A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data

Paul Little (), Si Liu, Vasyl Zhabotynsky, Yun Li, Dan-Yu Lin and Wei Sun ()
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Paul Little: Fred Hutchinson Cancer Center
Si Liu: Fred Hutchinson Cancer Center
Vasyl Zhabotynsky: University of North Carolina at Chapel Hill
Yun Li: University of North Carolina at Chapel Hill
Dan-Yu Lin: University of North Carolina at Chapel Hill
Wei Sun: Fred Hutchinson Cancer Center

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-38795-w

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