Genome-Wide Association Studies Identify CHRNA5/3 and HTR4 in the Development of Airflow Obstruction

Jemma B Wilk; Nick R G Shrine; Laura R Loehr; Jing Hua Zhao; Ani Manichaikul; Lorna M Lopez; Albert Vernon Smith; Susan R Heckbert; Joanna Smolonska; Wenbo Tang; Daan W Loth; Ivan Curjuric; Jennie Hui; Michael H Cho; Jeanne C Latourelle; Amanda P Henry; Melinda Aldrich; Per Bakke; Terri H Beaty; Amy R Bentley; Ingrid B Borecki; Guy G Brusselle; Kristin M Burkart; Ting-hsu Chen; David Couper; James D Crapo; Gail Davies; Josée Dupuis; Nora Franceschini; Amund Gulsvik; Dana B Hancock; Tamara B Harris; Albert Hofman; Medea Imboden; Alan L James; Kay-Tee Khaw; Lies Lahousse; Lenore J Launer; Augusto Litonjua; Yongmei Liu; Kurt K Lohman; David A Lomas; Thomas Lumley; Kristin D Marciante; Wendy L McArdle; Bernd Meibohm; Alanna C Morrison; Arthur W Musk; Richard H Myers; Kari E North; Dirkje S Postma; Bruce M Psaty; Stephen S Rich; Fernando Rivadeneira; Thierry Rochat; Jerome I Rotter; María Soler Artigas; John M Starr; André G Uitterlinden; Nicholas J Wareham; Cisca Wijmenga; Pieter Zanen; Michael A Province; Edwin K Silverman; Ian J Deary; Lyle J Palmer; Patricia A Cassano; Vilmundur Gudnason; R Graham Barr; Ruth J F Loos; David P Strachan; Stephanie J London; H Marike Boezen; Nicole Probst-Hensch; Sina A Gharib; Ian P Hall; George T O’Connor; Martin D Tobin; Bruno H Stricker

doi:10.1164/rccm.201202-0366OC

. 2012 Oct 1;186(7):622–632. doi: 10.1164/rccm.201202-0366OC

Genome-Wide Association Studies Identify CHRNA5/3 and HTR4 in the Development of Airflow Obstruction

Jemma B Wilk ^1,^2,^*,^✉, Nick R G Shrine ^3,^*, Laura R Loehr ^4,^*, Jing Hua Zhao ^5,^*, Ani Manichaikul ^6,^7,^*, Lorna M Lopez ^8,^9,^*, Albert Vernon Smith ^10,^11,^*, Susan R Heckbert ^12,¹³, Joanna Smolonska ¹⁴, Wenbo Tang ¹⁵, Daan W Loth ^16,¹⁷, Ivan Curjuric ^18,¹⁹, Jennie Hui ^20,^21,^22,²³, Michael H Cho ^24,^25,^✉, Jeanne C Latourelle ²⁶, Amanda P Henry ²⁷, Melinda Aldrich ²⁸, Per Bakke ²⁹, Terri H Beaty ³⁰, Amy R Bentley ³¹, Ingrid B Borecki ³², Guy G Brusselle ³³, Kristin M Burkart ³⁴, Ting-hsu Chen ^35,³⁶, David Couper ³⁷, James D Crapo ³⁸, Gail Davies ^8,⁹, Josée Dupuis ^2,³⁹, Nora Franceschini ⁴, Amund Gulsvik ²⁹, Dana B Hancock ^40,⁴¹, Tamara B Harris ⁴², Albert Hofman ^16,⁴³, Medea Imboden ^18,¹⁹, Alan L James ^20,^44,⁴⁵, Kay-Tee Khaw ⁴⁶, Lies Lahousse ^16,³³, Lenore J Launer ⁴², Augusto Litonjua ^24,²⁵, Yongmei Liu ⁴⁷, Kurt K Lohman ⁴⁸, David A Lomas ⁴⁹, Thomas Lumley ⁵⁰, Kristin D Marciante ¹², Wendy L McArdle ⁵¹, Bernd Meibohm ⁵², Alanna C Morrison ⁵³, Arthur W Musk ^20,^23,^45,⁵⁴, Richard H Myers ²⁶, Kari E North ⁴, Dirkje S Postma ⁵⁵, Bruce M Psaty ^12,^13,⁵⁶, Stephen S Rich ⁶, Fernando Rivadeneira ^16,^43,⁵⁷, Thierry Rochat ⁵⁸, Jerome I Rotter ⁵⁹, María Soler Artigas ³, John M Starr ^8,⁶⁰, André G Uitterlinden ^16,^43,⁵⁷, Nicholas J Wareham ⁵, Cisca Wijmenga ¹⁴, Pieter Zanen ⁶¹, Michael A Province ³², Edwin K Silverman ^24,²⁵, Ian J Deary ^8,⁹, Lyle J Palmer ^62,⁶³, Patricia A Cassano ^15,⁶⁴, Vilmundur Gudnason ^10,¹¹, R Graham Barr ^34,⁶⁵, Ruth J F Loos ⁵, David P Strachan ⁶⁶, Stephanie J London ⁴⁰, H Marike Boezen ^67,^*, Nicole Probst-Hensch ^18,^19,^*, Sina A Gharib ^68,^*, Ian P Hall ^27,^*, George T O’Connor ^2,^36,^*, Martin D Tobin ^3,^*, Bruno H Stricker ^16,^17,^43,^57,^69,^*

¹Division of Aging, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts

²The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts

³Departments of Health Sciences and Genetics, University of Leicester, Leicester, United Kingdom

⁴Department of Epidemiology University of North Carolina Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

⁵MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, United Kingdom

⁶Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia

⁷Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia

⁸Centre for Cognitive Ageing and Cognitive Epidemiology The University of Edinburgh, Edinburgh, United Kingdom

⁹Department of Psychology, The University of Edinburgh, Edinburgh, United Kingdom

¹⁰Icelandic Heart Association, Kopavogur, Iceland

¹¹University of Iceland, Reykjavik, Iceland

¹²Cardiovascular Health Research Unit, Department of Medicine University of Washington, Seattle, Washington

¹³Department of Epidemiology, University of Washington, Seattle, Washington

¹⁴Department of Genetics University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

¹⁵Division of Nutritional Sciences, Cornell University, Ithaca, New York

¹⁶Department of Epidemiology Erasmus Medical Center, Rotterdam, The Netherlands

¹⁷Inspectorate of Healthcare, The Hague, The Netherlands

¹⁸Swiss Tropical and Public Health Institute SwissTPH, Basel, Switzerland

¹⁹University of Basel, Basel, Switzerland

²⁰Busselton Population Medical Research Institute Sir Charles Gairdner Hospital, Nedlands, Australia

²¹PathWest Laboratory Medicine of Western Australia, QEII Medical Centre, Nedlands, Australia

²²School of Pathology and Laboratory Medicine University of Western Australia, Nedlands, Australia

²³School of Population Health, University of Western Australia, Nedlands, Australia

²⁴Channing Laboratory, Brigham and Women\x{2019}s Hospital and Harvard Medical School, Boston, Massachusetts

²⁵Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women\x{2019}s Hospital and Harvard Medical School, Boston, Massachusetts

²⁶Department of Neurology, Boston University School of Medicine, Boston, Massachusetts

²⁷Division of Therapeutics and Molecular Medicine, University of Nottingham, Nottingham, United Kingdom

²⁸Department of Thoracic Surgery and Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee

²⁹Department of Thoracic Medicine, Haukeland University Hospital and Institute of Medicine, University of Bergen, Bergen, Norway

³⁰Johns Hopkins School of Public Health, Baltimore, Maryland

³¹Center for Research in Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland

³²Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri

³³Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium

³⁴Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York

³⁵Section of Pulmonary and Critical Care Medicine, Department of Medicine, Veterans Administration Boston Healthcare System, Boston, Massachusetts

³⁶Section of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts

³⁷Department of Biostatistics, University of North Carolina Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

³⁸National Jewish Health, Denver, Colorado

³⁹Biostatistics Department, Boston University School of Public Health, Boston, Massachusetts

⁴⁰Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina

⁴¹Behavioral Health Epidemiology Program, Research Triangle Institute International, Research Triangle Park, North Carolina

⁴²Laboratory of Epidemiology, Demography, and Biometry, Intramural Research Program, National Institute on Aging, Bethesda, Maryland

⁴³Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands

⁴⁴Department of Pulmonary Physiology and Sleep Medicine, Sir Charles Gairdner Hospital, Nedlands, Australia

⁴⁵The School of Medicine and Pharmacology, University of Western Australia, Nedlands, Australia

