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Deconvolution of bulk blood eQTL effects into immune cell subpopulations.
- Source :
-
BMC Bioinformatics . 6/12/2020, Vol. 21 Issue 1, p1-23. 23p. 1 Diagram, 1 Chart, 4 Graphs. - Publication Year :
- 2020
-
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]
- Subjects :
- *DECONVOLUTION (Mathematics)
*BLOOD
*CELLS
*LOCUS (Genetics)
*BLOOD sampling
Subjects
Details
- Language :
- English
- ISSN :
- 14712105
- Volume :
- 21
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- BMC Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 143737972
- Full Text :
- https://doi.org/10.1186/s12859-020-03576-5