Sharon M. Lutz

Risk loci for chronic obstructive pulmonary disease: a genome-wide association study and meta-analysis

BACKGROUND The genetic risk factors for susceptibility to chronic obstructive pulmonary disease (COPD) are still largely unknown. Additional genetic variants are likely to be identified by genome-wide association studies in larger cohorts or specific subgroups. We sought to identify risk loci for moderate to severe and severe COPD with data from several cohort studies. METHODS We combined genome-wide association analysis data from participants in the COPDGene study (non-Hispanic white and African-American ethnic origin) and the ECLIPSE, NETT/NAS, and Norway GenKOLS studies (self-described white ethnic origin). We did analyses comparing control individuals with individuals with moderate to severe COPD and with a subset of individuals with severe COPD. Single nucleotide polymorphisms yielding a p value of less than 5 × 10(-7) in the meta-analysis at loci not previously described were genotyped in individuals from the family-based ICGN study. We combined results in a joint meta-analysis (threshold for significance p<5 × 10(-8)). FINDINGS Analysis of 6633 individuals with moderate to severe COPD and 5704 control individuals confirmed association at three known loci: CHRNA3 (p=6·38 × 10(-14)), FAM13A (p=1·12 × 10(-14)), and HHIP (p=1·57 × 10(-12)). We also showed significant evidence of association at a novel locus near RIN3 (p=5·25 × 10(-9)). In the overall meta-analysis (ie, including data from 2859 ICGN participants), the association with RIN3 remained significant (p=5·4 × 10(-9)). 3497 individuals were included in our analysis of severe COPD. The effect estimates for the loci near HHIP and CHRNA3 were significantly stronger in severe disease than in moderate to severe disease (p<0·01). We also identified associations at two additional loci: MMP12 (overall joint meta-analysis p=2·6 × 10(-9)) and TGFB2 (overall joint meta-analysis p=8·3 × 10(-9)). INTERPRETATION We have confirmed associations with COPD at three known loci and identified three new genome-wide significant associations. Genetic variants other than in α-1 antitrypsin increase the risk of COPD. FUNDING US National Heart, Lung, and Blood Institute; the Alpha-1 Foundation; the COPD Foundation through contributions from AstraZeneca, Boehringer Ingelheim, Novartis, and Sepracor; GlaxoSmithKline; Centers for Medicare and Medicaid Services; Agency for Healthcare Research and Quality; and US Department of Veterans Affairs.

Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

The interest in performing gene–environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene–environment interaction studies and the technical challenges related to these studies—when the number of environmental or genetic risk factors is relatively small—has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene–gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to “joining” two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.

Cluster analysis in the COPDGene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema

Background There is notable heterogeneity in the clinical presentation of patients with COPD. To characterise this heterogeneity, we sought to identify subgroups of smokers by applying cluster analysis to data from the COPDGene study. Methods We applied a clustering method, k-means, to data from 10 192 smokers in the COPDGene study. After splitting the sample into a training and validation set, we evaluated three sets of input features across a range of k (user-specified number of clusters). Stable solutions were tested for association with four COPD-related measures and five genetic variants previously associated with COPD at genome-wide significance. The results were confirmed in the validation set. Findings We identified four clusters that can be characterised as (1) relatively resistant smokers (ie, no/mild obstruction and minimal emphysema despite heavy smoking), (2) mild upper zone emphysema-predominant, (3) airway disease-predominant and (4) severe emphysema. All clusters are strongly associated with COPD-related clinical characteristics, including exacerbations and dyspnoea (p<0.001). We found strong genetic associations between the mild upper zone emphysema group and rs1980057 near HHIP, and between the severe emphysema group and rs8034191 in the chromosome 15q region (p<0.001). All significant associations were replicated at p<0.05 in the validation sample (12/12 associations with clinical measures and 2/2 genetic associations). Interpretation Cluster analysis identifies four subgroups of smokers that show robust associations with clinical characteristics of COPD and known COPD-associated genetic variants.

