Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study
Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Fr...
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| Veröffentlicht in: | Scientific reports Jg. 13; H. 1; S. 12952 - 11 |
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10.08.2023
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| Abstract | Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant
cis
CpG-transcript pairs at
p
< 1E−7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant
trans
CpG-transcript pairs at
p
< 1E−14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant
cis
and
trans
CpG-transcript pairs was completed in the Women’s Health Initiative and Jackson Heart Study cohorts. Using significant
cis
CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease. |
|---|---|
| AbstractList | Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant
cis
CpG-transcript pairs at
p
< 1E−7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant
trans
CpG-transcript pairs at
p
< 1E−14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant
cis
and
trans
CpG-transcript pairs was completed in the Women’s Health Initiative and Jackson Heart Study cohorts. Using significant
cis
CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease. Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E−7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E−14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women’s Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease. Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E-7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E-14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women's Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease.Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E-7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E-14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women's Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease. Abstract Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E−7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E−14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women’s Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease. Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E-7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E-14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women's Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease. |
| ArticleNumber | 12952 |
| Author | Yao, Chen Vasan, Ramachandran S. Li, Yun Feolo, Mike Hwang, Shih-Jen Liu, Ching-Ti Castaldi, Peter Hersh, Craig P. Munson, Peter J. Keshawarz, Amena Huan, Tianxiao Lundin, Jessica DeMeo, Dawn L. Du, Zhaohui Raffield, Laura M. Wang, Yuxuan Haessler, Jeffrey Heard-Costa, Nancy L. Bui, Helena Lin, Honghuang Courchesne, Paul Wen, Jia Joehanes, Roby Ma, Jiantao Pitsillides, Achilleas N. Kooperberg, Charles Liu, Chunyu Demirkale, Cumhur Y. Cho, Michael Sharopova, Nataliya Levy, Daniel Sooda, Meera Peloso, Gina M. Carson, April P. Tejada, Brandon Reiner, Alexander P. |
| Author_xml | – sequence: 1 givenname: Amena surname: Keshawarz fullname: Keshawarz, Amena organization: Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health – sequence: 2 givenname: Helena surname: Bui fullname: Bui, Helena organization: Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health – sequence: 3 givenname: Roby surname: Joehanes fullname: Joehanes, Roby organization: Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health – sequence: 4 givenname: Jiantao surname: Ma fullname: Ma, Jiantao organization: Framingham Heart Study, Friedman School of Nutrition Science and Policy, Tufts University – sequence: 5 givenname: Chunyu surname: Liu fullname: Liu, Chunyu organization: Framingham Heart Study, Department of Biostatistics, Boston University School of Public Health – sequence: 6 givenname: Tianxiao surname: Huan fullname: Huan, Tianxiao organization: Framingham Heart Study, Department of Ophthalmology and Visual Sciences, University of Massachusetts Chan Medical School – sequence: 7 givenname: Shih-Jen surname: Hwang fullname: Hwang, Shih-Jen organization: Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health – sequence: 8 givenname: Brandon surname: Tejada fullname: Tejada, Brandon organization: Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health – sequence: 9 givenname: Meera surname: Sooda fullname: Sooda, Meera organization: Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood 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givenname: Peter surname: Castaldi fullname: Castaldi, Peter organization: Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, General Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School – sequence: 26 givenname: Laura M. surname: Raffield fullname: Raffield, Laura M. organization: Department of Genetics, University of North Carolina at Chapel Hill – sequence: 27 givenname: Jia surname: Wen fullname: Wen, Jia organization: Department of Genetics, University of North Carolina at Chapel Hill – sequence: 28 givenname: Yun surname: Li fullname: Li, Yun organization: Department of Genetics, University of North Carolina at Chapel Hill, Department of Biostatistics, University of North Carolina at Chapel Hill – sequence: 29 givenname: Alexander P. surname: Reiner fullname: Reiner, Alexander P. organization: Fred Hutchinson Cancer Center, Department of Epidemiology, University of Washington – sequence: 30 givenname: Mike surname: Feolo fullname: Feolo, Mike organization: National Center for Biotechnology Information, National Institutes of Health – sequence: 31 givenname: Nataliya surname: Sharopova fullname: Sharopova, Nataliya organization: National Center for Biotechnology Information, National Institutes of Health – sequence: 32 givenname: Ramachandran S. surname: Vasan fullname: Vasan, Ramachandran S. organization: Framingham Heart Study, Department of Medicine, Preventive Medicine and Epidemiology, Boston University School of Medicine – sequence: 33 givenname: Dawn L. surname: DeMeo fullname: DeMeo, Dawn L. organization: Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School – sequence: 34 givenname: April P. surname: Carson fullname: Carson, April P. organization: Department of Medicine, University of Mississippi Medical Center – sequence: 35 givenname: Charles surname: Kooperberg fullname: Kooperberg, Charles organization: Fred Hutchinson Cancer Center – sequence: 36 givenname: Daniel surname: Levy fullname: Levy, Daniel email: levyd@nhlbi.nih.gov organization: Framingham Heart Study, Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health |
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| Snippet | Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study... Abstract Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The... |
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| SubjectTerms | 631/337/176 631/337/2019 Blood Cardiovascular Diseases - genetics Cell signaling CpG islands CpG Islands - genetics Deoxyribonucleic acid DNA DNA Methylation DNA sequencing Female Gene expression Gene Expression Regulation Genome-Wide Association Study Health promotion Heart Humanities and Social Sciences Humans Immune response Longitudinal Studies multidisciplinary Quantitative Trait Loci Science Science (multidisciplinary) |
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| Title | Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study |
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