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

Innovation in animal health under Regulation (EU) 2019/6: Review and recommendations.

Frontiers in veterinary science·2026

Same author

Discovery of gene-alcohol interaction loci influencing blood pressure in 1.1 million individuals from multiple populations.

Research square·2026

Same author

Cross-organ analysis reveals associations between vascular properties of the retina, the carotid and aortic arteries, and the brain.

Communications medicine·2026

Same author

Bidirectional genetic and phenotypic links between smoking and striatal iron content involving dopaminergic and inflammatory pathways.

Addiction (Abingdon, England)·2026

Same author

Large-Scale Gene-Smoking Interactions and Fine Mapping Study Identifies Multiple Novel Blood Pressure Loci in over 1 Million Individuals.

medRxiv : the preprint server for health sciences·2026

Same author

PLUME-OCT: A quality control tool to enhance statistical power in the analysis of biomarkers from 3D biomedical datasets of OCT images.

Computers in biology and medicine·2026

Same journal

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

Bioinformatics (Oxford, England)·2026

Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026

Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026

Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026

Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 31, 2025

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

PascalX: a Python library for GWAS gene and pathway enrichment tests.

Daniel Krefl^1,2, Alessandro Brandulas Cammarata¹, Sven Bergmann^1,2,3

¹Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.

Bioinformatics (Oxford, England)

|May 3, 2023

Summary

This summary is machine-generated.

PascalX is a Python library for analyzing genome-wide association studies (GWAS) summary statistics. It efficiently scores genes for enrichment signals, accounting for SNP correlations, and supports multithreading and GPU acceleration.

More Related Videos

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Related Experiment Videos

Last Updated: Jul 31, 2025

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

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

Genetics and Bioinformatics
Statistical Genomics

Background:

Genome-wide association studies (GWAS) generate large datasets of single nucleotide polymorphism (SNP) associations.
Identifying genes and pathways enriched for association signals from GWAS data is crucial for understanding disease mechanisms.
Existing methods for gene-set enrichment analysis often do not fully account for complex linkage disequilibrium patterns between SNPs.

Purpose of the Study:

To introduce PascalX, a novel Python library for fast and accurate mapping of SNP-wise GWAS summary statistics.
To enable gene and gene-set scoring for enrichment signals using single or paired GWAS datasets.
To provide a flexible and open-source platform for developing new GWAS enrichment testing methods.

Main Methods:

PascalX implements a scoring method based on the cumulative density function of a linear combination of chi-squared distributed random variables.
The method accounts for SNP correlation patterns to provide accurate gene scores.
The library supports approximate and exact calculations for high precision, with acceleration via multithreading and GPU.

Main Results:

PascalX offers fast and accurate tools for mapping SNP-wise GWAS summary statistics.
It enables the scoring of genes and annotated gene sets for enrichment signals.
The library is well-suited for method development in GWAS enrichment testing.

Conclusions:

PascalX provides an efficient and accurate computational tool for GWAS enrichment analysis.
Its open-source nature and flexibility facilitate further research and method development in the field.
The library's ability to handle SNP correlations enhances the reliability of gene-level association signals.