Dissecting childhood asthma with nasal transcriptomics distinguishes subphenotypes of disease

BACKGROUND Bronchial airway expression profiling has identified inflammatory subphenotypes of asthma, but the invasiveness of this technique has limited its application to childhood asthma. OBJECTIVES We sought to determine whether the nasal transcriptome can proxy expression changes in the lung airway transcriptome in asthmatic patients. We also sought to determine whether the nasal transcriptome can distinguish subphenotypes of asthma. METHODS Whole-transcriptome RNA sequencing was performed on nasal airway brushings from 10 control subjects and 10 asthmatic subjects, which were compared with established bronchial and small-airway transcriptomes. Targeted RNA sequencing nasal expression analysis was used to profile 105 genes in 50 asthmatic subjects and 50 control subjects for differential expression and clustering analyses. RESULTS We found 90.2% overlap in expressed genes and strong correlation in gene expression (ρ = .87) between the nasal and bronchial transcriptomes. Previously observed asthmatic bronchial differential expression was strongly correlated with asthmatic nasal differential expression (ρ = 0.77, P = 5.6 × 10(-9)). Clustering analysis identified TH2-high and TH2-low subjects differentiated by expression of 70 genes, including IL13, IL5, periostin (POSTN), calcium-activated chloride channel regulator 1 (CLCA1), and serpin peptidase inhibitor, clade B (SERPINB2). TH2-high subjects were more likely to have atopy (odds ratio, 10.3; P = 3.5 × 10(-6)), atopic asthma (odds ratio, 32.6; P = 6.9 × 10(-7)), high blood eosinophil counts (odds ratio, 9.1; P = 2.6 × 10(-6)), and rhinitis (odds ratio, 8.3; P = 4.1 × 10(-6)) compared with TH2-low subjects. Nasal IL13 expression levels were 3.9-fold higher in asthmatic participants who experienced an asthma exacerbation in the past year (P = .01). Several differentially expressed nasal genes were specific to asthma and independent of atopic status. CONCLUSION Nasal airway gene expression profiles largely recapitulate expression profiles in the lung airways. Nasal expression profiling can be used to identify subjects with IL13-driven asthma and a TH2-skewed systemic immune response.

Paired inspiratory-expiratory chest CT scans to assess for small airways disease in COPD

BackgroundGas trapping quantified on chest CT scans has been proposed as a surrogate for small airway disease in COPD. We sought to determine if measurements using paired inspiratory and expiratory CT scans may be better able to separate gas trapping due to emphysema from gas trapping due to small airway disease.MethodsSmokers with and without COPD from the COPDGene Study underwent inspiratory and expiratory chest CT scans. Emphysema was quantified by the percent of lung with attenuation < −950HU on inspiratory CT. Four gas trapping measures were defined: (1) Exp−856, the percent of lung < −856HU on expiratory imaging; (2) E/I MLA, the ratio of expiratory to inspiratory mean lung attenuation; (3) RVC856-950, the difference between expiratory and inspiratory lung volumes with attenuation between −856 and −950 HU; and (4) Residuals from the regression of Exp−856 on percent emphysema.ResultsIn 8517 subjects with complete data, Exp−856 was highly correlated with emphysema. The measures based on paired inspiratory and expiratory CT scans were less strongly correlated with emphysema. Exp−856, E/I MLA and RVC856-950 were predictive of spirometry, exercise capacity and quality of life in all subjects and in subjects without emphysema. In subjects with severe emphysema, E/I MLA and RVC856-950 showed the highest correlations with clinical variables.ConclusionsQuantitative measures based on paired inspiratory and expiratory chest CT scans can be used as markers of small airway disease in smokers with and without COPD, but this will require that future studies acquire both inspiratory and expiratory CT scans.

