simple and reliable technique to measure epistatic interactions of any sign from sequence data.<br />Author summary Interactions between genomic sites create a fitness landscape. The knowledge of topology and strength of interactions is vital for predicting the escape of viruses from drugs and immune response and their passing through fitness valleys. Many efforts have been invested into measuring these interactions from DNA sequence sets. Unfortunately, reproducibility of the results remains low due partly to a very small fraction of interaction pairs and partly to stochastic linkage noise masking true interactions. Here we propose a method to separate stochastic linkage and indirect interactions from epistatic interactions and apply it to influenza virus sequence data.
- Subjects :
- Evolutionary Genetics
RNA viruses
Viral Diseases
Heredity
Fitness landscape
[SDV]Life Sciences [q-bio]
Medical Conditions
Influenza A Virus, H1N1 Subtype
Medicine and Health Sciences
Drug Interactions
Biology (General)
Pathology and laboratory medicine
education.field_of_study
Genomics
Medical microbiology
Biological Evolution
[SDV] Life Sciences [q-bio]
Genetic Mapping
Infectious Diseases
Fitness Epistasis
Influenza A virus
Mutation (genetic algorithm)
Viruses
Pathogens
Algorithms
Research Article
QH301-705.5
Population
Context (language use)
Computational biology
Biology
Microbiology
Influenza, Human
Genetics
Influenza viruses
Humans
education
Gene
Linkage (software)
Pharmacology
Evolutionary Biology
Models, Genetic
Haplotype
Organisms
Viral pathogens
Biology and Life Sciences
Epistasis, Genetic
RC581-607
Influenza
Microbial pathogens
Haplotypes
Epistasis
Immunologic diseases. Allergy
Orthomyxoviruses
Subjects
Details
- Language :
- English
- ISSN :
- 15537366 and 15537374
- Database :
- OpenAIRE
- Journal :
- PLoS Pathogens, PLoS Pathogens, Public Library of Science, 2021, 17 (6), pp.e1009669. ⟨10.1371/journal.ppat.1009669⟩, PLoS Pathogens, 2021, 17 (6), pp.e1009669. ⟨10.1371/journal.ppat.1009669⟩, PLoS Pathogens, Vol 17, Iss 6, p e1009669 (2021)
- Accession number :
- edsair.doi.dedup.....f9343b89a364628c3893e0f0cc569047