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

Article (PDF Available)inAmerican Journal of Respiratory and Critical Care Medicine 186(7):622-32 · July 2012with61 Reads
DOI: 10.1164/rccm.201202-0366OC · Source: PubMed
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.
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
, Jose
´
e 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
, Marı
´
a Soler Artigas
3
,
John M. Starr
8,60
, Andre
´
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
*, and
Bruno H. Stricker
16,17,43,57,69
*
1
Division of Aging, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts;
2
The National
Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts;
3
Departments of Health Sciences and Genetics,
University of Leicester, Leicester, United Kingdom;
4
Department of Epidemiology University of North Carolina Gillings School of Global Public
Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina;
5
MRC Epidemiology Unit, Institute of Metabolic Science,
Addenbrooke’s Hospital, Cambridge, United Kingdom;
6
Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia;
7
Division
of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia;
8
Centre for Cognitive
Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, United Kingdom;
9
Department of Psychology, The University of
Edinburgh, Edinburgh, United Kingdom;
10
Icelandic Heart Association, Kopavogur, Iceland;
11
University of Iceland, Reykjavik, Iceland;
12
Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington;
13
Department of Epidemiology,
University of Washington, Seattle, Washington;
14
Department of Genetics, University of Groningen, University Medical Center Gron ingen,
Groningen, The Netherlands;
15
Division of Nutritional Sciences, Cornell University, Ithaca, New York;
16
Department of Epidemiology, Erasmus
Medical Center, Rotterdam, The Netherlands;
17
Inspectorate of Healthcare, The Hague, The Netherlands;
18
Swiss Tropical and Public Health
Institute SwissTPH, Basel, Switzerland;
19
University of Basel, Basel, Switzerland;
20
Busselton Population Medical Research Institute, Sir Charles
Gairdner Hospital, Nedlands, Australia;
21
PathWest Laboratory Medicine of Western Australia, QEII Medical Centre, Nedlands, Australia;
22
School of
Pathology and Laboratory Medicine, University of Western Australia, Nedlands, Australia;
23
School of Population Health, University of Western
Australia, Nedlands, Australia;
24
Channing Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts;
25
Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hosp ital and Harvard Medical School, Boston,
Massachusetts;
26
Department of Neurology, Boston University School of Medicine, Boston, Massachusetts;
27
Division of Therapeutics and
Molecular Medicine, University of Nottingham, Nottingham, United Kingdom;
28
Department of Thoracic Surgery and Division of Epidemiology,
Vanderbilt University Medical Center, Nashville, Tennessee;
29
Department of Thoracic Medicine, Haukeland University Hospital and Institute of
Medicine, Universit y of Bergen, Bergen, Norway;
30
Johns Hopkins School of Public Health, Baltimore, Maryland;
31
Center for Research in Genomics
and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland;
32
Division of Statistical
Genomics, Department of Genetics, Washing ton University School of Medicine, St. Louis, Missouri;
33
Department of Respiratory Medicine, Ghent
University Hospital, Ghent, Belgium;
34
Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York;
35
Section of Pulmonary and Critical Care Medicine, Department of Medicine, Veterans Adminis tration Boston Healthcare System, Boston,
Massachusetts;
36
Section of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Boston University School of Medicine,
Boston, Massachusetts;
37
Department of Biostatistics, University of North Carolina Gillings School of Global Public Health, University of North
Carolina at Chapel Hill, Chapel Hill, North Carolina;
38
National Jewish Health, Denver, Colorado;
39
Biostatistics Department, Boston University
School of Public Health, Boston, Massachusetts;
40
Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of
Health, Research Triangle Park, North Carolina;
41
Behavioral Health Epidemiology Program, Research Triangle Institute International, Research
Triangle Park, North Carolina;
42
Laboratory of Epidemiology, Demography, and Biometry, Intramural Research Program, National Institute on
Aging, Bethesda, Maryland;
43
Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The
Netherlands;
44
Department of Pulmonary Physiology and Sleep Medicine, Sir Charles Gairdner Hospital, Nedlands, Australia;
45
The School of
Medicine and Pharmacology, University of Western Australia, Nedlands, Australia;
46
Department of Public Health and Primary Care, University of
Cambridge, Cambridge, United Kingdom;
47
Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School
of Medicine, Winston-Salem, North Carolina;
48
Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of
Medicine, Winston-Salem, North Carolina;
49
Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom;
50
Department of Statistics, University of Auckland, Auckland, New Zealand;
51
ALSPAC Laboratory, School of Social and Community Medicine,
University of Bristol, Bristol, United Kingdom;
52
College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee;
53
University
of Texas Health Science Center at Houston, Human Genetics Center, Houston, Texas;
54
Department of Respiratory Medicine, Sir Charles Gairdner
Hospital, Nedlands, Australia;
55
Department of Pulmonology and Groningen Research Institute for Asthma and COPD, University of Groningen,
University Medical Center Groningen, Groningen, The Netherlands;
56
Group Health Research Unit, Group Health Cooperative, Seattle, Washington;
57
Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands;
58
Division of Pulmonary Medicine, University Hospitals of
Am J Respir Crit Care Med Vol 186, Iss. 7, pp 622–632, Oct 1, 2012
Geneva, Geneva, Switzerland;
59
Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California;
60
Alzheimer Scotland Dementia
Research Centre, The University of Edinburgh, Edinburgh, United Kingdom;
61
Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht,
The Netherlands;
62
Genetic Epidemiology and Biostatistics Platform, Ontario Institute for Cancer Research, Toronto, Ontario, Canada;
63
Prosserman
Centre for Health Research, Samuel Lunenfeld Research Institute, Toronto, Ontario, Canada;
64
Department of Public Health, Weill Cornell Medical
College, New York, New York;
65
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York;
66
Division
of Population Health Sciences and Education, St George’s, University of London, London, United Kingdom
67
Department of Epidemiology and
Groningen Research Institute for Asthma and COPD, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands;
68
Center for Lung Biology, Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Washington, Seattle, Washington;
and
69
Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
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 keypathophysiologiccharacteristicofCOPDassessedbyspirometry,
in population-based cohorts examiningall 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 FEV
1
anditsratiotoFVC(FEV
1
/FVC) both less than
their respective lower limits of normal as determin ed by published
reference equations.
