The transcriptional landscape of age in human peripheral blood

Marjolein Peters, R Joehanes, LC Pilling, C Schurmann, KN Conneely, J Powell, E Reinmaa, GL Sutphin, A Zhernakova, K Schramm, YA Wilson, S Kobes, T Tukiainen, YF Ramos, HHH Goring, M Fornage, YM Liu, SA Gharib, BE Stranger, PL De JagerA Aviv, D Levy, JM Murabito, PJ Munson, T Huan, Bert Hofman, André Uitterlinden, Fernando Rivadeneira, J van Rooij, Lisette Stolk, Linda Broer, Michael Verbiest, M Jhamai, Pascal Arp, A Metspalu, L Tserel, L Milani, NJ Samani, P Peterson, S Kasela, V Codd, A Peters, CK Ward-Caviness, Cindy Herder, M Waldenberger, M Roden, P Singmann, S Zeilinger, T Illig, G Homuth, HJ Grabe, H Voelzke, L Steil, T Kocher, A Murray, D Melzer, H Yaghootkar, S Bandinelli, EK Moses, JW Kent, JE Curran, MP Johnson, S Williams-Blangero, HJ Westra, AF Mcrae, JA Smith, SLR Kardia, I Hovatta, M Perola, S Ripatti, V Salomaa, AK Henders, NG Martin, AK Smith, D Mehta, EB Binder, KM Nylocks, EM Kennedy, T Klengel, J Ding, AM Suchy-Dicey, DA Enquobahrie, J Brody, JI Rotter, YDI Chen, J Houwing-Duistermaat, M Kloppenburg, PE (Eline) Slagboom, Q Helmer, W den Hollander, S Bean, T Raj, N Bakhshi, QP Wang, LJ Oyston, BM Psaty, RP Tracy, GW Montgomery, ST Turner, J Blangero, I Meulenbelt, KJ Ressler, Jiaqi Yang, L Franke, J Kettunen, PM Visscher, GG Neely, R Korstanje, RL Hanson, H Prokisch, L Ferrucci, T Esko, A Teumer, Joyce van Meurs, D Andrew

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Abstract

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the 'transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.
Original languageUndefined/Unknown
JournalNature Communications
Volume6
DOIs
Publication statusPublished - 2015

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