{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Little P"],"funding":["U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences","NIGMS NIH HHS"],"pagination":["3030"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10212972"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(1)"],"pubmed_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."],"journal":["Nature communications"],"pubmed_title":["A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data."],"pmcid":["PMC10212972"],"funding_grant_id":["R01 GM105785"],"pubmed_authors":["Li Y","Little P","Liu S","Sun W","Zhabotynsky V","Lin DY"],"additional_accession":[]},"is_claimable":false,"name":"A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data.","description":"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.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2025-04-20T00:15:33.841Z","creation":"2025-02-19T02:57:08.27Z"},"accession":"S-EPMC10212972","cross_references":{"pubmed":["37231002"],"doi":["10.1038/s41467-023-38795-w"]}}