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

Genome-wide genetic overlap between fear-based disorders and generalised anxiety disorder.

Molecular psychiatry·2026

Same author

Strategic Amyotrophic Lateral Sclerosis Australia-Systems Genomics Consortium (SALSA-SGC): cohort profile.

BMJ open·2026

Same author

Developmentally dynamic, non-convergent transcriptomic profiles in CNV models for schizophrenia risk.

Molecular psychiatry·2026

Same author

Genome-wide meta-analysis of quantitatively measured generalized anxiety symptoms in individuals of European ancestry.

Nature human behaviour·2026

Same author

Publisher Correction: Multi-ancestry genome-wide association analyses of refractive error augment genetic discovery and polygenic prediction.

Nature genetics·2026

Same author

Sleep chart of biological ageing clocks in middle and late life.

Nature·2026

Same journal

Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans.

medRxiv : the preprint server for health sciences·2026

Same journal

Nocturnal Respiratory Rate and Variability Predict Long-term Mortality in Stable Outpatients with Cardiovascular Disease.

medRxiv : the preprint server for health sciences·2026

Same journal

MOSAIC: Methylation-Oriented Site Analysis and Information Classifier for Robust Epigenomic Classification of Acute Leukemia in Clinical Cohorts with Variable Tumor Purity.

medRxiv : the preprint server for health sciences·2026

Same journal

Risk beliefs, intensive digital information and demand for a new preventative health product in public clinics: Evidence from an experiment in Zimbabwe.

medRxiv : the preprint server for health sciences·2026

Same journal

Development of an automated, imaging-based preoperative screening model for early identification of malnutrition in an abdominal surgery cohort.

medRxiv : the preprint server for health sciences·2026

Same journal

A Pilot Project Leveraging Large Language Models for Automated Screening and Variable Extraction in Observational Studies.

medRxiv : the preprint server for health sciences·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 16, 2025

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Benchmarking methods integrating GWAS and single-cell transcriptomic data for mapping trait-cell type associations.

Ang Li^1,2, Tian Lin¹, Alicia Walker²

¹Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.

Medrxiv : the Preprint Server for Health Sciences

|June 6, 2025

Summary

This summary is machine-generated.

This study evaluates methods for linking genetic variants to specific cell types. A new Cauchy approach is proposed to improve the accuracy of identifying cell types influenced by trait-associated variants.

More Related Videos

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Related Experiment Videos

Last Updated: Jun 16, 2025

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

Genomics
Computational Biology
Cell Biology

Background:

Genome-wide association studies (GWAS) identify genetic variants linked to traits, but their cellular mechanisms are often unknown.
Single-cell RNA sequencing (scRNA-seq) provides cell-type-specific gene expression data, offering a potential avenue to interpret GWAS findings.

Purpose of the Study:

To systematically evaluate existing computational strategies for integrating GWAS summary statistics with scRNA-seq data.
To assess the statistical power and false positive rates of different integration approaches.
To propose an improved method for identifying trait-associated cell types.

Main Methods:

Evaluation of 19 different computational methods for integrating GWAS and scRNA-seq data.
Comparison against established "ground truth" trait-cell type associations.
Development and proposal of a novel Cauchy-based integration strategy.

Main Results:

The study systematically benchmarked 19 methods, revealing varying performance in statistical power and false positive rates.
Key conclusions were drawn to guide the selection and application of integration strategies in future research.
The proposed Cauchy approach demonstrated potential for enhanced detection of trait-cell type associations.

Conclusions:

Current methods for integrating GWAS and scRNA-seq data have limitations in accuracy and reliability.
Systematic evaluation is crucial for understanding the strengths and weaknesses of different bioinformatic approaches.
The novel Cauchy approach offers a promising strategy to improve the identification of cell types mediating genetic effects on complex traits.