⁴⁶Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom

⁴⁷Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina

⁴⁸Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina

⁴⁹Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom

⁵⁰Department of Statistics, University of Auckland, Auckland, New Zealand

⁵¹ALSPAC Laboratory, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom

⁵²College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee

⁵³University of Texas Health Science Center at Houston, Human Genetics Center, Houston, Texas

⁵⁴Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, Australia

⁵⁵Department of Pulmonology and Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

⁵⁶Group Health Research Unit, Group Health Cooperative, Seattle, Washington

⁵⁷Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands

⁵⁸Division of Pulmonary Medicine, University Hospitals of Geneva, Geneva, Switzerland

⁵⁹Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California

⁶⁰Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, United Kingdom

⁶¹Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht, The Netherlands

⁶²Genetic Epidemiology and Biostatistics Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada

⁶³Prosserman Centre for Health Research, Samuel Lunenfeld Research Institute, Toronto, Ontario, Canada

⁶⁴Department of Public Health, Weill Cornell Medical College, New York, New York

⁶⁵Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York

⁶⁶Division of Population Health Sciences and Education, St George’s, University of London, London, United Kingdom

⁶⁷Department of Epidemiology and Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

⁶⁸Center for Lung Biology, Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Washington, Seattle, Washington; and

⁶⁹Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands

^✉

Correspondence and requests for reprints should be addressed to Jemma B. Wilk, D.Sc., Division of Aging, Brigham and Women’s Hospital and Harvard Medical School, 1620 Tremont Street, 3rd Floor, Boston, MA 02120. E-mail: jwilk@rics.bwh.harvard.edu

These authors contributed equally to this work.

^✉

Corresponding author.

PMCID: PMC3480517 PMID: 22837378

Abstract

Rationale: Genome-wide association studies (GWAS) have identified loci influencing lung function, but fewer genes influencing chronic obstructive pulmonary disease (COPD) are known.

Objectives: Perform meta-analyses of GWAS for airflow obstruction, a key pathophysiologic characteristic of COPD assessed by spirometry, in population-based cohorts examining all participants, ever smokers, never smokers, asthma-free participants, and more severe cases.

Methods: Fifteen cohorts were studied for discovery (3,368 affected; 29,507 unaffected), and a population-based family study and a meta-analysis of case-control studies were used for replication and regional follow-up (3,837 cases; 4,479 control subjects). Airflow obstruction was defined as FEV₁ and its ratio to FVC (FEV₁/FVC) both less than their respective lower limits of normal as determined by published reference equations.

Measurements and Main Results: The discovery meta-analyses identified one region on chromosome 15q25.1 meeting genome-wide significance in ever smokers that includes AGPHD1, IREB2, and CHRNA5/CHRNA3 genes. The region was also modestly associated among never smokers. Gene expression studies confirmed the presence of CHRNA5/3 in lung, airway smooth muscle, and bronchial epithelial cells. A single-nucleotide polymorphism in HTR4, a gene previously related to FEV₁/FVC, achieved genome-wide statistical significance in combined meta-analysis. Top single-nucleotide polymorphisms in ADAM19, RARB, PPAP2B, and ADAMTS19 were nominally replicated in the COPD meta-analysis.

Conclusions: These results suggest an important role for the CHRNA5/3 region as a genetic risk factor for airflow obstruction that may be independent of smoking and implicate the HTR4 gene in the etiology of airflow obstruction.

Keywords: chronic obstructive pulmonary disease, single-nucleotide polymorphism, genes

At a Glance Commentary

Scientific Knowledge on the Subject

Genome-wide association studies of pulmonary function in population-based studies have discovered numerous loci, but association to a standardized definition of airflow obstruction has not yet been evaluated within population-based studies.

What This Study Adds to the Field

This is the largest study to date to evaluate genetic predictors of airflow obstruction. We confirm the association to the chromosome 15 CHRNA5/CHRNA3 gene cluster and demonstrate nominal association to the region in never smokers with airflow obstruction. We also implicate the HTR4 gene in the pathogenesis of airflow obstruction.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, and cigarette smoking is the most widely recognized risk factor for this disease. COPD is defined based on spirometry as airflow obstruction that is not fully reversible after administration of a bronchodilator. Airflow obstruction is a key pathophysiologic characteristic of COPD that is assessed by spirometry. Both COPD and spirometry measures of lung function have been demonstrated to have a genetic component. Family studies have reported an increased risk for COPD in relatives of a COPD proband (1) as well as significant heritability of pulmonary function measured by spirometry in population-based cohorts (2). The α₁-antitrypsin gene (SERPINA1/A1AT) is known to be associated with COPD and leads to increased risk for early-onset disease among individuals carrying the susceptibility alleles, but few other genes have such a conclusive relationship to COPD.

Recent genome-wide association studies (GWAS) have examined two spirometry measures of lung function, FEV₁ and its ratio to FVC (FEV₁/FVC). Two large-scale GWAS meta-analyses identified a total of 11 loci related to FEV₁ or FEV₁/FVC (3, 4), and a larger meta-analysis incorporating these studies along with new studies identified an additional 16 loci (5). Two genetic loci identified by the above studies, HHIP and FAM13A, have been demonstrated to influence risk of COPD at genome-wide levels of statistical significance (6–9). GWAS of COPD have also identified associations with SNPs in a region on chromosome 15q25.1 that includes cholinergic nicotinic receptor genes (CHRNA5-CHRNA3-CHRNB4) and the iron-responsive element binding protein 2 (IREB2) (7), but some questions remain as to the underlying genetic signal because of substantial linkage disequilibrium in the region. This region has also been associated with lung cancer (10, 11) and nicotine dependence (12–15), leading to the hypothesis that the association with the various disease endpoints may be mediated through the nicotinic receptor genes and thus smoking, smoking intensity, and cessation (16). In a meta-analysis of lung cancer among never smokers, no association to the CHRNA genes was observed, supporting the hypothesis that association was mediated through smoking behavior (17). However, the observation of increased IREB2 protein and mRNA expression in COPD lung tissue compared with controls supports its potential involvement as well (18).

The standard definition of COPD is based on the presence of airflow obstruction that persists after administration of bronchodilator (19). In large population-based cohorts, post-bronchodilator spirometry is not generally available, so we have studied prebronchodilator airflow obstruction as a proxy for COPD. In this study, we performed GWAS using a standardized definition of airflow obstruction and control subjects across 15 population-based cohort studies and conducted a meta-analysis. We then sought replication of our top single-nucleotide polymorphisms (SNPs) and regions in a set of four COPD case-control studies previously included in a meta-analysis and in a population-based family study that used the same airflow obstruction phenotype definitions used in the discovery analyses.

Methods

Discovery Phase

Most of the cohorts used in the discovery phase of this meta-analysis were included in meta-analyses of cross-sectional quantitative pulmonary function measures in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium (3), the SpiroMeta consortium (4), and/or their joint analysis (5). Cohorts not included in previous GWAS discovery sets for pulmonary function include Rotterdam Study III (RS3), Swiss Study on Air Pollution and Lung and Heart Disease in Adults (SAPALDIA), Lothian Birth Cohort (LBC1936), Multi-Ethnic Study of Atherosclerosis (MESA), and COPD Pathology: Addressing Critical gaps, Early Treatment and diagnosis, and Innovative Concepts (COPACETIC). All of the included participants are white and of European descent.

Standardized definitions of airflow obstruction based on the lower limit of normal of FEV₁ and FEV₁/FVC from the National Health and Nutrition Examination Survey III prediction equations (20) were used across all cohorts. The presence of airflow obstruction was defined as an FEV₁ and FEV₁/FVC both less than the lower limit of normal (21) based on prediction equations that include age, age², and height² calculated separately by sex. Unaffected participants were defined by FEV₁, FVC, and FEV₁/FVC all above the lower limit of normal. Individuals below the lower limit of normal for FEV₁ or FEV₁/FVC but not both were excluded from these analyses. Logistic regression models were adjusted for current and former smoking dummy variables, pack-years of smoking, age, sex, standing height, center/cohort as needed, and principal components for genetic ancestry as needed.

Genome-wide imputation and analyses were performed by the cohort investigators, and results were shared for meta-analysis. Details of individual cohorts’ imputation and GWAS methods are provided in the online supplement text and Table E1 in the online supplement. Genome-wide and regional meta-analyses were performed using METAL software (22) with inverse variance weighting to combine effect size estimates after applying a genomic control correction (23).

Five discovery analyses were performed. GWAS were performed in (1) all cohorts with both ever and never smokers, (2) ever smokers, (3) never smokers, (4) asthma-free participants, and (5) the subset of more severe airflow obstruction with FEV₁ less than 65% predicted (excluding milder cases from analysis). Never smoking GWAS were performed in eight cohorts. In the 10 cohorts that collected self-reported asthma data, an analysis was performed excluding all participants reporting a history of asthma with diagnosis before age 40 years or missing onset age.

