ArticleNature communications2023
A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- A scalable framework for single-cell eQTL mapping uncovers genetic regulators of meat production traits in pigs.Journal of animal science and biotechnology · 2026Article
- SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation.Nature communications · 2025Article
- IPGCA: A Comprehensive Single Cell Atlas of 1 074 127 Porcine Intestinal Cells Revealing Cellular Dynamics, Genetic Regulation, and Cross-Species Conservation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A New Family-Based Approach for Detecting Allele-Specific Expression and for Mapping Possible eQTLs.Animals : an open access journal from MDPI · 2025Article
- SCIG: Machine learning uncovers cell identity genes in single cells by genetic sequence codes.Nucleic acids research · 2025Article
- A unified framework for cell-type-specific eQTL prioritization by integrating bulk and scRNA-seq data.American journal of human genetics · 2025Article
- Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data.Journal of the American Statistical Association · 2024Article
- eQTL studies: from bulk tissues to single cells.Journal of genetics and genomics = Yi chuan xue bao · 2023Review
- eQTL Studies: from Bulk Tissues to Single Cells.ArXiv · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Mapping cell type-specific gene expression quantitative trait loci (ct-eQTLs) is a powerful way to investigate the genetic basis of complex traits. A popular method for ct-eQTL mapping is to assess the interaction between the genotype of a genetic locus and the abundance of a specific cell type using a linear model. However, this approach requires transforming RNA-seq count data, which distorts the relation between gene expression and cell type proportions and results in reduced power and/or inflated type I error. To address this issue, we have developed a statistical method called CSeQTL that allows for ct-eQTL mapping using bulk RNA-seq count data while taking advantage of allele-specific expression. We validated the results of CSeQTL through simulations and real data analysis, comparing CSeQTL results to those obtained from purified bulk RNA-seq data or single cell RNA-seq data. Using our ct-eQTL findings, we were able to identify cell types relevant to 21 categories of human traits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.