학술논문
Deconvolution of bulk blood eQTL effects into immune cell subpopulations.
Document Type
Article
Author
Aguirre-Gamboa, Raúl; de Klein, Niek; di Tommaso, Jennifer; Claringbould, Annique; van der Wijst, Monique GP; de Vries, Dylan; Brugge, Harm; Oelen, Roy; Võsa, Urmo; Zorro, Maria M.; Chu, Xiaojin; Bakker, Olivier B.; Borek, Zuzanna; Ricaño-Ponce, Isis; Deelen, Patrick; Xu, Cheng-Jiang; Swertz, Morris; Jonkers, Iris; Withoff, Sebo; Joosten, Irma
Source
Subject
*DECONVOLUTION (Mathematics)
*BLOOD
*CELLS
*LOCUS (Genetics)
*BLOOD sampling
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Language
ISSN
1471-2105
Abstract
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, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as 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 could predict the proportions of 34 circulating cell types for 3194 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 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 that is useful for pinpointing the most relevant cell type for a given 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). [ABSTRACT FROM AUTHOR]