Regional Meta-analysis and Replication

Two strategies were implemented for follow-up of top results. In two regions with association signals spanning multiple genes in discovery meta-analyses, results across the whole region were requested from the replication studies, and combined meta-analyses were performed to refine the association signal. These regions were located on chromosome 6 (27,599,278–32,787,304 bp) and chromosome 15 (76,499,754–76,711,042 bp). In addition, 60 SNPs with P values less than or equal to 1 × 10⁻⁵ in any of the five discovery meta-analyses were selected for replication. Combined meta-analysis was performed with the Family Heart Study (FamHS), which evaluated the same airflow obstruction phenotype as used in the discovery phase (331 affected and 2,550 unaffected). Replication was further evaluated in a meta-analysis of studies with clinically ascertained COPD (3,499 cases and 1,922 control subjects) (24). Gene expression in lung tissues was evaluated for two genes on chromosome 15. Additional details are included in the online supplement.

Results

Descriptive characteristics of the 15 discovery cohorts are provided in Tables 1 and 2. The mean FEV₁ % predicted for participants with airflow obstruction ranged from 48.9 to 68.7% across cohorts, and for unaffected participants the means were generally around 100%. The mean FEV₁/FVC ratio ranged from 49.5 to 62.5% among affected participants and 74.1 to 81% among unaffected participants across the cohorts. The mean ages at measurement of spirometry across the cohorts ranged from 45 to 76 years. The number of participants contributing to each of the five discovery GWAS meta-analyses are provided in Table 3. The genomic control (λ_GC) values ranged from 0.946 to 1.045 for each cohort’s GWAS and from 1.011 to 1.060 in the meta-analysis (Table E2). Figures E1 to E5 present the Manhattan and quantile-quantile (QQ) plots for the five discovery meta-analyses.

TABLE 1.

DESCRIPTIVE CHARACTERISTICS OF COHORTS INCLUDED IN DISCOVERY META-ANALYSIS

	ARIC	FHS	CHS	COPACETIC	B58C	EPIC	MESA
No. affected,	914	571	402	312	264	127	104
No. unaffected	6,602	5,866	2,183	996	4,374	1,023	979
Age, yr	54.3 (5.7)	51.6 (14.6)	72.3 (5.3)	60.2 (5.6)	45.2 (0.39)	58.2 (9.0)	66.1 (9.8)
Male, %	47.2	46.4	39.4	100	49.6	46.8	49.6
Height, cm	169 (9)	168 (10)	165 (9)	179 (6)	169 (9)	167 (9)	169 (10)
BMI, kg/m²	27.0 (4.7)	27.2 (5.2)	26.2 (4.3)	—	27.4 (4.9)	26.4 (3.9)	28.0 (5.2)
Current smoker, %	21.8	14.2	9.3	55.1	21.3	10.1	7.9
Former smoker, %	35.8	38.1	39.7	44.9	49.1	44.6	50.8
Pack-years smoking^*	27.5 (21.4)	21.9 (21.2)	32.2 (26.7)	39.6 (17.0)	14.7 (11.7)	17.6 (16.0)	29.4 (28.5)
FEV₁/FVC
Affected	58.1 (8.6)	58.6 (8.3)	52.0 (10.7)	51.0 (9.1)	62.5 (7.0)	57.0 (8.5)	56.6 (9.2)
Unaffected	76.6 (4.4)	77.6 (5.3)	74.1 (5.6)	75.5 (4.8)	80.9 (5.6)	81.0 (6.4)	75.5 (5.8)
FEV₁ % predicted
Affected	62.2 (13.4)	63.2 (12.8)	52.1 (15.5)	58.9 (11.1)	68.7 (9.5)	57.1 (13.8)	61.0 (12.7)
Unaffected	99.5 (11.1)	100.4 (11.8)	98.4 (13.5)	105.8 (12.8)	100.7 (10.7)	96.6 (9.9)	98.2 (12.0)

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Definition of abbreviations: ARIC = Atherosclerosis Risk in Communities; B58C = British 1958 Birth Cohort; CHS = Cardiovascular Health Study; COPACETIC = COPD Pathology: Addressing Critical gaps, Early Treatment and diagnosis, and Innovative Concepts; EPIC = European Prospective Investigation into Cancer and Nutrition; FHS = Framingham Heart Study; MESA = Multi-Ethnic Study of Atherosclerosis.

Data are presented as mean (SD) unless otherwise indicated.

Pack-years calculated among current and former smokers.

TABLE 2.

DESCRIPTIVE CHARACTERISTICS OF ADDITIONAL COHORTS INCLUDED IN DISCOVERY META-ANALYSIS

	AGES	Health ABC	RS1	SAPALDIA	BHS	RS3	LBC1936	RS2
No. affected,	109	108	99	98	89	70	61	40
No. unaffected	1,562	1,129	1,003	833	661	1,001	627	668
Age, yr	76.2 (5.6)	73.8 (2.8)	74.4 (5.7)	51.0 (11.1)	54.6 (16.3)	56.6 (5.5)	69.6 (0.9)	67.1 (6.3)
Male, %	40.6	52.1	42.9	48.1	44.0	42.3	49.4	43.8
Height, cm,	167 (9)	167 (9)	167 (9)	169 (9)	168 (9)	171 (9)	166 (9)	168 (9)
BMI, kg/m²	27.1 (4.5)	26.5 (4.1)	27.4 (4.0)	25.7 (4.3)	25.8 (4.0)	27.4 (4.6)	27.4 (4.1)	27.6 (4.0)
Current smoker, %	9.7	6.6	10.7	20.4	16.4	19.0	11.1	13.4
Former smoker, %	42.2	49.0	56.2	32.9	26.8	47.0	40.7	51.7
Pack-years smoking^*	24.5 (22.0)	36.0 (32.4)	25.8 (23.0)	21.5 (23.0)	20.1 (19.6)	18.5 (18.0)	34.3 (23.1)	23.8 (22.9)
FEV₁/FVC
Affected	49.5 (18.1)	56.3 (6.1)	56.0 (7.2)	59.2 (7.3)	58.2 (9.8)	59.2 (8.4)	54.6 (8.6)	56.7 (6.9)
Unaffected	75.6 (7.2)	76.0 (5.1)	75.6 (5.4)	76.8 (5.0)	78.4 (5.4)	80.2 (5.8)	80.4 (6.7)	78.2 (5.9)
FEV₁ % predicted
Affected	48.9 (20.2)	55.3 (11.6)	56.9 (11.0)	67.6 (10.4)	57.3 (16)	60.4 (13.3)	50.8 (10)	58.8 (10.5)
Unaffected	93.2 (18.3)	100.2 (14.0)	102.8 (18.7)	103.5 (11.3)	98.9 (11.7)	104.6 (13.1)	100.4 (12.2)	105.5 (17.3)

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Definition of abbreviations: AGES = Age, Gene, Environment Susceptibility; BHS = Bussleton Health Study; Health ABC = Health, Aging and Body Composition; LBC1936 = Lothian Birth Cohort; RS1 = Rotterdam Study I; RS2 = Rotterdam Study II; RS3 = Rotterdam Study III; SAPALDIA = Swiss Study on Air Pollution and Lung and Heart Disease in Adults.

Data are presented as mean (SD) unless otherwise indicated.

Pack-years calculated among current and former smokers.

TABLE 3.

SAMPLE SIZES CONTRIBUTED BY EACH COHORT FOR THE FIVE DISCOVERY META-ANALYSES OF AIRFLOW OBSTRUCTION

	All Participants		Ever Smokers		Never Smokers		Asthma-Free^*		FEV₁ < 65%^†
	Affected	Unaffected	Affected	Unaffected	Affected	Unaffected	Affected	Unaffected	Affected	Unaffected
ARIC	914	6,602	821	3,510	93	3,092	814	6,355	452	6,602
FHS	571	5,866	457	2,909	114	2,957	391	5,210	274	5,866
CHS	402	2,183	317	950	85	1,233	363	2,135	292	2,183
RS1	99	1,003	87	650	12^‡	353^‡	97^§	967^§	68	1,003
RS2	40	668	37	424	3^‡	244^‡	NA	NA	29	668
RS3	70	1,001	57	650	13^‡	351^‡	NA	NA	39	1,001
Health ABC	108	1,129	94	593	14^‡	536^‡	70	1077	80	1,129
AGES	109	1,562	81	787	28	775	NA	NA	34	1,562
EPIC	127	1,023	79	490	48	533	110	992	88	1,023
BHS	89	661	46	278	43	383	20	421	53	661
SAPALDIA	98	833	59	437	39	396	42	620	38	833
LBC1936	61	627	50	306	11^‡	321^‡	60	622	56	627
B58C	264	4,374	210	3,053	54	1,321	183	4,036	75	4,374
COPACETIC	0	0	312	996	NA	NA	NA	NA	142	996
MESA	104	979	89	533	15^‡	531^‡	85	923	54	979
Total	3,056	28,511	2,796	16,566	504	10,690	2,138	22,391	1,774	29,507

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Definition of abbreviations: AGES = Age, Gene, Environment Susceptibility; ARIC = Atherosclerosis Risk in Communities; B58C = British 1958 Birth Cohort; BHS = Bussleton Health Study; CHS = Cardiovascular Health Study; COPACETIC = COPD Pathology: Addressing Critical gaps, Early Treatment and diagnosis, and Innovative Concepts; EPIC = European Prospective Investigation into Cancer and Nutrition; FHS = Framingham Heart Study; Health ABC = Health, Aging and Body Composition; LBC1936 = Lothian Birth Cohort; MESA = Multi-Ethnic Study of Atherosclerosis; RS1 = Rotterdam Study I; RS2 = Rotterdam Study II; RS3 = Rotterdam Study III; SAPALDIA = Swiss Study on Air Pollution and Lung and Heart Disease in Adults.

