Abstract
Introduction
Cigarette smoking is a major environmental risk factor for many diseases, including chronic obstructive pulmonary disease (COPD). There are shared genetic influences on cigarette smoking and COPD. Genetic risk factors for cigarette smoking in cohorts enriched for COPD are largely unknown.
Methods
We performed genome-wide association analyses for average cigarettes per day (CPD) across the Genetic Epidemiology of COPD (COPDGene) non-Hispanic white (NHW) (n = 6659) and African American (AA) (n = 3260), GenKOLS (the Genetics of Chronic Obstructive Lung Disease) (n = 1671), and ECLIPSE (the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) (n = 1942) cohorts. In addition, we performed exome array association analyses across the COPDGene NHW and AA cohorts. We considered analyses across the entire cohort and stratified by COPD case–control status.
Results
We identified genome-wide significant associations for CPD on chromosome 15q25 across all cohorts (lowest p = 1.78 × 10−15), except in the COPDGene AA cohort alone. Previously reported associations on chromosome 19 had suggestive and directionally consistent associations (RAB4, p = 1.95 × 10−6; CYP2A7, p = 7.50 × 10−5; CYP2B6, p = 4.04 × 10−4). When we stratified by COPD case–control status, single nucleotide polymorphisms on chromosome 15q25 were nominally associated with both NHW COPD cases (β = 0.11, p = 5.58 × 10−4) and controls (β = 0.12, p = 3.86 × 10−5) For the gene-based exome array association analysis of rare variants, there were no exome-wide significant associations. For these previously replicated associations, the most significant results were among COPDGene NHW subjects for CYP2A7 (p = 5.2 × 10−4).
Conclusions
In a large genome-wide association study of both common variants and a gene-based association of rare coding variants in ever-smokers, we found genome-wide significant associations on chromosome 15q25 with CPD for common variants, but not for rare coding variants. These results were directionally consistent among COPD cases and controls.
Implications
We examined both common and rare coding variants associated with CPD in a large population of heavy smokers with and without COPD of NHW and AA descent. We replicated genome-wide significant associations on chromosome 15q25 with CPD for common variants among NHW subjects, but not for rare variants. We demonstrated for the first time that common variants on chromosome 15q25 associated with CPD are similar among COPD cases and controls. Previously reported associations on chromosome 19 showed suggestive and directionally consistent associations among common variants (RAB4, CYP2A7, and CYP2B6) and for rare variants (CYP2A7) among COPDGene NHW subjects. Although the genetic effect sizes for these single nucleotide polymorphisms on chromosome 15q25 are modest, we show that this creates a substantial smoking burden over the lifetime of a smoker.
Introduction
The most recent Surgeon General’s report “The Health Consequences of Smoking—50 Years of Progress” notes tobacco continues to be an immense public health burden in the United States, and tobacco smoking has killed more than 20 million people prematurely since the first Surgeon General’s report on smoking in 1964.1 The prevalence of current cigarette smoking among adults in the United States has declined from 42% in 1965 to 18% in 2012; however, more than 42 million Americans still smoke. The findings in this recent Surgeon General’s report show the decline in the prevalence of smoking has slowed in recent years, but the burden of mortality related to smoking is expected to remain at high levels for decades to come.1
Smoking is known to have both environmental and genetic influences. The heritability of smoking behaviors has been estimated between 37% and 59%.2 Genetic studies of smoking have consistently identified an association with chromosome 15q25, which includes a cluster of genes coding for nicotinic acetylcholine receptor subunits, CHRNA5–CHRNA3–CHRNB4. Single nucleotide polymorphisms (SNPs) on chromosome 15q25 have been associated with smoking quantity and nicotine dependence among subjects of European ancestry,3–10 and African Americans (AAs).11 Chromosome 15q25 has also been associated with spirometric measures of pulmonary function,12 and respiratory diseases such as chronic obstructive pulmonary disorder (COPD)13,14 lung cancer,15–18 and time to lung cancer diagnosis19 where smoking may mediate the relationship between these genes and clinically relevant outcomes.20,21 Recently, rare coding variants in CHRNA5 were marginally associated with increased risk of nicotine dependence among European Americans (odds ratio [OR] = 12.9, p = .01) and in the same risk direction among AAs (OR = 1.5, p = .37).22 In addition to chromosome 15q25, common variants in several other regions have been associated with smoking-related traits such as chromosome 1 (LPPR5), chromosome 2 (TEX41/PABPC1P2), chromosome 6 (DNAH8), chromosome 7 (PDE1C), chromosome 8 (CHRNB3/CHRNA6), chromosome 9 (DBH), chromosome 10 (LOC1001889), chromosome 11 (BDNF, NCAM1), chromosome 19 (RAB4B, EGLN2, CYP2A7, CYP2B6), and chromosome 20 (NOL4L).23
The associations between SNPs on chromosome 15q25 and smoking-related diseases such as COPD and lung cancer are partially mediated through smoking.24,25 It is not clear if the impact of chromosome 15q25 SNPs on cigarettes per day (CPD) differs among individuals susceptible to the adverse clinical effects of cigarette smoking. It is possible that the genetic susceptibility to smoking-related diseases may be due to a stronger genetic influence on CPD that subsequently leads to increased disease risk. Given the observed relationship between chromosome 15q25 with COPD and CPD, we performed a genome-wide association (GWA) analysis of CPD in the Genetic Epidemiology of COPD (COPDGene) study, a large population of current and former smokers and a meta-analysis across the COPDGene, the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE), and the Genetics of Chronic Obstructive Lung Disease (GenKOLS) studies, which were all enriched for COPD cases. To examine potential differences in the genetic susceptibility to CPD among COPD cases and controls, we examined the genome-wide significant SNPs for CPD stratified by COPD case–control status and formally tested for interactions between SNPs and COPD on CPD.
Methods
COPDGene Study
The COPDGene study is a multicenter observational study designed to identify genetic factors associated with COPD and to characterize COPD-related phenotypes.26 This study recruited 10 192 adult smokers (current and former) who were non-Hispanic whites (NHWs) or AAs aged 44–81 years with at least 10 pack-years of smoking history. Table 1 details characteristics of the COPDGene participants included in the GWA analysis. We excluded subjects with severe α-1 antitrypsin deficiency or genotyping failure, which resulted in 9978 unrelated subjects. There were a total of 6659 NHWs and 3260 AAs with complete genotype and phenotype data available. There were 2819 NHW COPD cases (GOLD stage 2–4), 2543 NHW COPD controls (GOLD stage 0), 821 AA cases (GOLD stage 2–4), and 1749 AA controls (GOLD stage 0).
Table 1.
