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Am J Respir Crit Care Med. 2010 December 15; 182(12): 1498–1505.
Published online 2010 July 23. doi:  10.1164/rccm.201002-0151OC
PMCID: PMC3029936

Loci Identified by Genome-wide Association Studies Influence Different Disease-related Phenotypes in Chronic Obstructive Pulmonary Disease

Abstract

Rationale: Genome-wide association studies have shown significant associations between variants near hedgehog interacting protein HHIP, FAM13A, and cholinergic nicotinic acetylcholine receptor CHRNA3/5 with increased risk of chronic obstructive pulmonary disease (COPD) in smokers; however, the disease mechanisms behind these associations are not well understood.

Objectives: To identify the association between replicated loci and COPD-related phenotypes in well-characterized patient populations.

Methods: The relationship between these three loci and COPD-related phenotypes was assessed in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-point (ECLIPSE) cohort. The results were validated in the family-based International COPD Genetics Network (ICGN).

Measurements and Main Results: The CHRNA3/5 locus was significantly associated with pack-years of smoking (P = 0.002 and 3 × 10−4), emphysema assessed by a radiologist using high-resolution computed tomography (P = 2 × 10−4 and 4.8 × 10−5), and airflow obstruction (P = 0.004 and 1.8 × 10−5) in the ECLIPSE and ICGN populations, respectively. However, variants in the IREB2 gene were only significantly associated with FEV1. The HHIP locus was not associated with smoking intensity but was associated with FEV1/FVC (P = 1.9 × 10−4 and 0.004 in the ECLIPSE and ICGN populations). The HHIP locus was also associated with fat-free body mass (P = 0.007) and with both retrospectively (P = 0.015) and prospectively (P = 0.024) collected COPD exacerbations in the ECLIPSE cohort. Single-nucleotide polymorphisms in the FAM13A locus were associated with lung function.

Conclusions: The CHRNA3/5 locus was associated with increased smoking intensity and emphysema in individuals with COPD, whereas the HHIP and FAM13A loci were not associated with smoking intensity. The HHIP locus was associated with the systemic components of COPD and with the frequency of COPD exacerbations. FAM13A locus was associated with lung function.

Keywords: COPD exacerbations, nicotine addiction, high-resolution CT, genetic association analysis, emphysema

AT A GLANCE COMMENTARY

Scientific Knowledge on the Subject

After more than a decade of inconsistent genetic association analysis results in chronic obstructive pulmonary disease (COPD), recent studies have identified three genetic loci that are unequivocally associated with COPD. These results provide a major step forward in the understanding of COPD; however, the disease mechanisms behind these associations are not well understood.

What This Study Adds to the Field

Our study in well-characterized COPD cohorts demonstrated that the CHRNA3/5 locus was associated with increased smoking intensity, airflow obstruction, and emphysema in individuals with COPD. The HHIP locus was associated with the systemic components of COPD and with the frequency of COPD exacerbations. The FAM13A locus was associated with airflow obstruction in patients with COPD. These results suggest that these three genetic loci influence different biologic processes in COPD.

Chronic obstructive pulmonary disease (COPD) is a common condition that is intimately linked with cigarette smoking and is predicted to be the third leading cause of mortality and the fifth leading cause of morbidity in the world by the year 2020 (1). COPD is characterized by the presence of airflow limitation resulting from airway inflammation and remodelling or lung parenchymal destruction with emphysema. Although COPD severity is typically determined by spirometric measures (2), there is extensive heterogeneity in the amount of emphysema, degree of functional impairment, and frequency of exacerbations among patients with COPD with the same level of airflow obstruction (3). COPD can be associated with systemic manifestations, such as cachexia, which can effectively result in impaired functional capacity, worsening dyspnea, reduced health-related quality of life, and increased mortality (4).

Familial aggregation studies suggest a strong genetic component to the risk of developing COPD (510). However, the only proven genetic risk factor for COPD is a severe deficiency of α1-antitrypsin (11), which is present in only 1–2% of individuals with COPD. Major COPD susceptibility loci have recently been identified on chromosomes 4 (near hedgehog interacting protein [HHIP] and FAM13A) and 15 (in a linkage disequilibrium block including components of the cholinergic nicotinic acetylcholine receptor, CHRNA3/5, and the IREB2 gene) in genome-wide association studies (GWAS) of COPD and related phenotypes (1214). We hypothesized that different COPD-related phenotypes would be influenced by the HHIP, FAM13A, and CHRNA3/5 loci. We have evaluated these loci in individuals recruited to the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-point (ECLIPSE) cohort (15) and have replicated the findings in the International COPD Genetics Network (ICGN) population.

METHODS

ECLIPSE is a longitudinal prospective study being conducted at 46 clinical centers in 12 countries (15). All consenting subjects with COPD (n = 1,719) underwent spirometry and a low-dose multislice computed tomography (CT) scan of the chest (120-kVp, 40 mAs, 1-mm contiguous images). Extent of emphysema was assessed qualitatively by two independent radiologists and quantitatively using the Pulmonary Workstation 2.0 Software (VIDA Diagnostics, Iowa City, IA). The airway wall measurements were undertaken using the Pulmonary Workstation software. Reversibility to salbutamol was defined as greater than or equal to 12% improvement from the baseline FEV1. The body mass index, airflow obstruction, and exercise capacity (BODE) index was calculated using the method previously described (16). Whole-body impedance was measured using the bioelectrical impedance method (Bodystat 1500; Bodystat Ltd, Isle of Man, UK) and fat-free body mass (FFM) was calculated (17).

COPD exacerbation frequency in ECLIPSE was assessed in two ways. For retrospective exacerbations, a questionnaire was administered at the time of recruitment and representing COPD-related exacerbations in the previous 12 months (subject-reported episodes that required antibiotics, systemic corticosteroids, or hospitalization). For prospective exacerbations, subjects were followed for 2 years based on monthly telephone calls (exacerbations over the first 2 years of the study were defined by the requirement for antibiotics, systemic corticosteroids, or hospitalization).