Asthma-free: no history of an asthma diagnosis before age 40 years; participants reporting asthma with missing data on age at diagnosis were also excluded.

^†

FEV₁ < 65%: cases were restricted to those with FEV₁ < 65% and FEV₁/FVC less than the lower limit of normal; the definition of control subjects was the same as used for all participants.

^‡

Not analyzed due to low number of cases.

^§

Results were not available for discovery meta-analyses.

Discovery Meta-analyses

One region on chromosome 15 had 11 SNPs with genome-wide significant results (P values < 5 × 10⁻⁸) in discovery meta-analysis of ever smokers (Table 4). An SNP in the AGPHD1 gene between the IREB2 gene and CHRN gene cluster was the top association with airflow obstruction among ever smokers (rs8031948, P value = 2.8 × 10⁻⁹) with the minor allele conferring a 22% higher risk of airflow obstruction. Among 14 cohorts with both smoking and never-smoking participants, the top SNP results for all subjects combined were found in the same chromosome 15 region but localized to the CHRNA5 gene (rs17486278, P value = 1.9 × 10⁻⁷). For comparison, results among never smokers (504 affected, 10,690 unaffected from eight cohorts) are included in Table 4, and the smallest P value in the region (8.4 × 10⁻⁵) occurs at a synonymous SNP (rs1051730) in CHRNA3. The odds ratios (OR) shown in Table 4 demonstrate consistency in the effect size for the tested allele across the analyses of all cohorts with both ever and never smoking participants (14 cohorts), ever smokers (15 cohorts), and never smokers (8 cohorts). The results in Table 4 were based on meta-analyses that included different cohorts as presented in Table 3, and thus the ever and never smoker results do not reflect a straightforward stratified analysis of all participants. The inclusion of the COPACETIC study in the ever smokers meta-analysis contributed to the improved association signal in the region. In the GWAS of a more severe airflow obstruction phenotype defined by FEV₁ less than 65% predicted, a missense SNP in the CHRNA5 gene (rs16969968, Asp398Asn) had the third ranked P value (5 × 10⁻⁷) with an OR of 1.22.

TABLE 4.

GENOME-WIDE SIGNIFICANT RESULTS ON CHROMOSOME 15 FROM DISCOVERY META-ANALYSIS OF AIRFLOW OBSTRUCTION

						All^*		Never Smokers		Ever Smokers
				Coded Allele		14 Cohorts		8 Cohorts		15 Cohorts
SNP	Position	Gene	Function	Allele	Frequency	OR	P Value	OR	P Value	OR	P Value
rs8031948	76603112	AGPHD1	Intronic	t	0.35	1.17	4.76 × 10⁻⁷	1.15	1.53 × 10⁻³	1.22	2.78 × 10⁻⁹
rs931794	76613235	AGPHD1	Intronic	a	0.65	0.86	6.18 × 10⁻⁷	0.87	1.46 × 10⁻³	0.82	4.69 × 10⁻⁹
rs10519203	76601101	AGPHD1	Intronic	a	0.65	0.86	7.16 × 10⁻⁷	0.86	1.27 × 10⁻³	0.82	5.67 × 10⁻⁹
rs9788721	76589924	AGPHD1	Intronic	t	0.65	0.86	1.04 × 10⁻⁶	0.86	1.17 × 10⁻³	0.82	9.76 × 10⁻⁹
rs2036527	76638670			a	0.34	1.18	3.51 × 10⁻⁷	1.18	2.85 × 10⁻⁴	1.22	1.72 × 10⁻⁸
rs17486278	76654537	CHRNA5	Intronic	a	0.66	0.85	1.91 × 10⁻⁷	0.84	1.06 × 10⁻⁴	0.83	2.43 × 10⁻⁸
rs7180002	76661048	CHRNA5	Intronic	a	0.66	0.85	2.23 × 10⁻⁷	0.84	1.20 × 10⁻⁴	0.83	2.68 × 10⁻⁸
rs1051730	76681394	CHRNA3	Synonymous	a	0.33	1.17	3.29 × 10⁻⁷	1.20	8.36 × 10⁻⁵	1.21	3.36 × 10⁻⁸
rs16969968	76669980	CHRNA5	Missense	a	0.34	1.17	3.46 × 10⁻⁷	1.19	1.25 × 10⁻⁴	1.20	3.47 × 10⁻⁸
rs951266	76665596	CHRNA5	Intronic	a	0.34	1.17	3.64 × 10⁻⁷	1.19	1.41 × 10⁻⁴	1.21	3.47 × 10⁻⁸
rs1317286	76683184	CHRNA3	Intronic	a	0.66	0.85	3.93 × 10⁻⁷	0.84	1.18 × 10⁻⁴	0.83	4.74 × 10⁻⁸

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Definition of abbreviation: OR = odds ratio.

All cohorts with both ever and never smoking participants.

No other genome-wide significant associations were identified among discovery meta-analyses. In the meta-analysis of never smokers (504 affected and 10,690 control subjects), several top SNPs were observed 570 kb away from ADARB2 (the closest gene), and results from the FEV₁ less than 65% meta-analysis also implicated this locus. Among never smokers, the chromosome 6 major histocompatability locus (MHC) region was among top results. The discovery meta-analysis results for the 60 SNPs selected for replication are included in Table 5. Genome-wide results for all five definitions of airflow obstruction are available in the online supplement.

TABLE 5.

ODDS RATIOS AND P VALUES FOR THE 60 SINGLE-NUCLEOTIDE POLYMORPHISMS IDENTIFIED IN THE DISCOVERY META-ANALYSIS AND SELECTED FOR REPLICATION AND COMBINED META-ANALYSIS WITH THE FAMILY HEART STUDY