Measurements and Main Results: The discovery meta-analyses identi-
fied one region on chromosome 15q25.1 meeting genome-wide sig-
nificance 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 FEV
1
/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
Chronic obstructive pulmonary disease (COPD) is the third lead-
ing 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 revers-
ible 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 func-
tion 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 a
1
-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 exam-
ined two spirometry measures of lung function, FEV
1
and its
ratiotoFVC(FEV
1
/FVC). Two large-scale GWAS meta-analyses
identified a total of 11 loci related to FEV
1
or FEV
1
/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 iden-
tified 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 un-
derlying genetic signal because of substantial linkage disequilib-
rium 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
maybemediatedthroughthenicotinicreceptorgenesandthus
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).
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 predic-
tors of airflow obstruction. We confirm the association to the
chromosome 15 CHRNA5/CHRNA3 gene cluster and dem-
onstrate nominal association to the region in never smokers
with airflow obstruction. We also implicate the HTR4 gene
in the pathogenesis of airflow obstruction.
(Received in original form February 29, 2012; accepted in final form July 4, 2012)
* These authors contributed equally to this work.
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. Phe-
notype 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.Criticalrevisionofmanuscript:allauthors.
Information about sources of funding can be found before the R
EFERENCES.
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
This article has an online supplement, which is accessible from this issue’s table of
contents at www.atsjournals.org
Am J Respir Crit Care Med Vol 186, Iss. 7, pp 622–632, Oct 1, 2012
Published 2012 by the American Thoracic Society
Originally Published in Press as DOI: 10.1164/rccm.201202-0366OC on July 26, 2012
Internet address: www.atsjournals.org
Wilk, Shrine, Loehr, et al.: GWAS of Airflow Obstruction 623
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 bronchodi-
lator (19). In large population-based cohorts, post-bronchodilator
spirometry is not generally available, so we have studied prebron-
chodilator 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 co-
hort studies and conducted a meta-analysis. We then sought rep-
lication 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 Ge-
nomic Epidemiology (CHARGE) consortium (3), the SpiroMeta con-
sortium (4), and/or their joint analysis (5). Cohorts not included in
previous GWAS discovery sets for pulmonary function include Rotter-
dam 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 FEV
1
and FEV
1
/FVC from the National Health and Nutri-
tion Examination Survey III prediction equations (20) were used across all
cohorts. The presence of airflow obstruction was defined as an FEV
1
and
FEV
1
/FVC both less than the lower limit of normal (21) based on pre-
diction equations that include age, age
2
, and height
2
calculated sepa-
rately by sex. Unaffected participants were defined by FEV
1
,FVC,
and FEV
1
/FVC all above the lower limit of normal. Individuals be-
low the lower limit of normal for FEV
1
or FEV
1
/FVC but not both
were excluded from these analyses. Logistic regression models were ad-
justed 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 individ-
ual 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 FEV
1
less than 65% predicted (exclud-
ing 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 his-
tory 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 chromo-
some 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 3 10
25
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 un-
affected). Replication was further evaluated in a meta-analysis of stud-
ies 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 pro-
vided in Tables 1 and 2. The mean FEV
1
% predicted for partic-
ipants with airflow obstruction ranged from 48.9 to 68.7% across
cohorts, and for unaffected participants the means were generally
around 100%. The mean FEV
1
/FVC ratio ranged from 49.5 to
62.5% among affected participants and 74.1 to 81% among un-
affected participants across the cohorts. The mean ages at
measurement of spirometry across the cohorts ran ged from 45
to 76 years. The number of participants contributing to each of
the five discovery GWAS meta-analyses are provided in Table 3.
