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    The transcriptional landscape of age in human peripheral blood

    Access Status
    Open access via publisher
    Authors
    Peters, M.
    Joehanes, R.
    Pilling, L.
    Schurmann, C.
    Conneely, K.
    Powell, J.
    Reinmaa, E.
    Sutphin, G.
    Zhernakova, A.
    Schramm, K.
    Wilson, Y.
    Kobes, S.
    Tukiainen, T.
    Ramos, Y.
    Göring, H.
    Fornage, M.
    Liu, Y.
    Gharib, S.
    Stranger, B.
    De Jager, P.
    Aviv, A.
    Levy, D.
    Murabito, J.
    Munson, P.
    Huan, T.
    Hofman, A.
    Uitterlinden, A.
    Rivadeneira, F.
    Van Rooij, J.
    Stolk, L.
    Broer, L.
    Verbiest, M.
    Jhamai, M.
    Arp, P.
    Metspalu, A.
    Tserel, L.
    Milani, L.
    Samani, N.
    Peterson, P.
    Kasela, S.
    Codd, V.
    Peters, A.
    Ward-Caviness, C.
    Herder, C.
    Waldenberger, M.
    Roden, M.
    Singmann, P.
    Zeilinger, S.
    Illig, T.
    Homuth, G.
    Grabe, H.
    Völzke, H.
    Steil, L.
    Kocher, T.
    Murray, A.
    Melzer, D.
    Yaghootkar, H.
    Bandinelli, S.
    Moses, Eric
    Kent, J.
    Curran, J.
    Johnson, M.
    Williams-Blangero, S.
    Westra, H.
    McRae, A.
    Smith, J.
    Kardia, S.
    Hovatta, I.
    Perola, M.
    Ripatti, S.
    Salomaa, V.
    Henders, A.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Peters, M. and Joehanes, R. and Pilling, L. and Schurmann, C. and Conneely, K. and Powell, J. and Reinmaa, E. et al. 2015. The transcriptional landscape of age in human peripheral blood. Nature Communications. 6: Article ID 8570.
    Source Title
    Nature Communications
    DOI
    10.1038/ncomms9570
    School
    School of Biomedical Sciences
    URI
    http://hdl.handle.net/20.500.11937/14537
    Collection
    • Curtin Research Publications
    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.

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