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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: Jun 1, 2025

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

A unified framework for cell-type-specific eQTL prioritization by integrating bulk and scRNA-seq data.

Xinyi Yu¹, Xianghong Hu², Xiaomeng Wan²

¹Shenzhen Research Institute of Big Data, Shenzhen 518172, China; School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Shenzhen 518172, China.

American Journal of Human Genetics

|January 17, 2025

Summary

We developed IBSEP, a new method integrating bulk and single-cell RNA sequencing data to improve cell-type-specific eQTL (ct-eQTL) discovery. This approach enhances understanding of genetic influences on complex diseases at the cellular level.

Keywords:

GWAS eQTLs expression quantitative trait loci genome-wide association study scRNA-seq single-cell RNA sequencing statistical genetics

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Area of Science:

Genomics
Bioinformatics
Systems Biology

Background:

Genome-wide association studies (GWASs) identify genetic variants for complex traits, but biological interpretation, especially for non-coding regions, is difficult.
Expression quantitative trait locus (eQTL) studies link genetic variation to gene expression, aiding disease gene identification.
Bulk RNA sequencing (bulk RNA-seq) eQTL analyses lack cellular resolution due to heterogeneity, while single-cell RNA sequencing (scRNA-seq) offers higher resolution but has smaller sample sizes.

Purpose of the Study:

To present IBSEP, a novel statistical framework for enhanced cell-type-specific eQTL (ct-eQTL) prioritization.
To integrate bulk RNA-seq and scRNA-seq data to overcome limitations of individual methods.
To improve the identification of cell-type-specific genetic regulatory mechanisms in complex traits.

Main Methods:

Developed a statistical framework, IBSEP, utilizing a hierarchical linear model.
Integrated summary statistics from both bulk RNA-seq and scRNA-seq data.
Validated the method through extensive simulations and analysis of peripheral blood mononuclear cells and brain cortex datasets.

Main Results:

IBSEP demonstrated superior performance in identifying ct-eQTLs compared to existing methods.
The framework effectively combined strengths of bulk and single-cell data, mitigating individual limitations.
Analysis revealed cell-type-specific transcriptional regulatory mechanisms.

Conclusions:

IBSEP provides a robust approach for ct-eQTL analysis, enhancing the biological interpretation of GWAS findings.
The method offers deeper insights into the genetic basis of complex diseases at a cellular resolution.
This work facilitates the discovery of cell-type-specific genetic regulation relevant to human health and disease.