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/m
2
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)
FEV
1
/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)
FEV
1
% 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 )
Definition of abbreviations: ARIC ¼ Atherosclerosis Risk in Communities; B58C ¼ British 1958 Birth Cohort; CHS ¼ Cardiovascular Health Study; COPACETIC ¼ COPD
Pathology: Addressing Critical gaps, Early Treatment and diagnosis, and Innovative Concepts; EPIC ¼ European Prospective Investigation into Cancer and Nutrition; FHS ¼
Framingham Heart Study; MESA ¼ Multi-Ethnic Study of Atherosclerosis.
Data are presented as mean (SD) unless otherwise indicated.
* Pack-years calculated among current and former smokers.
624 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 186 2012
The genomic control (l
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 ve discovery meta-
analyses.
Discovery Meta-analyses
One region on chromosome 15 had 11 SNPs with genome-wide
significant results (P values , 5 3 10
28
) in discovery meta-analysis
of ever smokers (Table 4). An SNP in the AGPHD1 gene be-
tween the IREB2 gene and CHRN gene cluster was the top
association with airflow obstruction among ever smokers
(rs8031948, P value ¼ 2.8 3 10
29
)withtheminorallele
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 chromosom e 15 region but localized to the CHRNA5
gene (rs17486278, P value ¼ 1.9 3 10
27
). 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 3 10
25
) 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 anal-
yses 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
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/m
2
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)
FEV
1
/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)
FEV
1
% 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)
Definition of abbreviations: AGES ¼ Age, Gene, Environment Susceptibility; BHS ¼ Bussleton Health Study; Health ABC ¼ Health, Aging and Body Composition;
LBC1936 ¼ Lothian Birth Cohort; RS1 ¼ Rotterdam Study I; RS 2 ¼ Rotterdam Study II; RS3 ¼ Rotterdam Study III; SAPALDIA ¼ 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* FEV
1
, 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
x
967
x
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
Definition of abbreviations: AGES ¼ Age, Gene, Environment Susceptibility; ARIC ¼ Atherosclerosis Risk in Communities; B58C ¼ British 1958 Birth Cohort; BHS ¼
Bussleton Health Study; CHS ¼ Cardiovascular Health Study; COPACETIC ¼ COPD Pathology: Addressing Critical gaps, Early Treatment and diagnosis, and Innovative
Concepts; EPIC ¼ European Prospective Investigation into Cancer and Nutrition; FHS ¼ Framingham Heart Study; Health ABC ¼ Health, Aging and Body Composition;
LBC1936 ¼ Lothian Birth Cohort; MESA ¼ Multi-Ethnic Study of Atherosclerosis; RS1 ¼ Rotterdam Study I; RS2 ¼ Rotterdam Study II; RS3 ¼ Rotterdam Study III;
SAPALDIA ¼ 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.
y
FEV
1
, 65%: cases were restricted to those with FEV
1
, 65% and FEV
1
/FVC less than the lower limit of normal; the definition of control subjects was the same as used
for all participants.
z
Not analyzed due to low number of cases.
x
Results were not available for discovery meta-analyses.
Wilk, Shrine, Loehr, et al.: GWAS of Airflow Obstruction 625
that included different cohorts as presented in Table 3, and thus
the ever and never smoker results do not reflect a straightfor-
ward stratified analysis of all participants. The inclusion of the
COPACETIC study in the ever smokers meta-analysis contrib-
uted to the improved association signal in the region. In the
GWAS of a more severe airflow obstruction phenotype defined
by FEV
1
less than 65% predicted, a missense SNP in the
CHRNA5 gene (rs16969968, Asp398Asn) had the third ranked
P value (5 3 10
27
) with an OR of 1.22.
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 FEV
1
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 se-
lected for replication are included in Table 5. Genome-wide
results for all five definitions of airflow obstruction are available
in the online supplement.
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 rep-
lication studies. In discovery analysis of all airflow obstruction,
the chromosome 6 MHC locus at 6p21.33 was among the top
results (smallest P value ¼ 6.8 3 10
27
, rs3094013). The closest
gene to the top SNP was HLA complex P6 (HCP5), although
the extensive linkage disequilibrium in the region makes inter-
pretation difficult. When discovery results were meta-analyzed
with the replication studies, the previous associations were at-
tenuated. 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 3 10
26
near the HLA-A gene.
Thirty-six SNPs in chromosome 6 with combined meta-analysis
P values less than 1 3 10
24
are provided in Table E3.
On chromosome 15, after meta-analysis of airflow obstruc-
tion in ever smokers from discovery populations with replica-
tion studies, the order of the top hits was generally unchanged
and P values improved, rea ching 2.6 3 10
215
.TheCOPD
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 presenti ng the study-specific results
among never smokers that demonstrates sim ilar effect sizes
across the cohorts.
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 3 10
24
, representing
Bonferroni correction for 60 tests at the a ¼ 0.05 level, was
selected apriorias th e threshold for st atistically significant
replication. No SNPs achieved the replicat ion criterion. In
a meta-analysis combining the di scovery results with the
FamHS, one SNP achieved genome-wide statistical signifi-
cance (rs7733088 in HTR4) with a 38% frequent minor allele
conferring an OR of 0.81 (P ¼ 4.09 3 10
29
). Of the top 60
SNPs, four had nominal association (P values , 0.05) in the
COPD meta-analysis and a consistent risk allele; these S NPs
were located in ADAM19, RARB, PPAP2B,andADAMTS19
(Table 6).
