The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank
Open accessAbstract
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.
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Miller, M. R., Hankinson, J., Brusasco, V., Burgos, F., Casaburi, R., Coates, A., Crapo, R., Enright, P., van der Grinten, C. P. M., Gustafsson, P., et al. (2005) · European Respiratory Journal · doi:10.1183/09031936.05.00034805 · Cited by 15,627
Lozano, R., Naghavi, M., Foreman, K., Lim, S., Shibuya, K., Aboyans, V., Abraham, J., Adair, T., Aggarwal, R., Ahn, S. Y., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61728-0 · Cited by 14,070
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821
Hundreds of variants clustered in genomic loci and biological pathways affect human height
Lango Allen, H., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., Willer, C. J., Jackson, A. U., Vedantam, S., Raychaudhuri, S., et al. (2010) · Nature · doi:10.1038/nature09410 · Cited by 1,945
Self-citationGenetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712
Self-citationThe UK10K project identifies rare variants in health and disease
The UK10K Consortium, Writing group, Walter, K., Min, J. L., Huang, J., Crooks, L., Memari, Y., McCarthy, S., Perry, J. R. B., Xu, C., et al. (2015) · Nature · doi:10.1038/nature14962 · Cited by 1,040
Self-citationGenome-wide association study identifies multiple susceptibility loci for pulmonary fibrosis
Fingerlin, T. E., Murphy, E., Zhang, W., Peljto, A. L., Brown, K. K., Steele, M. P., Loyd, J. E., Cosgrove, G. P., Lynch, D., Groshong, S., et al. (2013) · Nature Genetics · doi:10.1038/ng.2609 · Cited by 809
Sequence variants at CHRNB3–CHRNA6 and CYP2A6 affect smoking behavior
The ENGAGE Consortium, Thorgeirsson, T. E., Gudbjartsson, D. F., Surakka, I., Vink, J. M., Amin, N., Geller, F., Sulem, P., Rafnar, T., Esko, T., et al. (2010) · Nature Genetics · doi:10.1038/ng.573 · Cited by 689
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Self-citationThe Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease
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Self-citationAfter asthma: redefining airways diseases
Pavord, I. D., Beasley, R., Agusti, A., Anderson, G. P., Bel, E., Brusselle, G., Cullinan, P., Custovic, A., Ducharme, F. M., Fahy, J. V., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(17)30879-6 · Cited by 1,073
Self-citationChronic obstructive pulmonary disease
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Metadata
- Authors
- Wain, L. V., Shrine, N., Miller, S., Jackson, V. E., Ntalla, I., Artigas, M. S., Billington, C. K., Kheirallah, A. K., Allen, R., Cook, J. P., Probert, K., Obeidat, M., Bossé, Y., Hao, K., Postma, D. S., Paré, P. D., Ramasamy, A., Mägi, R., Mihailov, E., Reinmaa, E., Melén, E., O'Connell, J., Frangou, E., Delaneau, O., Freeman, C., Petkova, D., McCarthy, M., Sayers, I., Deloukas, P., Hubbard, R., Pavord, I., Hansell, A. L., Thomson, N. C., Zeggini, E., Morris, A. P., Marchini, J., Strachan, D. P., Tobin, M. D., & Hall, I. P.
- Venue
- The Lancet Respiratory Medicine
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 3
- Issue
- 10
- Pages
- 769-781
- ISSN
- 2213-2600 (ISSN-L)
- OpenAlex
- W2149200222
- Cited by
- 400
- References
- 12
- FWCI
- 35.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- CoChat. (2026). Citation check: Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. https://app.cochat.ai/papers/10.1016/s2213-2600(15)00283-0
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- Wain, L. V., Shrine, N., Miller, S., Jackson, V. E., Ntalla, I., Artigas, M. S., Billington, C. K., Kheirallah, A. K., Allen, R., Cook, J. P., Probert, K., Obeidat, M., Bossé, Y., Hao, K., Postma, D. S., Paré, P. D., Ramasamy, A., Mägi, R., Mihailov, E., . . . Hall, I. P. (2015). Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. The Lancet Respiratory Medicine. https://doi.org/10.1016/s2213-2600(15)00283-0
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