Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects
Background The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expres...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
van Eijk, Kristel R [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Anmerkung: |
© van Eijk et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
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| Übergeordnetes Werk: |
Enthalten in: BMC genomics - London : BioMed Central, 2000, 13(2012), 1 vom: 17. Nov. |
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| Übergeordnetes Werk: |
volume:13 ; year:2012 ; number:1 ; day:17 ; month:11 |
| Links: |
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| DOI / URN: |
10.1186/1471-2164-13-636 |
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SPR027073033 |
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| 520 | |a Background The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. Results Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. Conclusions Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated. | ||
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| 650 | 4 | |a Epigenetics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a WGCNA |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a de Jong, Simone |4 aut | |
| 700 | 1 | |a Boks, Marco PM |4 aut | |
| 700 | 1 | |a Langeveld, Terry |4 aut | |
| 700 | 1 | |a Colas, Fabrice |4 aut | |
| 700 | 1 | |a Veldink, Jan H |4 aut | |
| 700 | 1 | |a de Kovel, Carolien GF |4 aut | |
| 700 | 1 | |a Janson, Esther |4 aut | |
| 700 | 1 | |a Strengman, Eric |4 aut | |
| 700 | 1 | |a Langfelder, Peter |4 aut | |
| 700 | 1 | |a Kahn, René S |4 aut | |
| 700 | 1 | |a van den Berg, Leonard H |4 aut | |
| 700 | 1 | |a Horvath, Steve |4 aut | |
| 700 | 1 | |a Ophoff, Roel A |4 aut | |
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10.1186/1471-2164-13-636 doi (DE-627)SPR027073033 (SPR)1471-2164-13-636-e DE-627 ger DE-627 rakwb eng van Eijk, Kristel R verfasserin aut Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van Eijk et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. Results Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. Conclusions Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated. DNA methylation (dpeaa)DE-He213 Gene expression (dpeaa)DE-He213 Association (dpeaa)DE-He213 Epigenetics (dpeaa)DE-He213 WGCNA (dpeaa)DE-He213 de Jong, Simone aut Boks, Marco PM aut Langeveld, Terry aut Colas, Fabrice aut Veldink, Jan H aut de Kovel, Carolien GF aut Janson, Esther aut Strengman, Eric aut Langfelder, Peter aut Kahn, René S aut van den Berg, Leonard H aut Horvath, Steve aut Ophoff, Roel A aut Enthalten in BMC genomics London : BioMed Central, 2000 13(2012), 1 vom: 17. Nov. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:13 year:2012 number:1 day:17 month:11 https://dx.doi.org/10.1186/1471-2164-13-636 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 1 17 11 |
| spelling |
10.1186/1471-2164-13-636 doi (DE-627)SPR027073033 (SPR)1471-2164-13-636-e DE-627 ger DE-627 rakwb eng van Eijk, Kristel R verfasserin aut Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van Eijk et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. Results Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. Conclusions Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated. DNA methylation (dpeaa)DE-He213 Gene expression (dpeaa)DE-He213 Association (dpeaa)DE-He213 Epigenetics (dpeaa)DE-He213 WGCNA (dpeaa)DE-He213 de Jong, Simone aut Boks, Marco PM aut Langeveld, Terry aut Colas, Fabrice aut Veldink, Jan H aut de Kovel, Carolien GF aut Janson, Esther aut Strengman, Eric aut Langfelder, Peter aut Kahn, René S aut van den Berg, Leonard H aut Horvath, Steve aut Ophoff, Roel A aut Enthalten in BMC genomics London : BioMed Central, 2000 13(2012), 1 vom: 17. Nov. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:13 year:2012 number:1 day:17 month:11 https://dx.doi.org/10.1186/1471-2164-13-636 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 1 17 11 |
| allfields_unstemmed |
10.1186/1471-2164-13-636 doi (DE-627)SPR027073033 (SPR)1471-2164-13-636-e DE-627 ger DE-627 rakwb eng van Eijk, Kristel R verfasserin aut Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van Eijk et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. Results Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. Conclusions Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated. DNA methylation (dpeaa)DE-He213 Gene expression (dpeaa)DE-He213 Association (dpeaa)DE-He213 Epigenetics (dpeaa)DE-He213 WGCNA (dpeaa)DE-He213 de Jong, Simone aut Boks, Marco PM aut Langeveld, Terry aut Colas, Fabrice aut Veldink, Jan H aut de Kovel, Carolien GF aut Janson, Esther aut Strengman, Eric aut Langfelder, Peter aut Kahn, René S aut van den Berg, Leonard H aut Horvath, Steve aut Ophoff, Roel A aut Enthalten in BMC genomics London : BioMed Central, 2000 13(2012), 1 vom: 17. Nov. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:13 year:2012 number:1 day:17 month:11 https://dx.doi.org/10.1186/1471-2164-13-636 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 1 17 11 |