Association of Spirometry-associated SNPs
with Airflow Obstruction
Previous meta-analyses in the CHARGE and SpiroMeta consor-
tia (3–5) identified 75 SNPs associated with either FEV
1
or FEV
1
/
FVC at genome-wide significance (P value < 5 3 10
28
). 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 con-
sidered a P value of 8.6 3 10
24
as the criterion for statistically
significant association with airflow obstruction (Bonferroni cor-
rection for 58 tests at the a ¼ 0.05 level) given the aprioriasso-
ciation with spirometry. Among all participants, SNPs in RARB,
GPR126, HTR4, C10orf11, near HHIP, and near HLA-DRA
were statistically significantly associated with airow obst ruc-
tion. 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, a nd
COPACETIC) as an independent sample, power was reduced,
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 3 10
27
1.15 1.53 3 10
23
1.22 2.78 3 10
29
rs931794 76613235 AGPHD1 Intronic a 0.65 0.86 6.18 3 10
27
0.87 1.46 3 10
23
0.82 4.69 3 10
29
rs10519203 76601101 AGPHD1 Intronic a 0.65 0.86 7.16 3 10
27
0.86 1.27 3 10
23
0.82 5.67 3 10
29
rs9788721 76589924 AGPHD1 Intronic t 0.65 0.86 1.04 3 10
26
0.86 1.17 3 10
23
0.82 9.76 3 10
29
rs2036527 76638670 a 0.34 1.18 3.51 3 10
27
1.18 2.85 3 10
24
1.22 1.72 3 10
28
rs17486278 76654537 CHRNA5 Intronic a 0.66 0.85 1.91 3 10
27
0.84 1.06 3 10
24
0.83 2.43 3 10
28
rs7180002 76661048 CHRNA5 Intronic a 0.66 0.85 2.23 3 10
27
0.84 1.20 3 10
24
0.83 2.68 3 10
28
rs1051730 76681394 CHRNA3 Synonymous a 0.33 1.17 3.29 3 10
27
1.20 8.36 3 10
25
1.21 3.36 3 10
28
rs16969968 76669980 CHRNA5 Missense a 0.34 1.17 3.46 3 10
27
1.19 1.25 3 10
24
1.20 3.47 3 10
28
rs951266 76665596 CHRNA5 Intronic a 0.34 1.17 3.64 3 10
27
1.19 1.41 3 10
24
1.21 3.47 3 10
28
rs1317286 76683184 CHRNA3 Intronic a 0.66 0.85 3.93 3 10
27
0.84 1.18 3 10
24
0.83 4.74 3 10
28
Definition of abbreviation:OR¼ odds ratio.
* All cohorts with both ever and never smoking participants.
626 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 186 2012
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 3 10
28
Smoker 0.75 0.015 0.81 4.09 3 10
29
rs2044029 A 0.4 15 69467013 THSD4 1.16 4.95 3 10
27
All 1.20 0.078 1.17 1.06 3 10
27
rs181654 A 0.28 10 119369646 EMX2 0.82 1.24 3 10
26
No asthma 0.76 0.030 0.81 1.31 3 10
27
rs4597955 A 0.59 5 147827466 HTR4 0.84 3.12 3 10
26
Smoker 0.76 0.011 0.83 1.57 3 10
27
rs12905014 T 0.95 15 90684844 ST8SIA2 0.58 5.24 3 10
26
FEV
1
65% 0.47 0.020 0.57 3.83 3 10
27
rs11744671 T 0.92 5 156853809 ADAM19 0.72 8.25 3 10
27
No asthma 0.77 0.264 0.72 4.51 3 10
27
rs8033889 T 0.21 15 69467134 THSD4 1.18 2.49 3 10
26
All 1.23 0.066 1.19 4.67 3 10
27
rs6684428 A 0.16 1 56132401 PPAP2B 1.24 3.60 3 10
27
Smoker 1.08 0.574 1.23 4.73 3 10
27
rs4767234 A 0.59 12 113122231 TBX5 1.18 1.28 3 10
26
Smoker 1.15 0.242 1.17 6.32 3 10
27
rs4534959 A 0.97 18 60028136 SERPINB8 0.57 4.19 3 10
27
FEV
1
65% 0.91 0.844 0.58 6.90 3 10
27
rs715921 A 0.31 13 23693489 SPATA13 1.22 5.18 3 10
27
No asthma 1.07 0.551 1.20 7.60 3 10
27
rs16889038 T 0.92 6 24414366 DCDC2 0.7 4.15 3 10
27
FEV
1
65% 0.95 0.845 0.72 7.94 3 10
27
rs9536318 A 0.83 13 52392695 PCDH8 0.81 5.47 3 10
26
No asthma 0.75 0.051 0.80 8.24 3 10
27
rs1997352 A 0.26 3 25513321 RARB 0.85 4.29 3 10
26
All 0.82 0.076 0.84 8.64 3 10
27
rs10759102 A 0.33 9 9900123 PTPRD 0.81 4.66 3 10
26
FEV
1
65% 0.77 0.083 0.81 1.04 3 10
26
rs13144621 T 0.32 4 109437378 LEF1 0.85 5.87 3 10
27
All 0.98 0.850 0.86 1.20 3 10
26
rs1982234 C 0.63 15 69478345 THSD4 1.16 4.80 3 10
26
All 1.16 0.136 1.16 1.55 3 10
26
