Abstract
Case-control studies have successfully identified many significant genetic associations for complex diseases, but lack of replication has been a criticism of case-control genetic association studies in general. We selected 12 candidate genes with reported associations to chronic obstructive pulmonary disease (COPD) and genotyped 29 polymorphisms in a family-based study and in a case-control study. In the Boston Early-Onset COPD Study families, significant associations with quantitative and/or qualitative COPD-related phenotypes were found for the tumor necrosis factor (TNF)-α −308G>A promoter polymorphism (P < 0.02), a coding variant in surfactant protein B (SFTPB Thr131Ile) (P = 0.03), and the (GT)31 allele of the heme oxygenase (HMOX1) promoter short tandem repeat (P = 0.02). In the case-control study, the SFTPB Thr131Ile polymorphism was associated with COPD, but only in the presence of a gene-by-environment interaction term (P = 0.01 for both main effect and interaction). The 30-repeat, but not the 31-repeat, allele of HMOX1 was associated (P = 0.04). The TNF −308G>A polymorphism was not significant. In addition, the microsomal epoxide hydrolase “fast” allele (EPHX1 His139Arg) was significantly associated in the case-control study (P = 0.03). Although some evidence for replication was found for SFTPB and HMOX1, none of the previously published COPD genetic associations was convincingly replicated across both study designs.
Case-control association analysis is a commonly used study design in the field of complex trait genetics. Susceptibility genes successfully identified using this approach include the associations between Factor V Leiden and venous thromboembolism (1), Apolipoprotein E4 and Alzheimer's disease (2), and peroxisome proliferator–activated receptor-γ (PPARγ) and type 2 diabetes mellitus (3). However, candidate gene case-control association studies have been criticized because of a lack of replication (4, 5). Often the first published study reports a significant association between a candidate gene polymorphism and a disease of interest, but subsequent studies are unable to confirm the association (6).
Candidate genes can be selected based on previous genetic linkage analysis results (positional candidate genes), but they are more commonly chosen based on known or presumed mechanisms in disease pathophysiology or based on the results of previous association studies. In the example of PPARγ above, Altshuler and colleagues identified 16 published genetic associations for type 2 diabetes mellitus (3). They genotyped the previously associated polymorphisms in a parent-child trios study, to protect against spurious results due to population stratification. Significant associations (P < 0.05) were then tested for replication in two case-control studies and 1 sibling-pair study. By the use of this method, they confirmed only one of the previously reported associations. The common allele of the PPARγ Pro12Ala polymorphism conferred a modest (1.25-fold), but significant increase in risk for type 2 diabetes mellitus.
In this study of chronic obstructive pulmonary disease (COPD), we used a study design similar to that of Altshuler and colleagues in their study of diabetes. We sought to determine whether any of the published COPD candidate gene associations would withstand the test of replication and whether a convincing COPD genetic association could be found using this approach. Twelve candidate genes with reported significant associations to COPD were identified in the published literature (Table 1
Genes/Pathways | Study Author and Year | Association Results | Country (Ethnicity) | Case Definition |
|---|---|---|---|---|
| Protease-antiprotease | ||||
| α1-Antichymotrypsin (SERPINA3) | Poller 1992, 1993 | Initial positive | Sweden | Airway resistance > 0.4kPa/liter/s |
| Ishii 2000 | Replicated | Japan | Clinical diagnosis, including PFTs | |
| Sandford 1998 | Negative | Canada | FEV1 < 80% pred, FEV1/FVC < 0.7 | |
