Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genome-wide Association Studies-GWAS

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

Chemoenzymatic Syntheses of Tumor-Associated Carbohydrate Antigen Globo-H and Stage-Specific Embryonic Antigen 4.

Advanced synthesis & catalysis·2011

Same author

Identification of tandem mass spectra of mixtures of isomeric peptides.

Journal of proteome research·2010

Same author

Increasing the sensitivity and single-base mismatch selectivity of the molecular beacon using graphene oxide as the "nanoquencher".

Chemistry (Weinheim an der Bergstrasse, Germany)·2010

Same author

Optimal use of silver nitrate and marginal leakage at the sealant-enamel interface using micro-CT.

American journal of dentistry·2010

Same author

The use of hsa-miR-21, hsa-miR-181b and hsa-miR-106a as prognostic indicators of astrocytoma.

European journal of cancer (Oxford, England : 1990)·2010

Same author

Simultaneous elimination of T- and B-cell epitope by structure-based mutagenesis of single Glu80 residue within recombinant staphylokinase.

Acta biochimica et biophysica Sinica·2010

Search research articles

Related Experiment Video

Updated: May 17, 2026

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

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

Published on: July 1, 2020

Adaptive elastic-net sparse principal component analysis for pathway association testing.

¹Vanderbilt University, USA. steven.chen@vanderbilt.edu

Statistical Applications in Genetics and Molecular Biology

|October 24, 2012

Summary

This summary is machine-generated.

This study introduces a novel two-stage pathway analysis method for high-throughput biological data. The approach efficiently identifies key pathways linked to clinical outcomes, improving upon existing methods.

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Related Experiment Videos

Last Updated: May 17, 2026

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

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

Published on: July 1, 2020

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Pathway analysis is crucial for interpreting high-throughput biological data, like microarray gene expression.
Identifying key pathways associated with clinical outcomes presents significant challenges.
Efficiently selecting relevant genes and testing pathway associations are critical needs.

Purpose of the Study:

To develop an efficient and robust two-stage strategy for pathway analysis.
To address challenges in gene subset selection and statistical testing for pathway association.
To enable analysis of diverse outcome types (binary, continuous, survival).

Main Methods:

A two-stage approach utilizing dimension reduction via adaptive elastic-net sparse principal component analysis.
Extraction of latent variables representing pathway activity.
Integration of latent variables into a regression modeling framework for outcome analysis.

Main Results:

The proposed method is computationally efficient, requiring latent variable calculation only once per pathway.
Demonstrated favorable performance compared to five existing pathway testing methods.
Successfully applied to both simulated and real biological datasets.

Conclusions:

The novel two-stage strategy offers an efficient and effective solution for pathway analysis.
The method enhances the identification of biologically relevant pathways associated with clinical phenotypes.
This approach provides a valuable tool for analyzing complex biological datasets across various outcome types.