rs7799265 C 0.95 7 28399001 CREB5 0.63 8.84 3 10
27
FEV
1
65% 0.91 0.742 0.65 1.71 3 10
26
rs181652 A 0.54 10 119369077 EMX2 1.18 8.12 3 10
26
No asthma 1.19 0.097 1.18 1.94 3 10
26
rs11766496 C 0.12 7 71026786 CALN1 1.34 3.90 3 10
27
All 0.97 0.855 1.30 2.01 3 10
26
rs2263638 A 0.37 10 94158777 IDE 0.78 4.68 3 10
26
Never smoker 0.80 0.275 0.78 2.53 3 10
26
rs7850092 A 0.21 9 9899119 PTPRD 0.79 4.00 3 10
26
FEV
1
65% 0.85 0.324 0.79 2.60 3 10
26
rs1329705 A 0.2 6 142795031 GPR126 0.79 3.75 3 10
26
FEV
1
65% 0.85 0.342 0.79 2.63 3 10
26
rs11209261 A 0.76 1 68557801 GPR177 0.81 6.76 3 10
26
FEV
1
65% 0.80 0.191 0.81 2.78 3 10
26
rs7607316 A 0.21 2 237186581 CXCR7 1.28 9.19 3 10
26
Never smoker 1.35 0.152 1.29 3.21 3 10
26
rs9975851 T 0.57 21 26638525 CYYR1 1.18 4.92 3 10
26
No asthma 1.11 0.332 1.17 3.66 3 10
26
rs12505749 C 0.92 4 57028869 SRP72 0.76 3.06 3 10
27
All 1.22 0.258 0.79 4.69 3 10
26
rs1207393 C 0.36 22 24983362 SEZ6L 0.83 8.69 3 10
26
FEV
1
65% 0.85 0.283 0.84 4.78 3 10
26
rs12744110 T 0.25 1 56168897 PPAP2B 1.18 4.51 3 10
26
Smoker 1.06 0.681 1.17 5.95 3 10
26
rs11097912 T 0.33 4 107219911 MGC16169 0.85 6.04 3 10
26
Smoker 0.92 0.497 0.85 5.95 3 10
26
rs17086172 T 0.94 18 68378001 CBLN2 0.73 7.13 3 10
26
No asthma 0.86 0.492 0.74 7.23 3 10
26
rs2322734 A 0.96 3 4608492 ITPR1 0.67 7.19 3 10
26
FEV
1
65% 0.83 0.518 0.69 7.52 3 10
26
rs8036030 A 0.39 15 72503662 SEMA7A 0.84 5.71 3 10
26
No asthma 0.95 0.653 0.85 8.31 3 10
26
rs892961 A 0.41 17 72911695 SEPT9 0.85 8.00 3 10
26
No asthma 0.93 0.479 0.85 8.91 3 10
26
rs7629245 T 0.15 3 18662455 1 MAP3K13 1.23 0.00001 Smoker 1.12 0.456 1.22 9.06 3 10
26
rs2830165 T 0.59 21 26598463 APP 0.81 5.27 3 10
26
Never smoker 0.98 0.892 0.82 9.55 3 10
26
rs7686928 T 0.14 4 18897082 3 ZFP42 1.21 8.77 3 10
26
Smoker 1.08 0.630 1.21 9.65 3 10
26
rs9632471 C 0.72 5 128761894 ADAMTS19 0.76 5.54 3 10
26
FEV
1
65% 0.95 0.776 0.77 1.07 3 10
25
rs4837614 T 0.15 9 11835018 6 ASTN2 0.76 4.56 3 10
26
FEV
1
65% 0.98 0.911 0.78 1.20 3 10
25
rs12960805 A 0.41 18 7909707 PTPRM 1.27 6.22 3 10
26
Never smoker 1.00 0.995 1.25 1.27 3 10
25
rs1799257 A 0.12 19 53664351 PSCD2 1.37 2.08 3 10
26
FEV
1
65% 0.78 0.337 1.33 1.38 3 10
25
rs1868466 A 0.79 16 76301059 KIAA1576 0.85 5.45 3 10
26
All 1.00 0.997 0.86 1.39 3 10
25
rs10518948 C 0.93 15 69415023 THSD4 0.68 9.91 3 10
26
Never smoker 0.97 0.931 0.69 1.51 3 10
25
rs6901575 A 0.1 6 28358963 PGBD1 1.24 6.76 3 10
26
All 0.97 0.853 1.22 1.55 3 10
25
rs3790728 T 0.97 1 21573715 0 GPATCH2 0.57 3.12 3 10
26
FEV
1
65% 4.24 0.060 0.60 1.58 3 10
25
rs9511117 A 0.09 13 23660045 SPATA13 0.73 6.78 3 10
26
Smoker 1.01 0.979 0.75 1.65 3 10
25
rs4957070 A 0.63 5 600858 SLC9A3 0.85 9.09 3 10
26
Smoker 0.98 0.874 0.86 1.65 3 10
25
rs3814818 T 0.9 14 94263130 GSC 0.74 2.51 3 10
26
FEV
1
65% 1.24 0.357 0.77 1.68 3 10
25
rs1895493 C 0.09 16 78122404 MAF 1.41 5.51 3 10
26
Never smoker 0.77 0.460 1.37 1.72 3 10
25
rs12872078 A 0.91 13 64002324 PCDH9 1.43 4.81 3 10
26
FEV
1
65% 0.96 0.876 1.37 1.75 3 10
25
rs764593 T 0.11 3 3687236 LRRN1 0.76 3.89 3 10
26
Smoker 1.11 0.563 0.79 2.64 3 10
25
rs1408298 T 0.7 6 17199273 RBM24 0.81 8.19 3 10
26
No asthma 1.00 0.972 0.83 2.95 3 10
25
rs2164220 T 0.08 7 15744798 6 PTPRN2 1.42 9.80 3 10
26
Never smoker 0.61 0.266 1.38 3.14 3 10
25
rs10496694 A 0.09 2 133252637 NAP5 1.31 7.42 3 10
26
Smoker 0.86 0.512 1.27 3.30 3 10
25
rs11023434 C 0.23 11 15083724 INSC 1.33 7.78 3 10
26
Never smoker 0.95 0.766 1.28 3.70 3 10
25
rs1567398 T 0.43 8 8764214 MFHAS1 1.2 8.20 3 10
26
FEV
1
65% 0.91 0.519 1.17 3.98 3 10
25
rs1125729 T 0.81 8 93427586 RUNX1T1 0.8 1.63 3 10
