Year: 2020
Type: preprint
Study design: Observational Study
Abstract: Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNAm signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample, (Generation Scotland; n=9,537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore – disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.
Authors: Danni A Gadd, Robert F Hillary, Daniel L McCartney, Shaza B Zaghlool, Anna J Stevenson, Cliff Nangle, Archie Campbell, Robin Flaig, Sarah E Harris, Rosie M Walker, Liu Shi, Elliot M Tucker-Drob, Christian Gieger, Annette Peters, Melanie Waldenberger, Johannes Graumann, Allan F McRae, Ian J Deary, David J Porteous, Caroline Hayward, Peter M Visscher, Simon R Cox, Kathryn L Evans, Andrew M McIntosh, Karsten Suhre, Riccardo E Marioni
Institutions: Institute of Genetics and Cancer, University of Edinburgh, Weill Cornell Medical College in Qatar, Weill Cornell Medicine, University of Oxford, The University of Texas at Austin, Helmholtz Munich, German Centre for Cardiovascular Research, German Center for Diabetes Research, Max Planck Society, Max Planck Institute for Heart and Lung Research, The University of Queensland, Institute for Molecular Bioscience, MRC Human Genetics Unit, Royal Edinburgh Hospital
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funders: Qatar Foundation, Wellcome Trust, Scottish Government, National Institute for Health and Care Research, Age UK, University of Oxford, University of Edinburgh, Bundesministerium für Bildung und Forschung, Dementias Platform UK, Oxford Health NHS Foundation Trust, Directorate for Biological Sciences, Weill Cornell Medical College, Qatar National Research Fund, Weill Cornell Medicine - Qatar, Medical Research Council, Biotechnology and Biological Sciences Research Council, Helmholtz Zentrum München
Awards: G1123430464, G1255019943, UK Dementias Platform, G1675756823, Translational Neuroscience - training the next generation of basic neuroscientists to embrace clinical research., Brain imaging and cognitive ageing in the Lothian Birth Cohort 1936: III, G2064318019, G2277832307, G259633682, G2730595978, University of Edinburgh - Translational Neuroscience, G3996179666, Stratifying Resilience and Depression Longitudinally (STRADL), University of Edinburgh - Translational Neuroscience, Brain imaging and cognitive ageing in the Lothian Birth Cohort 1936: IV, G4684299935, G4942547004, G5492613641, G5931154709, Quantitative Traits in Health and Disease, Stratifying Resilience and Depression Longitudinally (STRADL), G666546157, G6671908157, G6868616705, A genome-wide association study of non-pathological cognitive ageing, G718988473, MICA: Application for a Mental Health Data Pathfinder award (Oxford), G7749460998, G8330560607, G8733687393, G9000063527, G9364917
OpenAlex ID: W3106897828