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Meta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function

Nature Genetics volume 42, pages 45–52 (2010)Cite this article

Abstract

Spirometric measures of lung function are heritable traits that reflect respiratory health and predict morbidity and mortality. We meta-analyzed genome-wide association studies for two clinically important lung-function measures: forced expiratory volume in the first second (FEV1) and its ratio to forced vital capacity (FEV1/FVC), an indicator of airflow obstruction. This meta-analysis included 20,890 participants of European ancestry from four CHARGE Consortium studies: Atherosclerosis Risk in Communities, Cardiovascular Health Study, Framingham Heart Study and Rotterdam Study. We identified eight loci associated with FEV1/FVC (HHIP, GPR126, ADAM19, AGER-PPT2, FAM13A, PTCH1, PID1 and HTR4) and one locus associated with FEV1 (INTS12-GSTCD-NPNT) at or near genome-wide significance (P < 5 × 10−8) in the CHARGE Consortium dataset. Our findings may offer insights into pulmonary function and pathogenesis of chronic lung disease.

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Figure 1: Meta-analyses of approximately 2,534,500 SNPs tested for association with pulmonary function measures in all participants from the CHARGE Consortium.
Figure 2: Regional association plots for loci associated with FEV1/FVC in the CHARGE consortium at or near genome-wide significance.
Figure 3: Regional association plot for the chromosome 4q24 locus associated with FEV1 in the CHARGE consortium at genome-wide significance, which includes FLJ20184, INTS12, GSTCD and NPNT.

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References

  1. Wilk, J.B. et al. Evidence for major genes influencing pulmonary function in the NHLBI family heart study. Genet. Epidemiol. 19, 81–94 (2000).

    Article  CAS  Google Scholar 

  2. Hole, D.J. et al. Impaired lung function and mortality risk in men and women: findings from the Renfrew and Paisley prospective population study. Br. Med. J. 313, 711–716 (1996).

    Article  CAS  Google Scholar 

  3. Schünemann, H.J., Dorn, J., Grant, B.J., Winkelstein, W. Jr. & Trevisan, M. Pulmonary function is a long-term predictor of mortality in the general population: 29-year follow-up of the Buffalo Health Study. Chest 118, 656–664 (2000).

    Article  Google Scholar 

  4. DeMeo, D.L. & Silverman, E.K. Genetics of chronic obstructive pulmonary disease. Semin. Respir. Crit. Care Med. 24, 151–160 (2003).

    Article  Google Scholar 

  5. Pauwels, R.A., Buist, A.S., Calverley, P.M., Jenkins, C.R. & Hurd, S.S. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. NHLBI/WHO Global Initiative for Chronic Obstructive Lung Disease (GOLD) Workshop summary. Am. J. Respir. Crit. Care Med. 163, 1256–1276 (2001).

    Article  CAS  Google Scholar 

  6. Silverman, E.K. & Sandhaus, R.A. Clinical practice. Alpha1-antitrypsin deficiency. N. Engl. J. Med. 360, 2749–2757 (2009).

    Article  CAS  Google Scholar 

  7. Crapo, R.O. Pulmonary-function testing. N. Engl. J. Med. 331, 25–30 (1994).

    Article  CAS  Google Scholar 

  8. Pellegrino, R. et al. Interpretative strategies for lung function tests. Eur. Respir. J. 26, 948–968 (2005).

    Article  CAS  Google Scholar 

  9. Wilk, J.B., Walter, R.E., Laramie, J.M., Gottlieb, D.J. & O'Connor, G.T. Framingham Heart Study genome-wide association: results for pulmonary function measures. BMC Med. Genet. 8 Suppl 1, S8 (2007).

    Article  Google Scholar 

  10. Wilk, J.B. et al. A genome-wide association study of pulmonary function measures in the Framingham Heart Study. PLoS Genet. 5, e1000429 (2009).

    Article  Google Scholar 

  11. Pillai, S.G. et al. A genome-wide association study in chronic obstructive pulmonary disease (COPD): identification of two major susceptibility loci. PLoS Genet. 5, e1000421 (2009).