	Coded Allele					Discovery Meta-analysis			Family Heart Study^*		Combined Meta-analysis
SNP	Allele	Freq	Chr	Position	Closest Gene	OR	P Value	Analysis	OR	P Value	OR	P Value
rs7733088	A	0.38	5	147836526	HTR4	0.82	6.53 × 10⁻⁸	Smoker	0.75	0.015	0.81	4.09 × 10⁻⁹
rs2044029	A	0.4	15	69467013	THSD4	1.16	4.95 × 10⁻⁷	All	1.20	0.078	1.17	1.06 × 10⁻⁷
rs181654	A	0.28	10	119369646	EMX2	0.82	1.24 × 10⁻⁶	No asthma	0.76	0.030	0.81	1.31 × 10⁻⁷
rs4597955	A	0.59	5	147827466	HTR4	0.84	3.12 × 10⁻⁶	Smoker	0.76	0.011	0.83	1.57 × 10⁻⁷
rs12905014	T	0.95	15	90684844	ST8SIA2	0.58	5.24 × 10⁻⁶	FEV₁ 65%	0.47	0.020	0.57	3.83 × 10⁻⁷
rs11744671	T	0.92	5	156853809	ADAM19	0.72	8.25 × 10⁻⁷	No asthma	0.77	0.264	0.72	4.51 × 10⁻⁷
rs8033889	T	0.21	15	69467134	THSD4	1.18	2.49 × 10⁻⁶	All	1.23	0.066	1.19	4.67 × 10⁻⁷
rs6684428	A	0.16	1	56132401	PPAP2B	1.24	3.60 × 10⁻⁷	Smoker	1.08	0.574	1.23	4.73 × 10⁻⁷
rs4767234	A	0.59	12	113122231	TBX5	1.18	1.28 × 10⁻⁶	Smoker	1.15	0.242	1.17	6.32 × 10⁻⁷
rs4534959	A	0.97	18	60028136	SERPINB8	0.57	4.19 × 10⁻⁷	FEV₁ 65%	0.91	0.844	0.58	6.90 × 10⁻⁷
rs715921	A	0.31	13	23693489	SPATA13	1.22	5.18 × 10⁻⁷	No asthma	1.07	0.551	1.20	7.60 × 10⁻⁷
rs16889038	T	0.92	6	24414366	DCDC2	0.7	4.15 × 10⁻⁷	FEV₁ 65%	0.95	0.845	0.72	7.94 × 10⁻⁷
rs9536318	A	0.83	13	52392695	PCDH8	0.81	5.47 × 10⁻⁶	No asthma	0.75	0.051	0.80	8.24 × 10⁻⁷
rs1997352	A	0.26	3	25513321	RARB	0.85	4.29 × 10⁻⁶	All	0.82	0.076	0.84	8.64 × 10⁻⁷
rs10759102	A	0.33	9	9900123	PTPRD	0.81	4.66 × 10⁻⁶	FEV₁ 65%	0.77	0.083	0.81	1.04 × 10⁻⁶
rs13144621	T	0.32	4	109437378	LEF1	0.85	5.87 × 10⁻⁷	All	0.98	0.850	0.86	1.20 × 10⁻⁶
rs1982234	C	0.63	15	69478345	THSD4	1.16	4.80 × 10⁻⁶	All	1.16	0.136	1.16	1.55 × 10⁻⁶
rs7799265	C	0.95	7	28399001	CREB5	0.63	8.84 × 10⁻⁷	FEV₁ 65%	0.91	0.742	0.65	1.71 × 10⁻⁶
rs181652	A	0.54	10	119369077	EMX2	1.18	8.12 × 10⁻⁶	No asthma	1.19	0.097	1.18	1.94 × 10⁻⁶
rs11766496	C	0.12	7	71026786	CALN1	1.34	3.90 × 10⁻⁷	All	0.97	0.855	1.30	2.01 × 10⁻⁶
rs2263638	A	0.37	10	94158777	IDE	0.78	4.68 × 10⁻⁶	Never smoker	0.80	0.275	0.78	2.53 × 10⁻⁶
rs7850092	A	0.21	9	9899119	PTPRD	0.79	4.00 × 10⁻⁶	FEV₁ 65%	0.85	0.324	0.79	2.60 × 10⁻⁶
rs1329705	A	0.2	6	142795031	GPR126	0.79	3.75 × 10⁻⁶	FEV₁ 65%	0.85	0.342	0.79	2.63 × 10⁻⁶
rs11209261	A	0.76	1	68557801	GPR177	0.81	6.76 × 10⁻⁶	FEV₁ 65%	0.80	0.191	0.81	2.78 × 10⁻⁶
rs7607316	A	0.21	2	237186581	CXCR7	1.28	9.19 × 10⁻⁶	Never smoker	1.35	0.152	1.29	3.21 × 10⁻⁶
rs9975851	T	0.57	21	26638525	CYYR1	1.18	4.92 × 10⁻⁶	No asthma	1.11	0.332	1.17	3.66 × 10⁻⁶
rs12505749	C	0.92	4	57028869	SRP72	0.76	3.06 × 10⁻⁷	All	1.22	0.258	0.79	4.69 × 10⁻⁶
rs1207393	C	0.36	22	24983362	SEZ6L	0.83	8.69 × 10⁻⁶	FEV₁ 65%	0.85	0.283	0.84	4.78 × 10⁻⁶
rs12744110	T	0.25	1	56168897	PPAP2B	1.18	4.51 × 10⁻⁶	Smoker	1.06	0.681	1.17	5.95 × 10⁻⁶
rs11097912	T	0.33	4	107219911	MGC16169	0.85	6.04 × 10⁻⁶	Smoker	0.92	0.497	0.85	5.95 × 10⁻⁶
rs17086172	T	0.94	18	68378001	CBLN2	0.73	7.13 × 10⁻⁶	No asthma	0.86	0.492	0.74	7.23 × 10⁻⁶
rs2322734	A	0.96	3	4608492	ITPR1	0.67	7.19 × 10⁻⁶	FEV₁ 65%	0.83	0.518	0.69	7.52 × 10⁻⁶
rs8036030	A	0.39	15	72503662	SEMA7A	0.84	5.71 × 10⁻⁶	No asthma	0.95	0.653	0.85	8.31 × 10⁻⁶
rs892961	A	0.41	17	72911695	SEPT9	0.85	8.00 × 10⁻⁶	No asthma	0.93	0.479	0.85	8.91 × 10⁻⁶
rs7629245	T	0.15	3	186624551	MAP3K13	1.23	0.00001	Smoker	1.12	0.456	1.22	9.06 × 10⁻⁶
rs2830165	T	0.59	21	26598463	APP	0.81	5.27 × 10⁻⁶	Never smoker	0.98	0.892	0.82	9.55 × 10⁻⁶
rs7686928	T	0.14	4	188970823	ZFP42	1.21	8.77 × 10⁻⁶	Smoker	1.08	0.630	1.21	9.65 × 10⁻⁶
rs9632471	C	0.72	5	128761894	ADAMTS19	0.76	5.54 × 10⁻⁶	FEV₁ 65%	0.95	0.776	0.77	1.07 × 10⁻⁵
rs4837614	T	0.15	9	118350186	ASTN2	0.76	4.56 × 10⁻⁶	FEV₁ 65%	0.98	0.911	0.78	1.20 × 10⁻⁵
rs12960805	A	0.41	18	7909707	PTPRM	1.27	6.22 × 10⁻⁶	Never smoker	1.00	0.995	1.25	1.27 × 10⁻⁵
rs1799257	A	0.12	19	53664351	PSCD2	1.37	2.08 × 10⁻⁶	FEV₁ 65%	0.78	0.337	1.33	1.38 × 10⁻⁵
rs1868466	A	0.79	16	76301059	KIAA1576	0.85	5.45 × 10⁻⁶	All	1.00	0.997	0.86	1.39 × 10⁻⁵
rs10518948	C	0.93	15	69415023	THSD4	0.68	9.91 × 10⁻⁶	Never smoker	0.97	0.931	0.69	1.51 × 10⁻⁵
rs6901575	A	0.1	6	28358963	PGBD1	1.24	6.76 × 10⁻⁶	All	0.97	0.853	1.22	1.55 × 10⁻⁵
rs3790728	T	0.97	1	215737150	GPATCH2	0.57	3.12 × 10⁻⁶	FEV₁ 65%	4.24	0.060	0.60	1.58 × 10⁻⁵
rs9511117	A	0.09	13	23660045	SPATA13	0.73	6.78 × 10⁻⁶	Smoker	1.01	0.979	0.75	1.65 × 10⁻⁵
rs4957070	A	0.63	5	600858	SLC9A3	0.85	9.09 × 10⁻⁶	Smoker	0.98	0.874	0.86	1.65 × 10⁻⁵
rs3814818	T	0.9	14	94263130	GSC	0.74	2.51 × 10⁻⁶	FEV₁ 65%	1.24	0.357	0.77	1.68 × 10⁻⁵
rs1895493	C	0.09	16	78122404	MAF	1.41	5.51 × 10⁻⁶	Never smoker	0.77	0.460	1.37	1.72 × 10⁻⁵
rs12872078	A	0.91	13	64002324	PCDH9	1.43	4.81 × 10⁻⁶	FEV₁ 65%	0.96	0.876	1.37	1.75 × 10⁻⁵
rs764593	T	0.11	3	3687236	LRRN1	0.76	3.89 × 10⁻⁶	Smoker	1.11	0.563	0.79	2.64 × 10⁻⁵
rs1408298	T	0.7	6	17199273	RBM24	0.81	8.19 × 10⁻⁶	No asthma	1.00	0.972	0.83	2.95 × 10⁻⁵
rs2164220	T	0.08	7	157447986	PTPRN2	1.42	9.80 × 10⁻⁶	Never smoker	0.61	0.266	1.38	3.14 × 10⁻⁵
rs10496694	A	0.09	2	133252637	NAP5	1.31	7.42 × 10⁻⁶	Smoker	0.86	0.512	1.27	3.30 × 10⁻⁵
rs11023434	C	0.23	11	15083724	INSC	1.33	7.78 × 10⁻⁶	Never smoker	0.95	0.766	1.28	3.70 × 10⁻⁵
rs1567398	T	0.43	8	8764214	MFHAS1	1.2	8.20 × 10⁻⁶	FEV₁ 65%	0.91	0.519	1.17	3.98 × 10⁻⁵
rs1125729	T	0.81	8	93427586	RUNX1T1	0.8	1.63 × 10⁻⁶	FEV₁ 65%	1.59	0.014	0.83	4.89 × 10⁻⁵
rs7163331	A	0.04	15	96260720	ARRDC4	1.55	6.81 × 10⁻⁶	FEV₁ 65%	0.56	0.132	1.46	6.34 × 10⁻⁵
rs12265908	A	0.97	10	2339319	ADARB2	0.59	6.92 × 10⁻⁶	FEV₁ 65%	1.81	0.157	0.64	7.54 × 10⁻⁵
rs7719062	T	0.08	5	1222044	SLC6A19	1.42	9.79 × 10⁻⁶	Smoker	0.81	0.393	1.34	7.71 × 10⁻⁵

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Definition of abbreviations: Chr = chromosome; Freq = frequency; OR = odds ratio; SNP = single-nucleotide polymorphism.

