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...
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Autor*in:	Aguirre-Gamboa, Raúl [verfasserIn] de Klein, Niek [verfasserIn] di Tommaso, Jennifer [verfasserIn] Claringbould, Annique [verfasserIn] van der Wijst, Monique GP [verfasserIn] de Vries, Dylan [verfasserIn] Brugge, Harm [verfasserIn] Oelen, Roy [verfasserIn] Võsa, Urmo [verfasserIn] Zorro, Maria M. [verfasserIn] Chu, Xiaojin [verfasserIn] Bakker, Olivier B. [verfasserIn] Borek, Zuzanna [verfasserIn] Ricaño-Ponce, Isis [verfasserIn] Deelen, Patrick [verfasserIn] Xu, Cheng-Jiang [verfasserIn] Swertz, Morris [verfasserIn] Jonkers, Iris [verfasserIn] Withoff, Sebo [verfasserIn] Joosten, Irma [verfasserIn] Sanna, Serena [verfasserIn] Kumar, Vinod [verfasserIn] Koenen, Hans J. P. M. [verfasserIn] Joosten, Leo A. B. [verfasserIn] Netea, Mihai G. [verfasserIn] Wijmenga, Cisca [verfasserIn] Franke, Lude [verfasserIn] Li, Yang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	eQTL Deconvolution Cell types Immune cells

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 21(2020), 1 vom: 12. Juni
Übergeordnetes Werk:	volume:21 ; year:2020 ; number:1 ; day:12 ; month:06

Links:	Volltext

DOI / URN:	10.1186/s12859-020-03576-5

Katalog-ID:	SPR040021238

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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). 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).
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700	1		\|a van der Wijst, Monique GP \|e verfasserin \|4 aut
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700	1		\|a Li, Yang \|e verfasserin \|4 aut
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allfields	10.1186/s12859-020-03576-5 doi (DE-627)SPR040021238 (SPR)s12859-020-03576-5-e DE-627 ger DE-627 rakwb eng 004 570 610 ASE 42.11 bkl 54.00 bkl Aguirre-Gamboa, Raúl verfasserin aut Deconvolution of bulk blood eQTL effects into immune cell subpopulations 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). eQTL (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Cell types (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213 de Klein, Niek verfasserin aut di Tommaso, Jennifer verfasserin aut Claringbould, Annique verfasserin aut van der Wijst, Monique GP verfasserin aut de Vries, Dylan verfasserin aut Brugge, Harm verfasserin aut Oelen, Roy verfasserin aut Võsa, Urmo verfasserin aut Zorro, Maria M. verfasserin aut Chu, Xiaojin verfasserin aut Bakker, Olivier B. verfasserin aut Borek, Zuzanna verfasserin aut Ricaño-Ponce, Isis verfasserin aut Deelen, Patrick verfasserin aut Xu, Cheng-Jiang verfasserin aut Swertz, Morris verfasserin aut Jonkers, Iris verfasserin aut Withoff, Sebo verfasserin aut Joosten, Irma verfasserin aut Sanna, Serena verfasserin aut Kumar, Vinod verfasserin aut Koenen, Hans J. P. M. verfasserin aut Joosten, Leo A. B. verfasserin aut Netea, Mihai G. verfasserin aut Wijmenga, Cisca verfasserin aut Franke, Lude verfasserin aut Li, Yang verfasserin aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 21(2020), 1 vom: 12. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:21 year:2020 number:1 day:12 month:06 https://dx.doi.org/10.1186/s12859-020-03576-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.11 ASE 54.00 ASE AR 21 2020 1 12 06