    Article  Google Scholar 

  12. Berrettini, W. et al. Alpha-5/alpha-3 nicotinic receptor subunit alleles increase risk for heavy smoking. Mol. Psychiatry 13, 368–373 (2008).

    Article  CAS  Google Scholar 

  13. Saccone, S.F. et al. Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs. Hum. Mol. Genet. 16, 36–49 (2007).

    Article  CAS  Google Scholar 

  14. Psaty, B.M. et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ. Cardiovasc. Genet. 2, 73–80 (2009).

    Article  Google Scholar 

  15. Repapi, E. et al. Genome-wide association study identifies five new loci associated with lung function. Nat. Genet. advance online publication, doi:10.1038/ng.501 (13 December 2009).

  16. Kent, W.J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    Article  CAS  Google Scholar 

  17. Eyre, T.A. et al. The HUGO gene nomenclature database, 2006 updates. Nucleic Acids Res. 34, D319–D321 (2006).

    Article  CAS  Google Scholar 

  18. Ramensky, V., Bork, P. & Sunyaev, S. Human non-synonymous SNPs: server and survey. Nucleic Acids Res. 30, 3894–3900 (2002).

    Article  CAS  Google Scholar 

  19. Ferguson, G.T., Enright, P.L., Buist, A.S. & Higgins, M.W. Office spirometry for lung health assessment in adults: a consensus statement from the National Lung Health Education Program. Chest 117, 1146–1161 (2000).

    Article  CAS  Google Scholar 

  20. Hankinson, J.L., Odencrantz, J.R. & Fedan, K.B. Spirometric reference values from a sample of the general U.S. population. Am. J. Respir. Crit. Care Med. 159, 179–187 (1999).

    Article  CAS  Google Scholar 

  21. Chen, M.H., Wilson, C.W. & Chuang, P.T. SnapShot: hedgehog signaling pathway. Cell 130, 386 (2007).

    Article  CAS  Google Scholar 

  22. Warburton, D. et al. Molecular mechanisms of early lung specification and branching morphogenesis. Pediatr. Res. 57, 26R–37R (2005).

    Article  Google Scholar 

  23. Weedon, M.N. et al. Genome-wide association analysis identifies 20 loci that influence adult height. Nat. Genet. 40, 575–583 (2008).

    Article  CAS  Google Scholar 

  24. Villavicencio, E.H., Walterhouse, D.O. & Iannaccone, P.M. The sonic hedgehog-patched-gli pathway in human development and disease. Am. J. Hum. Genet. 67, 1047–1054 (2000).

    Article  CAS  Google Scholar 

  25. Whitsett, J.A., Wert, S.E. & Trapnell, B.C. Genetic disorders influencing lung formation and function at birth. Hum. Mol. Genet. 13 (Spec. No. 2), R207–R215 (2004).

    Article  CAS  Google Scholar 

  26. Lemjabbar-Alaoui, H. et al. Wnt and Hedgehog are critical mediators of cigarette smoke-induced lung cancer. PLoS One 1, e93 (2006).

    Article  Google Scholar 

  27. Pogach, M.S., Cao, Y., Millien, G., Ramirez, M.I. & Williams, M.C. Key developmental regulators change during hyperoxia-induced injury and recovery in adult mouse lung. J. Cell. Biochem. 100, 1415–1429 (2007).

    Article  CAS  Google Scholar 

  28. Hofmann, M.A. et al. RAGE and arthritis: the G82S polymorphism amplifies the inflammatory response. Genes Immun. 3, 123–135 (2002).

    Article  CAS  Google Scholar 

  29. Chavakis, T. et al. The pattern recognition receptor (RAGE) is a counterreceptor for leukocyte integrins: a novel pathway for inflammatory cell recruitment. J. Exp. Med. 198, 1507–1515 (2003).

    Article  CAS  Google Scholar 

  30. Englert, J.M. et al. A role for the receptor for advanced glycation end products in idiopathic pulmonary fibrosis. Am. J. Pathol. 172, 583–591 (2008).

    Article  CAS  Google Scholar 

  31. Queisser, M.A. et al. Loss of RAGE in pulmonary fibrosis: molecular relations to functional changes in pulmonary cell types. Am. J. Respir. Cell Mol. Biol. 39, 337–345 (2008).

