ArticleBMC bioinformatics2020
Deconvolution of bulk blood eQTL effects into immune cell subpopulations.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data.Genome biology · 2023Pooled it
- Identifying causal genetic variants for high-altitude adaptation through blood eQTL analysis in plateau populations.Communications biology · 2026Article
- Article
- Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.Genes · 2026Article
- Review
- Multi-omics Data Integration.Advances in experimental medicine and biology · 2026Review
- Transcriptomics and eQTLs reveal inflammatory heterogeneity in the duodenal lining in coeliac disease.Genes and immunity · 2025Article
- Dynamic and reversible transcriptomic age shifts induced by COVID-19 in Korean whole blood.Aging · 2025Article
- Sex differences in adenosine deaminase activity associate with disparities in SARS-CoV-2 innate immunity.iScience · 2025Article
- Trans-eQTL hotspots shape complex traits by modulating cellular states.Cell genomics · 2025Article
- The identification of blood-derived response eQTLs reveals complex effects of regulatory variants on inflammatory and infectious disease risk.PLoS genetics · 2025Article
- Multiomics reveal key inflammatory drivers of severe obesity: IL4R, LILRA5, and OSM.Cell genomics · 2025Article
- Oxidative phosphorylation is a key feature of neonatal monocyte immunometabolism promoting myeloid differentiation after birth.Nature communications · 2025Article
- A unified framework for cell-type-specific eQTL prioritization by integrating bulk and scRNA-seq data.American journal of human genetics · 2025Article
- Dog10K: an integrated Dog10K database summarizing canine multi-omics.Nucleic acids research · 2025Article
- Hierarchical annotation of eQTLs by H-eQTL enables identification of genes with cell type-divergent regulation.Genome biology · 2024Article
- Antibody signatures against viruses and microbiome reflect past and chronic exposures and associate with aging and inflammation.iScience · 2024Article
- Quantitative and Longitudinal Assessment of Systemic Innate Immunity in Health and Disease Using a 2D Gene Model.Biomedicines · 2024Article
- Adenylate cyclase activates the cAMP signalling pathway to enhance platelet-rich plasma-treated Achilles tendon disease, a theoretical bioinformatics-based study.World journal of orthopedics · 2024Article
- Cell-type deconvolution of bulk-blood RNA-seq reveals biological insights into neuropsychiatric disorders.American journal of human genetics · 2024Article
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Authors and funding
29 authors.
Funding
Abstract
backgroundExpression 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).
resultsThe 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.
conclusionsDecon2 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).
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