Characteristics of COPDGene, ECLIPSE, and GenKOLS Subjects Included in Genome-Wide Association Analysis
| COPDGene NHW | COPDGene AA | ECLIPSE | GenKOLS | |
|---|---|---|---|---|
| Sample size (no. of COPD cases) | 6658 (2812) | 3280 (821) | 1942 (1764) | 1671 (863) |
| Sex (%male) | 47.73 | 44.79 | 66.17 | 58.71 |
| Age (y) | 62.0 (8.9) | 54.6 (7.2) | 63.1 (7.6) | 60.7 (11.1) |
| Pack-years | 47.2 (26.0) | 38.3 (21.6) | 48.6 (27.7) | 26.0 (17.4) |
| Cigarettes per day | 25.8 (11.4) | 21.3 (10.4) | 25.3 (12.7) | 14.7 (7.6) |
| Smoking initiation age (years) | 16.9 (4.3) | 17.0 (5.3) | 16.9 (4.4) | 18.5 (5.0) |
| Current smoking (%) | 38.9 | 80.0 | 34.8 | 43.8 |
COPD = chronic obstructive pulmonary disorder, COPDGene = Genetic Epidemiology of COPD, ECLIPSE = Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints, NHW = non-Hispanic white. For continuous variables, the mean is given first followed by the standard deviation.
Meta-Analysis Study Populations: ECLIPSE and GenKOLS
The ECLIPSE study was a longitudinal, observational study conducted at 46 clinical centers in 12 countries.27 For this study, we included 1764 COPD cases and 178 current or former smoking controls from the ECLIPSE study; we also included 863 COPD cases and 808 controls from the GenKOLS study in Bergen, Norway. For both cohorts, the COPD controls were chosen from the cohort as GOLD stage 0 subjects who were ever-smokers. In all cohorts, COPD cases were chosen as GOLD stage 2–4 COPD subjects (moderate, severe, and very severe COPD; post-bronchodilator FEV1 < 80% predicted with FEV1/FVC < 0.7). Individuals with severe α-1 antitrypsin deficiency were excluded. Both the ECLIPSE and GenKOLS cohorts were of NHW ancestry. Genotyping methods for the genome-wide SNP panels and study descriptions for the GenKOLS and ECLIPSE cohorts have been described previously.14,28
Comparison to Previously Published GWA Studies: UK BiLEVE Study
We considered genome-wide significant results from a previously published genome-wide analysis of smoking-related traits in the UK Biobank Lung Exome Variant Evaluation (BiLEVE) study.23 We chose this UK BiLEVE analysis because the study used similar enrollment criteria. This study focused on individuals of European ancestry from the UK Biobank with the middle and extremes of the FEV1 distribution among heavy smokers (mean 35 pack-years) and never-smokers. Heavy smokers were defined as individuals with a percentage of life span smoking of at least 42% (equivalent to a minimum pack-years of 10 in the youngest participants). The study had 50 008 subjects: 10 002 individuals with low FEV1, 10 000 with average FEV1, and 5 002 with high FEV1 from each of the heavy smoker and never-smoker groups. Genotyping was done on a custom Affymetrix Axiom array (UK BiLEVE array; Santa Clara, CA) and non-genotyped variants were imputed using a combined 1000 Genomes Project Phase and UK10K Project reference panel. Further details regarding the genotyping methods and study design have been described previously.29
Phenotypes of Smoking-Related Traits
Due to non-normality, we categorized CPD into seven categories (eg, 1 if CPD is ≤10, up to 7 if the CPD is ≥60, and intermediate numbers 2–6 for each increment of 10). Supplementary Figure 2a–c shows histograms of CPD and CPD binned to show that while right skewed the distribution of CPD binned has more of a bell curve than CPD. This is due to the way CPD is reported as subjects tend to report CPD values that are divisible by 5 or 10. We also considered pack-years of cigarettes smoked, but due to the similarity and high correlation (r = 0.92) with CPD, we did not present these results here. We also considered age of smoking initiation and current smoking status but did not find any significant results for these phenotypes. As a result, we did not present these results here.
Genotyping, Quality Control, and Imputation for Common Variants in the COPDGene Study
All COPDGene subjects were genotyped using the Illumina HumanOmniExpress-12 v1 by Illumina (San Diego, CA). Details of genotyping quality control have been previously described.13 Imputation on the COPDGene cohorts was performed using MaCH and minimac.30,31 Prephasing and imputation were both performed using 30 rounds and 200 states, with regions divided into 1 Mb segments with 500-kb flanks. Reference panels for the NHW and AA subjects were the 1000 Genomes Phase I (v3) European (EUR) and cosmopolitan reference panels, respectively.32 Imputed variants with an R-squared value of less than 0.3 were dropped from further analysis. SNPs with minor allele frequency less than 1% were excluded. Further details concerning genotyping, quality control, and imputation are posted on the COPDGene Web site (www.copdgene.org). All SNP locations are based on the NCBI37/hg19 assembly.
Genotyping and Quality Control for Exome Chip Variants in the COPDGene Study
Genotyping with the exome chip was done for this same COPDGene study cohort on Illumina Human exome array (v1.1 and v1.2). Standard QC protocols were followed to clean called variants on this exome array data as detailed previously.29
Statistical Analyses for Common Variants
GWA tests were performed in Plink (v.1.07, Boston, MA) stratified by race.33 Linear regression analyses of CPD were adjusted for age, sex, and genetic ancestry (as summarized by principal components computed within racial group) by including these covariates in the model.34 We chose this set of precision variables and confounders to match other GWA studies. We assumed additive model for each SNP (ie, 0,1,2 for 0,1,2 copies of the minor allele, respectively). For the primary analysis, we did not adjust for current smoking status due to the correlation with CPD (r = −0.19). For Supplementary Tables 1–3, we re-ran all analyses adjusting for current smoking status in addition to age, sex, and genetic ancestry (as summarized by principal components computed within racial group) by including these covariates in the model. The results summarized in Supplementary Tables 1–3 are very similar to those shown in Tables 2–5. A fixed-effects meta-analysis was performed using METAL (v 2010-08-01, Ann Arbor, MI)35 for CPD adjusting for the same covariates mentioned earlier (age, sex, and genetic ancestry as summarized by principal components) for the COPDGene, ECLIPSE, and GenKOLS cohorts. We used a fixed-effects meta-analysis because in the presence of between-study heterogeneity, fixed-effects models are known to produce tighter confidence intervals than random-effects models.36 Genome-wide significant associations were defined by p < 5 × 10−8. The power analysis for these linear regressions that examines the common variants associated with CPD in the NHW COPDGene, AA COPDGene, and meta-analysis cohorts is included in Supplementary Table 3a.
