Background Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, the current methods are labor-intensive and expensive. Here we introduce a new method, Decon2, a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL).
Results The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell we can predict the proportions of 34 circulating cell types for 3,194 samples from a population-based cohort. Next we identified 16,362 whole blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with those of eQTL(≥ 96%-100%) and chromatin mark QTL (≥87%-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect.
Conclusions Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs, which is useful in pinpointing the most relevant cell type for a certain complex disease. Decon2 is available as an R package and Java application. (https://github.com/molgenis/systemsgenetics/tree/master/Decon2), and as a web tool (www.molgenis.org/deconvolution).