Individuals with COPD were recruited as probands to the ICGN study (18), and siblings and available parents were ascertained through the probands. In total, 1,891 whites from 606 pedigrees were included in the current analysis. High-resolution CT (120-kVp, 200-mAs, 1-slice thickness, 20-mm slice interval) measurements were available on 561 probands (48.5%) and 663 siblings (34.2%) using custom software developed at the University of British Columbia, Vancouver, Canada (18). All of the CT scans from the ECLIPSE and ICGN studies were processed at University of British Columbia. The ICGN population was originally used to identify the HHIP, FAM13A, and CHRNA3/5 single-nucleotide polymorphisms (SNP) using the COPD affection status phenotype in follow-up to genome-wide association analysis (12, 14), and a previously reported association analysis of FEV1 in the ICGN is included for comparison in this manuscript. The ECLIPSE population was not a part of the original GWAS that identified those loci, but was included in the collaborative study that identified FAM13A as an additional COPD susceptibility locus (12). Additional information about these cohorts is available in the online supplement. A total of 110 subjects from the ICGN study used in these analyses participated in the ECLIPSE cohort, and these subjects were eliminated in the analyses involving both ECLIPSE and ICGN cohorts.

Genotyping

The Illumina 550Kv3 SNP array was used for SNP genotyping in the ECLIPSE subjects. Details on the procedures used for genotyping and quality control evaluation are described elsewhere (14). Data from two SNPs in the CHRNA3/5 locus (rs8034191 and rs1051730) and two SNPs in the HHIP locus (rs1828591 and rs13118928) and one SNP from FAM13A locus (rs7671167) were used in the current analysis. The ICGN subjects were genotyped using Sequenom's (San Diego, CA) iPLEX SNP genotyping protocol developed for measurement with the MassARRAY mass spectrometer (19). FAM13A SNP in the ICGN population was genotyped by the 5′ to 3′ exonuclease TaqMan (Applied Biosystems, Foster City, CA) method using Applied Biosystems Pre-Designed SNP Genotyping assays as per standard protocol.

Statistical Analyses

In the ECLIPSE population, logistic regression models for the binary phenotypes and linear regression models for the quantitative phenotypes were used; covariates included age, sex, pack-years of smoking, current smoking status, and EIGENSTRAT principal components for control of population stratification. Multivariate regression models were evaluated to identify the contribution of the HHIP, FAM13A, and CHRNA3/5 SNPs to phenotypes including spirometric measures, FFM, body mass index (BMI), bronchodilator responsiveness, and quantitative and qualitative measures of emphysema. The associations of the HHIP, FAM13A, and CHRNA3/5 SNPs with COPD exacerbations in ECLIPSE were assessed with negative binomial regression models.

Genotype imputations in the ECLIPSE population were done using the genotypes from the Illumina HumanHap550 Beadchip (Illumina, San Diego, CA) and phased chromosomes for the 60 HapMap Phase 2 CEU founders. Genotypes were imputed using a hidden Markov model as programmed in MACH (20).

Family-based association analysis was conducted in the ICGN families using PBAT version 3.6 (Golden Helix, Bozeman, MT) (21). In the analysis of quantitative and qualitative phenotypes, adjustments for age, gender, pack-years of smoking, and current smoking status were performed. Height was also included as a covariate in lung function analyses.

RESULTS

The demographics and clinical characteristics of the subjects assessed in the ECLIPSE and ICGN cohorts are shown in Table 1. Five SNPs from three loci (HHIP and FAM13A on chromosome 4 and CHRNA3/5/IREB2 on chromosome 15) that were previously significantly associated with COPD (12, 14) were analyzed in this study. The SNPs in the HHIP region on chromosome 4 (rs1828591 and rs13118928) were in strong LD (D' = 0.98; r2 = 0.97), so the results from only SNP rs13118928 are reported. Likewise, the SNPs on chromosome 15 (rs8034191 and rs1051730) were in high LD (D' = 0.99; r2 = 0.9) and hence only the results from one SNP (rs8034191) are reported. The most significantly associated SNP in the FAM13 locus rs7671167 was analyzed and the results are reported. A detailed LD plot of the three loci in the ECLIPSE population is shown in the online supplement (see Figure E1) for CHRNA3/5, HHIP, and FAM13A loci, respectively.

TABLE 1.
BASELINE DEMOGRAPHICS AND CLINICAL CHARACTERISTICS OF THE ECLIPSE AND ICGN POPULATIONS

Smoking Behavior

We first assessed the association of the three loci with smoking behavior in the ECLIPSE cohort, and we found a significant association of the CHRNA3/5 SNP with number of cigarettes smoked per day and the number of pack-years smoked, but not the age of starting to smoke (Table 2). Subjects with COPD who were current or former smokers that are homozygous for the rs8034191 risk allele smoked on average 3.47 more cigarettes per day (P = 0.0009) and 7.5 more cumulative pack-years (P = 0.0016) than did individuals with COPD who did not carry the risk allele. These findings were replicated in the ICGN family-based association analysis (P = 0.0003 for pack-years smoked) (Table 3). In contrast, the SNPs in the HHIP and FAM13A loci were not associated with any of these smoking phenotypes in either the ECLIPSE or ICGN populations.