26
FEV
1
65% 1.59 0.014 0.83 4.89 3 10
25
rs7163331 A 0.04 15 96260720 ARRDC4 1.55 6.81 3 10
26
FEV
1
65% 0.56 0.132 1.46 6.34 3 10
25
rs12265908 A 0.97 10 2339319 ADARB2 0.59 6.92 3 10
26
FEV
1
65% 1.81 0.157 0.64 7.54 3 10
25
rs7719062 T 0.08 5 1222044 SLC6A19 1.42 9.79 3 10
26
Smoker 0.81 0.393 1.34 7.71 3 10
25
Definition of abbreviations: Chr ¼ chromosome ; Freq ¼ frequency; OR ¼ odds ratio; SNP ¼ 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 FEV
1
65%.
Wilk, Shrine, Loehr, et al.: GWAS of Airflow Obstruction 627
and only the ADAM19 SNP in smokers achieved the Bonfer-
roni 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 pla-
centa: 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.
Figure 1. Regional association plot for chromosome
15 presenting results from combined meta-analysis
of discovery and replication studies. X-axis is meg-
abase (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 leg-
end. Nonsynonymous SNPs are depicted by an
inverted triangle and other coding SNPs by a square.
(A) Ever smokers. (B) Never smokers.
628 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 186 2012
DISCUSSION
These meta-analyses included 32,875 participants from population-
based studies for discovery of loci associated with airflow ob-
struction. 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 sam-
ples, 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 t he minor allele was
associated with lower expression of CHRNA5 (28). The correla-
tion between associated SNP genotypes and CHRNA5 expres-
sion 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 re-
cently 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 ex-
pression 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 FEV
1
/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 hu-
man airway epithelial cells and have been implicated in the path-
ogenesis of asthma (32). The reduced risk of airflow obstruction
was strongest when limited to ever smokers, suggesting that var-
iation in HTR4 may contribute to the inflammatory response to
cigarette smoke.
Several genes represented among the top SNP results were
nominall y replicated in the COPD case- control meta-analysis
(ADAM19, RARB, PPAP2B,andADAMTS19). 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 FEV
1
/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 epithe-
lial cells, bronchial smooth muscle, and interstitial inflammatory
cells and may have a role in immune defense and the inflamma-
tory process (34). ADAMTS19 (a disintegrin and metalloprotei-
nase 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 gen-
erally believed to influence surfactant secretion and have a role in
lung injury and repair (36).
0 0.5 1 1.5 2
Odds Ratio
Meta-analysis
FamHS
SAPALDIA
B58C
BHS
EPIC
AGES
FHS
CHS
ARIC
1.18 [ 1.08 , 1.29 ]
0.96 [ 0.67 , 1.37 ]
1.12 [ 0.69 , 1.83 ]
0.91 [ 0.61 , 1.37 ]
1.22 [ 0.79 , 1.88 ]
1.05 [ 0.71 , 1.57 ]
1.15 [ 0.76 , 1.75 ]
1.28 [ 0.99 , 1.66 ]
1.01 [ 0.73 , 1.38 ]
1.26 [ 1.12 , 1.42 ]
Figure 2. Forest plot depicting the association results for rs10517 30
(CHRNA3) and airflow obstruction among never smokers in each cohort
and the meta-analysis. AGES ¼ Age, Gene, Environment Susceptibility;
ARIC ¼ Atherosclerosis Risk in Communities; B58C ¼ British 1958 Birth Co-
hort; BHS ¼ Bussleton Health Study; CHS ¼ Cardiovascular Health Study;
EPIC ¼ Euro pean Prospective Investigation into Cancer and Nutrition;
FamHS ¼ Family Heart Study; FHS ¼ Framingham Heart Study; SAPALDIA ¼
Swiss Study on Air Pollution and Lung and Heart Disease in Adults.
