Deconvolution of bulk blood eQTL effects into immune cell subpopulations.
Cell types
Deconvolution
Immune cells
eQTL
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
12 Jun 2020
12 Jun 2020
Historique:
received:
17
01
2020
accepted:
01
06
2020
entrez:
14
6
2020
pubmed:
14
6
2020
medline:
31
7
2020
Statut:
epublish
Résumé
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). 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. 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).
Sections du résumé
BACKGROUND
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
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
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).
Identifiants
pubmed: 32532224
doi: 10.1186/s12859-020-03576-5
pii: 10.1186/s12859-020-03576-5
pmc: PMC7291428
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
243Subventions
Organisme : ZonMW-VIDI
ID : 917.164.455
Organisme : ZonMW-VIDI
ID : 917.14.374
Organisme : ERC Starting Grant
ID : 637640
Organisme : ZonMW-OffRoad
ID : 91215206
Organisme : IN-CONTROL CVON
ID : CVON2012-03
Organisme : Netherlands Organization for Scientific Research (NWO) Spinoza prize
ID : NWO SPI 94-212
Organisme : ERC advanced
ID : FP/2007-2013/ERC grant 2012-322698
Organisme : European Research Council (ERC) Consolidator grant
ID : ERC 310372
Organisme : NWO Spinoza prize
ID : NWO SPI 92-266
Organisme : European Union Seventh Framework Programme grant (EU FP7) TANDEM project
ID : HEALTH-F3-2012-305279
Organisme : European Union Seventh Framework Programme grant (EU FP7) TANDEM project
ID : HEALTH-F3-2012-305279
Organisme : National Institutes of Health (NIH)
ID : DK43351, AT009708, AI137325
Organisme : CONACYT-I2T2 scholarship
ID : 382117
Organisme : Institute for Biospheric Studies, Yale University (US)
ID : NWO 184.021.007
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