Deconvolution of bulk blood eQTL effects into immune cell subpopulations
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-contex...
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BMC
2020-06-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-020-03576-5 |
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| 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 |
| author_facet | 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 |
| author_sort | Raúl Aguirre-Gamboa |
| collection | DOAJ |
| description | 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 ). |
| first_indexed | 2024-04-12T05:21:50Z |
| format | Article |
| id | doaj.art-d968288a4faf422088c9b27830e918ca |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T05:21:50Z |
| publishDate | 2020-06-01 |
| publisher | BMC |
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| series | BMC Bioinformatics |
| spelling | doaj.art-d968288a4faf422088c9b27830e918ca2022-12-22T03:46:26ZengBMCBMC Bioinformatics1471-21052020-06-0121112310.1186/s12859-020-03576-5Deconvolution of bulk blood eQTL effects into immune cell subpopulationsRaúl Aguirre-Gamboa0Niek de Klein1Jennifer di Tommaso2Annique Claringbould3Monique GP van der Wijst4Dylan de Vries5Harm Brugge6Roy Oelen7Urmo Võsa8Maria M. Zorro9Xiaojin Chu10Olivier B. Bakker11Zuzanna Borek12Isis Ricaño-Ponce13Patrick Deelen14Cheng-Jiang Xu15Morris Swertz16Iris Jonkers17Sebo Withoff18Irma Joosten19Serena Sanna20Vinod Kumar21Hans J. P. M. Koenen22Leo A. B. Joosten23Mihai G. Netea24Cisca Wijmenga25BIOS ConsortiumLude Franke26Yang Li27Department of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, Oncode Institute, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, Oncode Institute, University of Groningen, University Medical Center GroningenDepartment of Genetics, Oncode Institute, University of Groningen, University Medical Center GroningenDepartment of Genetics, Oncode Institute, University of Groningen, University Medical Center GroningenDepartment of Genetics, Oncode Institute, University of Groningen, University Medical Center GroningenDepartment of Genetics, Oncode Institute, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, Oncode Institute, University of Groningen, University Medical Center GroningenCentre for Individualised Infection Medicine (CiiM) & TWINCORE, joint ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH)Department of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical CentreDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical CentreDepartment of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical CenterDepartment of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical CenterDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenDepartment of Genetics, University of Groningen, University Medical Center GroningenAbstract 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 ).http://link.springer.com/article/10.1186/s12859-020-03576-5eQTLDeconvolutionCell typesImmune cells |
| spellingShingle | 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 Deconvolution of bulk blood eQTL effects into immune cell subpopulations BMC Bioinformatics eQTL Deconvolution Cell types Immune cells |
| title | Deconvolution of bulk blood eQTL effects into immune cell subpopulations |
| title_full | Deconvolution of bulk blood eQTL effects into immune cell subpopulations |
| title_fullStr | Deconvolution of bulk blood eQTL effects into immune cell subpopulations |
| title_full_unstemmed | Deconvolution of bulk blood eQTL effects into immune cell subpopulations |
| title_short | Deconvolution of bulk blood eQTL effects into immune cell subpopulations |
| title_sort | deconvolution of bulk blood eqtl effects into immune cell subpopulations |
| topic | eQTL Deconvolution Cell types Immune cells |
| url | http://link.springer.com/article/10.1186/s12859-020-03576-5 |
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