| Benetazzo 1999 | Negative | Italy | FEV1 < 70% pred, BD response < 12% | |
| Matrix metalloproteinase-1 (MMP1) | Joos 2002 | Initial positive | United States & Canada (whites) | Rapid decline vs. no decline in FEV1 over 5 yr in patients with COPD (Lung Health Study) |
| Matrix metalloproteinase-9 (MMP9) | Minematsu 2001 | Initial positive | Japan | Emphysema on chest CT scan, ⩾ 10 pack-years |
| Joos 2002 | Negative | United States & Canada (whites) | Rapid decline vs. no decline in FEV1 over 5 yr in patients with COPD (Lung Health Study) | |
| Tissue inhibitor of metalloproteases-2 (TIMP2) | Hirano 2001 | Initial positive | Japan | FEV1 < 70% pred, FEV1/FVC < 0.7 |
| Antioxidant | ||||
| Heme oxygenase (HMOX1) | Yamada 1999 | Initial positive | Japan | Reduced FEV1/FVC, DLCO; chest CT scan |
| He 2002 | Negative | United States & Canada (whites) | Rapid decline vs. no decline in FEV1 over 5 yr in patients with COPD (Lung Health Study) | |
| Xenobiotic metabolism | ||||
| Microsomal epoxide hydrolase (EPHX1) | Smith 1997 | Initial positive | Scotland | COPD: FEV1 < 1.5 liters Emphysema: lung pathology at lung cancer resection |
| Sandford 2001 | Replicated | United States & Canada (whites) | Rapid decline vs. no decline in FEV1 over 5 yr in patients with COPD (Lung Health Study) | |
| Yoshikawa 2000 | Negative | Japan | FEV1 < 70% pred | |
| Takeyabu 2000 | Negative | Japan | FEV1/FVC < 0.7; chest CT scan | |
| Yim 2000 | Negative | Korea | FEV1 < 80% pred, FEV1/FVC < 0.7, BD response < 12% | |
| Glutathione S-Transferase P1 | Ishii 1999 | Initial positive | Japan | FEV1/FVC < 0.7 |
| (GSTP1) | He 2002 | Replicated | United States & Canada (whites) | Rapid decline vs. no decline in FEV1 over 5 yr in patients with COPD (Lung Health Study) |
| Yim 2002 | Negative | Korea | FEV1 < 80% pred, FEV1/FVC < 0.7, BD response < 12% | |
| Glutathione S-transferase M1 | Harrison 1997 | Initial positive | Scotland | Emphysema on lung pathology at lung cancer resection |
| (GSTM1) | Baranova 1997 | Replicated | France | Chronic bronchitis with “moderate” or “severe” airflow obstruction |
| Yim 2002 | Negative | Korea | FEV1 < 80% pred, FEV1/FVC < 0.7, BD response < 12% | |
| He 2002 | Negative | United States & Canada (whites) | Rapid decline vs. no decline in FEV1 over 5 yr in patients with COPD (Lung Health Study) | |
| Inflammation | ||||
| Tumor Necrosis Factor-α (TNF) | Huang 1997 | Initial positive | Taiwan | FEV1 < 80% pred, FEV1/FVC < 0.7, chronic bronchitis |
| Sakao 2001 | Replicated | Japan | FEV1 < 80% pred, FEV1/FVC < 0.7; chronic bronchitis excluded | |
| Sandford 2001 | Negative | United States & Canada (whites) | Rapid decline vs. no decline in FEV1 over 5 yr in patients with COPD (Lung Health Study) | |
| Higham 2000 | Negative | England | FEV1/FVC ⩽ 80% pred, BD response ⩽ 15%, ⩾ 10 pack-years | |
| Patuzzo 2000 | Negative | Italy | FEV1 < 70% pred, BD response < 12% | |
| Ferrarotti 2003 | Negative | Italy | FEV1 < 50% pred, FEV1/FVC < 70% pred, BD response < 12%, DLCO < 50% pred, | |
| Ishii 2000 | Negative | Japan | Clinical diagnosis, including PFTs | |
| Immune response | ||||
| Surfactant protein B (SFTPB) | Guo 2001 | Initial positive | Mexico | FEV1 < 70% pred, FEV1/FVC < 0.7, > 10 pack-years |
| Seifart 2002 | Replicated | Germany | FEV1 < 70% pred, FEV1/FVC < 0.8, chronic bronchitis | |
| Surfactant protein D (SFTPD) | Guo 2001 | Initial positive | Mexico | FEV1 < 70% pred, FEV1/FVC < 0.7, > 10 pack-years |
| Vitamin D binding protein | Horne 1990 | Initial positive | Canada | Clinical diagnosis |
| (group-specific complement, GC) | Schellenberg 1998 | Replicated | Canada | FEV1 < 80% pred, FEV1/FVC < 0.7 |
| Ishii 2001 | Replicated | Japan | FEV1/FVC < 70%, ⩾ 10 pack-years | |
| Ito 2004 | Replicated | Japan | Clinical diagnosis, > 20 pack-years | |
| Kauffmann 1983 | Negative | France | Never-smokers, low lung function, ⩽ 40 yr old | |
| Kasuga 2003 | Negative | United States & Canada (whites) | Low lung function among patients with COPD (Lung Health Study) | |