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 3 10
27
0.8 0.027
rs1997352 A 0.26 3 25513321 RARB All 0.84 8.64 3 10
27
0.88 0.038
rs12744110 T 0.25 1 56168897 PPAP2B Smoker 1.17 5.95 3 10
26
1.13 0.045
rs9632471 C 0.72 5 128761894 ADAMTS19 FEV
1
65% 0.77 1.07 3 10
25
0.86 0.045
Definition of abbreviations: Chr ¼ chromosome; COPD ¼ chronic obstructive pulmonary disease; Freq ¼ frequency; OR ¼ odds ratio; SNP ¼
single-nucleotide polymorphism.
Wilk, Shrine, Loehr, et al.: GWAS of Airflow Obstruction 629
RARB (retinoic acid receptor b) 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 demon-
strated reversibility of experimentally induced emphysema with
administration of RA (37); however, subsequent studies in ani-
mal models had conflicting results (38), and a small feasibility
study of RA for the treatment of emphysema did not show sig-
nificant 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 thera-
peutics to target the b 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 stud-
ies 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 hap-
lotype 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 obstruc-
tion (P ¼ 3.2 3 10
24
), 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 asso-
ciation with COPD in a prior report focusing on clinically ascer-
tained 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 sepa-
rately, in this reduced sample size (567 affected, 2,922 unaf-
fected) 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 3 10
24
) across five megabases
in the HLA region, including 17 SNPs in the histone gene clus-
ter 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 pre-
bronchodilator 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 ven-
tilatory impairment, and the participants who meet this defini-
tion 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 ad-
dress 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 ob-
struction 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 obstruc-
tion 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 rele-
vant lung and airway tissues. Thus, CHRNA3/5 variants may me-
diate airflow obstruction in both ever and never smokers.
Author disclosures are available with the text of this article at www.atsjournals.org.
Acknowledgment: The authors thank all the participants and research team mem-
bers. 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 ex pertise. The
LBC1936 authors thank the nurses and staff at the Wellcome Trust Clinical Re-
search 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 resour-
ces from the Framingham Heart Study of the National Heart, Lung, and Blood In-
stitute (NHLBI) of the National Institutes of Health (NIH) and Boston University
School of Medicine. The analyses reflect intellectual input and resource develop-
ment from the Framingham investigators participating in the SNP Health Associ-
ation Resource (SHARe) project. This work was partially supported by the NHLBI’s
Framingham Heart Study (contract N01-HC-25195) and its contract with Affy-
metrix, 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 Envi-
ronmental 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 HL0 80295, HL075366,
HL087652, HL105756 with additional contribution from National Institute of Neu-
rological 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 pro-
gram, grant agreem ent 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 spon-
sored 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 Institut e of Child Health and Human
630 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 186 2012
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 Parliame nt).
The Health, Aging, and Body Composition Study was supported by NIA contracts
N01AG62101, N01AG2103, and N01AG62106 and in part by the Intramural Re-
search 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 Re-
search, w hich 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 Insti-
tute 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 Commissi on (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 Trans-
port, 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 Unfall-
versicherungsanstalt (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, West-
ern Australia. The Busselton Health Study is supported by The Great Wine Estates
of the Margaret River region of Western Australia. The study gratefully acknowl-
edges the assistance of the Western Australian DNA Bank (NHMRC Enabling Fa-
cility) 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 postdo ctoral 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 Treat-
ment Trial was supported by the National Heart, Lung, and Blood Institute, the
Centers for Medicare and Medicaid Services, and the Agency for Healthcare Re-
search 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 Ag-
ing Study is supported by the Cooperative Studies Program/ERIC of the U.S. Depart-
ment 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 com-
prised 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-H L-087700
and R01-HL-088215 (M.A.P., PI) from NHLBI; a nd R01-DK-8925 601 a nd R 01-
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.
