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 Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

5.4K
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...
5.4K
Epistasis01:39

Epistasis

48.1K
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...
48.1K
Protein Networks02:26

Protein Networks

4.2K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Bifurcation of StCRY1-StHY5 axis orchestrates blue light-enhanced glycoalkaloid and chlorophyll accumulation in potato tubers.

The Plant cell·2026
Same author

Cluster-guided adversarial graph contrastive learning.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Neurochondrin promotes U5 snRNP maturation by regulating AAR2 release from PRPF8.

Nucleic acids research·2026
Same author

Prevalence and Epidemiological Characteristics of <i>Mycoplasma synoviae</i> Infection in Chickens in Mainland China.

Animals : an open access journal from MDPI·2026
Same author

β-TrCP targets nucleolin and modulates phase separation to restrict ribosome biogenesis in myocardial infarction hearts.

Cellular signalling·2026
Same author

Bilateral Myeloid Sarcoma of Breast: A Case Report and Discussion.

European journal of breast health·2026

Related Experiment Video

Updated: Oct 19, 2025

Detection of Heterodimerization of Protein Isoforms Using an in Situ Proximity Ligation Assay
09:18

Detection of Heterodimerization of Protein Isoforms Using an in Situ Proximity Ligation Assay

Published on: October 20, 2018

7.7K

EpiHNet: Detecting epistasis by heterogeneous molecule network.

Xin Wang1, Huiling Zhang2, Jun Wang1

  • 1School of Software, Shandong University, Jinan, China; Joint SDU-NTU Centre For AI Research (C-FAIR), Shandong University, Jinan, China.

Methods (San Diego, Calif.)
|September 23, 2021
PubMed
Summary

EpiHNet identifies complex disease genetic interactions by integrating SNP data with other biomolecules. This novel network-based approach improves the detection of high-order single nucleotide polymorphism (SNP) interactions.

Keywords:
Epistasis detectionGenome-wide association studiesHeterogeneous molecular networkModularity clusteringSNP network

More Related Videos

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

46.6K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.7K

Related Experiment Videos

Last Updated: Oct 19, 2025

Detection of Heterodimerization of Protein Isoforms Using an in Situ Proximity Ligation Assay
09:18

Detection of Heterodimerization of Protein Isoforms Using an in Situ Proximity Ligation Assay

Published on: October 20, 2018

7.7K
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

46.6K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.7K

Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Epistasis, or interactions between single nucleotide polymorphisms (SNPs), is crucial for understanding complex disease heritability.
  • Current methods for detecting SNP interactions often overlook valuable connections with other biomolecules like microRNAs (miRNAs) and long non-coding RNAs (lncRNAs).

Purpose of the Study:

  • To introduce EpiHNet, a novel heterogeneous bio-molecular network-based solution for detecting high-order SNP interactions.
  • To comprehensively model disease-related molecules, including SNPs, genes, miRNAs, and lncRNAs, for improved interaction detection.

Main Methods:

  • Constructed an SNP statistical network using case/control data.
  • Defined an SNP relational network using meta-path based similarity on a heterogeneous network (SNPs, genes, lncRNAs, miRNAs, diseases).
  • Integrated both networks and applied modularity-based clustering to identify SNP interactions within clusters.

Main Results:

  • EpiHNet demonstrated superior performance compared to existing methods in synthetic experiments for two-locus and three-locus disease models.
  • The method showed significant results in detecting high-order SNP interactions in real-world WTCCC breast cancer data.
  • Performance gains were observed even when the heterogeneous network component was excluded, highlighting the robustness of the approach.

Conclusions:

  • EpiHNet effectively detects high-order SNP interactions by integrating diverse biomolecular data within a network framework.
  • The approach offers a comprehensive strategy for modeling complex disease genetics, advancing the understanding of missing heritability.
  • This method holds promise for identifying complex genetic architectures underlying various diseases.