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

Protein Networks02:26

Protein Networks

4.7K
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,...
4.7K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

16.6K
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...
16.6K
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

76
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
76

You might also read

Related Articles

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

Sort by
Same author

Publisher Correction: Tumor transcriptional state predicts survival in immune-checkpoint-blockade-treated glioblastoma.

Nature cancer·2026
Same author

Quantum ensembling methods for healthcare and life science.

Briefings in bioinformatics·2026
Same author

Tumor transcriptional state predicts survival in immune-checkpoint-blockade-treated glioblastoma.

Nature cancer·2026
Same author

Multimodal predictions of end stage chronic kidney disease from asymptomatic individuals for discovery of genomic biomarkers.

BMC nephrology·2026
Same author

Remics: a redescription-based framework for multi-omics analysis.

Frontiers in cell and developmental biology·2026
Same author

Genomic modifiers of malignant and neurodevelopmental phenotypes in individuals with PTEN hamartoma tumor syndrome.

NPJ genomic medicine·2026

Related Experiment Video

Updated: Mar 26, 2026

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

47.1K

Characterizing redescriptions using persistent homology to isolate genetic pathways contributing to pathogenesis.

Daniel E Platt1, Saugata Basu2, Pierre A Zalloua3,4

  • 1Computational Biology Center, IBM T. J. Watson Research Center, 1101 Kitchawan Rd., Yorktown Hgts, 10598, NY, USA. watplatt@us.ibm.com.

BMC Systems Biology
|January 29, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces composite phenotypes to better understand complex diseases like metabolic syndrome. By analyzing genetic data, it identifies specific patterns that improve the power of Genome-Wide Association Studies (GWAS), potentially uncovering hidden genetic links.

More Related Videos

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.3K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.5K

Related Experiment Videos

Last Updated: Mar 26, 2026

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

47.1K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.3K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.5K

Area of Science:

  • Genetics and Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Complex diseases involve multiple, often correlated, causal pathways.
  • Metabolic syndrome presents a challenge due to highly correlated pathways.
  • Identifying distinct phenotype clusters (composite phenotypes) is crucial for understanding disease etiology.

Purpose of the Study:

  • To develop a method for identifying composite phenotypes that reflect distinct etiologies of complex diseases.
  • To leverage composite phenotypes to enhance the power of Genome-Wide Association Studies (GWAS) and address the missing heritability problem.

Main Methods:

  • Phenotype patterns were identified and fuzzy redescriptions were determined using Jaccard distances.
  • Vietoris-Rips complexes were constructed from Jaccard distances to compute persistent homology.
  • Composite phenotypes were identified based on topological features and their genetic associations were explored using logistic regression and GWAS.

Main Results:

  • Distinct composite phenotypes within metabolic syndrome were identified.
  • Specific SNPs within the RAAS complex showed associations with different composite phenotypes.
  • GWAS identified SNPs associated with composite phenotypes, with greater power observed in composite combinations compared to individual phenotypes.

Conclusions:

  • Systematic associations and distinctive genetic profiles were found among metabolic syndrome variates.
  • Composite phenotype descriptions enhance the power of GWAS, allowing for the detection of SNPs that might otherwise be missed (false negatives).
  • This approach offers a new window into understanding the genetic architecture of complex diseases.