TABLE 2.
EFFECT OF SNPS IN THE HHIP (RS13118928), FAM13A (RS7671167), AND CHRNA3/5/IREB2 (RS8034191) LOCI IN COPD SUBJECTS FROM THE ECLIPSE STUDY
TABLE 3.
RESULTS OF THE FAMILY-BASED ASSOCIATION ANALYSES IN THE ICGN POPULATION

Lung Function Measurements

The CHRNA3/5 SNP was significantly associated with FEV1 and FEV1/FVC ratio within COPD cases in the ECLIPSE cohort (Table 2). The HHIP SNP was also significantly associated with FEV1/FVC ratio in ECLIPSE, and there was a trend toward association with FEV1. The ICGN family-based association analyses showed similar results with significant associations between the CHRNA3/5 and HHIP SNPs with FEV1/FVC ratio (Table 3). We have previously reported the significant association of CHRNA3/5 SNPs with post-bronchodilator FEV1 in the ICGN population (14).

FAM13A was not associated with FEV1 in the ECLIPSE population, although it showed significant association in the ICGN cohort (12). This locus showed a trend toward association for FEV1/FVC in the ECLIPSE cohort and significant association in the ICGN cohort (Table 3).

BMI, FFM, and BODE Index

There were no significant associations between BMI or FFM and the CHRNA3/5 SNP, but this SNP was associated with an increased BODE index in the ECLIPSE cohort (P = 0.03). In contrast, the HHIP risk allele showed a trend toward lower BMI (P = 0.07) and significant association with FFM (P = 0.007) in subjects with COPD in the ECLIPSE cohort. In the ICGN study, no significant association was observed between the HHIP SNP and BMI (P = 0.18); however, the direction of association was the same in both populations. The FAM13A SNP was not associated with BMI, FFM, or BODE index in the ECLIPSE population, whereas a borderline association with BMI in the ICGN population was observed (P = 0.05). FFM measurements and BODE index were not available in the ICGN population.

CT-defined Emphysema and Airway Wall Thickness

The CHRNA3/5 (P = 0.002) and FAM13A (P = 0.04) SNPs were associated with quantitative emphysema (−950 HU) in the ECLIPSE cohort, whereas the HHIP SNP only showed a trend toward significance (P = 0.07). We then evaluated the SNPs for association with the presence or absence of clinically significant emphysema based on the radiologist's assessment of the CT scans. The CHRNA3/5 SNP (P = 0.0002) and the HHIP SNP (P = 0.006) showed significant association with the radiologist's score of emphysema, whereas the FAM13A SNP did not show any association. In the ICGN population, the associations were significant for the quantitative and radiologist's classification of emphysema (P = 0.0082 and 4.82 × 10−5, respectively) at the CHRNA3/5 locus. The HHIP SNP also showed association with quantitative (P = 0.01) and radiologist's score (P = 0.004) of emphysema in the ICGN population. FAM13A SNP did not show any association to emphysema phenotypes in the ICGN population. There was no association with airway wall thickness measurements (Pi10 and wall area percent) with any of these SNPs in either population.

Multiple Regression Analyses With HHIP, FAM13A, and CHRNA3/5 Loci

The results of multivariate analyses, including the HHIP, FAM13A, and CHRNA3/5/IREB2 SNPs, are shown in Table 4. These analyses were conducted to determine whether the HHIP, FAM13A, and CHRNA3/5 loci were independently associated with different COPD-related phenotypes. The results show that CHRNA3/5 and HHIP loci contribute significantly and independently to the airflow obstruction phenotypes (FEV1 and FEV1/FVC) in individuals with COPD. The FAM13A SNP did not show any association with FEV1, but showed a trend toward association with FEV1/FVC (P = 0.059). The effect of the HHIP variant was statistically more significant than the CHRNA3/5 variant in contributing to airflow obstruction in these multivariate models. All three loci contributed to the quantitative emphysema measurements. The CHRNA3/5 variant had the most significant association to quantitative emphysema, but the FAM13A SNP also had evidence for association to emphysema in this multivariate model. The HHIP variant had a significant effect on FFM and BMI.

TABLE 4.
MULTIVARIATE ANALYSES INCLUDING HHIP, FAM13A, AND CHRNA3/5 SINGLE-NUCLEOTIDE POLYMORPHISMS OF THE COPD-RELATED PHENOTYPES IN ECLIPSE COPD PATIENTS*

The results of the multiple regression analyses for the outcome of COPD exacerbations in the ECLIPSE population are shown in Table 5. The HHIP SNP was associated with previous patient-reported exacerbations (P = 0.015) and exacerbations prospectively collected over 2 years of follow-up in subjects with COPD (P = 0.024). There was no association of exacerbations with the CHRNA or FAM13A SNPs. Because exacerbations are more frequent in subjects with COPD with more severely reduced FEV1 values, we also conducted an analysis adjusting for baseline percent predicted FEV1. The results from the HHIP SNP were statistically significant for prior exacerbations (P = 0.025; incidence ratio, 0.895). The trends were similar for prospectively collected exacerbations, but were not statistically significant (P = 0.397; incidence ratio, 0.966). We also analyzed the data by dividing the subjects into frequent exacerbators (two or more exacerbations per year) versus nonfrequent exacerbators (no more than one exacerbation per year), and the results were again significant for prior exacerbations (P = 0.013; odds ratio, 0.77). In the prospective exacerbation data, the association of the HHIP SNP with exacerbation frequency was not statistically significant (P = 0.66; odds ratio, 0.97). Because COPD exacerbations have been reported to be associated with BODE index (22) and serum surfactant protein D levels (23), we explored a multiple regression model including serum surfactant protein D level, BODE index, and HHIP SNP genotypes in predicting exacerbations. The results indicated that the effect of HHIP in predicting exacerbations is independent of BODE index and serum surfactant protein D levels (data not shown).

