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A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data
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DOI：10.1038/s41467-023-38795-w.png)
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Abstract
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Detecting cell type-specific genetic effects on gene expression is challenging in bulk RNA-seq data. Here, the authors develop a method to increase power which incorporates allele-specific expression and does not transform the gene expression 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.
Keywords:
FALSE DISCOVERY RATE
GENE-EXPRESSION
HERITABILITY
RISK
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