Table 2.
Genome-Wide Significant Results for CPD and Comparison of Previously Replicated Regions From the UK BiLEVE Study23
| SNP | Position (bp) | CHR | Nearest gene | Coded allele | COPDGene NHW (n = 6658) | COPDGene AA (n = 3260) | Meta-analysis of COPDGene NHW, COPDGene AA, ECLIPSE, and GenKOLS (n= 13551) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Allele freq | β | p | Allele freq | β | p | Allele freq | β | p | ||||||
| Chromosome 15q25 | rs8192482 | 78886198 | 15 | CHRNA3 | T/T/T | 0.37 | 0.13 | 5.37E-11 | 0.06 | 0.05 | .41 | 0.37 | 0.12 | 1.78E-15 |
| rs11633958 | 78866445 | 15 | CHRNA5 | T/T/T | 0.37 | 0.13 | 9.41E-11 | 0.06 | 0.04 | .46 | 0.37 | 0.12 | 2.18E-15 | |
| rs72738786 | 78828086 | 15 | AGPHD1 | T/T/T | 0.37 | 0.13 | 3.41E-11 | 0.14 | 0.02 | .61 | 0.37 | 0.12 | 5.78E-15 | |
| rs2869548 | 78922638 | 15 | CHRNB4 | A/A/A | 0.38 | 0.12 | 2.17E-10 | 0.06 | 0.05 | .31 | 0.39 | 0.12 | 2.82E-14 | |
| rs11858836 | 78783277 | 15 | IREB2 | A/A/A | 0.36 | 0.11 | 1.42E-8 | 0.07 | 0.01 | .83 | 0.36 | 0.10 | 1.26E-10 | |
| Replicated regions from the UK BiLEVE study | rs61784651 | 99445471 | 1 | LPPR5 | T/T/T | 0.16 | -0.03 | 0.27 | 0.06 | -0.08 | .15 | 0.16 | 0.02 | 0.32 |
| rs10193706 | 146316319 | 2 | TEX41/ PABPC1P2 | A/C/A | 0.46 | 0.01 | 0.46 | 0.19 | 0.01 | .96 | 0.51 | 0.01 | 0.4 | |
| rs10807199 | 38901867 | 6 | DNAH8 | T/T/T | 0.46 | 0.01 | 0.86 | 0.15 | 0.02 | .65 | 0.47 | -0.01 | 0.56 | |
| rs215605 | 32336965 | 7 | PDE1C | G/T/T | 0.38 | 0.02 | 0.21 | 0.28 | 0.01 | .83 | 0.37 | -0.01 | 0.43 | |
| rs13280604 | 42559586 | 8 | CHRNB3 | G/A/A | 0.22 | 0.04 | 0.27 | 0.45 | -0.06 | .03 | 0.78 | -0.02 | 0.24 | |
| rs3025343 | 136478355 | 9 | DBH | A/A/A | 0.12 | -0.02 | 0.52 | 0.02 | 0.07 | .40 | 0.12 | 0.001 | 0.99 | |
| rs1329650 | 93348120 | 10 | LOC100188947 | T/T/T | 0.27 | -0.01 | 0.58 | 0.10 | 0.02 | .63 | 0.28 | -0.03 | 0.12 | |
| rs6265 | 27679916 | 11 | BDNF | T/T/T | 0.18 | -0.02 | 0.48 | 0.04 | -0.02 | .75 | 0.18 | -0.02 | 0.39 | |
| rs4466874 | 112861434 | 11 | NCAM1 | C/C/T | 0.41 | -0.06 | 9.34E-4 | 0.41 | 0.05 | .06 | 0.58 | 0.03 | 0.02 | |
| rs7937 | 41302706 | 19 | RAB4B | C/T/T | 0.43 | -0.09 | 1.95E-6 | 0.30 | 0.03 | .34 | 0.42 | 0.07 | 1.05E-5 | |
| rs3733829 | 41310571 | 19 | EGLN2 | G/G/A | 0.37 | 0.06 | 3.86E-3 | 0.08 | 0.03 | .49 | 0.47 | -0.04 | 0.01 | |
| rs12461383 | 41370338 | 19 | CYP2A7 | C/G/C | 0.47 | -0.09 | 2.47E-4 | 0.23 | 0.04 | .26 | 0.47 | -0.09 | 7.50E-5 | |
| rs7260329 | 41521638 | 19 | CYP2B6 | A/A/A | 0.31 | -0.07 | 4.04E-4 | 0.15 | -0.04 | .23 | 0.34 | -0.05 | 8.20E-4 | |
| rs4911243 | 31162568 | 20 | NOL4L | A/G/A | 0.33 | 0.04 | 0.03 | 0.17 | -0.03 | .46 | 0.35 | 0.02 | 0.18 | |
AA = African American, CPD = cigarettes per day, COPD = chronic obstructive pulmonary disorder, COPDGene = Genetic Epidemiology of COPD, ECLIPSE = Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints, NHW = non-Hispanic white, SNP = single nucleotide polymorphism. The “Coded allele” column shows alleles for COPDGene NHW cohort, COPDGene AA cohort, the meta-analysis of COPDGene NHW, COPDGene AA, ECLIPSE and GenKOLS. Correcting for the 12 regions listed, all p values less than 4.17E-3 (0.05/12) are in bold. Note that due to the categorization of CPD, the β coefficients listed are for an average increase of 10 CPD.
In addition, using SNPs from Table 2, we stratified by COPD case–control status (GOLD spirometry stage 2–4 for cases and GOLD spirometry stage 0 for controls). GOLD stage 1 subjects are an indeterminate group regarding COPD status, meeting spirometric criteria for mild COPD but likely also including normal older subjects, because FEV1/FVC typically falls with age. Because we wanted to focus on subjects with definite COPD or normal spirometry, we excluded GOLD stage 0 and PRISm subjects.37 The power analysis for these methods that examine the common variants associated with CPD stratified by COPD case–control status are in Supplementary Table 3b. In addition, we tested for an SNP by COPD interaction on CPD. The power analysis for these linear regressions that examines the SNP by COPD interaction on CPD is given in Supplementary Table 3c. Using the SNPs from Table 2, we also stratified by sex, and in addition, tested for an SNP by sex interaction on CPD. We did not find any significant sex differences of SNPs for CPD, and the results of these analyses are in Supplementary Tables 4a and b.