| allfieldsGer |
10.1186/1471-2164-13-636 doi (DE-627)SPR027073033 (SPR)1471-2164-13-636-e DE-627 ger DE-627 rakwb eng van Eijk, Kristel R verfasserin aut Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van Eijk et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. Results Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. Conclusions Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated. DNA methylation (dpeaa)DE-He213 Gene expression (dpeaa)DE-He213 Association (dpeaa)DE-He213 Epigenetics (dpeaa)DE-He213 WGCNA (dpeaa)DE-He213 de Jong, Simone aut Boks, Marco PM aut Langeveld, Terry aut Colas, Fabrice aut Veldink, Jan H aut de Kovel, Carolien GF aut Janson, Esther aut Strengman, Eric aut Langfelder, Peter aut Kahn, René S aut van den Berg, Leonard H aut Horvath, Steve aut Ophoff, Roel A aut Enthalten in BMC genomics London : BioMed Central, 2000 13(2012), 1 vom: 17. Nov. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:13 year:2012 number:1 day:17 month:11 https://dx.doi.org/10.1186/1471-2164-13-636 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 1 17 11 |
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10.1186/1471-2164-13-636 doi (DE-627)SPR027073033 (SPR)1471-2164-13-636-e DE-627 ger DE-627 rakwb eng van Eijk, Kristel R verfasserin aut Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © van Eijk et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Background The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. Results Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. Conclusions Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated. DNA methylation (dpeaa)DE-He213 Gene expression (dpeaa)DE-He213 Association (dpeaa)DE-He213 Epigenetics (dpeaa)DE-He213 WGCNA (dpeaa)DE-He213 de Jong, Simone aut Boks, Marco PM aut Langeveld, Terry aut Colas, Fabrice aut Veldink, Jan H aut de Kovel, Carolien GF aut Janson, Esther aut Strengman, Eric aut Langfelder, Peter aut Kahn, René S aut van den Berg, Leonard H aut Horvath, Steve aut Ophoff, Roel A aut Enthalten in BMC genomics London : BioMed Central, 2000 13(2012), 1 vom: 17. Nov. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:13 year:2012 number:1 day:17 month:11 https://dx.doi.org/10.1186/1471-2164-13-636 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2012 1 17 11 |
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Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects |
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Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects |
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van Eijk, Kristel R |
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van Eijk, Kristel R de Jong, Simone Boks, Marco PM Langeveld, Terry Colas, Fabrice Veldink, Jan H de Kovel, Carolien GF Janson, Esther Strengman, Eric Langfelder, Peter Kahn, René S van den Berg, Leonard H Horvath, Steve Ophoff, Roel A |
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10.1186/1471-2164-13-636 |
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genetic analysis of dna methylation and gene expression levels in whole blood of healthy human subjects |
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Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects |
| abstract |
Background The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. Results Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. Conclusions Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated. © van Eijk et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
| abstractGer |
Background The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. Results Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. Conclusions Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated. © van Eijk et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
| abstract_unstemmed |
Background The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. Results Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. Conclusions Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated. © van Eijk et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( |
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