Reduced Bone Density and Vertebral Fractures in Smokers. Men and COPD Patients at Increased Risk

RATIONALE Former smoking history and chronic obstructive pulmonary disease (COPD) are potential risk factors for osteoporosis and fractures. Under existing guidelines for osteoporosis screening, women are included but men are not, and only current smoking is considered. OBJECTIVES To demonstrate the impact of COPD and smoking history on the risk of osteoporosis and vertebral fracture in men and women. METHODS Characteristics of participants with low volumetric bone mineral density (vBMD) were identified and related to COPD and other risk factors. We tested associations of sex and COPD with both vBMD and fractures adjusting for age, race, body mass index (BMI), smoking, and glucocorticoid use. MEASUREMENTS AND MAIN RESULTS vBMD by calibrated quantitative computed tomography (QCT), visually scored vertebral fractures, and severity of lung disease were determined from chest CT scans of 3,321 current and ex-smokers in the COPDGene study. Low vBMD as a surrogate for osteoporosis was calculated from young adult normal values. Male smokers had a small but significantly greater risk of low vBMD (2.5 SD below young adult mean by calibrated QCT) and more fractures than female smokers. Low vBMD was present in 58% of all subjects, was more frequent in those with worse COPD, and rose to 84% among subjects with very severe COPD. Vertebral fractures were present in 37% of all subjects and were associated with lower vBMD at each Global Initiative for Chronic Obstructive Lung Disease stage of severity. Vertebral fractures were most common in the midthoracic region. COPD and especially emphysema were associated with both low vBMD and vertebral fractures after adjustment for steroid use, age, pack-years of smoking, current smoking, and exacerbations. Airway disease was associated with higher bone density after adjustment for other variables. Calibrated QCT identified more subjects with abnormal values than the standard dual-energy X-ray absorptiometry in a subset of subjects and correlated well with prevalent fractures. CONCLUSIONS Male smokers, with or without COPD, have a significant risk of low vBMD and vertebral fractures. COPD was associated with low vBMD after adjusting for race, sex, BMI, smoking, steroid use, exacerbations, and age. Screening for low vBMD by using QCT in men and women who are smokers will increase opportunities to identify and treat osteoporosis in this at-risk population.

Quantitative Computed Tomography Measures of Pectoralis Muscle Area and Disease Severity in Chronic Obstructive Pulmonary Disease. A Cross-Sectional Study

RATIONALE Muscle wasting in chronic obstructive pulmonary disease (COPD) is associated with a poor prognosis and is not readily assessed by measures of body mass index (BMI). BMI does not discriminate between relative proportions of adipose tissue and lean muscle and may be insensitive to early pathologic changes in body composition. Computed tomography (CT)-based assessments of the pectoralis muscles may provide insight into the clinical significance of skeletal muscles in smokers. OBJECTIVES We hypothesized that objective assessment of the pectoralis muscle area on chest CT scans provides information that is clinically relevant and independent of BMI. METHODS Data from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) Study (n = 73) were used to assess the relationship between pectoralis muscle area and fat-free mass. We then used data in a subset (n = 966) of a larger cohort, the COPDGene (COPD Genetic Epidemiology) Study, to explore the relationship between pectoralis muscle area and COPD-related traits. MEASUREMENTS AND MAIN RESULTS We first investigated the correlation between pectoralis muscle area and fat-free mass, using data from a subset of participants in the ECLIPSE Study. We then further investigated pectoralis muscle area in COPDGene Study participants and found that higher pectoralis muscle area values were associated with greater height, male sex, and younger age. On subsequent clinical correlation, compared with BMI, pectoralis muscle area was more significantly associated with COPD-related traits, including spirometric measures, dyspnea, and 6-minute-walk distance (6MWD). For example, on average, each 10-cm(2) increase in pectoralis muscle area was associated with a 0.8-unit decrease in the BODE (Body mass index, Obstruction, Dyspnea, Exercise) index (95% confidence interval, -1.0 to -0.6; P < 0.001). Furthermore, statistically significant associations between pectoralis muscle area and COPD-related traits remained even after adjustment for BMI. CONCLUSIONS CT-derived pectoralis muscle area provides relevant indices of COPD morbidity that may be more predictive of important COPD-related traits than BMI. However, the relationship with clinically relevant outcomes such as hospitalization and death requires additional investigation. Pectoralis muscle area is a convenient measure that can be collected in the clinical setting in addition to BMI.