| Sandford 2001 | Negative | United States &
Canada (whites) | Rapid decline vs. no decline in FEV1 over 5 yr in patients
with COPD (Lung Health Study) |
Details of subject recruitment and phenotyping in the Boston Early-Onset COPD Study have been reported previously (8). Severe, early-onset COPD probands had an FEV1 < 40% predicted, age < 53 yr, and did not have severe α1-antitrypsin deficiency. First-degree relatives, older second-degree relatives, spouses, and other family members with COPD were invited to participate. This analysis included 949 individuals from 127 extended pedigrees. Ninety-eight percent of the Boston Early-Onset COPD Study participants were white.
The case-control study identified cases from the National Emphysema Treatment Trial (NETT) (9, 10). Subjects participating in NETT had an FEV1 ⩽ 45% predicted (11), evidence of hyperinflation on pulmonary function testing, and bilateral emphysema on high-resolution chest CT scan. For this analysis, cases included 304 non-Hispanic white participants in the NETT Genetics Ancillary Study; nearly all were current or former smokers. Control subjects were participants in the Normative Aging Study (NAS), a longitudinal study of healthy men conducted by the Veterans Administration (VA) in Greater Boston, starting in the 1960s (12). Control subjects included 441 white male NAS participants with a history of at least 10 pack-years of cigarette smoking and without airflow obstruction at their most recent visit (FEV1 > 80% predicted [13] and FEV1/FVC > 90% predicted [11]).
All studies were approved by the appropriate institutional review boards. Participants in the Boston Early-Onset COPD Study and the NETT Genetics Ancillary Study gave written informed consent. Anonymized data were used for the Normative Aging Study participants, as approved by the IRBs of Partners Healthcare System and of the Boston VA.
Twelve candidate genes with reported associations to COPD were identified from the published literature (Tables 1 and E1). Nineteen polymorphisms were initially genotyped in both the family-based and case-control studies (Table 2)
Gene | Polymorphism | Role | Presumed Function |
|---|---|---|---|
| SERPINA3 | rs4934 | Ala-15Thr | Signal peptide |
| rs17473 | Pro229Ala (Bonn-1) | Reduced serum α1-antichymostrypsin levels | |
| rs1800463 | Leu55Pro (Bochum-1) | ||
| MMP1 | rs1799750 | G-1607GG | Increased gene expression |
| MMP9 | D20S838 | (CA) repeat | Reduced gene expression with increased length |
| TIMP2 | rs2277698 | Ser101Ser | of repeat |
| HMOX1 | — | (GT) repeat | May affect gene expression |
| EPHX1 | rs868966 | Promoter | |
| rs1877724 | Intron | ||
| rs1051740 | Tyr113His (“slow”) | Reduced enzyme activity | |
| rs2292566 | Exon, synonymous | ||
| rs2260863 | Intron | ||
| rs2234922 | His139Arg (“fast”) | Increased enzyme activity | |
| rs1051741 | Exon, synonymous | ||
| rs360063 | 3′ UTR | ||
| GSTP1 | rs947894 rs1799811 | Ile105Val Ala114Val | Altered enzyme activity |
| GSTM1 | — | Null | Absent enzyme activity |
| TNF | rs1800629 | Promoter (−308G>A) | Higher baseline and induced expression |
| rs361525 | Promoter (−238) | ||
| rs1800610 | Intron (+489) | ||
| rs3091257* | 3′ UTR | ||
| rs769178 | 3′ UTR | ||
| SFTPB | rs1130866 | Thr131Ile | Alters glycosylation site |
| D2S388 | (CA) Repeat | ||
| SFTPD | rs2243639 | Thr180Ala | |
| rs721917 | Met31Thr | ||
| GC | rs4588 | Thr436Lys (Gc-2) | Decreased conversion into macrophage |
| rs7041 | Asp432Glu (Gc-1S) | activating factor |
Four additional SNPs in TNF (Table 2) were selected using a linkage disequilibrium tagging algorithm (http://www.innateimmunity.net), based on genotype data available from SeattleSNPs (http://pga.mbt.washington.edu/). Tag SNPs were selected using an LD threshold defined by r2 ⩾ 0.9 and a minor allele frequency ⩾ 0.05. Six additional SNPs in EPHX1 (Table 2) were selected from public databases (http://snpper.chip.org/, http://snp500cancer.nci.nih.gov/).