Statistical Analyses for Exome Array
Using data from the exome array, variants with minor allele frequency value of less than 5% and minimum minor allele count of 5 were collapsed into gene sets and analyzed using SKAT-O38 separately for NHW and AA COPDGene subjects. These analyses were restricted to putatively functional variants based on GENCODE v14 annotations via EPACTS where the annotations were nonsynonymous, splice site, or stop gain or loss.29 The same outcomes and covariates were used for both the rare and common variant analyses with the addition of a covariate for exome array platform (v1.1 vs. v1.2) in the NHW analyses and the inclusion of exome array–specific ancestry-based principal components. Among the AA and NHW in COPDGene, 14 559 and 14 677 genes were included in the analysis, respectively, which generated a Bonferroni critical value of p equal to 3.4 × 10−6 for each cohort.
Results
Common Variants: NHWs and AAs in the COPDGene Study and a Meta-Analysis of COPDGene, ECLIPSE, and GenKOLS Cohorts
Among all NHW COPDGene participants, multiple SNPs within chromosome 15q25 reached genome-wide significance (p < 5.0E-8) for CPD as shown in Table 2. There were no genome-wide significant results for CPD among AA in the COPDGene cohort. For the meta-analysis of COPDGene, ECLIPSE, and GenKOLS cohorts, all genome-wide significant results were again within chromosome 15q25. Supplementary Figure 1 includes regional plots for CPD for this chromosome 15 region from the meta-analysis, among NHW and AA subjects in the COPDGene study. Note that there is a similar association within chromosome 15q25 for the meta-analysis and among NHW subjects in the COPDGene study, but there was no significant GWA between any SNPs within chromosome 15q25 and CPD among AA subjects in the COPDGene study.
Comparison to Previously Published GWA Studies
We considered genome-wide significant results from a previously published genome-wide analysis of smoking-related traits in the UK BiLEVE study.23Table 2 shows p values of these SNPs from the UK BiLEVE analyses in the COPDGene subpopulations analyzed above. Previously reported associations on chromosome 19 (RAB4, p = 1.95 × 10−6; CYP2A7, p = 7.50 × 10−5; CYP2B6, 4.04 × 10−4) had suggestive and directionally consistent associations. For the exome array association analysis of rare variants in these previously replicated associations, the most significant results were among the COPDGene NHW subjects for CYP2A7 (p = 5.2 × 10−4), as shown in Table 5. In particular, chromosome 15q25 did not reach exome chip significance with CPD among the NHW cohort (CHRNA3, p = .17; CHRNA5, p = .06; AGPHD1, p = .27; CHRNB4, p = .87; IREB2, p = .27) and the AA cohort (CHRNA3, p = .52; CHRNA5, p = .30; AGPHD1, p = .02; CHRNB4, p = .87; IREB2, p = .02).
Table 5.
p Values for the Associations of Rare Variants in Each Gene With CPD Using the Genes From Table 2
| CHR | Nearest gene | COPDGene NHW | COPDGene AA | |||||
|---|---|---|---|---|---|---|---|---|
| All (n = 6658) | Cases (n = 2819) | Controls (n = 2534) | All (n = 3260) | Cases (n = 821) | Controls (n = 1749) | |||
| Chromosome 15q25 | 15 | CHRNA3 | 0.17 | 0.24 | 0.41 | 0.52 | 0.52 | 0.49 |
| 15 | CHRNA5 | 0.06 | 0.15 | 1.00 | 0.30 | 0.33 | 0.45 | |
| 15 | AGPHD1 | 0.27 | 0.01 | 0.28 | 0.02 | 0.43 | 0.05 | |
| 15 | CHRNB4 | 0.87 | 0.82 | 0.84 | 0.87 | 0.19 | 0.50 | |
| 15 | IREB2 | 0.27 | 0.77 | 0.82 | 0.02 | NA | 0.15 | |
| Replicated regions from the UK BiLEVE study | 1 | LPPR5 | 0.83 | 0.76 | 0.81 | 0.19 | 0.88 | 0.11 |
| 2 | TEX41/PABPC1P2 | 0.56 | 0.85 | 0.22 | 0.64 | 1.00 | 0.33 | |
| 6 | DNAH8 | 0.87 | 0.35 | 0.87 | 0.38 | 0.42 | 0.51 | |
| 7 | PDE1C | 1.00 | 0.05 | 0.14 | 0.60 | 0.32 | 0.74 | |
| 8 | CHRNB3 | 0.64 | 1.00 | 0.65 | 0.86 | NA | 1.00 | |
| 9 | DBH | 0.52 | 0.64 | 0.50 | 0.70 | 1.00 | 0.45 | |
| 10 | LOC100188947 | 0.87 | 0.79 | 0.71 | 0.11 | 0.51 | 0.69 | |
| 11 | BDNF | 1.00 | 0.76 | 0.46 | 0.26 | 0.48 | 0.56 | |
| 11 | NCAM1 | 0.76 | 0.30 | 0.82 | 0.03 | 0.27 | 0.28 | |
| 19 | RAB4B-EGLN2 | 0.44 | 0.69 | 0.57 | 0.04 | 0.15 | 0.27 | |
| 19 | CYP2A7 | 5.2E-4 | 0.05 | 0.08 | 0.47 | 0.51 | 0.97 | |
| 19 | CYP2B6 | 0.56 | 0.45 | 0.03 | 0.48 | 0.51 | 0.21 | |
| 20 | NOL4L | 0.32 | 0.55 | 0.89 | 0.84 | NA | NA | |
AA = African American, CHR = chromosome, CPD = cigarettes per day, COPD = chronic obstructive pulmonary disorder, COPDGene = Genetic Epidemiology of COPD, NA = not applicable, NHW = non-Hispanic white. Correcting for the 12 regions listed, all p values less than 4.17E-3 (0.05/12) are in bold. NAs are given if there were not enough rare variants in the region for the test statistic.
CPD Genetic Associations by COPD Case–Control Status
When we examined the SNPs listed in Table 2 stratified by COPD case–control status, SNPs on chromosome 15q25 were nominally associated in both NHW COPD cases (β = 0.11, p = 5.58 × 10−4) and controls (β = 0.12, p = 3.86 × 10−5) (Tables 3 and 4) However, on chromosome 19, there was a stronger but directionally consistent signal with CPD in COPD cases than controls for RAB4 (β = −0.13 vs. β = −0.05) and there was a stronger but directionally consistent signal with CPD in COPD controls than cases for CYP2B6 (β = −0.10 vs. β = −0.03). When we tested for an SNP by COPD interaction on CPD, none of these interactions in Tables 3 and 4 were significant. The SNP by COPD interaction on CPD for RAB4 was marginally significant (p = .07), but the SNP by COPD interaction on CPD for CYP2B6 was not significant (p = .12).
Table 3.
