Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits
MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to...
Ausführliche Beschreibung
Autor*in: |
Michelle M. J. Mens [verfasserIn] Silvana C. E. Maas [verfasserIn] Jaco Klap [verfasserIn] Gerrit Jan Weverling [verfasserIn] Paul Klatser [verfasserIn] Just P. J. Brakenhoff [verfasserIn] Joyce B. J. van Meurs [verfasserIn] André G. Uitterlinden [verfasserIn] M. Arfan Ikram [verfasserIn] Maryam Kavousi [verfasserIn] Mohsen Ghanbari [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2020 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Frontiers in Genetics - Frontiers Media S.A., 2011, 11(2020) |
---|---|
Übergeordnetes Werk: |
volume:11 ; year:2020 |
Links: |
---|
DOI / URN: |
10.3389/fgene.2020.00110 |
---|
Katalog-ID: |
DOAJ039846172 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ039846172 | ||
003 | DE-627 | ||
005 | 20230308032828.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230227s2020 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3389/fgene.2020.00110 |2 doi | |
035 | |a (DE-627)DOAJ039846172 | ||
035 | |a (DE-599)DOAJa9f2b98ff0da4b9ca197bd8ea55a6c4a | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a QH426-470 | |
100 | 0 | |a Michelle M. J. Mens |e verfasserin |4 aut | |
245 | 1 | 0 | |a Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits |
264 | 1 | |c 2020 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases. | ||
650 | 4 | |a cardiometabolic traits | |
650 | 4 | |a microRNA | |
650 | 4 | |a multi-omics data | |
650 | 4 | |a GWAS | |
650 | 4 | |a EWAS | |
653 | 0 | |a Genetics | |
700 | 0 | |a Silvana C. E. Maas |e verfasserin |4 aut | |
700 | 0 | |a Silvana C. E. Maas |e verfasserin |4 aut | |
700 | 0 | |a Jaco Klap |e verfasserin |4 aut | |
700 | 0 | |a Gerrit Jan Weverling |e verfasserin |4 aut | |
700 | 0 | |a Paul Klatser |e verfasserin |4 aut | |
700 | 0 | |a Just P. J. Brakenhoff |e verfasserin |4 aut | |
700 | 0 | |a Joyce B. J. van Meurs |e verfasserin |4 aut | |
700 | 0 | |a Joyce B. J. van Meurs |e verfasserin |4 aut | |
700 | 0 | |a André G. Uitterlinden |e verfasserin |4 aut | |
700 | 0 | |a André G. Uitterlinden |e verfasserin |4 aut | |
700 | 0 | |a M. Arfan Ikram |e verfasserin |4 aut | |
700 | 0 | |a Maryam Kavousi |e verfasserin |4 aut | |
700 | 0 | |a Mohsen Ghanbari |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Frontiers in Genetics |d Frontiers Media S.A., 2011 |g 11(2020) |w (DE-627)65799829X |w (DE-600)2606823-0 |x 16648021 |7 nnns |
773 | 1 | 8 | |g volume:11 |g year:2020 |
856 | 4 | 0 | |u https://doi.org/10.3389/fgene.2020.00110 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/a9f2b98ff0da4b9ca197bd8ea55a6c4a |z kostenfrei |
856 | 4 | 0 | |u https://www.frontiersin.org/article/10.3389/fgene.2020.00110/full |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/1664-8021 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 11 |j 2020 |
author_variant |
m m j m mmjm s c e m scem s c e m scem j k jk g j w gjw p k pk j p j b jpjb j b j v m jbjvm j b j v m jbjvm a g u agu a g u agu m a i mai m k mk m g mg |
---|---|
matchkey_str |
article:16648021:2020----::utoisnlsseelmconssoitdiha |
hierarchy_sort_str |
2020 |
callnumber-subject-code |
QH |
publishDate |
2020 |
allfields |
10.3389/fgene.2020.00110 doi (DE-627)DOAJ039846172 (DE-599)DOAJa9f2b98ff0da4b9ca197bd8ea55a6c4a DE-627 ger DE-627 rakwb eng QH426-470 Michelle M. J. Mens verfasserin aut Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases. cardiometabolic traits microRNA multi-omics data GWAS EWAS Genetics Silvana C. E. Maas verfasserin aut Silvana C. E. Maas verfasserin aut Jaco Klap verfasserin aut Gerrit Jan Weverling verfasserin aut Paul Klatser verfasserin aut Just P. J. Brakenhoff verfasserin aut Joyce B. J. van Meurs verfasserin aut Joyce B. J. van Meurs verfasserin aut André G. Uitterlinden verfasserin aut André G. Uitterlinden verfasserin aut M. Arfan Ikram verfasserin aut Maryam Kavousi verfasserin aut Mohsen Ghanbari verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00110 kostenfrei https://doaj.org/article/a9f2b98ff0da4b9ca197bd8ea55a6c4a kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00110/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 11 2020 |
spelling |
