The transcriptional landscape of age in human peripheral blood

Marjolein J. Peters*, Roby Joehanes, Luke C. Pilling, Claudia Schurmann, Karen N. Conneely, Joseph Powell, Eva Reinmaa, George L. Sutphin, Alexandra Zhernakova, Katharina Schramm, Yana A. Wilson, Sayuko Kobes, Taru Tukiainen, Yolande F. Ramos, Harald H. H. Goering, Myriam Fornage, Yongmei Liu, Sina A. Gharib, Barbara E. Stranger, Philip L. De JagerAbraham Aviv, Daniel Levy, Joanne M. Murabito, Peter J. Munson, Tianxiao Huan, Albert Hofman, Andre G. Uitterlinden, Fernando Rivadeneira, Jeroen van Rooij, Lisette Stolk, Linda Broer, Michael M. P. J. Verbiest, Mila Jhamai, Pascal Arp, Andres Metspalu, Liina Tserel, Lili Milani, Nilesh J. Samani, Paert Peterson, Silva Kasela, Veryan Codd, Annette Peters, Cavin K. Ward-Caviness, Christian Herder, Melanie Waldenberger, Michael Roden, Paula Singmann, Sonja Zeilinger, Harm-Jan Westra, Lude Franke, NABEC UKBEC Consortium

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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 languageEnglish
Article number8570
Number of pages14
JournalNature Communications
Volume6
DOIs
Publication statusPublished - Oct-2015

Keywords

  • GENE-EXPRESSION CHANGES
  • GENOME-WIDE ASSOCIATION
  • DROSOPHILA-MELANOGASTER
  • HUMAN LONGEVITY
  • LIFE-SPAN
  • CAENORHABDITIS-ELEGANS
  • OXIDATIVE STRESS
  • CELL-TYPES
  • METHYLATION
  • DISEASE

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