Population Genomics of Cardiometabolic Traits: Design of the University College London-London School of Hygiene and Tropical Medicine-Edinburgh-Bristol (UCLEB) Consortium

dataset

posted on 2013-08-20, 01:36 authored by Tina Shah, Jorgen Engmann, Caroline Dale, Sonia Shah, Jon White, Claudia Giambartolomei, Stela McLachlan, Delilah Zabaneh, Alana Cavadino, Chris Finan, Andrew Wong, Antoinette Amuzu, Ken Ong, Tom Gaunt, Michael V. Holmes, Helen Warren, Teri-Louise Davies, Fotios Drenos, Jackie Cooper, Reecha Sofat, Mark Caulfield, Shah Ebrahim, Debbie A. Lawlor, Philippa J. Talmud, Steve E. Humphries, Christine Power, Elina Hypponen, Marcus Richards, Rebecca Hardy, Diana Kuh, Nicholas Wareham, Yoav Ben-Shlomo, Ian N. Day, Peter Whincup, Richard Morris, Mark W. J. Strachan, Jacqueline Price, Meena Kumari, Mika Kivimaki, Vincent Plagnol, Frank Dudbridge, John C. Whittaker, Juan P. Casas, Aroon D. Hingorani

Substantial advances have been made in identifying common genetic variants influencing cardiometabolic traits and disease outcomes through genome wide association studies. Nevertheless, gaps in knowledge remain and new questions have arisen regarding the population relevance, mechanisms, and applications for healthcare. Using a new high-resolution custom single nucleotide polymorphism (SNP) array (Metabochip) incorporating dense coverage of genomic regions linked to cardiometabolic disease, the University College-London School-Edinburgh-Bristol (UCLEB) consortium of highly-phenotyped population-based prospective studies, aims to: (1) fine map functionally relevant SNPs; (2) precisely estimate individual absolute and population attributable risks based on individual SNPs and their combination; (3) investigate mechanisms leading to altered risk factor profiles and CVD events; and (4) use Mendelian randomisation to undertake studies of the causal role in CVD of a range of cardiovascular biomarkers to inform public health policy and help develop new preventative therapies.