10.3389/fgene.2020.00110 doi (DE-627)DOAJ039846172 (DE-599)DOAJa9f2b98ff0da4b9ca197bd8ea55a6c4a DE-627 ger DE-627 rakwb eng QH426-470 Michelle M. J. Mens verfasserin aut Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases. cardiometabolic traits microRNA multi-omics data GWAS EWAS Genetics Silvana C. E. Maas verfasserin aut Silvana C. E. Maas verfasserin aut Jaco Klap verfasserin aut Gerrit Jan Weverling verfasserin aut Paul Klatser verfasserin aut Just P. J. Brakenhoff verfasserin aut Joyce B. J. van Meurs verfasserin aut Joyce B. J. van Meurs verfasserin aut André G. Uitterlinden verfasserin aut André G. Uitterlinden verfasserin aut M. Arfan Ikram verfasserin aut Maryam Kavousi verfasserin aut Mohsen Ghanbari verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00110 kostenfrei https://doaj.org/article/a9f2b98ff0da4b9ca197bd8ea55a6c4a kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00110/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 11 2020 |
allfields_unstemmed |
10.3389/fgene.2020.00110 doi (DE-627)DOAJ039846172 (DE-599)DOAJa9f2b98ff0da4b9ca197bd8ea55a6c4a DE-627 ger DE-627 rakwb eng QH426-470 Michelle M. J. Mens verfasserin aut Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases. cardiometabolic traits microRNA multi-omics data GWAS EWAS Genetics Silvana C. E. Maas verfasserin aut Silvana C. E. Maas verfasserin aut Jaco Klap verfasserin aut Gerrit Jan Weverling verfasserin aut Paul Klatser verfasserin aut Just P. J. Brakenhoff verfasserin aut Joyce B. J. van Meurs verfasserin aut Joyce B. J. van Meurs verfasserin aut André G. Uitterlinden verfasserin aut André G. Uitterlinden verfasserin aut M. Arfan Ikram verfasserin aut Maryam Kavousi verfasserin aut Mohsen Ghanbari verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00110 kostenfrei https://doaj.org/article/a9f2b98ff0da4b9ca197bd8ea55a6c4a kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00110/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 11 2020 |
allfieldsGer |
10.3389/fgene.2020.00110 doi (DE-627)DOAJ039846172 (DE-599)DOAJa9f2b98ff0da4b9ca197bd8ea55a6c4a DE-627 ger DE-627 rakwb eng QH426-470 Michelle M. J. Mens verfasserin aut Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases. cardiometabolic traits microRNA multi-omics data GWAS EWAS Genetics Silvana C. E. Maas verfasserin aut Silvana C. E. Maas verfasserin aut Jaco Klap verfasserin aut Gerrit Jan Weverling verfasserin aut Paul Klatser verfasserin aut Just P. J. Brakenhoff verfasserin aut Joyce B. J. van Meurs verfasserin aut Joyce B. J. van Meurs verfasserin aut André G. Uitterlinden verfasserin aut André G. Uitterlinden verfasserin aut M. Arfan Ikram verfasserin aut Maryam Kavousi verfasserin aut Mohsen Ghanbari verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00110 kostenfrei https://doaj.org/article/a9f2b98ff0da4b9ca197bd8ea55a6c4a kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00110/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 11 2020 |
allfieldsSound |
10.3389/fgene.2020.00110 doi (DE-627)DOAJ039846172 (DE-599)DOAJa9f2b98ff0da4b9ca197bd8ea55a6c4a DE-627 ger DE-627 rakwb eng QH426-470 Michelle M. J. Mens verfasserin aut Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases. cardiometabolic traits microRNA multi-omics data GWAS EWAS Genetics Silvana C. E. Maas verfasserin aut Silvana C. E. Maas verfasserin aut Jaco Klap verfasserin aut Gerrit Jan Weverling verfasserin aut Paul Klatser verfasserin aut Just P. J. Brakenhoff verfasserin aut Joyce B. J. van Meurs verfasserin aut Joyce B. J. van Meurs verfasserin aut André G. Uitterlinden verfasserin aut André G. Uitterlinden verfasserin aut M. Arfan Ikram verfasserin aut Maryam Kavousi verfasserin aut Mohsen Ghanbari verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00110 kostenfrei https://doaj.org/article/a9f2b98ff0da4b9ca197bd8ea55a6c4a kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00110/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 11 2020 |
language |
English |
source |
In Frontiers in Genetics 11(2020) volume:11 year:2020 |
sourceStr |
In Frontiers in Genetics 11(2020) volume:11 year:2020 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
cardiometabolic traits microRNA multi-omics data GWAS EWAS Genetics |
isfreeaccess_bool |
true |
container_title |
Frontiers in Genetics |
authorswithroles_txt_mv |
Michelle M. J. Mens @@aut@@ Silvana C. E. Maas @@aut@@ Jaco Klap @@aut@@ Gerrit Jan Weverling @@aut@@ Paul Klatser @@aut@@ Just P. J. Brakenhoff @@aut@@ Joyce B. J. van Meurs @@aut@@ André G. Uitterlinden @@aut@@ M. Arfan Ikram @@aut@@ Maryam Kavousi @@aut@@ Mohsen Ghanbari @@aut@@ |