spelling	10.1186/s12859-020-03576-5 doi (DE-627)SPR040021238 (SPR)s12859-020-03576-5-e DE-627 ger DE-627 rakwb eng 004 570 610 ASE 42.11 bkl 54.00 bkl Aguirre-Gamboa, Raúl verfasserin aut Deconvolution of bulk blood eQTL effects into immune cell subpopulations 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). eQTL (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Cell types (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213 de Klein, Niek verfasserin aut di Tommaso, Jennifer verfasserin aut Claringbould, Annique verfasserin aut van der Wijst, Monique GP verfasserin aut de Vries, Dylan verfasserin aut Brugge, Harm verfasserin aut Oelen, Roy verfasserin aut Võsa, Urmo verfasserin aut Zorro, Maria M. verfasserin aut Chu, Xiaojin verfasserin aut Bakker, Olivier B. verfasserin aut Borek, Zuzanna verfasserin aut Ricaño-Ponce, Isis verfasserin aut Deelen, Patrick verfasserin aut Xu, Cheng-Jiang verfasserin aut Swertz, Morris verfasserin aut Jonkers, Iris verfasserin aut Withoff, Sebo verfasserin aut Joosten, Irma verfasserin aut Sanna, Serena verfasserin aut Kumar, Vinod verfasserin aut Koenen, Hans J. P. M. verfasserin aut Joosten, Leo A. B. verfasserin aut Netea, Mihai G. verfasserin aut Wijmenga, Cisca verfasserin aut Franke, Lude verfasserin aut Li, Yang verfasserin aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 21(2020), 1 vom: 12. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:21 year:2020 number:1 day:12 month:06 https://dx.doi.org/10.1186/s12859-020-03576-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.11 ASE 54.00 ASE AR 21 2020 1 12 06
allfields_unstemmed	10.1186/s12859-020-03576-5 doi (DE-627)SPR040021238 (SPR)s12859-020-03576-5-e DE-627 ger DE-627 rakwb eng 004 570 610 ASE 42.11 bkl 54.00 bkl Aguirre-Gamboa, Raúl verfasserin aut Deconvolution of bulk blood eQTL effects into immune cell subpopulations 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). eQTL (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Cell types (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213 de Klein, Niek verfasserin aut di Tommaso, Jennifer verfasserin aut Claringbould, Annique verfasserin aut van der Wijst, Monique GP verfasserin aut de Vries, Dylan verfasserin aut Brugge, Harm verfasserin aut Oelen, Roy verfasserin aut Võsa, Urmo verfasserin aut Zorro, Maria M. verfasserin aut Chu, Xiaojin verfasserin aut Bakker, Olivier B. verfasserin aut Borek, Zuzanna verfasserin aut Ricaño-Ponce, Isis verfasserin aut Deelen, Patrick verfasserin aut Xu, Cheng-Jiang verfasserin aut Swertz, Morris verfasserin aut Jonkers, Iris verfasserin aut Withoff, Sebo verfasserin aut Joosten, Irma verfasserin aut Sanna, Serena verfasserin aut Kumar, Vinod verfasserin aut Koenen, Hans J. P. M. verfasserin aut Joosten, Leo A. B. verfasserin aut Netea, Mihai G. verfasserin aut Wijmenga, Cisca verfasserin aut Franke, Lude verfasserin aut Li, Yang verfasserin aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 21(2020), 1 vom: 12. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:21 year:2020 number:1 day:12 month:06 https://dx.doi.org/10.1186/s12859-020-03576-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.11 ASE 54.00 ASE AR 21 2020 1 12 06
allfieldsGer	10.1186/s12859-020-03576-5 doi (DE-627)SPR040021238 (SPR)s12859-020-03576-5-e DE-627 ger DE-627 rakwb eng 004 570 610 ASE 42.11 bkl 54.00 bkl Aguirre-Gamboa, Raúl verfasserin aut Deconvolution of bulk blood eQTL effects into immune cell subpopulations 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). eQTL (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Cell types (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213 de Klein, Niek verfasserin aut di Tommaso, Jennifer verfasserin aut Claringbould, Annique verfasserin aut van der Wijst, Monique GP verfasserin aut de Vries, Dylan verfasserin aut Brugge, Harm verfasserin aut Oelen, Roy verfasserin aut Võsa, Urmo verfasserin aut Zorro, Maria M. verfasserin aut Chu, Xiaojin