    Article  CAS  Google Scholar 

  32. Lechin, F., van der Dijs, B., Orozco, B., Lechin, M. & Lechin, A.E. Increased levels of free serotonin in plasma of symptomatic asthmatic patients. Ann. Allergy Asthma Immunol. 77, 245–253 (1996).

    Article  CAS  Google Scholar 

  33. Dupont, L.J. et al. The effects of 5-HT on cholinergic contraction in human airways in vitro. Eur. Respir. J. 14, 642–649 (1999).

    Article  CAS  Google Scholar 

  34. Idzko, M. et al. The serotoninergic receptors of human dendritic cells: identification and coupling to cytokine release. J. Immunol. 172, 6011–6019 (2004).

    Article  CAS  Google Scholar 

  35. Bayer, H. et al. Serotoninergic receptors on human airway epithelial cells. Am. J. Respir. Cell Mol. Biol. 36, 85–93 (2007).

    Article  CAS  Google Scholar 

  36. Van Eerdewegh, P. et al. Association of the ADAM33 gene with asthma and bronchial hyperresponsiveness. Nature 418, 426–430 (2002).

    Article  CAS  Google Scholar 

  37. Jongepier, H. et al. Polymorphisms of the ADAM33 gene are associated with accelerated lung function decline in asthma. Clin. Exp. Allergy 34, 757–760 (2004).

    Article  CAS  Google Scholar 

  38. van Diemen, C.C. et al. A disintegrin and metalloprotease 33 polymorphisms and lung function decline in the general population. Am. J. Respir. Crit. Care Med. 172, 329–333 (2005).

    Article  Google Scholar 

  39. Dijkstra, A. et al. Expression of ADAMs (“a disintegrin and metalloprotease”) in the human lung. Virchows Arch. 454, 441–449 (2009).

    Article  CAS  Google Scholar 

  40. Bjarnadottir, T.K., Fredriksson, R. & Schioth, H.B. The adhesion GPCRs: a unique family of G protein-coupled receptors with important roles in both central and peripheral tissues. Cell. Mol. Life Sci. 64, 2104–2119 (2007).

    Article  CAS  Google Scholar 

  41. Moriguchi, T. et al. DREG, a developmentally regulated G protein-coupled receptor containing two conserved proteolytic cleavage sites. Genes Cells 9, 549–560 (2004).

    Article  CAS  Google Scholar 

  42. Gudbjartsson, D.F. et al. Many sequence variants affecting diversity of adult human height. Nat. Genet. 40, 609–615 (2008).

    Article  CAS  Google Scholar 

  43. Lettre, G. et al. Identification of ten loci associated with height highlights new biological pathways in human growth. Nat. Genet. 40, 584–591 (2008).

    Article  CAS  Google Scholar 

  44. Soranzo, N. et al. Meta-analysis of genome-wide scans for human adult stature identifies novel Loci and associations with measures of skeletal frame size. PLoS Genet. 5, e1000445 (2009).

    Article  Google Scholar 

  45. Baillat, D. et al. Integrator, a multiprotein mediator of small nuclear RNA processing, associates with the C-terminal repeat of RNA polymerase II. Cell 123, 265–276 (2005).

    Article  CAS  Google Scholar 

  46. Brandenberger, R. et al. Identification and characterization of a novel extracellular matrix protein nephronectin that is associated with integrin alpha8beta1 in the embryonic kidney. J. Cell Biol. 154, 447–458 (2001).

    Article  CAS  Google Scholar 

  47. Huang, J.T. & Lee, V. Identification and characterization of a novel human nephronectin gene in silico. Int. J. Mol. Med. 15, 719–724 (2005).

    CAS  PubMed  Google Scholar 

  48. Wey, E., Lyons, G.E. & Schafer, B.W. A human POU domain gene, mPOU, is expressed in developing brain and specific adult tissues. Eur. J. Biochem. 220, 753–762 (1994).

    Article  CAS  Google Scholar 

  49. Cardoso, W.V. Transcription factors and pattern formation in the developing lung. Am. J. Physiol. 269, L429–L442 (1995).