In the family-based study, deviations from Mendelian inheritance were tested with Pedcheck (18). The data were analyzed using the extended pedigree family-based association test, implemented in PBAT (http://www.biostat.harvard.edu/~clange/default.htm) (19). Quantitative (FEV1 and FEV1/FVC) and qualitative (mild-to-severe and moderate-to-severe airflow obstruction) traits were analyzed under additive genetic models, adjusting for age, sex, height, smoking status, and pack-years of cigarette smoking, including quadratic terms where appropriate. Dominant genetic models and gene-by-environment interactions were tested in secondary analyses.
The case-control data were analyzed in SAS/Genetics (SAS Institute, Cary, NC). Hardy-Weinberg equilibrium was assessed in control subjects using an exact test. Odds ratios and chi-square statistics were calculated from 2 × 2 tables of allele frequencies. Logistic regression was used to control for age and pack-years in both additive and dominant genetic models. Gene-by-smoking and gene-by-gene interactions were tested by including appropriate cross-product terms in the regression models. Haplotype analysis was performed using the expectation-maximization algorithm and score tests, implemented in haplo.stats (20).
In the case-control study, we had previously genotyped a panel of 44 unlinked SNPs to test for population stratification using the method of Pritchard and Rosenberg (21). There was no compelling evidence for overt population stratification (χ244 d.f. = 58.5, P = 0.07) (22).
Characteristics of participants in the Boston Early-Onset COPD Study are shown in Table 3A
Probands | First-Degree Relatives | Second-Degree Relatives | Spouses | |
|---|---|---|---|---|
| Subjects | 127 | 503 | 273 | 46 |
| Female (%) | 95 (74.8%) | 287 (57.1%) | 157 (57.5%) | 14 (30.4%) |
| Age (± SD) | 48.1 (± 4.7) | 42.0 (± 17.9) | 53.2 (± 19.5) | 51.0 (± 5.8) |
| Pack-years (± SD) | 38.9 (± 21.9) | 17.3 (± 23.9) | 19.5 (± 24.5) | 34.0 (± 34.1) |
| FEV1, % predicted (± SD)* | 19.2 (± 7.4) | 83.3 (± 21.9) | 84.7 (± 19.0) | 86.3 (± 20.4) |
| FEV1/FVC, % predicted (± SD)* | 39.1 (± 13.3) | 86.2 (± 15.1) | 89.7 (± 12.5) | 90.2 (± 11.3) |
| Mild-to-severe airflow obstruction (%)*,† | 127 (100%) | 133 (26.4%) | 71 (26.0%) | 10 (21.7%) |
| Moderate-to-severe airflow obstruction (%)*,‡ | 127 (100%) | 67 (13.3%) | 27 (9.9%) | 3 (6.5%) |
Cases: NETT | Control Subjects: NAS | P value* | |
|---|---|---|---|
| Total subjects | 304 | 441 | |
| Male (%) | 194 (63.8%) | 441 (100%) | < 0.0001 |
| Age (± SD) | 67.3 (± 6.0) | 67.5 (± 8.5) | n.s. |
| Pack-years (± SD) | 67.4 (± 31.6) | 38.5 (± 26.5) | < 0.0001 |
| FEV1, % predicted (± SD) | 24.8 (± 6.5) | 92.2 (± 11.4) | < 0.0001 |
