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pmid:33109080
Blood-based epigenetic estimators of chronological age in human adults using DNA methylation data from the Illumina MethylationEPIC array.
Yunsung Lee · Kristine L Haftorn · William R P Denault · Haakon E Nustad · Christian M Page · Robert Lyle · Sindre Lee-Ødegård · Gunn-Helen Moen · Rashmi B Prasad · Leif C Groop · Line Sletner · Christine Sommer · Maria C Magnus · Håkon K Gjessing · Jennifer R Harris · Per Magnus · Siri E Håberg · Astanand Jugessur · Jon Bohlin
BMC genomics · 2020 doi:10.1186/s12864-020-07168-8
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Complete file
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Existing epigenetic clocks are based on CpGs from the Illumina HumanMethylation450 BeadChip (450 K) which has now been replaced by the latest platform, Illumina MethylationEPIC BeadChip (EPIC). Thus, it remains unclear to what extent EPIC contributes to increased precision and accuracy in the prediction of chronological age.\nRESULTS: We developed three blood-based epigenetic clocks for human adults using EPIC-based DNA methylation (DNAm) data from the Norwegian Mother, Father and Child Cohort Study (MoBa) and the Gene Expression Omnibus (GEO) public repository: 1) an Adult Blood-based EPIC Clock (ABEC) trained on DNAm data from MoBa (n = 1592, age-span: 19 to 59 years), 2) an extended ABEC (eABEC) trained on DNAm data from MoBa and GEO (n = 2227, age-span: 18 to 88 years), and 3) a common ABEC (cABEC) trained on the same training set as eABEC but restricted to CpGs common to 450 K and EPIC. Our clocks showed high precision (Pearson correlation between chronological and epigenetic age (r) > 0.94) in independent cohorts, including GSE111165 (n = 15), GSE115278 (n = 108), GSE132203 (n = 795), and the Epigenetics in Pregnancy (EPIPREG) study of the STORK Groruddalen Cohort (n = 470). This high precision is unlikely due to the use of EPIC, but rather due to the large sample size of the training set.\nCONCLUSIONS: Our ABECs predicted adults' chronological age precisely in independent cohorts. As EPIC is now the dominant platform for measuring DNAm, these clocks will be useful in further predictions of chronological age, age-related diseases, and mortality.", "claims": [{"id": "claim-method", "statement": "The mgcv R package [51] was used to fit the smoothing splines in Fig. 4.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33109080", "locator": "sections[14] \"Comparison between EPIC-CpG clock and common CpG clock\" paragraph 2", "quote": "The mgcv R package [51] was used to fit the smoothing splines in Fig. 4."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The MoBa data can be accessed by applying directly to the Norwegian Institute of Public Health, http://www.fhi.no/en/. The EPIPREG data can be accessed by contacting Dr. Christine Sommer, Oslo University Hospital, https://www.oslodiabetes.no/christine-sommer. The publicly available DNAm data in this study (accession numbers: GSE116339, GSE111165, GSE115278, and GSE132203) are accessible on the GEO repository, https://www.ncbi.nlm.nih.gov/geo/. Public access to the GEO repository is open, and thus administrative permission to access and use the data is not needed.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33109080", "locator": "sections[23] \"notes\" paragraph 1", "quote": "The MoBa data can be accessed by applying directly to the Norwegian Institute of Public Health, http://www.fhi.no/en/. The EPIPREG data can be accessed by contacting Dr. Christine Sommer, Oslo University Hospital, https://www.oslodiabetes.no/christine-sommer. The publicly available DNAm data in this study (accession numbers: GSE116339, GSE111165, GSE115278, and GSE132203) are accessible on the GEO repository, https://www.ncbi.nlm.nih.gov/geo/. 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