학술논문
Deconvolution of bulk blood eQTL effects into immune cell subpopulations
Document Type
article
Author
Raúl Aguirre-Gamboa; Niek de Klein; Jennifer di Tommaso; Annique Claringbould; Monique GP van der Wijst; Dylan de Vries; Harm Brugge; Roy Oelen; Urmo Võsa; Maria M. Zorro; Xiaojin Chu; Olivier B. Bakker; Zuzanna Borek; Isis Ricaño-Ponce; Patrick Deelen; Cheng-Jiang Xu; Morris Swertz; Iris Jonkers; Sebo Withoff; Irma Joosten; Serena Sanna; Vinod Kumar; Hans J. P. M. Koenen; Leo A. B. Joosten; Mihai G. Netea; Cisca Wijmenga; BIOS Consortium; Lude Franke; Yang Li
Source
BMC Bioinformatics, Vol 21, Iss 1, Pp 1-23 (2020)
Subject
Language
English
ISSN
1471-2105
Abstract
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 ).