publishDateDaySort_date |
2020-01-01T00:00:00Z |
hierarchy_top_id |
65799829X |
id |
DOAJ039846172 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ039846172</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308032828.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fgene.2020.00110</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ039846172</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJa9f2b98ff0da4b9ca197bd8ea55a6c4a</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QH426-470</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Michelle M. J. Mens</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">cardiometabolic traits</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">microRNA</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-omics data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GWAS</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">EWAS</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Genetics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Silvana C. E. Maas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Silvana C. E. Maas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jaco Klap</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gerrit Jan Weverling</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Paul Klatser</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Just P. J. Brakenhoff</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Joyce B. J. van Meurs</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Joyce B. J. van Meurs</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">André G. Uitterlinden</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">André G. Uitterlinden</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">M. Arfan Ikram</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Maryam Kavousi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohsen Ghanbari</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Genetics</subfield><subfield code="d">Frontiers Media S.A., 2011</subfield><subfield code="g">11(2020)</subfield><subfield code="w">(DE-627)65799829X</subfield><subfield code="w">(DE-600)2606823-0</subfield><subfield code="x">16648021</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2020</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fgene.2020.00110</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/a9f2b98ff0da4b9ca197bd8ea55a6c4a</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/article/10.3389/fgene.2020.00110/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1664-8021</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2020</subfield></datafield></record></collection>
|
callnumber-first |
Q - Science |
author |
Michelle M. J. Mens |
spellingShingle |
Michelle M. J. Mens misc QH426-470 misc cardiometabolic traits misc microRNA misc multi-omics data misc GWAS misc EWAS misc Genetics Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits |
authorStr |
Michelle M. J. Mens |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)65799829X |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut aut aut aut aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
QH426-470 |
illustrated |
Not Illustrated |
issn |
16648021 |
topic_title |
QH426-470 Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits cardiometabolic traits microRNA multi-omics data GWAS EWAS |
topic |
misc QH426-470 misc cardiometabolic traits misc microRNA misc multi-omics data misc GWAS misc EWAS misc Genetics |
topic_unstemmed |
misc QH426-470 misc cardiometabolic traits misc microRNA misc multi-omics data misc GWAS misc EWAS misc Genetics |
topic_browse |
misc QH426-470 misc cardiometabolic traits misc microRNA misc multi-omics data misc GWAS misc EWAS misc Genetics |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Frontiers in Genetics |
hierarchy_parent_id |
65799829X |
hierarchy_top_title |
Frontiers in Genetics |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)65799829X (DE-600)2606823-0 |
title |
Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits |
ctrlnum |
(DE-627)DOAJ039846172 (DE-599)DOAJa9f2b98ff0da4b9ca197bd8ea55a6c4a |
title_full |
Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits |
author_sort |
Michelle M. J. Mens |
journal |
Frontiers in Genetics |
journalStr |
Frontiers in Genetics |
callnumber-first-code |
Q |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2020 |
contenttype_str_mv |
