Abstract
Background: The integration of different layers of omics information is an opportunity to tackle the complexity of cardiovascular diseases (CVD) and to identify new predictive biomarkers and potential therapeutic targets. Our aim was to integrate DNA methylation and gene expression data in an effort to identify biomarkers related to cardiovascular disease risk in a community-based population. We accessed data from the Framingham Offspring Study, a cohort study with data on DNA methylation (Infinium HumanMethylation450 BeadChip; Illumina) and gene expression (Human Exon 1.0 ST Array; Affymetrix). Using the MOFA2 R package, we integrated these data to identify biomarkers related to the risk of presenting a cardiovascular event.
Results: Four independent latent factors (9, 19, 21-only in women-and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination.
Conclusions: Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function.
Keywords: Cardiovascular disease; DNA methylation; Gene expression; MOFA; Multi-omics integration; Unsupervised integration.
Results: Four independent latent factors (9, 19, 21-only in women-and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination.
Conclusions: Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function.
Keywords: Cardiovascular disease; DNA methylation; Gene expression; MOFA; Multi-omics integration; Unsupervised integration.
| Original language | English |
|---|---|
| Article number | 75 |
| Number of pages | 13 |
| Journal | Clinical Epigenetics |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 9 Apr 2021 |
Bibliographical note
Funding Information:This project was funded by the Carlos III Health Institute-European Regional Development Fund (FIS PI18/00017, CIBERCV, CIBERESP), PERIS from Agència de Gestió d’Ajuts Universitaris i de Recerca (SLT002/16/00088) and the Government of Catalonia through the Agency for Management of University and Research Grants (2017SGR946). Fernández-Sanlés was funded by the Spanish Ministry of Economy and Competitiveness (BES-2014-069718).
Funding Information:
The authors declare that they have no competing interests. The Framingham Heart Study (FHS) is conducted and supported by the US National Heart, Lung and Blood Institute (NHLBI) in collaboration with Boston University (Contract No. N01-HC-25195 and HHSN268201500001I). This manuscript was not prepared in collaboration with investigators of the FHS, has not been reviewed and/or approved by the FHS and does not necessarily reflect the opinions or views of the FHS investigators or the NHLBI.
Publisher Copyright:
© 2021, The Author(s).
Keywords
- DNA methylation
- Gene expression
- Multi-omics integration
- Cardiovascular disease
- MOFA
- Unsupervised integration
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