SNPs From Table 2 With the Analysis Stratified by COPD Case–Control Status Among COPDGene NHW
| SNP | CHR | Nearest gene | Coded allele | COPDGene NHW COPD cases (n = 2819) | COPDGene NHW COPD controls (n = 2534) | Interaction of COPD and SNP on CPD (n = 5353) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Allele freq | β | p | Allele freq | β | p | Allele freq | β | p | |||||
| Chromosome 15q25 | rs8192482 | 15 | CHRNA3 | T/T/T/T | 0.39 | 0.11 | 5.58E-4 | 0.34 | 0.12 | 3.86E-5 | 0.37 | -0.02 | .61 |
| rs11633958 | 15 | CHRNA5 | T/T/T/T | 0.39 | 0.11 | 5.06E-4 | 0.34 | 0.12 | 5.34E-5 | 0.37 | -0.02 | .66 | |
| rs72738786 | 15 | AGPHD1 | T/T/T/T | 0.39 | 0.11 | 3.33E-4 | 0.34 | 0.12 | 5.81E-5 | 0.37 | -0.01 | .74 | |
| rs2869548 | 15 | CHRNB4 | A/A/A/A | 0.40 | 0.10 | 7.94E-4 | 0.36 | 0.11 | 2.81E-4 | 0.38 | -0.01 | .81 | |
| rs11858836 | 15 | IREB2 | A/A/A/A | 0.38 | 0.08 | 7.17E-4 | 0.33 | 0.11 | 2.36E-4 | 0.36 | -0.03 | .47 | |
| Replicated regions from the UK BiLEVE study | rs61784651 | 1 | LPPR5 | T/T/T/T | 0.15 | -0.01 | 0.90 | 0.16 | -0.05 | 0.24 | 0.15 | 0.04 | .48 |
| rs10193706 | 2 | TEX41/ PABPC1P2 | A/A/C/C | 0.46 | 0.01 | 0.83 | 0.47 | 0.04 | 0.12 | 0.47 | -0.04 | .37 | |
| rs10807199 | 6 | DNAH8 | T/T/T/T | 0.48 | 0.02 | 0.43 | 0.45 | -0.04 | 0.14 | 0.46 | 0.07 | .11 | |
| rs215605 | 7 | PDE1C | G/G/T/T | 0.39 | 0.02 | 0.57 | 0.37 | 0.02 | 0.56 | 0.38 | 0.001 | .98 | |
| rs13280604 | 8 | CHRNB3 | G/G/A/A | 0.21 | 0.05 | 0.15 | 0.22 | 0.02 | 0.53 | 0.22 | 0.03 | .51 | |
| rs3025343 | 9 | DBH | A/A/A/A | 0.12 | -0.02 | 0.64 | 0.12 | 0.01 | 0.82 | 0.12 | -0.03 | .65 | |
| rs1329650 | 10 | LOC100188947 | T/T/T/T | 0.28 | -0.01 | 0.79 | 0.27 | -0.03 | 0.39 | 0.28 | 0.02 | .72 | |
| rs6265 | 11 | BDNF | T/T/T/T | 0.18 | -0.02 | 0.64 | 0.19 | -0.01 | 0.76 | 0.19 | 0.001 | .93 | |
| rs4466874 | 11 | NCAM1 | C/C/C/C | 0.41 | -0.07 | 0.02 | 0.40 | -0.07 | 0.02 | 0.41 | 0.001 | .94 | |
| rs7937 | 19 | RAB4B | C/C/T/T | 0.40 | -0.13 | 2.49E-5 | 0.45 | -0.05 | 0.05 | 0.42 | -0.08 | .07 | |
| rs3733829 | 19 | EGLN2 | G/G/G/G | 0.38 | 0.08 | 0.02 | 0.36 | 0.06 | 0.04 | 0.37 | 0.02 | .72 | |
| rs12461383 | 19 | CYP2A7 | C/C/G/G | 0.46 | -0.1 | 9.63E-03 | 0.48 | -0.06 | 0.09 | 0.47 | -0.04 | .46 | |
| rs7260329 | 19 | CYP2B6 | A/A/A/A | 0.30 | -0.03 | 0.44 | 0.32 | -0.1 | 1.19E-3 | 0.31 | 0.07 | .12 | |
| rs4911243 | 20 | NOL4L | A/A/G/G | 0.34 | 0.04 | 0.24 | 0.33 | 0.05 | 0.12 | 0.34 | -0.01 | .90 | |
CHR = chromosome, CPD = cigarettes per day, COPD = chronic obstructive pulmonary disorder, COPDGene = Genetic Epidemiology of COPD, NHW = non-Hispanic whites, SNPs = single nucleotide polymorphisms. Correcting for the 12 regions listed, all p values less than 4.17E-3 (0.05/12) are in bold.
Table 4.
SNPs From Table 2 With the Analysis Stratified by COPD Case–Control Status Among COPDGene AA
| SNP | CHR | Nearest gene | Coded allele | COPDGene AA COPD cases (n = 821) | COPDGene AA COPD controls (n = 1749) | Interaction of COPD and SNP on CPD (n = 2570) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Allele freq | β | Allele freq | β | Allele freq | β | Allele Freq | β | p | |||||
| Chromosome 15q25 | rs8192482 | 15 | CHRNA3 | T/T/T/T | 0.06 | 0.08 | 0.06 | 0.08 | 0.06 | 0.08 | 0.06 | 0.06 | .65 |
| rs11633958 | 15 | CHRNA5 | T/T/T/T | 0.07 | 0.13 | 0.07 | 0.13 | 0.07 | 0.13 | 0.06 | 0.14 | .26 | |
| rs72738786 | 15 | AGPHD1 | T/T/T/T | 0.14 | 0.07 | 0.14 | 0.07 | 0.14 | 0.07 | 0.14 | 0.05 | .56 | |
| rs2869548 | 15 | CHRNB4 | A/A/A/A | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.06 | 0.02 | .86 | |
| rs11858836 | 15 | IREB2 | A/A/A/A | 0.07 | 0.09 | 0.07 | 0.09 | 0.07 | 0.09 | 0.07 | 0.13 | .29 | |
| Replicated regions from the UK BiLEVE study | rs61784651 | 1 | LPPR5 | T/T/T/T | 0.05 | -0.16 | 0.05 | -0.16 | 0.05 | -0.16 | 0.06 | -0.10 | .46 |
| rs10193706 | 2 | TEX41/PABPC1P2 | A/A/C/C | 0.19 | -0.02 | 0.19 | -0.02 | 0.19 | -0.02 | 0.19 | -0.01 | .93 | |
| rs10807199 | 6 | DNAH8 | T/T/T/T | 0.15 | -0.01 | 0.15 | -0.01 | 0.15 | -0.01 | 0.15 | -0.03 | .71 | |
| rs215605 | 7 | PDE1C | G/G/T/T | 0.28 | 0.03 | 0.28 | 0.03 | 0.28 | 0.03 | 0.28 | -0.02 | .79 | |
| rs13280604 | 8 | CHRNB3 | G/G/A/A | 0.27 | -0.08 | 0.27 | -0.08 | 0.27 | -0.08 | 0.28 | 0.05 | .47 | |
| rs3025343 | 9 | DBH | A/A/A/A | 0.03 | 0.06 | 0.03 | 0.06 | 0.03 | 0.06 | 0.02 | -0.05 | .81 | |
| rs1329650 | 10 | LOC100188947 | T/T/T/T | 0.11 | 0.01 | 0.11 | 0.01 | 0.11 | 0.01 | 0.10 | -0.02 | .82 | |
| rs6265 | 11 | BDNF | T/T/T/T | 0.04 | -0.08 | 0.04 | -0.08 | 0.04 | -0.08 | 0.04 | -0.01 | .94 | |
| rs4466874 | 11 | NCAM1 | C/C/C/C | 0.39 | 0.11 | 0.39 | 0.11 | 0.39 | 0.11 | 0.41 | 0.08 | .22 | |
| rs7937 | 19 | RAB4B | C/C/T/T | 0.31 | 0.01 | 0.31 | 0.01 | 0.31 | 0.01 | 0.31 | 0.001 | 1.00 | |
| rs3733829 | 19 | EGLN2 | G/G/G/G | 0.09 | 0.03 | 0.09 | 0.03 | 0.09 | 0.03 | 0.08 | -0.01 | .94 | |