    CAS  PubMed  Google Scholar 

  50. Warburton, D. et al. The molecular basis of lung morphogenesis. Mech. Dev. 92, 55–81 (2000).

    Article  CAS  Google Scholar 

  51. Uhl, G.R. et al. Genome-wide association for methamphetamine dependence: convergent results from 2 samples. Arch. Gen. Psychiatry 65, 345–355 (2008).

    Article  CAS  Google Scholar 

  52. Kohansal, R. et al. The natural history of chronic airflow obstruction revisited: an analysis of the Framingham offspring cohort. Am. J. Respir. Crit. Care Med. 180, 3–10 (2009).

    Article  Google Scholar 

  53. Eisner, M.D. et al. Secondhand smoke exposure, pulmonary function, and cardiovascular mortality. Ann. Epidemiol. 17, 364–373 (2007).

    Article  Google Scholar 

  54. Anonymous. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am. J. Epidemiol. 129, 687–702 (1989).

  55. Fried, L.P. et al. The Cardiovascular Health Study: design and rationale. Ann. Epidemiol. 1, 263–276 (1991).

    Article  CAS  Google Scholar 

  56. Dawber, T.R. & Kannel, W.B. The Framingham study. An epidemiological approach to coronary heart disease. Circulation 34, 553–555 (1966).

    Article  CAS  Google Scholar 

  57. Feinleib, M., Kannel, W.B., Garrison, R.J., McNamara, P.M. & Castelli, W.P. The Framingham Offspring Study. Design and preliminary data. Prev. Med. 4, 518–525 (1975).

    Article  CAS  Google Scholar 

  58. Hofman, A. et al. The Rotterdam Study: objectives and design update. Eur. J. Epidemiol. 22, 819–829 (2007).

    Article  Google Scholar 

  59. Hofman, A., Grobbee, D.E., de Jong, P.T. & van den Ouweland, F.A. Determinants of disease and disability in the elderly: the Rotterdam Elderly Study. Eur. J. Epidemiol. 7, 403–422 (1991).

    Article  CAS  Google Scholar 

  60. The National Heart and Blood Institute. Atherosclerosis Risk in Communities (ARIC) Study. Quality assurance and quality control, version 1.0. (ARIC Coordinating Center, School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA, 1989).

  61. Enright, P.L., Kronmal, R.A., Higgins, M., Schenker, M. & Haponik, E.F. Spirometry reference values for women and men 65 to 85 years of age. Cardiovascular health study. Am. Rev. Respir. Dis. 147, 125–133 (1993).

    Article  CAS  Google Scholar 

  62. Enright, P.L., Kronmal, R.A., Higgins, M.W., Schenker, M.B. & Haponik, E.F. Prevalence and correlates of respiratory symptoms and disease in the elderly. Cardiovascular Health Study. Chest 106, 827–834 (1994).

    Article  CAS  Google Scholar 

  63. Givelber, R.J. et al. Segregation analysis of pulmonary function among families in the Framingham Study. Am. J. Respir. Crit. Care Med. 157, 1445–1451 (1998).

    Article  CAS  Google Scholar 

  64. van Durme, Y.M. et al. Prevalence, incidence, and lifetime risk for the development of COPD in the elderly: the Rotterdam study. Chest 135, 368–377 (2009).

    Article  Google Scholar 

  65. Miller, M.R. et al. Standardisation of spirometry. Eur. Respir. J. 26, 319–338 (2005).

    Article  CAS  Google Scholar 

  66. Li, Y. & Mach Abecasis, G.R. 1.0: rapid haplotype reconstruction and missing genotype inference. Am. J. Hum. Genet. S79, 2290 (2006).

    Google Scholar 

  67. Guan, Y. & Stephens, M. Practical issues in imputation-based association mapping. PLoS Genet. 4, e1000279 (2008).

    Article  Google Scholar 

  68. Pei, Y.F., Li, J., Zhang, L., Papasian, C.J. & Deng, H.W. Analyses and comparison of accuracy of different genotype imputation methods. PLoS One 3, e3551 (2008).

