Journal article
New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries
Nick Shrine, Anna L Guyatt, A Mesut Erzurumluoglu, Victoria E Jackson, Brian D Hobbs, Carl A Melbourne, Chiara Batini, Katherine A Fawcett, Kijoung Song, Phuwanat Sakornsakolpat, Xingnan Li, Ruth Boxall, Nicola F Reeve, Ma'en Obeidat, Jing Hua Zhao, Matthias Wielscher, Stefan Weiss, Katherine A Kentistou, James P Cook, Benjamin B Sun Show all
NATURE GENETICS | NATURE PUBLISHING GROUP | Published : 2019
Abstract
Reduced lung function predicts mortality and is key to the diagnosis of chronic obstructive pulmonary disease (COPD). In a genome-wide association study in 400,102 individuals of European ancestry, we define 279 lung function signals, 139 of which are new. In combination, these variants strongly predict COPD in independent populations. Furthermore, the combined effect of these variants showed generalizability across smokers and never smokers, and across ancestral groups. We highlight biological pathways, known and potential drug targets for COPD and, in phenome-wide association studies, autoimmune-related and other pleiotropic effects of lung function-associated variants. This new genetic ev..
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Awarded by Wellcome Trust
Awarded by Medical Research Council (MRC)
Awarded by BBSRC
Awarded by ESRC
Awarded by MRC
Funding Acknowledgements
This research has been conducted using the UK Biobank Resource under applications 648, 4892 and 26041. L. Wain holds a GSK/British Lung Foundation Chair in Respiratory Research. M. Tobin is supported by a Wellcome Trust Investigator Award (WT202849/Z/16/Z). M. Tobin and L. Wain have been supported by the Medical Research Council (MRC) (MR/N011317/1). The research was partially supported by the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre; the views expressed are those of the author(s) and not necessarily those of the National Health Service (NHS), the NIHR or the Department of Health. I.H. was partially supported by the NIHR Nottingham Biomedical Research Centre; the views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. This research used the ALICE and SPECTRE High Performance Computing Facilities at the University of Leicester. Additional acknowledgments and funding details for other co-authors and contributing studies (including the SpiroMeta consortium) are in the Supplementary Note.