TABLE 5.
ANALYSES OF PRIOR AND PROSPECTIVE 2-YEAR COPD EXACERBATIONS IN THE SUBJECTS WITH COPD FROM THE ECLIPSE COHORT*

Results from the Genotype Imputation Analysis

Because the causative variants in these loci are not known, we conducted an imputation analysis using the HAPMAP Phase 2 data (200 kb upstream and downstream of the variant of interest at each of the three loci) in the ECLIPSE population and conducted association analyses for the key phenotypes. The results from the imputation analyses are provided in the online supplement (see Table E1) for CHRNA, HHIP, and FAM13A, respectively, and summarized next.

CHRNA3/5/IREB2 Locus

The most significant association with pack-years of smoking and cigarettes smoked per day were in an LD block containing the CHRNA3/5 genes. A nonsynonymous coding SNP in the CHRNA5 gene (rs16969968) and a synonymous coding SNP in the CHRNA3 showed the most significant associations. The quantitative and radiologist score of emphysema were also significantly associated with this LD block with the most significantly associated SNP in the CHRNA 3 gene (rs1051730) followed by the nonsynonymous SNP in CHRNA5 (rs16969968). The FEV1/FVC analyses also showed significant association with this LD block, whereas the most significant association for FEV1 was in the intronic region in the IREB2 gene (rs17483929; P = 0.0056).

FAM13A Locus

Imputation analysis of the ECLIPSE data showed that one of the SNPs in FAM13A (rs10007590; P = 0.02) was associated with FEV1. This SNP was 33 kb away from the SNP shown to be associated with COPD in the GWAS study (rs7671167), which did not show any association with FEV1 in the ECLIPSE population. Three SNPs in the FAM13A gene showed significant association with FEV1/FVC (rs2869966, rs2869967, and rs2045517). Quantitative emphysema analyses identified SNPs 118 kb upstream of rs7671167 (P = 0.035) with significant associations. Radiologist score of emphysema analysis found one SNP with marginally significant association.

HHIP Locus

Imputation analysis of FEV1 did not identify SNPs that are more significantly associated than the one identified in the GWAS study (rs13118928). However, the FEV1/FVC analyses identified a genomic SNP (rs6817273; P = 0.0001) that maps 5 kb downstream of rs13118928. The analyses of quantitative and radiologist score of emphysema identified a significant genomic SNP (rs10519717; P = 0.018) that maps 6 kb upstream of rs13118928.

DISCUSSION

Recent GWAS identified three major loci that are associated with COPD susceptibility. Here we show that a genetic variant in the CHRNA3/5 locus is significantly associated with smoking intensity and airflow obstruction as defined by reduced FEV1 and FEV1/FVC. More detailed analysis demonstrated that this association was with emphysema rather than with airway wall thickness phenotypes of COPD. These data are explicable by the CHRNA3/5 locus influencing addiction to tobacco with the resulting consequence being smoking-related lung diseases (i.e., COPD and lung cancer). However, the association of the CHRNA3/5 locus with smoking behavior is controversial. It was not observed in either COPD cases or controls from the discovery cohort in our original GWAS (14), nor was it clearly seen in two of the three GWAS linking the CHRNA3/5 locus to lung cancer (24, 25). These lung cancer reports argued that the association of lung cancer is not related to nicotine dependence. Another lung cancer study reported a very strong association of this locus with smoking quantity and nicotine dependence (26). This locus has been shown to be associated with nicotine dependence by other investigators (27, 28). Moreover, our conclusion is supported by the recent demonstration that this locus also influences the risk for alcohol dependence (29). Recent fine-mapping efforts on smoking dependence have shown the involvement of functional variants in the CHRNA3/5 gene (30). To localize the functional variants associated with these loci, we undertook a detailed genotypic imputation analysis and found that the SNPs in the CHRNA3/5 gene showed significant association with smoking, FEV1/FVC, and both quantitative and radiologist definition of emphysema. The most significant association with FEV1 was in an intronic region in the IREB2 gene. This raises the possibility of multiple functional genes in the locus controlling different aspects of COPD. Recent work suggests that the IREB2 gene, which is also located in the CHRNA3/5 locus, may confer COPD susceptibility (13).

Cholinergic activity in the airways primarily induces tracheobronchial smooth muscle contraction and mucous secretion. However, there is an increasing body of literature showing the importance of extraneuronal cholinergic signaling (31) in the lung. It has been shown that nicotinic acetylcholine receptors are active in the nonneuronal cells in the lung, including bronchial epithelial cells and a variety of inflammatory cells (32). These nicotinic acetylcholine receptors in lymphocytes are partially composed of the α4β2, α3β4, and α7 subtypes, whereas neutrophils express principally α3β4 (33). Nicotine reduces the integrin expression in freshly isolated peripheral blood neutrophils (34) and prolongs the survival of neutrophils by suppressing apoptosis (35). Neutrophils are important cells in COPD because they are increased in the conducting airways and are activated to release proteases and oxidants associated with the pathophysiology of the disease (36). It is possible that stimulation of nicotinic receptors by acetylcholine or tobacco-associated agonists could contribute to the pathogenesis of COPD by nicotinic regulation of cellular proliferation through the activation of Akt (37), by β-arrestin–mediated activation of Src and Rb-Raf-1 pathways (38), or by blocking apoptosis (39, 40). Of interest, we found the most significant association with emphysema within the CHRNA3/5 locus, whereas the strongest evidence of association for FEV1 was at the IREB2 locus. The protein product of IREB2 is an RNA binding protein that, together with IREB1, is involved in maintaining human cellular iron metabolism (41). Alterations in iron maintenance in the presence of hypoxia could lead to increased oxidative stress resulting in tissue damage and reduced FEV1.

