Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank
By L. Wain, N. Shrine, Suzanne M. Miller, V. Jackson, I. Ntalla, M. Artigas, C. Billington, Abdul Kader Kheirallah, R. Allen, J. Cook and 29 more. 2015
386 citations recorded by the source corpus.
Summary Background Understanding the genetic basis of airflow obstruction and smoking behaviour is key to determining the pathophysiology of chronic obstructive pulmonary disease (COPD). We used UK Biobank data to study the genetic causes of smoking behaviour and lung health. Methods We sampled individuals of European ancestry from UK Biobank, from the middle and extremes of the forced expiratory volume in 1 s (FEV1) distribution among heavy smokers (mean 35 pack-years) and never smokers. We developed a custom array for UK Biobank to provide optimum genome-wide coverage of common and low-frequency variants, dense coverage of genomic regions already implicated in lung health and disease, and to assay rare coding variants relevant to the UK population. We investigated whether there were shared genetic causes between different phenotypes defined by extremes of FEV1. We also looked for novel variants associated with extremes of FEV1 and smoking behaviour and assessed regions of the genome that had already shown evidence for a role in lung health and disease. We set genome-wide significance at p<5 × 10−8. Findings UK Biobank participants were recruited from March 15, 2006, to July 7, 2010. Sample selection for the UK BiLEVE study started on Nov 22, 2012, and was completed on Dec 20, 2012. We selected 50 008 unique samples: 10 002 individuals with low FEV1, 10 000 with average FEV1, and 5002 with high FEV1 from each of the heavy smoker and never smoker groups. We noted a substantial sharing of genetic causes of low FEV1 between heavy smokers and never smokers (p=2·29 × 10−16) and between individuals with and without doctor-diagnosed asthma (p=6·06 × 10−11). We discovered six novel genome-wide significant signals of association with extremes of FEV1, including signals at four novel loci (KANSL1, TSEN54, TET2, and RBM19/TBX5) and independent signals at two previously reported loci (NPNT and HLA-DQB1/HLA-DQA2). These variants also showed association with COPD, including in individuals with no history of smoking. The number of copies of a 150 kb region containing the 5′ end of KANSL1, a gene that is important for epigenetic gene regulation, was associated with extremes of FEV1. We also discovered five new genome-wide significant signals for smoking behaviour, including a variant in NCAM1 (chromosome 11) and a variant on chromosome 2 (between TEX41 and PABPC1P2) that has a trans effect on expression of NCAM1 in brain tissue. Interpretation By sampling from the extremes of the lung function distribution in UK Biobank, we identified novel genetic causes of lung function and smoking behaviour. These results provide new insight into the specific mechanisms underlying airflow obstruction, COPD, and tobacco addiction, and show substantial shared genetic architecture underlying airflow obstruction across individuals, irrespective of smoking behaviour and other airway disease. Funding Medical Research Council.
Subfields: Genetics, Physiology, Pulmonary and Respiratory Medicine
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In Genetics
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Citations in the corpus
- Comparison of DNA quantification methodology used in the DNA extraction protocol for the UK Biobank cohort (2017, background): “Genotyping was performed at the ARSL as per the Manufacturer’s Instructions [13] using the UK Biobank Axiom® Array or UK BiLEVE Axiom® Array.”
- Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes (2015, background): “Secondly, prospective studies such as UK Biobank [96, 97] are collecting genetic and highdimensional phenotypic data for hundreds of thousands of samples.”
- Collider scope: when selection bias can substantially influence observed associations (2016, methodology): “genetic studies using the first release of genome-wide association data in UK Biobank, which used two genotyping arrays, one of which was applied to a nested case-control study of smoking and lung function (UK BiLEVE).(41) The first”