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

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...

You might also read

Related Articles

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

Sort by
Same author

Parental Autonomy Support and Psychological Resilience in College Students: The Longitudinal Sequential Mediating Roles of Basic Psychological Need Satisfaction and Autonomous Motivation.

Psychological reports·2026
Same author

Identifying fate-determining transcription factors with single-cell omics.

Trends in genetics : TIG·2026
Same author

Integrated Gut-Brain Axis Response to Freezing and Recovery in Freeze-Tolerant Fish, <i>Perccottus glenii</i>.

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

Anti-<i>Candida albicans</i> natural products: convergent technologies revolutionizing discovery from bioactivity assessment to targeted mechanisms.

Critical reviews in microbiology·2026
Same author

Clonorchis sinensis excretory secretory products promote hepatic fibrosis through stimulating biliary epithelium to secrete IL-17A.

PLoS neglected tropical diseases·2026
Same author

A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation.

Nature communications·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 31, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Network-based group variable selection for detecting expression quantitative trait loci (eQTL).

Weichen Wang1, Xuegong Zhang

  • 1Mathematics and Physics, School of Sciences, Tsinghua University, Beijing 100084, China. wwc07@mails.tsinghua.edu.cn

BMC Bioinformatics
|July 2, 2011
PubMed
Summary
This summary is machine-generated.

We developed a network-based group variable selection (NGVS) method to improve expression quantitative trait loci (eQTL) detection. This approach enhances accuracy by integrating gene networks and linkage disequilibrium (LD) structures, outperforming traditional methods in high-dimensional biological data.

More Related Videos

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Related Experiment Videos

Last Updated: May 31, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

  • Genetics
  • Bioinformatics
  • Systems Biology

Background:

  • Expression quantitative trait loci (eQTL) analysis seeks genetic loci linked to gene expression levels.
  • High-dimensional biological data presents challenges for traditional statistical methods.
  • Incorporating biological network and linkage disequilibrium (LD) information can improve eQTL analysis.

Purpose of the Study:

  • To propose a novel network-based group variable selection (NGVS) method for enhanced QTL detection.
  • To leverage gene co-expression networks and LD structures for improved marker set grouping.
  • To reduce the dimensionality of eQTL problems by considering complex SNP interactions.

Main Methods:

  • Developed a network-based group variable selection (NGVS) method.
  • Grouped highly correlated expression traits by biological function and linked markers by LD.
  • Treated additive and dominant effects of loci as groups for analysis.

Main Results:

  • NGVS method successfully analyzed simulation data and a mouse obesity/diabetes dataset.
  • Replicated previously published findings on a mouse linkage dataset.
  • Identified potential sex-dependent loci and interactions among multiple SNPs.

Conclusions:

  • The NGVS method is effective for high-dimensional, high-noise biological data.
  • NGVS outperforms the classical Lasso method by incorporating biological knowledge.
  • Integrating gene expression and loci correlation information enhances causal marker detection and can yield novel biological insights.