| rs12461383 | 19 | CYP2A7 | C/C/G/G | 0.23 | 0.07 | 0.23 | 0.07 | 0.23 | 0.07 | 0.23 | 0.07 | .34 | |
| rs7260329 | 19 | CYP2B6 | A/A/A/A | 0.16 | -0.07 | 0.16 | -0.07 | 0.16 | -0.07 | 0.15 | -0.03 | .70 | |
| rs4911243 | 20 | NOL4L | A/A/G/G | 0.16 | 0.02 | 0.16 | 0.02 | 0.16 | 0.02 | 0.17 | 0.13 | .12 | |
AA = African American, CHR = chromosome, CPD = cigarettes per day, COPD = chronic obstructive pulmonary disorder, COPDGene = Genetic Epidemiology of COPD, SNPs = single nucleotide polymorphisms. Correcting for the 12 regions listed, all p values less than 4.17E-3 (0.05/12) are in bold.
Rare Variants: Exome Genotyping in COPDGene NHWs and AAs
Using a Bonferroni correction for the number of regions tested in the exome array, we considered a significance level of 3.4 × 10−6 in the SKAT-O gene-based tests for association between rare variants and CPD. No genes reached this significance threshold among NHW in the COPDGene study. Among AA in COPDGene study, one gene reached this significance threshold (MYLIP, p = 1.41 × 10−6). However, there were only seven SNPs in this region, and only one SNP (rs151199797, minor allele frequency = 0.005) was responsible for this signal. When this SNP (rs151199797) was excluded from this region in the rare variant analysis, the MYLIP gene was no longer associated with CPD among AA subjects (p = .51). This region on chromosome 6 (MYLIP) including rs151199797 was not replicated at the significance level of 3.4 × 10−6 among NHW in the COPDGene study (p = .034).
Discussion
In a large GWA study of current and former heavy smokers in a population of COPD cases and controls, we have replicated the association of common variants on chromosome 15q25 with CPD. This region contains a cluster of genes coding for nicotinic acetylcholine receptors and other genes that are associated with smoking behavior,3–11 pulmonary function,12 and diseases directly related to smoking including lung cancer and COPD.15–19 These findings were replicated in the COPDGene cohort and then confirmed in a meta-analysis of two other NHW ancestry cohorts (ie, ECLIPSE and GenKOLS). The SNPs associated with CPD on chromosome 15q25 do not represent independent associations. There is strong linkage disequilibrium (LD) between the SNPs on chromosome 15q25 associated with CPD in this study for NHW (R2 = 0.71–1.0 as seen in Supplementary Figure 4a). Also, a known functional variant (rs16969968), which leads to an amino acid change in CHRNA539 and has been associated with nicotine dependence,40 is in LD with the genome-wide significant SNPs associated with CPD in the current study for NHW (R2 = 0.655–1.0 as shown in Supplementary Figure 4a). Thus, the genome-wide significant SNPs on chromosome 15q25 for CPD among NHW in this study may be tagging rs16969968 or these SNPs may be tagging another variant on chromosome 15q25. Of note, the genome-wide significant SNPs for CPD on chromosome 15q25 among NHW in this study are not in strong LD with two known SNPs in CHRNA5, rs880395 and rs588765 (R2 = 0.229–0.332), that influence brain mRNA expression.41 In addition, rare variant gene–based analyses for chromosome 15q25 were not significantly associated with CPD.
Previously reported associations on chromosome 19 (RAB4, p = 1.95E-06; CYP2A7, p = 7.50E-05; CYP2B6, p = 4.04E-04) showed suggestive and directionally consistent associations among common variants and for rare variants among COPDGene NHW subjects for CYP2A7 (p = 5.2 × 10−4). The mechanism for the associations between these SNPs on chromosome 19 and CPD is not known; however, it appears that these associations represent distinct genetic associations given the weak LD among these SNPs on chromosome 19 for NHW subjects as seen in Supplementary Figure 4c. Previous studies have demonstrated the importance of the chromosome 19q13, CYP2A7/CYP2B6 region, and nicotine metabolism.42,43
To test the hypothesis that the genetic susceptibility to CPD was different among COPD cases and controls, analyses were stratified by COPD case–control status, and in addition, we tested for an SNP by COPD interaction on CPD. Although the association of SNPs on chromosome 15q25 was nominally associated with CPD in NHW COPD cases and controls, there was not a significant SNP by COPD interaction on CPD for these chromosome 15q25 SNPs. There was a nominal association between rs7937 in RAB4B and CPD among COPD cases. However, the SNP by COPD interaction on CPD for RAB4B among NHW was only marginally significant (p = .07). Among NHW COPD controls, rs7260329 in CYP2B6 on chromosome 19 was nominally associated with CPD. However, the SNP by COPD interaction on CPD for CYP2B6 was not statistically significant (p = .12). Although differences of the genetic susceptibility to CPD among COPD cases and controls were intriguing, convincing evidence for the SNP by COPD interaction was not found in this study.