The association of the HHIP locus with lung function measurements was convincingly demonstrated recently in two large general population lung function GWAS (42, 43). There was no significant association of the smoking-related phenotypes with the HHIP locus in our study. However, this locus was significantly associated with airflow obstruction and associated with emphysema defined by radiologist readings of chest CT scans. The association with quantitative measures of emphysema was not significant in the ECLIPSE population. The results from the multiple regression analyses also show that the contribution from the CHRNA3/5 locus is predominant in explaining the quantitative emphysema phenotype. The subjects with COPD with the HHIP risk genotypes had significantly lower FFM. Because the HHIP locus has been associated with height (44), we assessed the association of the HHIP SNP with height in our datasets but did not find an association (data not shown). A striking feature of our study was the association of variation at the HHIP locus and frequency of COPD exacerbations. The genetic factors underlying exacerbations in COPD are poorly understood. The only reports of genetic associations with COPD exacerbation are with variants in SFTPB and CCL1 genes (45, 46). Capturing reliable data on exacerbations in large COPD populations has been a challenge. We analyzed the historical exacerbation assessments and the ongoing exacerbation assessments in the ECLIPSE population for possible association with the genetic variants under study. We identified significant associations between the HHIP variant and patient-reported prior exacerbations and prospectively collected exacerbations. This observation increases the reliability of the association of HHIP with COPD exacerbations.

The FAM13A locus has been shown to be associated with COPD (12, 30) and lung function (30, 42). This locus showed significant association with FEV1 and FEV1/FVC in the ICGN population. Our genotypic imputation analysis showed that this locus is also significantly associated with FEV1 and FEV1/FVC in the ECLIPSE population. Association of this locus with emphysema was not consistent in the two populations.

Our study has limitations that require discussion. The associations that we identified with COPD-related phenotypes in the three replicated loci were restricted to the SNPs analyzed at these loci. None of the SNPs that we studied are known to be functional, and the functional variants related to these loci are still unknown. We conducted genotypic imputation analyses to localize potential functional variants, and we demonstrated the variants in CHRNA3/5 genes were associated with smoking behavior, airflow obstruction, and emphysema. The identification of functional variants in HHIP and FAM13A loci need fine-mapping efforts using large populations, potentially with different genetic ancestry. Our samples were limited only to white subjects. Although ECLIPSE is a well-characterized population with rich phenotypic assessments, some of the associations that we identified in ECLIPSE could not be replicated in the ICGN population because these phenotypes were not available in the ICGN cohort. The CT acquisition algorithms and software used for measuring the airway wall thickness were different in ECLIPSE and ICGN as evidenced by the mean values in Table 1. This technical issue might have contributed to the lack of association with airway phenotypes. The association analysis results from the quantitative and radiologist score of emphysema were consistent for the CHRNA locus, but not for the HHIP and FAM13A locus in the ECLIPSE population. In the radiologist definition of emphysema, we dichotomized the phenotype based on the presence or absence of clinically significant emphysema, whereas in the quantitative emphysema analysis the quantitative measurement from the algorithm was used and this may have contributed to the differences in the results.

Conclusions

Our data show that the CHRNA3/5 locus affects smoking behavior and has a significant effect on COPD-related phenotypes; it is unclear whether there are additional influences of this locus on COPD, independent of smoking. The HHIP locus is associated with significant airflow obstruction, increased exacerbations, reduced BMI, and FFM suggesting a relationship with the systemic component in COPD. FAM13A locus was not associated with smoking behavior; the association with CT-defined emphysema was also not consistent. However, this locus showed significant association with airway obstruction in subjects with COPD. The three COPD susceptibility loci identified by GWAS may influence distinct subtypes of COPD.

Supplementary Material

[Online Supplement]

Acknowledgments

The authors are grateful to the efforts of Linda Briley, Mark Hall, Sara Alalouf, Andrew Slater, Jennifer Johnson, Pam St. Jean, David Yarnall, Bo Zheng, and Sanjay Sharma of GlaxoSmithKline for the sample management, data management, genotyping, and analysis. The authors thank Drs. Nestor Müller and Paola Nasute Fauerbach for reading the CT scans and Tara Candido, Sebastian Cogswell, Heather Davis, Nima Farzaneh, Lukas Holy, Natasha Krowchuk, Helena Lee, Evan Phillips, Claudine Storness-Bliss, Nerissa Tai, Anh-Toan Tran, Nghia Tran, Eugene Wang, and Tomonori Yokogawa for technical assistance with the CT analysis and data management.

Notes

Supported by GlaxoSmithKline and by National Institutes of Health grants R01 HL084323 and R01 HL075478 (E.K.S.).

This work was funded by GlaxoSmithKline.

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.201002-0151OC on July 23, 2010

Author Disclosure: S.G.P. was a full time employee of GlaxoSmithKline until September 2009 and is currently a full time employee of Roche Pharmaceuticals since September 2009, and owns stock in GlaxoSmithKline. X.K. is an employee of GlaxoSmithKline. L.D.E. is a full-time employee of GlaxoSmithKline and holds $5,001–$10,000 in stock ownership/options in GlaxoSmithKline. M.C. received more than $100,001 from the National Institutes of Health (NIH) as a NIH K12 grant. W.H.A. is an employee of GlaxoSmithKline and holds more than $100,001 in stock in GlaxoSmithKline. W.H.A.'s employer, GlaxoSmithKline, provides more than $100,001 in sponsored grants as educational support for American Thoracic Society meetings. H.O.C. received up to $1,000 from GlaxoSmithKline and up to $1,000 from AstraZeneca in consultancy fees; $1,001–$5,000 from GlaxoSmithKline in advisory board fees; more than $100,001 from GlaxoSmithKline, $50,001–$100,000 from Spiration Inc., and more than $100,001 from Wyeth in industry-sponsored grants; and more than $100,001 from the NIH and more than $100,001 from the CIHR in sponsored grants. D.A.L. received $10,001–$50,000 from GlaxoSmithKline, $10,001–$5,000 from Novartis, up to $1,000 from Genzyme, and up to $1,000 from Amicus in consultancy fees; $10,001–$50,000 from GlaxoSmithKline, $1,001–$5,000 from Talecris, and $1,001–$5,000 from Thorax in advisory board fees; $1,001–$5,000 from GlaxoSmithKline, up to $1,000 from AstraZeneca, $1,001–$5,000 from Boehringer Ingelheim, and $1,001–$5,000 from LFB in lecture fees; more than $100,001 from GlaxoSmithKline and more than $100,001 from Merck, Sharpe & Dohme in industry-sponsored grants; more than $100,001 from the Medical Research Council, more than $100,001 from the Wellcome Trust, more than $100,001 from the British Lung Foundation, and $10,001–$50,000 from the Alpha-1 Foundation in sponsored grants; and $1,001–$5,000 from the Medical Research Council in advisory board fees. E.K.S. received $10,001–$50,000 from GlaxoSmithKline and $10,001–$50,000 from AstraZeneca in consultancy fees; $1,001–$5,000 from GlaxoSmithKline, $5,001–$10,000 from AstraZeneca, and $1,001–$5,000 from Bayer in lecture fees; more than $100,001 from GlaxoSmithKline in industry-sponsored grants; and more than $100,001 from the NIH in sponsored grants.

