The transcriptional landscape of age in human peripheral blood

• Type of Material: Article
• Authors: Karen Conneely, Emory University; Alicia Smith, Emory University; K Maria Nylocks, Emory University; Kerry Ressler, Emory University; Divya Mehta, Emory University; Elisabeth Binder, Emory University; Torsten Klengel, Emory University
• Language: English
• Date: 2015-10-01
• Publisher: Nature Publishing Group: Nature Communications
• Publication Version: Final Published Version
• Copyright Statement: © 2015, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/ncomms9570
• Title of Journal or Parent Work: Nature Communications
• ISSN: 2041-1723
• Volume: 6
• Start Page: 8570
• End Page: 8570
• Grant/Funding Information: 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.; The infrastructure for the CHARGE Consortium is supported in part by the National Heart, Lung, and Blood Institute grant R01HL105756.
• Keywords: HUMAN LONGEVITY; DISEASE; DROSOPHILA-MELANOGASTER; LIFE-SPAN; Multidisciplinary Sciences; METHYLATION; Science & Technology; GENOME-WIDE ASSOCIATION; CAENORHABDITIS-ELEGANS; Science & Technology - Other Topics; CELL-TYPES; OXIDATIVE STRESS; GENE-EXPRESSION CHANGES
• Research Categories: Psychology, Developmental; Psychology, General

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.