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632 AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 186 2012
    • "To study the genetic component of COPD, genome-wide association (GWA) studies have attempted to identify genetic determinants of human lung function in healthy subjects, using spirometry data on Forced Expiratory Volume in one second (FEV)1 and its ratio to Forced Vital Capacity (FVC) (FEV1/FVC). To date, a total of 26 genetic loci for human lung function have been identified, some of which also seem to be associated with COPD susceptibility, such as the loci at TNS1, RARB, FAM13A, GSTCD, HHIP, ADAM19, HTR4, AGER, GPR126, C10orf11 and THSD4 [7], [8], [9], [10], [11]. "
    [Show abstract] [Hide abstract] ABSTRACT: Chronic obstructive pulmonary disease (COPD) independently associates with an increased risk of coronary artery disease (CAD), but it has not been fully investigated whether this co-morbidity involves shared pathophysiological mechanisms. To identify potential common pathways across the two diseases, we tested all recently published single nucleotide polymorphisms (SNPs) associated with human lung function (spirometry) for association with carotid intima-media thickness (cIMT) in 3,378 subjects with multiple CAD risk factors, and for association with CAD in a case-control study of 5,775 CAD cases and 7,265 controls. SNPs rs2865531, located in the CFDP1 gene, and rs9978142, located in the KCNE2 gene, were significantly associated with CAD. In addition, SNP rs9978142 and SNP rs3995090 located in the HTR4 gene, were associated with average and maximal cIMT measures. Genetic risk scores combining the most robustly spirometry-associated SNPs from the literature were modestly associated with CAD, (odds ratio (OR) (95% confidence interval (CI95) = 1.06 (1.03, 1.09); P-value = 1.5×10-4, per allele). In conclusion, our study suggests that some genetic loci implicated in determining human lung function also influence cIMT and susceptibility to CAD. The present results should help elucidate the molecular underpinnings of the co-morbidity observed across COPD and CAD.
    Full-text · Article · Aug 2014
    • "Several genome-wide association studies have linked chromosome 15q24-q25.1, a region containing the genes encoding the α3, α5, and β4 subunits of neuronal nicotinic receptors, with nicotine dependence and smoking-related illnesses such as lung cancer, airflow obstruction, and chronic obstructive pulmonary disease [1]–[6]. In candidate gene association studies, variants in the CHRNA5-A3-B4 gene cluster have been associated with nicotine dependence [7]–[14], smoking behaviors [15], [16], level of response to alcohol [17], age of initiation of drinking [15] and cocaine dependence [11], [18]. "
    [Show abstract] [Hide abstract] ABSTRACT: Variants within the gene cluster encoding α3, α5, and β4 nicotinic receptor subunits are major risk factors for substance dependence. The strongest impact on risk is associated with variation in the CHRNA5 gene, where at least two mechanisms are at work: amino acid variation and altered mRNA expression levels. The risk allele of the non-synonymous variant (rs16969968; D398N) primarily occurs on the haplotype containing the low mRNA expression allele. In populations of European ancestry, there are approximately 50 highly correlated variants in the CHRNA5-CHRNA3-CHRNB4 gene cluster and the adjacent PSMA4 gene region that are associated with CHRNA5 mRNA levels. It is not clear which of these variants contribute to the changes in CHRNA5 transcript level. Because populations of African ancestry have reduced linkage disequilibrium among variants spanning this gene cluster, eQTL mapping in subjects of African ancestry could potentially aid in defining the functional variants that affect CHRNA5 mRNA levels. We performed quantitative allele specific gene expression using frontal cortices derived from 49 subjects of African ancestry and 111 subjects of European ancestry. This method measures allele-specific transcript levels in the same individual, which eliminates other biological variation that occurs when comparing expression levels between different samples. This analysis confirmed that substance dependence associated variants have a direct cis-regulatory effect on CHRNA5 transcript levels in human frontal cortices of African and European ancestry and identified 10 highly correlated variants, located in a 9 kb region, that are potential functional variants modifying CHRNA5 mRNA expression levels.
    Full-text · Article · Nov 2013
    • "In smokers, the 198 G/G genotype in ET-1 gene (coding for Endothelin-1) possesses higher risk to COPD than the TT genotype [45]. Most studies in different countries and races have congruously indicated that the cholinergic receptor-nicotinic-α 5/3 gene (CHRNA3/5) is strongly associated with COPD, whereas the rs6495309 CT or TT genotype in CHRNA3 gene is associated with a significantly decreased risk of COPD compared with the CC genotype464748495051. In addition, in a multistage genome-wide association study (GWAS) carried out by Pillai et al. in 2009, the rs8034191 and rs1051730 genotypes in CHRNA3/5 have been associated with COPD phenotypes (based on FEV 1 /FVC). "
    [Show abstract] [Hide abstract] ABSTRACT: Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality throughout the world and is mainly characterized by persistent airflow limitation. Given that multiple systems other than the lung can be impaired in COPD patients, the traditional FEV1/FVC ratio shows many limitations in COPD diagnosis and assessment. Certain heterogeneities are found in terms of clinical manifestations, physiology, imaging findings, and inflammatory reactions in COPD patients; thus, phenotyping can provide effective information for the prognosis and treatment. However, phenotypes are often based on symptoms or pathophysiological impairments in late-stage COPD, and the role of phenotypes in COPD prevention and early diagnosis remains unclear. This shortcoming may be overcome by the potential genotypes defined by the heterogeneities in certain genes. This review briefly describes the heterogeneity of COPD, with focus on recent advances in the correlations between genotypes and phenotypes. The potential roles of these genotypes and phenotypes in the molecular mechanisms and management of COPD are also elucidated.
    Full-text · Article · Nov 2013
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