txt |
author_browse |
Michelle M. J. Mens Silvana C. E. Maas Jaco Klap Gerrit Jan Weverling Paul Klatser Just P. J. Brakenhoff Joyce B. J. van Meurs André G. Uitterlinden M. Arfan Ikram Maryam Kavousi Mohsen Ghanbari |
container_volume |
11 |
class |
QH426-470 |
format_se |
Elektronische Aufsätze |
author-letter |
Michelle M. J. Mens |
doi_str_mv |
10.3389/fgene.2020.00110 |
author2-role |
verfasserin |
title_sort |
multi-omics analysis reveals micrornas associated with cardiometabolic traits |
callnumber |
QH426-470 |
title_auth |
Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits |
abstract |
MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases. |
abstractGer |
MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases. |
abstract_unstemmed |
MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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 |
title_short |
Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits |
url |
https://doi.org/10.3389/fgene.2020.00110 https://doaj.org/article/a9f2b98ff0da4b9ca197bd8ea55a6c4a https://www.frontiersin.org/article/10.3389/fgene.2020.00110/full https://doaj.org/toc/1664-8021 |
remote_bool |
true |
author2 |
Silvana C. E. Maas Jaco Klap Gerrit Jan Weverling Paul Klatser Just P. J. Brakenhoff Joyce B. J. van Meurs André G. Uitterlinden M. Arfan Ikram Maryam Kavousi Mohsen Ghanbari |
author2Str |
Silvana C. E. Maas Jaco Klap Gerrit Jan Weverling Paul Klatser Just P. J. Brakenhoff Joyce B. J. van Meurs André G. Uitterlinden M. Arfan Ikram Maryam Kavousi Mohsen Ghanbari |
ppnlink |
65799829X |
callnumber-subject |
QH - Natural History and Biology |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3389/fgene.2020.00110 |
callnumber-a |
QH426-470 |
up_date |
2024-07-04T00:58:21.816Z |
_version_ |
1803608079013511168 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ039846172</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308032828.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3389/fgene.2020.00110</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ039846172</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJa9f2b98ff0da4b9ca197bd8ea55a6c4a</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QH426-470</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Michelle M. J. Mens</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">cardiometabolic traits</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">microRNA</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multi-omics data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GWAS</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">EWAS</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Genetics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Silvana C. E. Maas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Silvana C. E. Maas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jaco Klap</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gerrit Jan Weverling</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Paul Klatser</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Just P. J. Brakenhoff</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Joyce B. J. van Meurs</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Joyce B. J. van Meurs</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">André G. Uitterlinden</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">André G. Uitterlinden</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">M. Arfan Ikram</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Maryam Kavousi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohsen Ghanbari</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Genetics</subfield><subfield code="d">Frontiers Media S.A., 2011</subfield><subfield code="g">11(2020)</subfield><subfield code="w">(DE-627)65799829X</subfield><subfield code="w">(DE-600)2606823-0</subfield><subfield code="x">16648021</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2020</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3389/fgene.2020.00110</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/a9f2b98ff0da4b9ca197bd8ea55a6c4a</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.frontiersin.org/article/10.3389/fgene.2020.00110/full</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1664-8021</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2020</subfield></datafield></record></collection>
|
score |
7.399534 |