Genetic Burden of Smoking
Among all NHW COPDGene participants, multiple SNPs within chromosome 15q25 reached genome-wide significance (p < 5.0E-8) for average CPD; however, the genetic effect size for the most significant SNP rs8192482 [CHRNA3] was modest (β = 0.13 for an average increase of 10 CPD). This modest effect size creates a substantial smoking burden over the lifetime of a smoker. For instance, given the average smoking duration in the COPDGene study of 36.7 years, each copy of the risk allele at rs8192482 would contribute an average of 474.5 cigarettes more per year and an average of 17 414 more cigarettes over the lifetime of this NHW COPDGene smoker adjusting for age, sex, and genetic ancestry (via principal components).
Differences Among NHW and AA COPDGene Subjects
Within the COPDGene study, on average, NHW subjects smoked more CPD (mean = 25.80, SD = 11.42) compared to AA subjects (mean = 21.29, SD = 10.40). However, there is a larger proportion of current smokers among AA subjects (80%) compared to NHW subjects (39%). This is consistent with previously observed difference in smoking patterns between AA and NHW.43 In that study, AAs made more attempts to quit smoking but overall were less successful in maintaining smoking cessation.44 The underlying causes of these differences are complex and likely relate both to underlying susceptibility to smoking behavior and to societal factors. For example, among NHW, the LD on chromosome 15q25 is relatively strong as shown in Supplementary Figure 4a as compared to AA as shown in Supplementary Figure 4b. There are also differences for allele frequencies among AA and NHW. For the most significant association (rs8192482 [CHRNA3]) for CPD, the allele frequency for the coded T allele is 37% for NHW COPDGene subjects but only 6% for AA subjects. There is also a significant difference in sample sizes among the NHW subjects (n = 6659) and AA COPDGene subjects (n = 3260). Given the differences in sample sizes and minor allele frequency, the power analysis in Supplementary Figure 3a–c shows that there is a large difference in power for the analyses among NHW and AA. These differences in the COPDGene AA and NHW subjects could contribute to the differing results between these the two populations.
Potential Limitations
The COPDGene cohort was ascertained based on smoking history and is deliberately enriched for COPD cases. Although this ascertainment scheme maximizes the efficiency of case–control studies from the COPDGene cohort, analyzing secondary phenotypes (eg, quantitative measure of smoking behaviors) in any case–control study can be biased due to the ascertainment scheme. This is only an issue for SNPs associated with both the ascertainment condition and the secondary phenotype.45 Because our analysis focused on CPD, when we adjusted for GOLD stage (a measure of severity of COPD) in an additional analysis and observed a decrease in the strength of the association (as measured by β), markers on chromosome 15q25 still gave the most significant signal. This suggests that our analysis should be robust against this source of sampling bias due to ascertainment.45
Although COPDGene included substantial numbers of both AA and NHW subjects, the sample size for AA subjects was considerably smaller and therefore had less statistical power. In addition, CPD is self-reported and may suffer from measurement error.46 Biomarkers for smoking history such as exhaled carbon monoxide, urine and blood cotinine, and the nicotine metabolite ratio could provide better measurements of smoking exposure than the reported number of CPD.20
Funding
This work was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (K01HL125858 to SML). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work was also supported by the National Heart, Lung, and Blood Institute (NHLBI P01 HL105339 and R01 HL089856 to EKS; K08 HL097029 and R01 HL113264 to MHC; R01 HL089897 to JDC.; R00 HL121087 to MNM; K08 HL136928 to BH), Parker B. Francis Research Opportunity Award (to MNM and BH), and a grant from the Alpha-1 Foundation (to MHC). The COPDGene study (NCT00608764) was supported by NHLBI U01 HL089897 and U01 HL089856. The COPDGene study is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprised of AstraZeneca, Boehringer-Ingelheim, GlaxoSmithKline, Novartis, and Sunovion. The Norway GenKOLS study (Genetics of Chronic Obstructive Lung Disease, GSK code RES11080) and the ECLIPSE study (NCT00292552; GSK code SCO104960) were funded by GlaxoSmithKline. The funding sources played no role in the design of the study or the decision to submit the manuscript for publication. A full list of the COPDGene and ECLIPSE investigators are given in the supplement.
Declaration of Interests
Regarding conflicts of interest, in the past 3 years, EKS received honoraria and consulting fees from Merck; grant support and consulting fees from GlaxoSmithKline; and honoraria from Novartis. DAL is a consultant and has received grant support and honoraria from GlaxoSmithKline. He chaired the Respiratory Therapy Area Board at GlaxoSmithKline (2012–2015). AG has participated in the advisory boards of Chiesi Pharma AB, Sverige; Novartis, Norge; Takeda Nycomed, Norge; AstraZeneca, Norge; and Boehringer Ingelheim, Norge. No other authors reported conflicts of interest.