verfasserin aut Bakker, Olivier B. verfasserin aut Borek, Zuzanna verfasserin aut Ricaño-Ponce, Isis verfasserin aut Deelen, Patrick verfasserin aut Xu, Cheng-Jiang verfasserin aut Swertz, Morris verfasserin aut Jonkers, Iris verfasserin aut Withoff, Sebo verfasserin aut Joosten, Irma verfasserin aut Sanna, Serena verfasserin aut Kumar, Vinod verfasserin aut Koenen, Hans J. P. M. verfasserin aut Joosten, Leo A. B. verfasserin aut Netea, Mihai G. verfasserin aut Wijmenga, Cisca verfasserin aut Franke, Lude verfasserin aut Li, Yang verfasserin aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 21(2020), 1 vom: 12. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:21 year:2020 number:1 day:12 month:06 https://dx.doi.org/10.1186/s12859-020-03576-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.11 ASE 54.00 ASE AR 21 2020 1 12 06
allfieldsSound	10.1186/s12859-020-03576-5 doi (DE-627)SPR040021238 (SPR)s12859-020-03576-5-e DE-627 ger DE-627 rakwb eng 004 570 610 ASE 42.11 bkl 54.00 bkl Aguirre-Gamboa, Raúl verfasserin aut Deconvolution of bulk blood eQTL effects into immune cell subpopulations 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). eQTL (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Cell types (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213 de Klein, Niek verfasserin aut di Tommaso, Jennifer verfasserin aut Claringbould, Annique verfasserin aut van der Wijst, Monique GP verfasserin aut de Vries, Dylan verfasserin aut Brugge, Harm verfasserin aut Oelen, Roy verfasserin aut Võsa, Urmo verfasserin aut Zorro, Maria M. verfasserin aut Chu, Xiaojin verfasserin aut Bakker, Olivier B. verfasserin aut Borek, Zuzanna verfasserin aut Ricaño-Ponce, Isis verfasserin aut Deelen, Patrick verfasserin aut Xu, Cheng-Jiang verfasserin aut Swertz, Morris verfasserin aut Jonkers, Iris verfasserin aut Withoff, Sebo verfasserin aut Joosten, Irma verfasserin aut Sanna, Serena verfasserin aut Kumar, Vinod verfasserin aut Koenen, Hans J. P. M. verfasserin aut Joosten, Leo A. B. verfasserin aut Netea, Mihai G. verfasserin aut Wijmenga, Cisca verfasserin aut Franke, Lude verfasserin aut Li, Yang verfasserin aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 21(2020), 1 vom: 12. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:21 year:2020 number:1 day:12 month:06 https://dx.doi.org/10.1186/s12859-020-03576-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.11 ASE 54.00 ASE AR 21 2020 1 12 06
language	English
source	Enthalten in BMC bioinformatics 21(2020), 1 vom: 12. Juni volume:21 year:2020 number:1 day:12 month:06
sourceStr	Enthalten in BMC bioinformatics 21(2020), 1 vom: 12. Juni volume:21 year:2020 number:1 day:12 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	eQTL Deconvolution Cell types Immune cells
dewey-raw	004
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container_title	BMC bioinformatics
authorswithroles_txt_mv	Aguirre-Gamboa, Raúl @@aut@@ de Klein, Niek @@aut@@ di Tommaso, Jennifer @@aut@@ Claringbould, Annique @@aut@@ van der Wijst, Monique GP @@aut@@ de Vries, Dylan @@aut@@ Brugge, Harm @@aut@@ Oelen, Roy @@aut@@ Võsa, Urmo @@aut@@ Zorro, Maria M. @@aut@@ Chu, Xiaojin @@aut@@ Bakker, Olivier B. @@aut@@ Borek, Zuzanna @@aut@@ Ricaño-Ponce, Isis @@aut@@ Deelen, Patrick @@aut@@ Xu, Cheng-Jiang @@aut@@ Swertz, Morris @@aut@@ Jonkers, Iris @@aut@@ Withoff, Sebo @@aut@@ Joosten, Irma @@aut@@ Sanna, Serena @@aut@@ Kumar, Vinod @@aut@@ Koenen, Hans J. P. M. @@aut@@ Joosten, Leo A. B. @@aut@@ Netea, Mihai G. @@aut@@ Wijmenga, Cisca @@aut@@ Franke, Lude @@aut@@ Li, Yang @@aut@@
publishDateDaySort_date	2020-06-12T00:00:00Z