    Article  Google Scholar 

  69. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  Google Scholar 

  70. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  Google Scholar 

  71. The International HapMap Project. Nature 426, 789–796 (2003).

  72. Aulchenko, Y.S., Ripke, S., Isaacs, A. & van Duijn, C.M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).

    Article  CAS  Google Scholar 

  73. Team, R.D.C. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2007).

  74. Abecasis, G.R., Cardon, L.R., Cookson, W.O., Sham, P.C. & Cherny, S.S. Association analysis in a variance components framework. Genet. Epidemiol. 21 Suppl 1, S341–S346 (2001).

    Article  Google Scholar 

  75. Estrada, K. et al. GRIMP: A web- and grid-based tool for high-speed analysis of large-scale genome-wide association using imputed data. Bioinformatics 15, 2750–2752 (2009).

    Article  Google Scholar 

  76. Pe'er, I., Yelensky, R., Altshuler, D. & Daly, M.J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol. 32, 381–385 (2008).

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the Intramural Research Program of the US National Institute of Environmental Health Sciences, National Institutes of Health (NIH), Department of Health and Human Services (Z01ES043012). The ARIC study is carried out as a collaborative study supported by US NIH National Heart, Lung, and Blood Institute contracts N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022, R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and US NIH contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions, along with G. Chiu, D. Howard and M. Quibrera for their analytic contributions.

The CHS research reported in this article was supported by contract numbers N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133, grant numbers U01 HL080295 and R01 HL087652 from the US NIH National Heart, Lung, and Blood Institute, with additional contribution from the US NIH National Institute of Neurological Disorders and Stroke. A full list of principal CHS investigators and institutions can be found at http://www.chs-nhlbi.org/pi.htm. DNA handling and genotyping was supported in part by US NIH National Center for Research Resources grant M01-RR00425 to the Cedars-Sinai General Clinical Research Center Genotyping core and National Institute of Diabetes and Digestive and Kidney Diseases grant DK063491 to the Southern California Diabetes Endocrinology Research Center.

Research was conducted in part using data and resources from the FHS of the National Heart, Lung, and Blood Institute of the US NIH and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the FHS investigators participating in the SNP Health Association Resource (SHARe) project. This work was partially supported by the US NIH National Heart, Lung, and Blood Institute's FHS (Contract No. N01-HC-25195) and its contract with Affymetrix, Inc. for genotyping services (Contract No. N02-HL-6-4278). A portion of this research used the Linux Cluster for Genetic Analysis (LinGA-II), which is funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. J.B.W. is supported by a Young Clinical Scientist Award from the Flight Attendant Medical Research Institute (FAMRI).

The Rotterdam Study was supported from grants from the Netherlands Organisation of Scientific Research (NOW) Investments (175.010.2005.011, 911-03-012); the Research Institute for Diseases in the Elderly (014-93-015; RIDE2); the Netherlands Genomics Initiative (NGI)/NWO (050-060-810); Erasmus Medical Center, Erasmus University, Rotterdam, The Netherlands; Organization for the Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Ministry of Education, Culture and Science; the Ministry for Health, Welfare and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. The authors thank P. Arp, M. Jhamai, M. Moorhouse, M. Verkerk, and S. Bervoets for their help in creating the Rotterdam GWAS database; T. A. Knoch, L. V. de Zeeuw, A. Abuseiris and R. de Graaf as well as their institutions, the Erasmus Computing Grid, Rotterdam, The Netherlands, and the national German MediGRID and Services@MediGRID part of the German D-Grid (German Bundesministerium fur Forschung und Technology) (#01 AK 803 A-H and # 01 IG 07015 G) for access to grid resources.

Author information

Author notes

  1. Dana B Hancock, Mark Eijgelsheim, Jemma B Wilk and Sina A Gharib: These authors contributed equally to this work.

  2. Thomas Lumley, Bruno H C Stricker, George T O'Connor and Stephanie J London: These authors jointly directed this work.

