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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.8K
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...
14.8K

You might also read

Related Articles

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

Sort by
Same author

Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study.

Journal of medical Internet research·2026
Same author

Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Observational Study.

Journal of medical Internet research·2026
Same author

The Common Fund Data Ecosystem (CFDE).

bioRxiv : the preprint server for biology·2026
Same author

LLM-Assessed Relatedness of Microbiome Study Descriptions Aligns more Strongly with Functional than with Taxonomic Profile Similarity.

Microbial ecology·2026
Same author

Single-cell atlas of transcriptomic vulnerability across multiple neurodegenerative and neuropsychiatric diseases.

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

KG2ML: integrating knowledge graphs and positive unlabeled learning for identifying disease-associated genes.

Frontiers in bioinformatics·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Nov 3, 2025

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

4.5K

TIGA: target illumination GWAS analytics.

Jeremy J Yang1,2, Dhouha Grissa3, Christophe G Lambert1

  • 1Division of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA.

Bioinformatics (Oxford, England)
|June 4, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a method to rank gene-trait associations for drug discovery by aggregating evidence across studies. It provides a confidence score for each association, aiding target prioritization.

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

3.5K
Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

7.1K

Related Experiment Videos

Last Updated: Nov 3, 2025

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

4.5K
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

3.5K
Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

7.1K

Area of Science:

  • Genomics
  • Pharmacology
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify genotype-phenotype links but face data quality and interpretability challenges.
  • Prioritizing drug targets requires robust evidence aggregation beyond single studies.

Purpose of the Study:

  • To develop a method for rational ranking, filtering, and interpretation of gene-trait associations.
  • To enhance drug target hypothesis generation by evaluating confidence in associations aggregated across multiple studies.

Main Methods:

  • Implemented a pipeline for evaluating gene-trait association confidence using aggregated statistics.
  • Incorporated bibliometric assessment (iCite relative citation ratio) and meanRank scores for evidence aggregation.
  • Leveraged existing curation and harmonization efforts for data integration.

Main Results:

  • Developed a scoring system for gene-trait associations based on aggregated evidence.
  • Created an analytical pipeline, open-source tool, and web application for usability.
  • Provided public datasets of results for drug discovery scientists.

Conclusions:

  • The proposed method provides a robust framework for assessing confidence in gene-trait associations.
  • This approach facilitates more reliable drug target hypothesis generation and prioritization.
  • The open-source implementation promotes accessibility and application in drug discovery research.