hierarchy_top_id	326644814
dewey-sort	14
id	SPR040021238
language_de	englisch
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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. 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author	Aguirre-Gamboa, Raúl
spellingShingle	Aguirre-Gamboa, Raúl ddc 004 bkl 42.11 bkl 54.00 misc eQTL misc Deconvolution misc Cell types misc Immune cells Deconvolution of bulk blood eQTL effects into immune cell subpopulations
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topic_title	004 570 610 ASE 42.11 bkl 54.00 bkl Deconvolution of bulk blood eQTL effects into immune cell subpopulations eQTL (dpeaa)DE-He213 Deconvolution (dpeaa)DE-He213 Cell types (dpeaa)DE-He213 Immune cells (dpeaa)DE-He213
topic	ddc 004 bkl 42.11 bkl 54.00 misc eQTL misc Deconvolution misc Cell types misc Immune cells
topic_unstemmed	ddc 004 bkl 42.11 bkl 54.00 misc eQTL misc Deconvolution misc Cell types misc Immune cells
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title	Deconvolution of bulk blood eQTL effects into immune cell subpopulations
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title_full	Deconvolution of bulk blood eQTL effects into immune cell subpopulations
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author_browse	Aguirre-Gamboa, Raúl de Klein, Niek di Tommaso, Jennifer Claringbould, Annique van der Wijst, Monique GP de Vries, Dylan Brugge, Harm Oelen, Roy Võsa, Urmo Zorro, Maria M. Chu, Xiaojin Bakker, Olivier B. Borek, Zuzanna Ricaño-Ponce, Isis Deelen, Patrick Xu, Cheng-Jiang Swertz, Morris Jonkers, Iris Withoff, Sebo Joosten, Irma Sanna, Serena Kumar, Vinod Koenen, Hans J. P. M. Joosten, Leo A. B. Netea, Mihai G. Wijmenga, Cisca Franke, Lude Li, Yang
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author-letter	Aguirre-Gamboa, Raúl
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title_sort	deconvolution of bulk blood eqtl effects into immune cell subpopulations
title_auth	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-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).
abstractGer	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_unstemmed	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).
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container_issue	1
title_short	Deconvolution of bulk blood eQTL effects into immune cell subpopulations
url	https://dx.doi.org/10.1186/s12859-020-03576-5
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author2	de Klein, Niek di Tommaso, Jennifer Claringbould, Annique van der Wijst, Monique GP de Vries, Dylan Brugge, Harm Oelen, Roy Võsa, Urmo Zorro, Maria M. Chu, Xiaojin Bakker, Olivier B. Borek, Zuzanna Ricaño-Ponce, Isis Deelen, Patrick Xu, Cheng-Jiang Swertz, Morris Jonkers, Iris Withoff, Sebo Joosten, Irma Sanna, Serena Kumar, Vinod Koenen, Hans J. P. M. Joosten, Leo A. B. Netea, Mihai G. Wijmenga, Cisca Franke, Lude Li, Yang
author2Str	de Klein, Niek di Tommaso, Jennifer Claringbould, Annique van der Wijst, Monique GP de Vries, Dylan Brugge, Harm Oelen, Roy Võsa, Urmo Zorro, Maria M. Chu, Xiaojin Bakker, Olivier B. Borek, Zuzanna Ricaño-Ponce, Isis Deelen, Patrick Xu, Cheng-Jiang Swertz, Morris Jonkers, Iris Withoff, Sebo Joosten, Irma Sanna, Serena Kumar, Vinod Koenen, Hans J. P. M. Joosten, Leo A. B. Netea, Mihai G. Wijmenga, Cisca Franke, Lude Li, Yang
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doi_str	10.1186/s12859-020-03576-5
up_date	2024-07-04T02:32:16.158Z
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Deconvolution of bulk blood eQTL effects into immune cell subpopulations

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