ECLIPSE Investigators: Yavor Ivanov, Pleven, Bulgaria; Kosta Kostov, Sofia, Bulgaria; Jean Bourbeau, Montreal, Quebec, Canada; Mark Fitzgerald, Vancouver, British Columbia, Canada; Paul Hernandez, Halifax, Nova Scotia, Canada; Kieran Killian, Hamilton, Ontario, Canada; Robert Levy, Vancouver, British Columbia, Canada; Francois Maltais, Montreal, Quebec, Canada; Denis O'Donnell, Kingston, Ontario, Canada; Jan Krepelka, Praha, Czech Republic; Jørgen Vestbo, Hvidovre, Denmark; Emiel Wouters, Horn-Maastricht, the Netherlands; Dean Quinn, Wellington, New Zealand; Per Bakke, Bergen, Norway; Mitja Kosnik, Golnik, Slovenia; Alvar Agusti, Jaume Sauleda, Palma de Mallorca, Spain; Yuri Feschenko, Kiev, Ukraine; Vladamir Gavrisyuk, Kiev, Ukraine; Lyudmila Yashina, Kiev, Ukraine; Nadezhda Monogarova, Donetsk, Ukraine; Peter Calverley, Liverpool, United Kingdom; David Lomas, Cambridge, United Kingdom; William MacNee, Edinburgh, United Kingdom; David Singh, Manchester, United Kingdom; Jadwiga Wedzicha, London, United Kingdom; Antonio Anzueto, San Antonio, Texas; Sidney Braman, Providence, Rhode Island; Richard Casaburi, Torrance, California; Bart Celli, Boston, Massachusetts; Glenn Giessel, Richmond, Virginia; Mark Gotfried, Phoenix, Arizona; Gary Greenwald, Rancho Mirage, California; Nicola Hanania, Houston, Texas; Don Mahler, Lebanon, New Hampshire; Barry Make, Denver, Colorado; Stephen Rennard, Omaha, Nebraska; Carolyn Rochester, New Haven, Connecticut; Paul Scanlon, Rochester, Minnesota; Dan Schuller, Omaha, Nebraska; Frank Sciurba, Pittsburgh, Pennsylvania; Amir Sharafkhaneh, Houston, Texas; Thomas Siler, St. Charles, Missouri, Edwin Silverman, Boston, Massachusetts; Adam Wanner, Miami, Florida; Robert Wise, Baltimore, Maryland; and Richard ZuWallack, Hartford, Connecticut.

ECLIPSE Steering Committee: Harvey Coxson (Canada); Lisa Edwards (GlaxoSmithKline, United States); Katharine Knobil (Co-chair, GlaxoSmithKline, United Kingdom); David Lomas (United Kingdom); William MacNee (United Kingdom); Edwin Silverman (United States); Ruth Tal-Singer (GlaxoSmithKline, United States); Jørgen Vestbo (Co-chair, Denmark); and Julie Yates (GlaxoSmithKline, United States).

ECLIPSE Scientific Committee: Alvar Agusti (Spain); Peter Calverley (United Kingdom); Bartolome Celli (United States); Courtney Crim (GlaxoSmithKline, United States); Gerry Hagan (GlaxoSmithKline, United Kingdom); William MacNee (Chair, United Kingdom); Stephen Rennard (United States); Ruth Tal-Singer (GlaxoSmithKline, United States); Emiel Wouters (the Netherlands); and Julie Yates (GlaxoSmithKline, United States).

ICGN Investigators: Alvar Agusti, Son Dureta Hospital, Fundación Caubet-Cimera and CIBER Enfermedades Respiratorias (CIBERES), Palma de Mallorca, Spain; Peter M.A. Calverley, University of Liverpool, United Kingdom; Claudio F. Donner, Division of Pulmonary Disease, S. Maugeri Foundation, Veruno (NO), Italy; Robert D. Levy, University of British Columbia, Vancouver, Canada; Barry J. Make, National Jewish Medical and Research Centre, Denver, Colorado; Peter D. Paré, University of British Columbia, Vancouver, Canada; Stephen I. Rennard, University of Nebraska, Omaha, Nebraska; Jørgen Vestbo, Department of Cardiology and Respiratory Medicine, Hvidovre Hospital, Copenhagen, Denmark; Emiel F.M. Wouters, University Hospital Maastricht, the Netherlands; David A. Lomas, University of Cambridge, Cambridge, United Kingdom (Co-PI); and Edwin K. Silverman, Brigham and Women's Hospital, Boston, Massachusetts (Co-PI).