| FEV1/FVC, % predicted (± SD) | 41.5 (± 8.2) | 102.6 (± 6.8) | < 0.0001 |
In the Boston Early-Onset COPD Study families, all markers were tested for association with the postbronchodilator phenotypes, including FEV1, FEV1/FVC, mild-to-severe airflow obstruction (FEV1 < 80% predicted, with FEV1/FVC < 90% predicted), and moderate-to-severe airflow obstruction (FEV1 < 60% predicted, with FEV1/FVC < 90% predicted). Significant results from the extended pedigree family-based association test are shown in Table 4
Candidate Gene Polymorphisms | |||||
|---|---|---|---|---|---|
| Phenotype (postbronchodilator) | TNF −308G>A | SFTPB Thr131Ile | HMOX1 137-bp Allele | ||
| (60 informative families) | (78 informative families) | (13 informative families) | |||
| FEV1 | 0.004 | n.s. | 0.02 | ||
| FEV1/FVC | 0.004 | n.s. | 0.02 | ||
| Mild-to-severe airflow obstruction* | 0.02 | n.s. | 0.08 | ||
| Moderate-to-severe airflow obstruction† | 0.04 | 0.03 | 0.05 | ||
In addition, the 31-repeat (137-bp) allele (allele frequency in the extended pedigrees = 0.07) of the Heme oxygenase (HMOX1) promoter STR also was found to be associated with postbronchodilator FEV1 and FEV1/FVC. None of the other variants—two additional STRs, the MMP1 indel, and the other 12 SNPs—were found to be associated with qualitative or quantitative COPD-related traits in the early-onset COPD families. The GSTM1 deletion could not be analyzed in the family-based study design, as heterozygotes for the deletion could not be distinguished consistently from the wild-type. One marker, the α1-antichymotrypsin Pro229Ala SNP (Bonn-1), was found to be monomorphic in the extended pedigrees and was not genotyped in the case-control study.
To assess for gene-by-environment interactions, the TNF −308G>A and SFTPB Thr131Ile variants were examined in an analysis stratified by smoking status as well as in a model including an interaction term for pack-years. These analyses did not show evidence of gene-by-environment interaction effects.
In the analysis of the additional TNF SNPs in the early-onset COPD families, one SNP in the 3′ untranslated region (UTR), rs3091257, had a high rate of Mendelian errors. In addition, this SNP deviated from Hardy-Weinberg proportions in the NAS control subjects and was not analyzed further in either cohort. None of the three additional TNF SNPs were associated with COPD-related phenotypes in the family-based study.
In the early-onset COPD families, none of the eight SNPs in EPHX1 were found to be associated in the primary analysis. In a secondary analysis using a dominant genetic model, one intronic SNP (rs1877724) was marginally associated with postbronchodilator values for both FEV1 (P = 0.02) and moderate-to-severe airflow obstruction (P = 0.02).