Supplementary Material
References
- 1. US Department of Health and Human Services.Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General, 2014. www.surgeongeneral.gov/library/reports/50-years-of-progress/exec-summary.pdf. [Google Scholar]
- 2. Li MD, Cheng R, Ma JZ, Swan GE. A meta-analysis of estimated genetic and environmental effects on smoking behavior in male and female adult twins. Addiction. 2003;98(1):23–31. [DOI] [PubMed] [Google Scholar]
- 3. Bierut LJ, Madden PA, Breslau N et al. . Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum Mol Genet. 2007;16(1):24–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Thorgeirsson TE, Geller F, Sulem P et al. . A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature. 2008;452(7187):638–642. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Liu JZ, Tozzi F, Waterworth DM et al. ; Wellcome Trust Case Control Consortium Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet. 2010;42(5):436–440. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Thorgeirsson TE, Gudbjartsson DF, Surakka I et al. ; ENGAGE Consortium Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior. Nat Genet. 2010;42(5):448–453. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. TAG Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet. 2010;42:441–447. https://www.nature.com/articles/ng.571. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Saccone NL, Culverhouse RC, Schwantes-An TH et al. . Multiple independent loci at chromosome 15q25.1 affect smoking quantity: a meta-analysis and comparison with lung cancer and COPD. PLoS Genet. 2010;6(8):1–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Bierut LJ, Stitzel JA, Wang JC et al. . Variants in nicotinic receptors and risk for nicotine dependence. Am J Psychiatry. 2008;165(9):1163–1171. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Hancock DB, Reginsson GW, Gaddis NC et al. . Genome-wide meta-analysis reveals common splice site acceptor variant in CHRNA4 associated with nicotine dependence. Transl Psychiatry. 2015;5:e651 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4930126/pdf/tp2015149a.pdf. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. David SP, Hamidovic A, Chen GK et al. . Genome-wide meta-analyses of smoking behaviors in African Americans. Transl Psychiatry. 2012;2:e119 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3365260/pdf/tp201241a.pdf. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Lutz SM, Cho MH, Young K et al. ; ECLIPSE Investigators; COPDGene Investigators A genome-wide association study identifies risk loci for spirometric measures among smokers of European and African ancestry. BMC Genet. 2015;16(138):1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Cho MH, McDonald ML, Zhou X et al. ; NETT Genetics, ICGN, ECLIPSE and COPDGene Investigators Risk loci for chronic obstructive pulmonary disease: a genome-wide association study and meta-analysis. Lancet Respir Med. 2014;2(3):214–225. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Pillai SG, Ge D, Zhu G et al. ; ICGN Investigators A genome-wide association study in chronic obstructive pulmonary disease (COPD): identification of two major susceptibility loci. PLoS Genet. 2009;5(3):e1000421. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Wang JC, Bierut LJ, Goate AM. Variants weakly correlated with CHRNA5 D398N polymorphism should be considered in transcriptional deregulation at the 15q25 locus associated with lung cancer risk. Clin Cancer Res. 2009;15(17):5599; author reply 5599. [DOI] [PubMed] [Google Scholar]
- 16. Falvella FS, Galvan A, Frullanti E et al. . Transcription deregulation at the 15q25 locus in association with lung adenocarcinoma risk. Clin Cancer Res. 2009;15(5):1837–1842. [DOI] [PubMed] [Google Scholar]
- 17. Amos CI, Wu X, Broderick P et al. . Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1. Nat Genet. 2008;40(5):616–622. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Thorgeirsson TE, Geller F, Sulem P et al. . A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature. 2008;452(7187):638–642. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Chen LS, Hung RJ, Baker T et al. . CHRNA5 risk variant predicts delayed smoking cessation and earlier lung cancer diagnosis: a meta-analysis. J Natl Cancer Inst. 2015;107(5):1–9. https://www.ncbi.nlm.nih.gov/pubmed/25873736. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Lutz SM, Hokanson JE. Genetic influences on smoking and clinical disease. Understanding behavioral and biological pathways with mediation analysis. Ann Am Thorac Soc. 2014;11(7):1082–1083. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Lutz SM, Hokanson JE. Mediation analysis in genome-wide association studies: current perspectives. Open Bioinforma J. 2015;2015(7):1–5. [Google Scholar]
- 22. Olfson E, Saccone NL, Johnson EO et al. . Rare, low frequency and common coding variants in CHRNA5 and their contribution to nicotine dependence in European and African Americans. Mol Psychiatry. 2016;21(5):601–607. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Wain LV, Shrine N, Miller S et al. . Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. Lancet Respir. 2015;3(10):769–781. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. VanderWeele TJ, Asomaning K, Tchetgen Tchetgen EJ et al. . Genetic variants on 15q25.1, smoking, and lung cancer: an assessment of mediation and interaction. Am J Epidemiol. 2012;175(10):1013–1020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Siedlinski M, Tingley D, Lipman PJ et al. ; COPDGene and ECLIPSE Investigators Dissecting direct and indirect genetic effects on chronic obstructive pulmonary disease (COPD) susceptibility. Hum Genet. 2013;132(4):431–441. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Regan EA, Hokanson JE, Murphy JR et al. . Genetic epidemiology of COPD (COPDGene) study design. COPD. 2010;7(1):32–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Vestbo J, Anderson W, Coxson HO et al. ; ECLIPSE investigators Evaluation of COPD longitudinally to identify predictive surrogate end-points (ECLIPSE). Eur Respir J. 2008;31(4):869–873. [DOI] [PubMed] [Google Scholar]
- 28. Cho MH, Boutaoui N, Klanderman BJ et al. . Variants in FAM13A are associated with chronic obstructive pulmonary disease. Nat Genet. 2010;42(3):200–202. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Hobbs BD, Parker MM, Chen H et al. ; NETT Genetics Investigators; ECLIPSE Investigators; COPDGene Investigators; International COPD Genetics Network Investigators Exome array analysis identifies a common variant in IL27 associated with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2016;194(1):48–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet. 2012;44(8):955–959. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010;34(8):816–834. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Abecasis GR, Auton A, Brooks LD et al. . An integrated map of genetic variation from 1,092 human genomes. Nature. 2010;491:56–65. https://www.nature.com/articles/nature11632. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Purcell S, Neale B, Todd-Brown K et al. . PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–575. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–909. [DOI] [PubMed] [Google Scholar]
- 35. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190–2191. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. de Bakker PIW, Ferreira MAR, Jia X et al. . Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet. 2008;17(2):122–128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Wan ES, Castaldi PJ, Cho MH et al. ; COPDGene Investigators Epidemiology, genetics, and subtyping of preserved ratio impaired spirometry (PRISm) in COPDGene. Respir Res. 2014;15(89):1–13 https://respiratory-research.biomedcentral.com/track/pdf/10.1186/s12931-014-0089-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Lee S, Emond MJ, Bamshad MJ et al. ; NHLBI GO Exome Sequencing Project—ESP Lung Project Team Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet. 2012;91(2):224–237. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Bierut LJ. Genetic vulnerability and susceptibility to substance dependence. Neuron. 2011;69(4):618–627. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Saccone NL, Wang JC, Breslau N et al. . The CHRNA5-CHRNA3-CHRNB4 nicotinic receptor subunit gene cluster affects risk for nicotine dependence in African-Americans and in European-Americans. Cancer Res. 2009;69(17):6848–6856. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Barrie ES, Hartmann K, Lee SH et al. . The CHRNA5/CHRNA3/CHRNB4 nicotinic receptor regulome: genomic architecture, regulatory variants, and clinical associations. Hum Mutat. 2017;38(1):112–119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Baurley JW, Edlund CK, Pardamean CI et al. . Genome-wide association of the laboratory-based nicotine metabolite ratio in three ancestries. Nicotine Tob Res. 2016;18(9):1837–1844. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Loukola A, Buchwald J, Gupta R et al. . A genome-wide association study of a biomarker of nicotine metabolism. PLoS Genet. 2015;11(9):e1005498. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Trinidad DR, Xie B, Fagan P et al. . Disparities in the population distribution of African American and non-Hispanic white smokers along the quitting continuum. Health Educ Behav. 2015;42(6):742–751. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Lutz SM, Hokanson JE, Lange C. An alternative hypothesis testing strategy for secondary phenotype data in case-control genetic association studies. Front Genet. 2014;4(188):1–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Bloom AJ, Hartz SM, Baker TB et al. . Beyond cigarettes per day. A genome-wide association study of the biomarker carbon monoxide. Ann Am Thorac Soc. 2014;11(7):1003–1010. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.