Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Inferring Genetic Interactions From Comparative Fitness Data

Kristina Crona, Alex Gavryushkin, Devin Greene, View ORCID ProfileNiko Beerenwinkel
doi: https://doi.org/10.1101/137372
Kristina Crona
American University, Washington DC;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alex Gavryushkin
ETH Zurich, Basel, Switzerland; SIB Swiss Institute of Bioinformatics, Basel, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Devin Greene
American University, Washington DC;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Niko Beerenwinkel
ETH Zurich, Basel, Switzerland; SIB Swiss Institute of Bioinformatics, Basel, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Niko Beerenwinkel
  • For correspondence: niko.beerenwinkel@bsse.ethz.ch
  • Abstract
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax, the fungus Aspergillus niger, and the TEM-family of β-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
  • Posted May 12, 2017.

Download PDF

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Inferring Genetic Interactions From Comparative Fitness Data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
Inferring Genetic Interactions From Comparative Fitness Data
Kristina Crona, Alex Gavryushkin, Devin Greene, Niko Beerenwinkel
bioRxiv 137372; doi: https://doi.org/10.1101/137372
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Inferring Genetic Interactions From Comparative Fitness Data
Kristina Crona, Alex Gavryushkin, Devin Greene, Niko Beerenwinkel
bioRxiv 137372; doi: https://doi.org/10.1101/137372

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Evolutionary Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (815)
  • Biochemistry (1128)
  • Bioengineering (718)
  • Bioinformatics (5733)
  • Biophysics (1946)
  • Cancer Biology (1383)
  • Cell Biology (1961)
  • Clinical Trials (71)
  • Developmental Biology (1340)
  • Ecology (2060)
  • Epidemiology (1096)
  • Evolutionary Biology (4336)
  • Genetics (3048)
  • Genomics (3931)
  • Immunology (840)
  • Microbiology (3301)
  • Molecular Biology (1221)
  • Neuroscience (8408)
  • Paleontology (62)
  • Pathology (169)
  • Pharmacology and Toxicology (304)
  • Physiology (401)
  • Plant Biology (1143)
  • Scientific Communication and Education (318)
  • Synthetic Biology (469)
  • Systems Biology (1601)
  • Zoology (211)