References

1. Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study. Lancet 1997;349:1498–1504. [PubMed]
2. Pauwels RA, Buist AS, Calverley PM, Jenkins CR, Hurd SS. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. NHLBI/WHO Global Initiative for Chronic Obstructive Lung Disease (GOLD) Workshop summary. Am J Respir Crit Care Med 2001;163:1256–1276. [PubMed]
3. Jones PW, Agusti AG. Outcomes and markers in the assessment of chronic obstructive pulmonary disease. Eur Respir J 2006;27:822–832. [PubMed]
4. Barnes PJ, Celli BR. Systemic manifestations and comorbidities of COPD. Eur Respir J 2009;33:1165–1185. [PubMed]
5. Cigarette smoking and health. American Thoracic Society. Am J Respir Crit Care Med 1996;153:861–865. [PubMed]
6. Kauffmann F, Tager IB, Munoz A, Speizer FE. Familial factors related to lung function in children aged 6–10 years. Results from the PAARC epidemiologic study. Am J Epidemiol 1989;129:1289–1299. [PubMed]
7. Lebowitz MD, Knudson RJ, Burrows B. Family aggregation of pulmonary function measurements. Am Rev Respir Dis 1984;129:8–11. [PubMed]
8. McCloskey SC, Patel BD, Hinchliffe SJ, Reid ED, Wareham NJ, Lomas DA. Siblings of patients with severe chronic obstructive pulmonary disease have a significant risk of airflow obstruction. Am J Respir Crit Care Med 2001;164:1419–1424. [PubMed]
9. Redline S, Tishler PV, Rosner B, Lewitter FI, Vandenburgh M, Weiss ST, Speizer FE. Genotypic and phenotypic similarities in pulmonary function among family members of adult monozygotic and dizygotic twins. Am J Epidemiol 1989;129:827–836. [PubMed]
10. Silverman EK, Chapman HA, Drazen JM, Weiss ST, Rosner B, Campbell EJ, O'Donnell WJ, Reilly JJ, Ginns L, Mentzer S, et al. Genetic epidemiology of severe, early-onset chronic obstructive pulmonary disease. Risk to relatives for airflow obstruction and chronic bronchitis. Am J Respir Crit Care Med 1998;157:1770–1778. [PubMed]
11. Ganrot PO, Laurell CB, Eriksson S. Obstructive lung disease and trypsin inhibitors in alpha-1-antitrypsin deficiency. Scand J Clin Lab Invest 1967;19:205–208. [PubMed]
12. Cho MH, Boutaoui N, Klanderman BJ, Sylvia JS, Ziniti JP, Hersh CP, DeMeo DL, Hunninghake GM, Litonjua AA, Sparrow D, et al. Variants in FAM13A are associated with chronic obstructive pulmonary disease. Nat Genet 2010;42:200–202. [PMC free article] [PubMed]
13. DeMeo DL, Mariani T, Bhattacharya S, Srisuma S, Lange C, Litonjua A, Bueno R, Pillai SG, Lomas DA, Sparrow D, et al. Integration of genomic and genetic approaches implicates IREB2 as a COPD susceptibility gene. Am J Hum Genet 2009;85:493–502. [PubMed]
14. Pillai SG, Ge D, Zhu G, Kong X, Shianna KV, Need AC, Feng S, Hersh CP, Bakke P, Gulsvik A, et al. A genome-wide association study in chronic obstructive pulmonary disease (COPD): identification of two major susceptibility loci. PLoS Genet 2009;5:e1000421. [PMC free article] [PubMed]
15. Vestbo J, Anderson W, Coxson HO, Crim C, Dawber F, Edwards L, Hagan G, Knobil K, Lomas DA, MacNee W, et al. Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). Eur Respir J 2008;31:869–873. [PubMed]
16. Celli BR, Cote CG, Marin JM, Casanova C, Montes de Oca M, Mendez RA, Pinto Plata V, Cabral HJ. The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med 2004;350:1005–1012. [PubMed]
17. Steiner MC, Barton RL, Singh SJ, Morgan MDL. Bedside methods versus dual energy X-ray absorptiometry for body composition measurement in COPD. Eur Respir J 2002;19:626–631. [PubMed]
18. Patel BD, Coxson HO, Pillai SG, Agusti AG, Calverley PM, Donner CF, Make BJ, Muller NL, Rennard SI, Vestbo J, et al. Airway wall thickening and emphysema show independent familial aggregation in COPD. Am J Respir Crit Care Med 2008;178:500–505. [PubMed]
19. Koren-Michowitz M, Shimoni A, Vivante A, Trakhtenbrot L, Rechavi G, Amariglio N, Loewenthal R, Nagler A, Cohen Y. A new MALDI-TOF-based assay for monitoring JAK2 V617F mutation level in patients undergoing allogeneic stem cell transplantation (allo SCT) for classic myeloproliferative disorders (MPD). Leuk Res 2008;32:421–427. [PubMed]
20. Li Y, Abecasis GR. Mach 1.0: rapid haplotype reconstruction and missing genotype inference. Am J Hum Genet 2006;S79:2290.
21. Lange C, DeMeo D, Silverman EK, Weiss ST, Laird NM. PBAT: tools for family-based association studies. Am J Hum Genet 2004;74:367–369. [PubMed]
22. Marin JM, Carrizo SJ, Casanova C, Martinez-Camblor P, Soriano JB, Agusti AGN, Celli BR. Prediction of risk of COPD exacerbations by the BODE index. Respir Med 2009;103:373–378. [PubMed]
23. Lomas DA, Silverman EK, Edwards LD, Locantore NW, Miller BE, Horstman DH, Tal-Singer R. Serum surfactant protein D is steroid sensitive and associated with exacerbations of COPD. Eur Respir J 2009;34:95–102. [PubMed]
24. Amos CI, Wu X, Broderick P, Gorlov IP, Gu J, Eisen T, Dong Q, Zhang Q, Gu X, Vijayakrishnan J, et al. Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1. Nat Genet 2008;40:616–622. [PMC free article] [PubMed]
