biostudies-literatureLittle PU.S. Department of Health & Human Services | NIH | National Institute of General Medical SciencesNIGMS NIH HHS3030https://www.ebi.ac.uk/biostudies/studies/S-EPMC10212972biostudies-literatureUnknown14(1)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.Nature communicationsA computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data.PMC10212972R01 GM105785Li YLittle PLiu SSun WZhabotynsky VLin DYfalseA computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data.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.2023-01-01T00:00:00Z2023 May2025-04-20T00:15:33.841Z2025-02-19T02:57:08.27ZS-EPMC102129723723100210.1038/s41467-023-38795-w