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

6.0K
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...
6.0K
Combinatorial Gene Control02:33

Combinatorial Gene Control

9.8K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
9.8K
Polygenic Traits01:18

Polygenic Traits

69.9K
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
69.9K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

16.4K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
16.4K
Multiple Allele Traits01:49

Multiple Allele Traits

38.5K
The Concept of Multiple Allelism
38.5K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.1K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
7.1K

You might also read

Related Articles

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

Sort by
Same author

New neurons flatten social hierarchies.

Scientific reports·2026
Same author

Ferroptosis susceptibility in hippocampal neural precursor cells influences neurogenesis and memory across aging.

Cell stem cell·2026
Same author

The Smarcal1-Usp37 locus modulates glycogen aggregation in astrocytes of the aged hippocampus.

Cell systems·2026
Same author

Lifestyle shapes preclinical social and microglial deficits in an Alzheimer's disease mouse model.

Molecular psychiatry·2025
Same author

Creating a biomedical knowledge base by addressing GPT inaccurate responses and benchmarking context.

bioRxiv : the preprint server for biology·2024
Same author

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Frontiers in immunology·2024

Related Experiment Video

Updated: Mar 10, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K

Integrating Multidimensional Data Sources to Identify Genes Regulating Complex Phenotypes.

Rupert W Overall1

  • 1CRTD - DFG Research Center for Regenerative Therapies Dresden, Technische Universität Dresden, Fetscherstrasse 105, 01307, Dresden, Germany. Rupert.overall@crt-dresden.de.

Methods in Molecular Biology (Clifton, N.J.)
|December 10, 2016
PubMed
Summary

Researchers can combine phenotype data with gene expression data to build network models. These models help identify key genes influencing traits, aiding in quantitative trait locus mapping and genetic research.

Keywords:
Complex traitsData integrationGene–gene interactionsMultilayer networksNetwork theory

More Related Videos

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

4.9K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Related Experiment Videos

Last Updated: Mar 10, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

4.9K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Quantitative trait locus (QTL) mapping relies on phenotype data.
  • Integrating diverse data types can enhance QTL analysis.
  • Whole-transcriptome expression data offers insights into gene function.

Purpose of the Study:

  • To describe a method for augmenting phenotype data with expression data.
  • To demonstrate the construction of multidimensional network models.
  • To identify key genes driving specific phenotypes.

Main Methods:

  • Assembling compatible whole-transcriptome expression data with phenotype data.
  • Constructing multidimensional network models.
  • Utilizing network models for gene identification.

Main Results:

  • The approach allows for the integration of multiple data sources.
  • Multidimensional network models can be built from these integrated data.
  • Key genes potentially driving phenotypes can be identified.

Conclusions:

  • This integrated network approach enhances QTL mapping capabilities.
  • The described workflow provides a framework for researchers.
  • Consideration of alternatives and limitations is crucial for application.