Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Detecting gene subnetworks under selection in biological pathways.

Alexandre Gouy1,2, Joséphine T Daub3, Laurent Excoffier1,2

  • 1Institute of Ecology and Evolution, University of Berne, Baltzerstrasse 6, 3012 Berne, Switzerland.

Nucleic Acids Research
|September 22, 2017
PubMed
Summary

Detecting polygenic selection on complex traits is challenging. Our new method analyzes gene networks to identify subnetworks under selection, revealing adaptation to high altitude in humans.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dual-kingdom necrobiome succession extends postmortem interval estimation into skeletonization.

Forensic science international. Genetics·2026
Same author

Evidence of long-term purging of mutation burden in killer whale Orcinus orca genomes.

Heredity·2025
Same author

Evolution of Functional Genomic Diversity During a Bottleneck.

Genome biology and evolution·2025
Same author

A generalized structured coalescent for purifying selection without recombination.

Genetics·2025
Same author

Author Correction: Considerations in the search for epistasis.

Genome biology·2025
Same author

STRAF 2: New features and improvements of the STR population data analysis software.

Forensic science international. Genetics·2024

Area of Science:

  • Genomics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • High-throughput sequencing generates vast genomic data, outpacing functional analysis capabilities.
  • Traditional methods often analyze genes individually, missing complex trait evolution influenced by gene interactions.
  • Detecting selection on multiple genes (polygenic selection) for complex traits remains a significant challenge.

Purpose of the Study:

  • To develop a novel computational method for detecting polygenic selection by analyzing gene networks.
  • To identify subnetworks of interacting genes exhibiting unusual evolutionary patterns indicative of selection.
  • To apply this methodology to human adaptation to high-altitude environments.

Main Methods:

  • Developed a simulated annealing approach to solve the combinatorial optimization problem of subnetwork search within biological pathways.

Related Experiment Videos

  • Implemented the methodology in an R package named 'signet'.
  • Applied the 'signet' package to analyze genome-wide data for signals of human adaptation to high altitude.

    • Main Results:

    • The study successfully identified signals of adaptation to high-altitude in human populations.
    • Demonstrated that high-altitude adaptation has a polygenic basis involving numerous genetic components.
    • The 'signet' approach identified new candidate genes and biological processes involved in altitude adaptation.

    Conclusions:

    • Gene network analysis offers a powerful framework for studying the evolution of adaptive traits and interpreting genome-wide data.
    • The developed method effectively detects polygenic selection by focusing on interacting gene subnetworks.
    • This approach enhances the identification of genetic factors underlying complex trait evolution, as exemplified by human adaptation to high altitude.