As noted above, the TNF rs3091257 SNP was out of Hardy-Weinberg Equilibrium (HWE) in the NAS control subjects and was removed from the analysis. All other SNPs and STRs tested in the study were found to be in HWE in the NAS control subjects. The results of the case-control association analysis of the biallelic markers (SNPs, insertion/deletion, and GSTM1 null allele) are shown in Table 5A
Gene | Polymorphism | Allele Frequency: Cases | Allele Frequency: Control Subjects | P Value Allele Test | P Value Genotype Trend Test (adjusted)* |
|---|---|---|---|---|---|
| SERPINA3 | Ala-15Thr | 0.47 | 0.49 | n.s. | n.s. |
| Leu55Pro (Bochum-1) | 0.01 | 0.005 | n.s. | n.s. | |
| MMP1 | G-1607GG | 0.49 | 0.48 | n.s. | n.s. |
| TIMP2 | Ser101Ser | 0.12 | 0.10 | n.s. | 0.08 |
| EPHX1 | His139Arg | 0.17 | 0.22 | 0.02 | 0.03 |
| Tyr113His | 0.29 | 0.28 | n.s. | n.s. | |
| GSTP1 | Ile105Val | 0.35 | 0.30 | 0.05 | 0.1 |
| Ala114Val | 0.08 | 0.09 | n.s. | n.s. | |
| GSTM1 | null | 0.58† | 0.54† | n.s. | n.s. |
| TNF | −308G>A | 0.15 | 0.17 | n.s. | n.s. |
| SFTPB | Thr131Ile | 0.46 | 0.44 | n.s. | n.s. |
| SFTPD | Thr180Ala | 0.40 | 0.43 | n.s. | n.s. |
| Met31Thr | 0.43 | 0.41 | n.s. | n.s. | |
| GC | Thr436Lys (GC-2) | 0.29 | 0.27 | n.s. | n.s. |
| Asp432Glu (GC-1S) | 0.43 | 0.42 | n.s. | n.s. |
The matrix metalloproteinase-9 (MMP9) STR (D20S838) was initially analyzed by grouping alleles into “Small” and “Large” repeat numbers, according to the method of Joos and colleagues (24); alleles ⩽ 110 bp (16 repeats in the previous report) were classified as small, and those ⩾ 112 bp (17 repeats) were considered large. The HMOX1 promoter polymorphism was analyzed as per Yamada and coworkers (16). Alleles were classified as Small (< 129 bp, < 27 repeats), Medium (129–139 bp, 27–32 repeats) and Large (⩾ 141 bp, ⩾ 33 repeats). Neither STR marker was significantly associated in these analyses.
The MMP9, HMOX1, and SFTPB STRs were then analyzed by comparing each allele with a frequency of at least 0.05 to all other alleles, using both additive and dominant genetic models (Table 5B)
Gene | Microsatellite | Allele | Allele Frequency: Cases | Allele Frequency: Control Subjects | P Value Trend Test (adjusted)* |
|---|---|---|---|---|---|
| MMP9 | D20S838 | 106 | 0.56 | 0.56 | n.s. |
| 120 | 0.19 | 0.17 | n.s. | ||
| 122 | 0.12 | 0.14 | n.s. | ||
| 124 | 0.05 | 0.06 | n.s. | ||
| HMOX1 | (GT) repeat | 121 | 0.20 | 0.19 | n.s. |
| 123 | 0.08 | 0.08 | n.s. | ||
| 135 | 0.43 | 0.48 | 0.04 | ||
| 137 | 0.08 | 0.07 | n.s. | ||
| 149† | 0.04 | 0.03 | n.s. | ||
| SFTPB | D2S388 | 259 | 0.23 | 0.19 | 0.09 |
| 263 | 0.36 | 0.38 | n.s. | ||
| 265 | 0.11 | 0.14 | n.s. | ||
| 269 | 0.24 | 0.26 | n.s. |
None of the three additional SNPs in TNF were significant in the case-control study; no evidence of gene-by-smoking interaction was found in this analysis. In the case-control study, none of the additional SNPs in EPHX1 was found to be associated with COPD. However, in a model that included a gene-by-smoking (pack-years) interaction, both the main effect (P = 0.02) and the interaction (P = 0.03) were significant for a silent coding variant in exon 3 (rs2292566). This SNP was not in linkage disequilibrium with the fast allele in exon 4 that was found to be associated with COPD (r2 < 0.1 in NAS control subjects).