CHRNA5 Risk Variant Predicts Delayed Smoking Cessation and Earlier Lung Cancer Diagnosis—A Meta-Analysis

BACKGROUND Recent meta-analyses show strong evidence of associations among genetic variants in CHRNA5 on chromosome 15q25, smoking quantity, and lung cancer. This meta-analysis tests whether the CHRNA5 variant rs16969968 predicts age of smoking cessation and age of lung cancer diagnosis. METHODS Meta-analyses examined associations between rs16969968, age of quitting smoking, and age of lung cancer diagnosis in 24 studies of European ancestry (n = 29 072). In each dataset, we used Cox regression models to evaluate the association between rs16969968 and the two primary phenotypes (age of smoking cessation among ever smokers and age of lung cancer diagnosis among lung cancer case patients) and the secondary phenotype of smoking duration. Heterogeneity across studies was assessed with the Cochran Q test. All statistical tests were two-sided. RESULTS The rs16969968 allele (A) was associated with a lower likelihood of smoking cessation (hazard ratio [HR] = 0.95, 95% confidence interval [CI] = 0.91 to 0.98, P = .0042), and the AA genotype was associated with a four-year delay in median age of quitting compared with the GG genotype. Among smokers with lung cancer diagnoses, the rs16969968 genotype (AA) was associated with a four-year earlier median age of diagnosis compared with the low-risk genotype (GG) (HR = 1.08, 95% CI = 1.04 to 1.12, P = 1.1*10(-5)). CONCLUSION These data support the clinical significance of the CHRNA5 variant rs16969968. It predicts delayed smoking cessation and an earlier age of lung cancer diagnosis in this meta-analysis. Given the existing evidence that this CHRNA5 variant predicts favorable response to cessation pharmacotherapy, these findings underscore the potential clinical and public health importance of rs16969968 in CHRNA5 in relation to smoking cessation success and lung cancer risk.

On the adjustment for covariates in genetic association analysis: a novel, simple principle to infer direct causal effects

In genetic association studies, different complex phenotypes are often associated with the same marker. Such associations can be indicative of pleiotropy (i.e. common genetic causes), of indirect genetic effects via one of these phenotypes, or can be solely attributable to non‐genetic/environmental links between the traits. To identify the phenotypes with the inducing genetic association, statistical methodology is needed that is able to distinguish between the different causes of the genetic associations. Here, we propose a simple, general adjustment principle that can be incorporated into many standard genetic association tests which are then able to infer whether an SNP has a direct biological influence on a given trait other than through the SNPs influence on another correlated phenotype. Using simulation studies, we show that, in the presence of a non‐marker related link between phenotypes, standard association tests without the proposed adjustment can be biased. In contrast to that, the proposed methodology remains unbiased. Its achieved power levels are identical to those of standard adjustment methods, making the adjustment principle universally applicable in genetic association studies. The principle is illustrated by an application to three genome‐wide association analyses. Genet. Epidemiol. 33:394–405, 2009.

Genome-wide meta-analysis reveals common splice site acceptor variant in CHRNA4 associated with nicotine dependence.

We conducted a 1000 Genomes–imputed genome-wide association study (GWAS) meta-analysis for nicotine dependence, defined by the Fagerström Test for Nicotine Dependence in 17 074 ever smokers from five European-ancestry samples. We followed up novel variants in 7469 ever smokers from five independent European-ancestry samples. We identified genome-wide significant association in the alpha-4 nicotinic receptor subunit (CHRNA4) gene on chromosome 20q13: lowest P=8.0 × 10−9 across all the samples for rs2273500-C (frequency=0.15; odds ratio=1.12 and 95% confidence interval=1.08–1.17 for severe vs mild dependence). rs2273500-C, a splice site acceptor variant resulting in an alternate CHRNA4 transcript predicted to be targeted for nonsense-mediated decay, was associated with decreased CHRNA4 expression in physiologically normal human brains (lowest P=7.3 × 10−4). Importantly, rs2273500-C was associated with increased lung cancer risk (N=28 998, odds ratio=1.06 and 95% confidence interval=1.00–1.12), likely through its effect on smoking, as rs2273500-C was no longer associated with lung cancer after adjustment for smoking. Using criteria for smoking behavior that encompass more than the single ‘cigarettes per day’ item, we identified a common CHRNA4 variant with important regulatory properties that contributes to nicotine dependence and smoking-related consequences.