SNPs are ordered by the combined meta-analysis P value.

Family Heart Study results are generated from phenotypes consistent with the discovery analysis indicated, with affected/unaffected sample sizes: 331/2,550 all, 248/1,003 smoker, 83/1,547 never smoker, 266/2,350 no asthma, 155/2,550 FEV₁ 65%.

Meta-analysis of Chromosome 6 and 15 Regions with Replication Studies

Regional meta-analyses were performed to further evaluate the regions on chromosomes 6 and 15 with the additional two replication studies. In discovery analysis of all airflow obstruction, the chromosome 6 MHC locus at 6p21.33 was among the top results (smallest P value = 6.8 × 10⁻⁷, rs3094013). The closest gene to the top SNP was HLA complex P6 (HCP5), although the extensive linkage disequilibrium in the region makes interpretation difficult. When discovery results were meta-analyzed with the replication studies, the previous associations were attenuated. The top SNP from the meta-analysis of discovery and replication results had an OR of 1.13 for the common allele (66%) and a P value of 6.03 × 10⁻⁶ near the HLA-A gene. Thirty-six SNPs in chromosome 6 with combined meta-analysis P values less than 1 × 10⁻⁴ are provided in Table E3.

On chromosome 15, after meta-analysis of airflow obstruction in ever smokers from discovery populations with replication studies, the order of the top hits was generally unchanged and P values improved, reaching 2.6 × 10⁻¹⁵. The COPD case-control studies meta-analysis included only ever smokers, so the FamHS served as a sole replication study for the never smoker regional results. Figure 1 depicts the chromosome 15 regional association of the meta-analysis of combined discovery and replication cohorts for the separate groups of ever smokers (A) and never smokers (B), created using LocusZoom (25). Figure 2 is a forest plot presenting the study-specific results among never smokers that demonstrates similar effect sizes across the cohorts.

Figure 1. — Regional association plot for chromosome 15 presenting results from combined meta-analysis of discovery and replication studies. X-axis is megabase (Mb) position. Y-axis is negative log of the P values. Linkage disequilibrium to the named single-nucleotide polymorphism (SNP) (*purple*) is depicted by degree of color according to the legend. Nonsynonymous SNPs are depicted by an *inverted triangle* and other coding SNPs by a *square*. (A) Ever smokers. (B) Never smokers.

Figure 2. — Forest plot depicting the association results for rs1051730 (*CHRNA3*) and airflow obstruction among never smokers in each cohort and the meta-analysis. AGES = Age, Gene, Environment Susceptibility; ARIC = Atherosclerosis Risk in Communities; B58C = British 1958 Birth Cohort; BHS = Bussleton Health Study; CHS = Cardiovascular Health Study; EPIC = European Prospective Investigation into Cancer and Nutrition; FamHS = Family Heart Study; FHS = Framingham Heart Study; SAPALDIA = Swiss Study on Air Pollution and Lung and Heart Disease in Adults.

Replication of Top 60 SNPs and Combined Meta-analysis

Table 5 presents the 60 SNPs selected for replication studies (not including the chromosome 6 and 15 SNPs included in the regional meta-analyses). A P value of 8 × 10⁻⁴, representing Bonferroni correction for 60 tests at the α = 0.05 level, was selected a priori as the threshold for statistically significant replication. No SNPs achieved the replication criterion. In a meta-analysis combining the discovery results with the FamHS, one SNP achieved genome-wide statistical significance (rs7733088 in HTR4) with a 38% frequent minor allele conferring an OR of 0.81 (P = 4.09 × 10⁻⁹). Of the top 60 SNPs, four had nominal association (P values < 0.05) in the COPD meta-analysis and a consistent risk allele; these SNPs were located in ADAM19, RARB, PPAP2B, and ADAMTS19 (Table 6).

TABLE 6.

FOUR SINGLE-NUCLEOTIDE POLYMORPHISMS WITH NOMINAL ASSOCIATION TO CHRONIC OBSTRUCTIVE PULMONARY DISEASE AND CONSISTENT RISK ALLELE OUT OF 60 SINGLE-NUCLEOTIDE POLYMORPHISMS SELECTED FOR REPLICATION FROM AIRFLOW OBSTRUCTION DISCOVERY GENOME-WIDE ASSOCIATION STUDIES

	Coded Allele					Airflow Obstruction Meta-analysis			COPD Meta-analysis
SNP	Allele	Freq	Chr	Position	Closest Gene	Analysis	OR	P Value	OR	P Value
rs11744671	T	0.92	5	156853809	ADAM19	No asthma	0.72	4.51 × 10⁻⁷	0.8	0.027
rs1997352	A	0.26	3	25513321	RARB	All	0.84	8.64 × 10⁻⁷	0.88	0.038
rs12744110	T	0.25	1	56168897	PPAP2B	Smoker	1.17	5.95 × 10⁻⁶	1.13	0.045
rs9632471	C	0.72	5	128761894	ADAMTS19	FEV₁ 65%	0.77	1.07 × 10⁻⁵	0.86	0.045

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Definition of abbreviations: Chr = chromosome; COPD = chronic obstructive pulmonary disease; Freq = frequency; OR = odds ratio; SNP = single-nucleotide polymorphism.

Association of Spirometry-associated SNPs with Airflow Obstruction

Previous meta-analyses in the CHARGE and SpiroMeta consortia (3–5) identified 75 SNPs associated with either FEV₁ or FEV₁/FVC at genome-wide significance (P value ≤ 5 × 10⁻⁸). We examined the association P values for airflow obstruction for these 75 SNPs in the meta-analysis results from all subjects and from ever smokers. Association for these 75 SNPs represents 58 independent tests using a multiple-testing correction that incorporates the linkage disequilibrium structure derived from HapMap European (CEU) samples (26). Accordingly, we considered a P value of 8.6 × 10⁻⁴ as the criterion for statistically significant association with airflow obstruction (Bonferroni correction for 58 tests at the α = 0.05 level) given the a priori association with spirometry. Among all participants, SNPs in RARB, GPR126, HTR4, C10orf11, near HHIP, and near HLA-DRA were statistically significantly associated with airflow obstruction. Among smokers, HTR4, RARB, GPR126, and THSD4 were associated with airflow obstruction. Results for the 75 SNPs are presented in Tables E4 and E5. When only cohorts that did not contribute to the published spirometry findings (3–5) were considered (RS3, SAPALDIA, LBC1936, MESA, and COPACETIC) as an independent sample, power was reduced, and only the ADAM19 SNP in smokers achieved the Bonferroni cutoff for significance (Table E6).

Gene Expression Results

Expression of CHRNA3 and CHRNA5 was evaluated in cDNA from human whole lung, peripheral blood mononuclear cells, and primary cultures of bronchial epithelial cells and airway myocytes, together with control tissues (kidney, brain, and placenta: see online supplement for methods). Both genes were expressed in all lung-derived tissues examined. Within the lung, expression of both CHRNA3 and CHRNA5 appeared strongest in airway myocytes and epithelial cells. The identity of reverse transcriptase–polymerase chain reaction products was confirmed by direct sequencing of bands of the relevant size from at least one tissue type for each gene.

Discussion

These meta-analyses included 32,875 participants from population-based studies for discovery of loci associated with airflow obstruction. In addition, we attempted to replicate results in 2,881 participants from a population-based family study and a meta-analysis of 5,421 participants from case-control studies of clinically ascertained COPD. The present study confirms the previously identified (7) association between the chromosome 15q25 region and airflow obstruction among smokers. Although the number of participants with airflow obstruction among never smokers was low (504 affected), and statistical power is therefore limited, analyses of airflow obstruction among never smokers also showed a nominal association to the Asp398Asn missense SNP in CHRNA5 and to a synonymous SNP in CHRNA3.

