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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jan 12, 2026

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Transcriptome-wide association studies at cell-state level using single-cell eQTL data.

Guanghao Qi¹, Eardi Lila¹, Zhicheng Ji²

¹Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.

|November 4, 2025

Summary

This summary is machine-generated.

We developed TWiST, a new method for gene-disease association studies using single-cell data. TWiST improves power by analyzing cell states, revealing dynamic gene effects in immune cell differentiation for autoimmune diseases.

Keywords:

autoimmune disease cell state dynamic effect genetics single-cell eQTL transcriptome-wide association studies

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Last Updated: Jan 12, 2026

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

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:

Genetics
Computational Biology
Immunology

Background:

Transcriptome-wide association studies (TWASs) are crucial for identifying genes linked to diseases.
Existing TWAS methods often overlook crucial cellular heterogeneity by analyzing bulk or pseudobulk tissues.
This limitation hinders the precise understanding of gene-disease relationships within complex biological systems.

Purpose of the Study:

To introduce TWiST, a novel statistical method for TWAS that leverages single-cell expression quantitative trait locus (eQTL) data.
To enable gene-disease association analysis at a finer cell-state resolution, accounting for intra-cell type heterogeneity.
To provide a flexible framework for testing global, dynamic, and nonlinear gene expression effects on traits.

Main Methods:

TWiST utilizes pseudotime to model cell states and represents gene expression effects as a continuous pseudotemporal curve.
The method integrates single-cell eQTL data to assess gene-disease associations dynamically.
Statistical power was evaluated through comprehensive simulation studies and real-world data analysis.

Main Results:

TWiST demonstrated significantly improved statistical power compared to traditional pseudobulk methods in simulations.
Analysis of the OneK1K study dataset identified hundreds of genes exhibiting dynamic associations with autoimmune diseases.
These dynamic effects were observed along the immune cell differentiation trajectory.

Conclusions:

TWiST offers a powerful approach to dissecting gene-disease associations by incorporating cell-state resolution.
The method enhances the ability to detect dynamic and nonlinear gene effects relevant to complex diseases.
TWiST holds significant promise for advancing genetic studies of diseases using single-cell technologies.