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

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

1.6K
The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
1.6K
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

27.5K
Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
27.5K
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

4.4K
4.4K

You might also read

Related Articles

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

Sort by
Same author

The Vertebrate Genomes Project Phase I: A global reference genome resource.

bioRxiv : the preprint server for biology·2026
Same author

Gene expression integration and similarity score-based modeling improve risk stratification in idiopathic venous thrombophilia.

Journal of thrombosis and haemostasis : JTH·2026
Same author

Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome.

Cell·2026
Same author

A new phased assembly of the Antarctic spiny plunderfish provides novel insights into the evolution of the notothenioid radiation.

bioRxiv : the preprint server for biology·2026
Same author

<i>Trans</i>-eQTLs reveal the architecture of human gene regulatory networks.

medRxiv : the preprint server for health sciences·2026
Same author

Cryptic Diversity and Impacts of Domestication in the Black Soldier Fly (Hermetia illucens) Genome.

Genome biology and evolution·2026

Related Experiment Video

Updated: Apr 16, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

8.0K

Pathway-based factor analysis of gene expression data produces highly heritable phenotypes that associate with age.

Andrew Anand Brown1, Zhihao Ding2, Ana Viñuela3

  • 1Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, United Kingdom NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.

G3 (Bethesda, Md.)
|March 12, 2015
PubMed
Summary

Statistical factor analysis creates robust "pathway phenotypes" from gene expression data, revealing significant associations between aging and biological pathways. These phenotypes enhance heritability and discovery power for genetic studies.

Keywords:
agingfactor analysisgene expressionheritabilitylinear mixed models

More Related Videos

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.9K
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

2.4K

Related Experiment Videos

Last Updated: Apr 16, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

8.0K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.9K
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

2.4K

Area of Science:

  • Genomics
  • Systems Biology
  • Statistical Genetics

Background:

  • Factor analysis is used to reduce noise in high-dimensional data for genetic association studies.
  • Derived factors can summarize biologically relevant variation.
  • Understanding relationships between gene expression, heritability, and aging is crucial.

Purpose of the Study:

  • To demonstrate how pathway expression factors can analyze relationships between expression, heritability, and aging.
  • To create more reliable phenotypes from gene expression data using factor analysis.
  • To increase the power of discovering biologically relevant associations.

Main Methods:

  • Applied statistical factor analysis to skin gene expression data from 647 twins (MuTHER Consortium).
  • Summarized gene expression patterns into 930 pathway phenotypes across 186 KEGG pathways.
  • Identified associations between age and pathway phenotypes using a stringent Bonferroni threshold.

Main Results:

  • Identified 69 significant age-phenotype associations across 57 KEGG pathways.
  • Pathway phenotypes exhibited higher heritability compared to individual gene expression levels.
  • Significant pathways involved sugar/fatty acid metabolism and insulin signaling.

Conclusions:

  • Factor analysis combined with biological knowledge generates reliable, low-noise phenotypes from gene expression data.
  • This approach enhances the power to discover biologically relevant associations, including those with aging.
  • Pathway phenotypes offer a promising tool for association studies with other environmental factors.