Results from gene expression studies demonstrated that both CHRNA5 and CHRNA3 were expressed in whole lung, airway smooth muscle, and bronchial epithelial cells. In a publication reporting CHRNA5 gene expression in normal lung tissue samples, the Asp398Asn genotype was strongly related to mRNA levels, with homozygosity of the risk allele (A) associated with 2.5-fold lower mRNA levels compared with homozygosity for the G allele (27). A similar pattern was observed for rs1051730 in the sputum of COPD cases, in which the minor allele was associated with lower expression of CHRNA5 (28). The correlation between associated SNP genotypes and CHRNA5 expression levels in the lung and sputum combined with our finding of increased risk for airflow obstruction in never smokers suggests that the variants in this region may have an influence on risk of airflow obstruction that is not simply mediated by an influence on nicotine dependence. Supporting a direct influence of variants in this region on lung phenotypes, a CHRNA3/5 variant was recently found to be associated with bronchial hyperresponsiveness in children not exposed to cigarette smoke (29). Silencing CHRNA5 in bronchial epithelial cells was found to reduce expression of adhesion molecules, thereby increasing cell motility, which may influence the repair and remodeling processes that lead to COPD (30). Our results suggest that the A allele of rs16969968 confers as much as a 20% increased odds of airflow obstruction, and based on the prior report, this increased risk may be mediated by lower mRNA levels in lung tissue (27).

In addition to the chromosome 15q region, SNPs in HTR4 met genome-wide statistical significance in ever smokers. The HTR4 (5-hydroxytryptamine [serotonin] receptor 4) gene was originally identified with association to FEV₁/FVC in CHARGE (3) and SpiroMeta (4), and subsequently showed a statistically significant association with COPD in a targeted gene analysis of six loci in the SpiroMeta cohorts (31). Serotoninergic receptors have been demonstrated to regulate cytokine and chemokine release in human airway epithelial cells and have been implicated in the pathogenesis of asthma (32). The reduced risk of airflow obstruction was strongest when limited to ever smokers, suggesting that variation in HTR4 may contribute to the inflammatory response to cigarette smoke.

Several genes represented among the top SNP results were nominally replicated in the COPD case-control meta-analysis (ADAM19, RARB, PPAP2B, and ADAMTS19). Of them, both ADAM19 and RARB have been previously implicated in GWAS of lung function as measured by spirometry (3–5). ADAM19 (a disintegrin and metalloprotease domain 19) was originally shown to be associated with FEV₁/FVC in the CHARGE GWAS (3), and these SNPs were subsequently reported to be associated with COPD in a case-control study (33). Here, we demonstrate that ADAM19 is associated with airflow obstruction in population-based cohort studies. ADAM19 is expressed in bronchial epithelial cells, bronchial smooth muscle, and interstitial inflammatory cells and may have a role in immune defense and the inflammatory process (34). ADAMTS19 (a disintegrin and metalloproteinase with thrombospondin motifs 19) has several of the same domains and has been shown to be expressed in fetal lung (35). PPAP2B is a lipid phosphate phosphohydrolase, which are generally believed to influence surfactant secretion and have a role in lung injury and repair (36).

RARB (retinoic acid receptor β) was recently demonstrated to be associated with lung function measures at genome-wide significance in the combined CHARGE and SpiroMeta meta-analysis (5). Retinoic acid (RA) has been evaluated as a potential therapeutic agent for emphysema after results in rats demonstrated reversibility of experimentally induced emphysema with administration of RA (37); however, subsequent studies in animal models had conflicting results (38), and a small feasibility study of RA for the treatment of emphysema did not show significant improvement in lung function (39). The finding that RARB minor alleles were associated with lower risk of airflow obstruction may provide insight into which patients may benefit from RA therapy or suggest modifying the design of RA therapeutics to target the β receptor.

The HHIP region was associated with airflow obstruction in our look-up replication of spirometry-associated SNPs, which was expected given the prior findings of association with COPD in earlier GWAS (7, 9) and further replication in targeted studies of HHIP and COPD (8). This region of chromosome 4q31 including SNPs in HHIP and GYPA has also been shown to be associated with lung cancer (40). Recently, a COPD risk haplotype upstream of HHIP was identified to be associated with reductions in HHIP promoter activity (41). Our meta-analysis is able to confirm that rs6537296 is associated with airflow obstruction (P = 3.2 × 10⁻⁴), but the other SNP in the haplotype (rs1542725) was not studied. Also, previously identified SNPs in GPR126, THSD4, and near HLA-DRA were associated with airflow obstruction, and GPR126 demonstrated a nominal association with COPD in a prior report focusing on clinically ascertained cases and control subjects (33). It should be noted that the look-up replication that supports the relation of these genes with airflow obstruction is not statistically independent from the original meta-analyses of spirometry traits because of overlap between the samples. When only the cohorts not included in the earlier published meta-analyses (3–5) were analyzed separately, in this reduced sample size (567 affected, 2,922 unaffected) only the ADAM19 gene achieved the cutoff criterion for significant association with airflow obstruction.

The chromosome 6 region identified in discovery meta-analysis did not replicate when additional studies were included in the meta-analysis. The regional meta-analysis results demonstrated modest association (P values < 1 × 10⁻⁴) across five megabases in the HLA region, including 17 SNPs in the histone gene cluster at 27.9 Mb. Our results are not able to clarify which gene or combination of genes may give rise to the underlying association signal given the extensive linkage disequilibrium in the MHC. Recently, a meta-analysis of the COPD case-control cohorts that served as replication cohorts in our study implicated a locus on chromosome 19q13 (24) as a COPD susceptibility locus; however, the rs7937 SNP identified is not replicated in the discovery meta-analyses described here (P values ranged from 0.12 among never smokers to 0.87 among ever smokers).

Our study has several limitations. Our cohorts had only prebronchodilator spirometry, and thus we could not examine the formal definition of COPD. Our main analysis used a definition of airflow obstruction that includes persons with very mild ventilatory impairment, and the participants who meet this definition may not all have COPD. Our definition of more severe airflow obstruction is likely to be more comparable to clinically ascertained COPD in the replication studies, but the numbers of affected participants were reduced. In addition, our ability to address asthma in the context of airflow obstruction was limited to a subset of cohorts with self-reported asthma diagnoses. Last, as our study was limited to white participants of European descent, the generalizability of these findings to other ethnic groups is unknown.

In summary, we performed meta-analyses and replication studies using data from more than 40,000 study participants of European ancestry to identify genetic loci influencing airflow obstruction as a categorical disease phenotype. We identified the CHRNA3/5 genes and HTR4 at genome-wide significance, and several genes that were implicated by previous GWAS of single spirometry measures as quantitative phenotypes (ADAM19, RARB) were among top results. Here we show, for the first time, that a CHRNA5 missense SNP is associated with airflow obstruction in never smokers, suggesting a main effect on risk of airflow obstruction that is independent of the influence mediated through effects on smoking habits. This was supported by gene expression findings demonstrating the CHRNA3/5 genes in relevant lung and airway tissues. Thus, CHRNA3/5 variants may mediate airflow obstruction in both ever and never smokers.

Supplementary Material

Online Supplement

supp_186_7_622_v2_index.html^{(708B, html)}

Acknowledgments

The authors thank all the participants and research team members. The ARIC authors acknowledge Grace Chiu, Ph.D. (Westat, Research Triangle Park, NC) and Dick Howard (University of North Carolina at Chapel Hill, Chapel Hill, NC) for computational support and computer programming expertise. The LBC1936 authors thank the nurses and staff at the Wellcome Trust Clinical Research Facility, where subjects were tested and the genotyping was performed. Additional members of the SAPALDIA study, COPDGene study group, ECLIPSE, and the NETT Genetics Ancillary Study are listed in the online supplement. The MESA authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.

Sources of Funding

J.B.W. is supported by a Young Clinical Scientist Award from the Flight Attendant Medical Research Institute. Research was conducted in part using data and resources from the Framingham Heart Study of the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (NIH) and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the Framingham investigators participating in the SNP Health Association Resource (SHARe) project. This work was partially supported by the NHLBI’s Framingham Heart Study (contract N01-HC-25195) and its contract with Affymetrix, Inc. for genotyping services (contract N02-HL-6-4278). A portion of this research used the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center.

The Atherosclerosis Risk in Communities Study is performed as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. Infrastructure was partly supported by grant number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. Work was supported in part by the Division of Intramural Research, National Institute of Environmental Health Sciences ZO1 ES43012.

This CHS research was supported by NHLBI contracts N01-HC-85239, N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133 and NHLBI grants HL080295, HL075366, HL087652, HL105756 with additional contribution from National Institute of Neurological Disorders and Stroke. Additional support was provided through AG-023629, AG-15928, AG-20098, and AG-027058 from the National Institute on Aging (NIA) and the Cedars-Sinai Board of Governors’ Chair in Medical Genetics (J.I.R.). DNA handling and genotyping was supported in part by National Center for Research Resources CTSI grant UL 1RR033176 and National Institute of Diabetes and Digestive and Kidney Diseases grant DK063491 to the Southern California Diabetes Endocrinology Research Center.

