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Nature Research (part of Springer Nature), Nature Communications, (6), p. 8570

DOI: 10.1038/ncomms9570

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The transcriptional landscape of age in human peripheral blood

Journal article published in 2015 by Marcel van der Brug, Marjolein J. Peters, Roby Joehanes, Luke C. Pilling ORCID, Claudia Schurmann ORCID, Karen N. Conneely, Joseph Powell, Eva Reinmaa, Michael A. Nalls, Dena G. Hernandez, George L. Sutphin, Mark R. Cookson, Alexandra Zhernakova, Consortium Nabec/Ukbec, Katharina Schramm and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

This is the final version of the article. Available from the publisher via the DOI in this record. ; 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. ; The infrastructure for the CHARGE Consortium is supported in part by the National Heart, Lung, and Blood Institute grant R01HL105756. This study was funded by the European Commission (HEALTH-F2-2008-201865, GEFOS; HEALTH-F2-2008 35627, TREAT-OA), the Netherlands Organization for Scientific Research (NWO) Investments (nr. 175.010.2005.011, 911-03-012), the Netherlands Consortium for Healthy Aging , the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) project nr. 050-060-810 and VIDI grant 917103521. Additional acknowledgments to specific cohorts and their support are found in Supplementary Notes 1 and 2