Previous authors have examined gene-by-gene interactions in COPD, specifically among genes involved in xenobiotic metabolism (25, 26). The GSTM1 deletion was tested in two-way interactions with GSTP1 Ile105Val, the EPHX1 slow allele, and the EPHX1 fast allele in the case-control study. In additive models for the SNPs, none of the interactions was significant, in models including or excluding the main SNP effects. Similarly, three-way interactions between the GSTM1 deletion, GSTP1 Ile105Val, and either the EPHX1 fast or slow alleles were not significant.
We also tested for interactions between the EPHX1 fast polymorphism, TNF −308 G>A, and SFTPB Thr131Ile, the SNPs that had been significant in either the family-based or case-control study. None of the two-way interactions nor the three-way interaction were significant.
In the case-control population, two-SNP haplotype analyses in SFTPD and GC were not significant, though there was a trend toward association for GSTP1 (global score test, P = 0.06). Haplotype analyses in TNF, using all four SNPs as well as using two SNP sliding windows, were not significant. The eight-SNP haplotype analysis in EPHX1 was not significant, and none of the two- or four-SNP sliding window haplotypes of EPHX1 was associated with COPD.
In this study, we selected 29 polymorphisms in 12 genes that had been reported to be associated with COPD in the published literature and genotyped these variants in a family-based study of early-onset COPD and a case-control COPD study. The most significant association in the family-based study (TNF −308G>A) was not replicated in the case-control study, and the strongest association in the case-control study (EPHX1 fast allele) was not found in the family-based analysis. Two variants showed modest evidence for replication across both study designs. A coding SNP in surfactant protein B (Thr131Ile) that was marginally associated (P = 0.03) with one qualitative trait (moderate-to-severe airflow obstruction) in the Boston Early-Onset COPD families was not associated in the primary case-control analysis, but did show association (P = 0.01) when an SNP-by-smoking interaction was included. An STR in HMOX1 was significant in both studies, though different alleles were associated in each cohort.
The associations with SFTPB and HMOX1 merit further investigation. The different effects of gene-by-smoking interaction in the analyses of SFTPB Thr131Ile in the family-based and case-control studies and the different alleles of the HMOX1 repeat driving the associations in the two study designs suggest that these polymorphisms are not the functional variants affecting COPD susceptibility. The effects that we detected may be due to linkage disequilibrium with nearby functional variants. Analysis of additional SNPs in these genes will be required to confirm these genetic associations. Despite our positive results, we cannot exclude that these may be spurious associations due to the multiple comparisons performed.
Several explanations have been proposed to explain the lack of replication that is commonly seen in case-control association studies in complex trait genetics (5, 6). Small sample sizes may lead to inadequate power to detect an association in the initial study or to replicate true associations in subsequent studies. In fact, the majority of the COPD candidate gene association studies listed in Table 1 enrolled fewer than 100 cases and 100 control subjects. Insufficient power is not likely to explain the lack of replication seen in our study. Using the example of TNF −308G>A with a 17% minor allele frequency in the NAS control subjects, our case-control study had 90% power (α = 0.05) to detect an odds ratio of 1.8, in an additive model; this odds ratio is less than reported in either of the two published COPD association studies with significant results (27, 28). However, if the true odds ratio were lower than 1.8 in other populations, then the power to detect significant associations would be reduced.
Spurious associations may result from multiple testing in studies that assess many genes, markers, and phenotypes (29). No consensus exists on the optimal method to adjust for multiple testing in case-control genetic association studies, though replication in an independent study may provide the strongest evidence for true association. Multiple testing was a potential problem in our family-based study, given the multiple genes and phenotypes tested, though the independent case-control sample provided an opportunity to confirm the findings from the family-based study.
Genotyping errors usually bias toward no association, though systematic errors may lead to false positive results. Deviation from Hardy-Weinberg equilibrium (HWE) in the control group may be a sign of genotyping error (29). We found that only one of the markers tested deviated from HWE, and that SNP was excluded from the association analyses. Departure from Mendelian transmission of alleles is another indication of genotyping error that is only applicable to family-based studies; besides the excluded SNP above, only a small number of Mendelian inconsistencies were found in our study.
