Work led by Cynthia Kalita, developing a new method to identify cell-type specific QTLs in bulk RNA-seq data by leveraging allele-specificity is now out in Genome Biology:

DeCAF: A novel method to identify cell-type specific regulatory variants and their role in cancer risk.
Kalita C, Gusev A. Genome Biology. 2022

Several recent studies have shown that variants influencing gene expression in a cell-type specific manner can be detected from bulk (i.e. non-specific) RNA-seq data by leveraging differences in cell type proportions across individuals. Such methods typically work by modeling the interaction between the genotypic effect on expression and individual cell type proportions: higher effects in individuals with more of a given cell type provide evidence of specificity. Here, we propose the method DeCAF to additionally incorporate allele-specific expression: the difference in expression between the two haplotypes of an individual. Individuals with more of a given cell type are expected to have more allele-specificity in their expression, which is a source of signal that is independent and complementary to the conventional eQTL interaction.

In simulation and in real data, DeCAF greatly boosts power to detect cell-type specific effects at low sample sizes. Surprisingly, we find the largest number of such effects coming from the tumor (where tumor purity is treated as โ€œcell typeโ€ proportion). These tumor-specific effects replicate strongly in immune cell types, suggesting they may be capturing genetic variants involved in tumor-immune interactions.

DeCAF can be broadly applied to cancer and non-cancer studies to identify additional interesting QTLs, and is open source.

Figure: Summary of the DeCAF model and results

DeCAF method