COPACETIC (i.e., COPD Pathology: Addressing Critical gaps, Early Treatment and diagnosis and Innovative Concepts) is funded by the European Union FP7 program, grant agreement number 201379.

We acknowledge use of phenotype and genotype data from the British 1958 Birth Cohort DNA collection, funded by the Medical Research Council grant G0000934 and the Wellcome Trust grant 068545/Z/02. (http://www.b58cgene.sgul.ac.uk/). Genotyping for the B58C-WTCCC subset was funded by the Wellcome Trust grant 076113/B/04/Z. The B58C-T1DGC genotyping used resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development, and Juvenile Diabetes Research Foundation International (JDRFI) and supported by U01 DK062418. B58C-T1DGC GWAS data were deposited by the Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research (CIMR), University of Cambridge, which is funded by JDRFI, the Wellcome Trust, and the National Institute for Health Research Cambridge Biomedical Research Centre; the CIMR is in receipt of a Wellcome Trust Strategic Award (079895). The B58C-GABRIEL genotyping was supported by a contract from the European Commission Framework Program 6 (018996) and grants from the French Ministry of Research.

The EPIC-Norfolk is supported by research program grant funding from Cancer Research UK and the Medical Research Council.

The AGES-Reykjavik Study is funded by NIH contract N01-AG-12100, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament).

The Health, Aging, and Body Composition Study was supported by NIA contracts N01AG62101, N01AG2103, and N01AG62106 and in part by the Intramural Research Program of the NIA, NIH. The genome-wide association study was funded by NIA grant 1R01AG032098–01A1 to Wake Forest Health Sciences, and genotyping services were provided by the Center for Inherited Disease Research, which is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C. This research was further supported by RC1AG035835.

The Rotterdam Studies were funded by the Netherlands Organization of Scientific Research NWO Investments, nr. 175.010.2005.011, 911-03-012, Research Institute for Diseases in the Elderly, 014-93-015; RIDE2, the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) project nr. 050-060-810, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII) Municipality of Rotterdam.

SAPALDIA was supported by the Swiss National Science Foundation (grants no 33CS30-134276/1, 33CSCO-108796, 3247BO-104283, 3247BO-104288, 3247BO-104284, 3247-065896, 3100-059302, 3200-052720, 3200-042532, 4026-028099, 3233-054996, PDFMP3-123171), the Federal Office for Forest, Environment, and Landscape, the Federal Office of Public Health, the Federal Office of Roads and Transport, the canton’s government of Aargau, Basel-Stadt, Basel-Land, Geneva, Luzern, Ticino, Valais, Zurich, the Swiss Lung League, the canton’s Lung League of Basel Stadt/Basel Landschaft, Geneva, Ticino, Valais and Zurich, Schweizerische Unfallversicherungsanstalt (SUVA), Freiwillige Akademische Gesellschaft, UBS Wealth Foundation, Talecris Biotherapeutics GmbH, Abbott Diagnostics. Genotyping in the GABRIEL framework was supported by grants European Commission 018996 and Wellcome Trust WT 084703MA.

The 1994 Busselton follow-up Health Study was supported by Healthways, Western Australia. The Busselton Health Study is supported by The Great Wine Estates of the Margaret River region of Western Australia. The study gratefully acknowledges the assistance of the Western Australian DNA Bank (NHMRC Enabling Facility) with DNA samples and the support provided by The Ark at University of Western Australia for this study.

L.M.L. is the beneficiary of a postdoctoral grant from the AXA Research Fund. The Lothian Birth Cohort 1936 (LBC1936) whole genome association study was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) (ref. BB/F019394/1). The LBC1936 research was initially supported by a program grant from Research Into Ageing (ref. 251) and continues with program grants from Age UK (Disconnected Mind). The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (ref. G0700704/84698). Funding from the BBSRC, Engineering and Physical Sciences Research Council, Economic and Social Research Council, and Medical Research Council (MRC) is gratefully acknowledged.

The COPD cohorts meta-analysis was supported by U.S. NIH grants R01 HL075478, R01 HL084323, P01 HL083069, U01 HL089856 (E.K.S.); K12HL089990 and K08 HL097029 (M.H.C); and U01 HL089897 (J.D.C.). The National Emphysema Treatment Trial was supported by the National Heart, Lung, and Blood Institute, the Centers for Medicare and Medicaid Services, and the Agency for Healthcare Research and Quality. The National Emphysema Treatment Trial was supported by NHLBI contracts N01HR76101, N01HR76102, N01HR76103, N01HR76104, N01HR76105, N01HR76106, N01HR76107, N01HR76108, N01HR76109, N01HR76110, N01HR76111, N01HR76112, N01HR76113, N01HR76114, N01HR76115, N01HR76116, N01HR76118, and N01HR76119. The Normative Aging Study is supported by the Cooperative Studies Program/ERIC of the U.S. Department of Veterans Affairs and is a component of the Massachusetts Veterans Epidemiology Research and Information Center. The Norway GenKOLS study (Genetics of Chronic Obstructive Lung Disease, GSK code RES11080) and the ECLIPSE study (clinicaltrials.gov identifier NCT00292552; GSK code SCO104960) are funded by GlaxoSmithKline. The COPDGene project is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, Novartis, and Sepracor.

This work was supported, in part, by Intramural Research Programs of the NIH, the National Institute of Environmental Health Sciences (Z01ES043012). The CHARGE Pulmonary Working Group acknowledges funding from the NHLBI (HL105756) and the CHARGE Consortium’s organizational support.

Martin D. Tobin is supported by U.K. MRC Senior Clinical Fellowship G0902313. I.P.H. is supported by MRC (G1000861).

The Family Heart Study (FamHS) was supported by NIH grants R01-HL-087700 and R01-HL-088215 (M.A.P., PI) from NHLBI; and R01-DK-8925601 and R01-DK-075681 (I.B.B, PI) from NIDDK.

MESA was supported by contracts N01-HC-95159 through N01-HC-95169 from the NHLBI and RR-024156. The MESA Lung study was supported by grants R01-HL077612 and RC1-HL100543 from the NHLBI. Funding for SHARe genotyping was provided by NHLBI contract N02-HL-6-4278.

Footnotes

Author Contributions: Study design: J.B.W., G.T.O., H.M.B., D.S.P., C.W., P.Z., S.J.L., E.K.S., A.L., J.D.C., T.H.B., M.H.C., P.B., D.A.L., A.G., A.H., B.H.S., N.P.-H., T.R., M.D.T., I.P.H., I.J.D., J.M.S., K.-T.K., N.J.W., R.G.B., B.M.P., V.G., T.B.H., L.J.L. Phenotype data acquisition and quality control: J.B.W., T.C., G.T.O., D.S., H.M.B., P.Z., L.R.L., E.K.S., A.L., J.D.C., P.B., D.A.L., A.G., D.W.L., L.L., G.G.B., B.H.S., N.P.-H., T.R., L.M.L., I.J.D., J.M.S., K.-T.K., N.J.W., A.L.J., W.J.M., K.M.B., R.G.B., S.R.H., B.M.P., V.G., W.T., P.A.C. Genotype data acquisition and quality control: J.D., W.L.M., H.M.B., J.S., C.W., K.E.N., M.H.C., T.H.B., F.R., A.G.U., Y.L., K.K.L., N.P.-H., T.R., I.C., M.I., T.R., N.R.G.S., M.D.T., M.S.A., L.M.L., G.D., J.H.Z., R.J.F.L., J.H., S.S.R., A.M., J.I.R. Data analysis: J.B.W., D.P.S., H.M.B., J.S., D.B.H., S.J.L., L.R.L., M.H.C., T.H.B., E.K.S., D.W.L., K.K.L., W.T., A.R.B., P.A.C., I.C., M.I., N.R.G.S., M.D.T., M.S.A., L.M.L., J.H.Z., A.M., S.A.G., K.D.M., T.L., S.R.H., A.V.S. Critical revision of manuscript: all authors.

Information about sources of funding can be found before the References.

This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org

Originally Published in Press as DOI: 10.1164/rccm.201202-0366OC on July 26, 2012

Author disclosures are available with the text of this article at www.atsjournals.org.

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PERMALINK

Genome-Wide Association Studies Identify CHRNA5/3 and HTR4 in the Development of Airflow Obstruction

Jemma B Wilk

Nick R G Shrine

Laura R Loehr

Jing Hua Zhao

Ani Manichaikul

Lorna M Lopez

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