Deconvolution of bulk blood eQTL effects into immune cell subpopulations
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).
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:21 |
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| Enthalten in: |
BMC bioinformatics - 21(2020), 1 vom: 12. Juni, Seite 243 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Aguirre-Gamboa, Raúl [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 30.07.2020 Date Revised 30.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12859-020-03576-5 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM31111363X |
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| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Cell types | |
| 650 | 4 | |a Deconvolution | |
| 650 | 4 | |a Immune cells | |
| 650 | 4 | |a eQTL | |
| 700 | 1 | |a de Klein, Niek |e verfasserin |4 aut | |
| 700 | 1 | |a di Tommaso, Jennifer |e verfasserin |4 aut | |
| 700 | 1 | |a Claringbould, Annique |e verfasserin |4 aut | |
| 700 | 1 | |a van der Wijst, Monique Gp |e verfasserin |4 aut | |
| 700 | 1 | |a de Vries, Dylan |e verfasserin |4 aut | |
| 700 | 1 | |a Brugge, Harm |e verfasserin |4 aut | |
| 700 | 1 | |a Oelen, Roy |e verfasserin |4 aut | |
| 700 | 1 | |a Võsa, Urmo |e verfasserin |4 aut | |
| 700 | 1 | |a Zorro, Maria M |e verfasserin |4 aut | |
| 700 | 1 | |a Chu, Xiaojin |e verfasserin |4 aut | |
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