25. Hung RJ, McKay JD, Gaborieau V, Boffetta P, Hashibe M, Zaridze D, Mukeria A, Szeszenia-Dabrowska N, Lissowska J, Rudnai P, et al. A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. Nature 2008;452:633–637. [PubMed]
26. Thorgeirsson TE, Geller F, Sulem P, Rafnar T, Wiste A, Magnusson KP, Manolescu A, Thorleifsson G, Stefansson H, Ingason A, et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature 2008;452:638–642. [PubMed]
27. Berrettini W, Yuan X, Tozzi F, Song K, Francks C, Chilcoat H, Waterworth D, Muglia P, Mooser V. Alpha-5/alpha-3 nicotinic receptor subunit alleles increase risk for heavy smoking. Mol Psychiatry 2008;13:368–373. [PMC free article] [PubMed]
28. Saccone SF, Hinrichs AL, Saccone NL, Chase GA, Konvicka K, Madden PA, Breslau N, Johnson EO, Hatsukami D, Pomerleau O, et al. Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs. Hum Mol Genet 2007;16:36–49. [PMC free article] [PubMed]
29. Wang JC, Grucza R, Cruchaga C, Hinrichs AL, Bertelsen S, Budde JP, Fox L, Goldstein E, Reyes O, Saccone N, et al. Genetic variation in the CHRNA5 gene affects mRNA levels and is associated with risk for alcohol dependence. Mol Psychiatry 2008;14:501–510. [PubMed]
30. Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, Berrettini W, Knouff CW, Yuan X, Waeber G, et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet 2010;42:436–440. [PubMed]
31. Wessler I, Kirkpatrick CJ, Racke K. Non-neuronal acetylcholine, a locally acting molecule, widely distributed in biological systems: expression and function in humans. Pharmacol Ther 1998;77:59–79. [PubMed]
32. Gwilt CR, Donnelly LE, Rogers DF. The non-neuronal cholinergic system in the airways: an unappreciated regulatory role in pulmonary inflammation? Pharmacol Ther 2007;115:208–222. [PubMed]
33. Benhammou K, Lee M, Strook M, Sullivan B, Logel J, Raschen K, Gotti C, Leonard S. [(3)H]Nicotine binding in peripheral blood cells of smokers is correlated with the number of cigarettes smoked per day. Neuropharmacology 2000;39:2818–2829. [PubMed]
34. Speer P, Zhang Y, Gu Y, Lucas MJ, Wang Y. Effects of nicotine on intercellular adhesion molecule expression in endothelial cells and integrin expression in neutrophils in vitro. Am J Obstet Gynecol 2002;186:551–556. [PubMed]
35. Aoshiba K, Nagai A, Yasui S, Konno K. Nicotine prolongs neutrophil survival by suppressing apoptosis. J Lab Clin Med 1996;127:186–194. [PubMed]
36. Barnes PJ. New molecular targets for the treatment of neutrophilic diseases. J Allergy Clin Immunol 2007;119:1055–1062. [PubMed]
37. West KA, Brognard J, Clark AS, Linnoila IR, Yang X, Swain SM, Harris C, Belinsky S, Dennis PA. Rapid Akt activation by nicotine and a tobacco carcinogen modulates the phenotype of normal human airway epithelial cells. J Clin Invest 2003;111:81–90. [PMC free article] [PubMed]
38. Dasgupta P, Rastogi S, Pillai S, Ordonez-Ercan D, Morris M, Haura E, Chellappan S. Nicotine induces cell proliferation by beta-arrestin-mediated activation of Src and Rb-Raf-1 pathways. J Clin Invest 2006;116:2208–2217. [PMC free article] [PubMed]
39. Dasgupta P, Chellappan SP. Nicotine-mediated cell proliferation and angiogenesis: new twists to an old story. Cell Cycle 2006;5:2324–2328. [PubMed]
40. Maneckjee R, Minna JD. Opioids induce while nicotine suppresses apoptosis in human lung cancer cells. Cell Growth Differ 1994;5:1033–1040. [PubMed]
41. Rouault TA. The role of iron regulatory proteins in mammalian iron homeostasis and disease. Nat Chem Biol 2006;2:406–414. [PubMed]
42. Hancock DB, Eijgelsheim M, Wilk JB, Gharib SA, Loehr LR, Marciante KD, Franceschini N, van Durme YM, Chen TH, Barr RG, et al. Meta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function. Nat Genet 2010;42:45–52. [PMC free article] [PubMed]
43. Repapi E, Sayers I, Wain LV, Burton PR, Johnson T, Obeidat M, Zhao JH, Ramasamy A, Zhai G, Vitart V, et al. Genome-wide association study identifies five loci associated with lung function. Nat Genet 2010;42:36–44. [PMC free article] [PubMed]
44. Weedon MN, Lango H, Lindgren CM, Wallace C, Evans DM, Mangino M, Freathy RM, Perry JR, Stevens S, Hall AS, et al. Genome-wide association analysis identifies 20 loci that influence adult height. Nat Genet 2008;40:575–583. [PMC free article] [PubMed]
45. Foreman MG, DeMeo DL, Hersh CP, Carey VJ, Fan VS, Reilly JJ, Shapiro SD, Silverman EK. Polymorphic variation in surfactant protein B is associated with COPD exacerbations. Eur Respir J 2008;32:938–944. [PMC free article] [PubMed]
46. Takabatake N, Shibata Y, Abe S, Wada T, Machiya J, Igarashi A, Tokairin Y, Ji G, Sato H, Sata M, et al. A single nucleotide polymorphism in the CCL1 gene predicts acute exacerbations in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2006;174:875–885. [PubMed]

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