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

MR2G: A novel framework for causal network inference using GWAS summary data.

PLoS genetics·2026

Same author

A novel machine learning-based algorithm for eQTL identification reveals complex pleiotropic effects in the MHC region.

Briefings in bioinformatics·2026

Same author

Extracting Genetically-Imputed Causal Features From ECG Data.

Statistical analysis and data mining·2026

Same author

scTWAS: A powerful statistical framework for single-cell transcriptome-wide association studies.

Research square·2025

Same author

scMultiMap: Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data.

Nature communications·2025

Same author

Single-cell transcriptomic and proteomic analysis of Parkinson's disease brains.

Science translational medicine·2024

Same journal

Inherent tissue homeostasis of the juvenile metaphysis provides a foundation for osteosarcoma development.

Nature communications·2026

Same journal

Slowing the spread of treatment failure to artemisinin-based combination therapies in Uganda.

Nature communications·2026

Same journal

Harnessing interfacial click polymerization using pyridinium-yne films as photochromic, radical generation and sensing platforms.

Nature communications·2026

Same journal

Potential role of a CRISPR-Cas-activated toxin-antitoxin system in bacterial immunity.

Nature communications·2026

Same journal

Diverse radical transformations of allenic C(sp)-C(sp<sup>2</sup>) and C(sp<sup>3</sup>)-C(sp<sup>2</sup>) bonds enabled by silyl substitution.

Nature communications·2026

Same journal

Experience-dependent modulation of collective behavior in larval zebrafish.

Nature communications·2026

See all related articles

Search research articles

Home
Sctwas: A Powerful Statistical Framework For Single-cell Transcriptome-wide Association Studies.

Home
Sctwas: A Powerful Statistical Framework For Single-cell Transcriptome-wide Association Studies.

Related Experiment Video

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

scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.

Zhaotong Lin¹, Chang Su^2,3

¹Department of Statistics, Florida State University, Tallahassee, FL, USA.

Nature Communications

|March 13, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces scTWAS, a new method for cell-type-specific transcriptome-wide association studies (TWAS) using single-cell data. scTWAS enhances the identification of gene-trait associations, particularly in complex diseases like Alzheimer's.

More Related Videos

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

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

Related Experiment Videos

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

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

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

Area of Science:

Genomics
Computational Biology
Statistical Genetics

Background:

Bulk RNA sequencing aggregates cell signals, limiting cell-type-specific genetic association studies.
Single-cell RNA sequencing (scRNA-seq) offers cell-type resolution but presents data challenges (noise, sparsity).

Purpose of the Study:

To develop a statistical method (scTWAS) for cell-type-specific transcriptome-wide association studies (TWAS) using scRNA-seq data.
To address the unique challenges posed by noisy and sparse single-cell gene expression data.

Main Methods:

Proposed scTWAS, a novel statistical method utilizing a latent-variable model and moment-based estimation.
Applied scTWAS to population-scale single-cell RNA sequencing datasets from blood and brain tissues.

Main Results:

scTWAS demonstrated improved prediction of genetically regulated gene expression across diverse cell types.
Identified significantly more gene-trait associations for hematological and immune-related traits compared to existing methods.
Revealed cell-subtype-specific associations for Alzheimer's disease, implicating specific genes in distinct microglial subtypes.

Conclusions:

scTWAS effectively overcomes challenges in single-cell data for cell-type-specific TWAS.
Provides a powerful tool for discovering cell-type-specific genetic contributions to complex traits and diseases.
Advances understanding of disease mechanisms at a granular cellular level.