Failure to demonstrate HWE may also be a sign of population stratification, which refers to differences in allele frequency between cases and controls due to ethnic differences and not due to disease status (30). Population stratification can lead to spurious association in case-control studies (21). Careful matching of cases and control subjects on ethnicity provides some protection against population stratification. Several statistical methods, based on genotype data from additional unlinked markers elsewhere in the genome, are available to test for stratification and control for its effects if present (21, 31, 32). None of the published COPD genetic association studies have employed these formal tests. We tested a modest sized panel of SNPs in our case-control study and found little evidence for stratification.
The issues described may lead to false positive (multiple testing, population stratification) or false negative (small sample size, genotyping error) results. However, true differences may lead to inconsistent results. COPD is a heterogeneous disease and published association studies have used different phenotype definitions. For example, studies of TNF have defined cases on the basis of airflow obstruction (28), emphysema (33), decline in lung function (34), or chronic bronchitis (27). It is possible that a given genetic variant may confer susceptibility to a specific COPD-related phenotype. In our case-control study, the NETT cases all had emphysema confirmed by chest CT scan. Radiographic evidence of emphysema was not a requirement for entry into the Boston Early-Onset COPD Study, though many probands did have chest CT scans showing emphysema (8). In our family-based study we analyzed quantitative and qualitative traits, based on spirometry, but the case-control study used COPD diagnosis as a binary outcome. However, we used strict spirometric criteria to define cases and controls, so the overall conclusions should not be affected. The power may be greater using quantitative versus qualitative traits, however.
For the majority of the genes studied, we genotyped only one or two markers per gene, as has been done in most of the previously reported studies. This method relies on the assumption that the variants tested have functional effects on COPD susceptibility. If another variant in or near the gene were the causal variant, then the true association could be easily missed. Different linkage disequilibrium patterns with the functional variant may lead to variable results in different populations. In two genes, TNF and EPHX1, we tested additional SNPs and used haplotype analysis to study these genes more thoroughly. However, this did not strengthen our findings.
Genetic heterogeneity may also explain the varying results among case-control association studies, especially those done in different ethnic groups. Many of the COPD association studies in Table 1 have shown inconsistent results in white and Asian populations. True differences may be the result of different genetic determinants of disease in diverse populations, variation in gene–environment interaction due to specific environmental exposures, or different patterns of linkage disequilibrium between the tested marker and the causal variant (35). Though most of our study subjects were whites from the United States, it is possible that severe early-onset COPD represents a unique disease subtype with different genetic determinants than the usually seen, later-onset COPD. However, many of the family members in the Boston Early-Onset COPD Study had less severe airflow obstruction, consistent with more usual forms of COPD. Nevertheless, variants in several genes studied, including TNF-α and surfactant protein B, may primarily increase susceptibility to severe early-onset COPD. These results should be interpreted with caution due to the multiple tests performed; replication in an independent cohort is still required.
This study highlights the major difficulty with using a candidate gene approach to uncover susceptibility genes for COPD, namely the lack of replication commonly seen in candidate gene studies. Future candidate gene association studies need to employ rigorous genetic epidemiology methods, including adequate sample sizes, control for multiple testing, and testing for population stratification. A more systematic approach to COPD genetics, starting with genome-wide linkage analysis followed by positional candidate gene association testing and/or SNP-based fine mapping, may lead to more consistent results in the search for genetic determinants of COPD.
The authors thank Salvatore Mazza, Michael Hagar, Molly Brown, Alison Brown, and Maura Regan for their genotyping work and Robert Welch (National Cancer Institute) for providing details of the GSTM1 assay. Co-investigators in the NETT Genetics Ancillary Study include: Marcia Katz, Rob McKenna, Malcolm DeCamp, Mark Ginsburg, Neil MacIntyre, James Utz, Barry Make, Philip Diaz, Gerard Criner, Andrew Ries, Mark Krasna, Fernando Martinez, Larry Kaiser, Frank Sciurba, Zab Mosenifar, and Joshua Benditt.
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