Authors and Affiliations

  1. Department of Health and Human Services, Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA

    Dana B Hancock, Laura R Loehr & Stephanie J London

  2. Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands

    Mark Eijgelsheim, Yannick M T A van Durme, Guy G Brusselle, Cornelia M van Duijn, André G Uitterlinden, Albert Hofman, Fernando Rivadeneira & Bruno H C Stricker

  3. Departments of Neurology and Medicine, Boston University School of Medicine, Boston, Massachusetts, USA

    Jemma B Wilk

  4. Center for Lung Biology, University of Washington, Seattle, Washington, USA

    Sina A Gharib

  5. Department of Medicine, University of Washington, Seattle, Washington, USA

    Sina A Gharib, Kristin D Marciante & Bruce M Psaty

  6. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Laura R Loehr, Nora Franceschini & Kari E North

  7. Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA

    Kristin D Marciante, Bruce M Psaty & Susan R Heckbert

  8. Department of Respiratory Medicine, Ghent University and Ghent University Hospital, Ghent, Belgium

    Yannick M T A van Durme & Guy G Brusselle

  9. Department of Medicine, Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts, USA

    Ting-hsu Chen & George T O'Connor

  10. The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA

    Ting-hsu Chen & George T O'Connor

  11. Division of General Medicine, Department of Medicine, Allergy and Critical Care, College of Physicians and Surgeons, Columbia University, New York, New York, USA

    R Graham Barr

  12. Division of Pulmonary, Department of Medicine, Allergy and Critical Care, College of Physicians and Surgeons, Columbia University, New York, New York, USA

    R Graham Barr

  13. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA

    R Graham Barr

  14. Risk Assessment, Detection, and Intervention, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA

    Matthew B Schabath

  15. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    David J Couper

  16. Department of Epidemiology, University of Washington, Seattle, Washington, USA

    Bruce M Psaty & Susan R Heckbert

  17. Center for Health Studies, Group Health, Seattle, Washington, USA

    Bruce M Psaty & Susan R Heckbert

  18. Department of Health Services, University of Washington, Seattle, Washington, USA

    Bruce M Psaty

  19. Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA

    Jerome I Rotter

  20. Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands

    André G Uitterlinden, Fernando Rivadeneira & Bruno H C Stricker

  21. Departments of Medicine and Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA

    Naresh M Punjabi

  22. Division of Epidemiology and Disease Control, Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, USA

    Alanna C Morrison

  23. College of Public Health, University of Arizona, Tucson, Arizona, USA

    Paul L Enright

  24. Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Kari E North

  25. Department of Biostatistics, University of Washington, Seattle, Washington, USA

    Thomas Lumley

  26. Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging, Rotterdam, The Netherlands

    Bruno H C Stricker

  27. Inspectorate of Health Care, The Hague, The Netherlands

    Bruno H C Stricker

  28. Department of Health and Human Services, Laboratory of Respiratory Biology, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA

    Stephanie J London

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  1. Dana B Hancock
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ARIC: D.B.H., L.R.L., N.F., M.B.S., D.J.C., N.M.P., A.C.M., K.E.N. and S.J.L. CHS: S.A.G., K.D.M., R.G.B., B.M.P., J.I.R., P.L.E., S.R.H. and T.L. FHS: J.B.W., T.-h.C. and G.T.O. RS: M.E., Y.M.T.A.v.D., G.G.B., C.M.v.D., A.G.U., A.H., F.R. and B.H.C.S. Study design: T.L., B.H.C.S., G.T.O. and S.J.L. Data analysis: D.B.H., M.E., J.B.W., L.R.L., K.D.M., N.F. and T.-h.C. Drafting of manuscript: D.B.H., M.E., J.B.W. and S.A.G. Critical revision of manuscript: D.B.H., M.E., J.B.W., S.A.G., L.R.L., K.D.M., N.F., Y.M.T.A.v.D., T.-h.C., R.G.B., M.B.S., D.J.C., G.G.B., B.M.P., C.M.v.D., J.I.R., A.G.U., A.H., N.M.P., F.R., A.C.M., P.L.E., K.E.N., S.R.H., T.L., B.H.C.S., G.T.O. and S.J.L.

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Correspondence to Stephanie J London.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 2–5 and Supplementary Figures 1–4. (PDF 725 kb)

Supplementary Table 1

The top 2,000 SNPs from meta-analysis of associations with FEV1/FVC and FEV1 with P>5×10−8 in all participants from the CHARGE consortium. (XLS 430 kb)

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Hancock, D., Eijgelsheim, M., Wilk, J. et al. Meta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function. Nat Genet 42, 45–52 (2010). https://doi.org/10.1038/ng.500

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