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BACKGROUND: Tracking longitudinal measurements of growth and decline in lung function in patients with persistent childhood asthma may reveal links between asthma and subsequent chronic airflow obstruction. METHODS: We classified children with asthma according to four characteristic patterns of lung-function growth and decline on the basis of graphs showing forced expiratory volume in 1 second (FEV1), representing spirometric measurements performed from childhood into adulthood. Risk factors associated with abnormal patterns were also examined. To define normal values, we used FEV1 values from participants in the National Health and Nutrition Examination Survey who did not have asthma. RESULTS: Of the 684 study participants, 170 (25%) had a normal pattern of lung-function growth without early decline, and 514 (75%) had abnormal patterns: 176 (26%) had reduced growth and an early decline, 160 (23%) had reduced growth only, and 178 (26%) had normal growth and an early decline. Lower baseline values for FEV1, smaller bronchodilator response, airway hyperresponsiveness at baseline, and male sex were associated with reduced growth (P<0.001 for all comparisons). At the last spirometric measurement (mean [±SD] age, 26.0±1.8 years), 73 participants (11%) met Global Initiative for Chronic Obstructive Lung Disease spirometric criteria for lung-function impairment that was consistent with chronic obstructive pulmonary disease (COPD); these participants were more likely to have a reduced pattern of growth than a normal pattern (18% vs. 3%, P<0.001). CONCLUSIONS: Childhood impairment of lung function and male sex were the most significant predictors of abnormal longitudinal patterns of lung-function growth and decline. Children with persistent asthma and reduced growth of lung function are at increased risk for fixed airflow obstruction and possibly COPD in early adulthood. (Funded by the Parker B. Francis Foundation and others; ClinicalTrials.gov number, NCT00000575.)

Hedgehog Interacting Protein (HHIP) was implicated in chronic obstructive pulmonary disease (COPD) by genome-wide association studies (GWAS). However, it remains unclear how HHIP contributes to COPD pathogenesis. To identify genes regulated by HHIP, we performed gene expression microarray analysis in a human bronchial epithelial cell line (Beas-2B) stably infected with HHIP shRNAs. HHIP silencing led to differential expression of 296 genes; enrichment for variants nominally associated with COPD was found. Eighteen of the differentially expressed genes were validated by real-time PCR in Beas-2B cells. Seven of 11 validated genes tested in human COPD and control lung tissues demonstrated significant gene expression differences. Functional annotation indicated enrichment for extracellular matrix and cell growth genes. Network modeling demonstrated that the extracellular matrix and cell proliferation genes influenced by HHIP tended to be interconnected. Thus, we identified potential HHIP targets in human bronchial epithelial cells that may contribute to COPD pathogenesis.

Recent breakthroughs in next-generation sequencing technologies allow cost-effective methods for measuring a growing list of cellular properties, including DNA sequence and structural variation. Next-generation sequencing has the potential to revolutionize complex trait genetics by directly measuring common and rare genetic variants within a genome-wide context. Because for a given gene both rare and common causal variants can coexist and have independent effects on a trait, strategies that model the effects of both common and rare variants could enhance the power of identifying disease-associated genes. To date, little work has been done on integrating signals from common and rare variants into powerful statistics for finding disease genes in genome-wide association studies. In this analysis of the Genetic Analysis Workshop 17 data, we evaluate various strategies for association of rare, common, or a combination of both rare and common variants on quantitative phenotypes in unrelated individuals. We show that the analysis of common variants only using classical approaches can achieve higher power to detect causal genes than recently proposed rare variant methods and that strategies that combine association signals derived independently in rare and common variants can slightly increase the power compared to strategies that focus on the effect of either the rare variants or the common variants.

Background: Numerous studies have demonstrated associations between genetic markers and COPD, but results have been inconsistent. One reason may be heterogeneity in disease definition. Unsupervised learning approaches may assist in understanding disease heterogeneity. Methods: We selected 31 phenotypic variables and 12 SNPs from five candidate genes in 308 subjects in the National Emphysema Treatment Trial (NETT) Genetics Ancillary Study cohort. We used factor analysis to select a subset of phenotypic variables, and then used cluster analysis to identify subtypes of severe emphysema. We examined the phenotypic and genotypic characteristics of each cluster. Results: We identified six factors accounting for 75% of the shared variability among our initial phenotypic variables. We selected four phenotypic variables from these factors for cluster analysis: 1) post-bronchodilator FEV1 percent predicted, 2) percent bronchodilator responsiveness, and quantitative CT measurements of 3) apical emphysema and 4) airway wall thickness. K-means cluster analysis revealed four clusters, though separation between clusters was modest: 1) emphysema predominant, 2) bronchodilator responsive, with higher FEV1; 3) discordant, with a lower FEV1 despite less severe emphysema and lower airway wall thickness, and 4) airway predominant. Of the genotypes examined, membership in cluster 1 (emphysema-predominant) was associated with TGFB1 SNP rs1800470. Conclusions: Cluster analysis may identify meaningful disease subtypes and/or groups of related phenotypic variables even in a highly selected group of severe emphysema subjects, and may be useful for genetic association studies.

Background: Rare loss-of-function folliculin (FLCN) mutations are the genetic cause of Birt-Hogg-Dubé syndrome, a monogenic disorder characterized by spontaneous pneumothorax, fibrofolliculomas, and kidney tumors. Loss-of-function folliculin mutations have also been described in pedigrees with familial spontaneous pneumothorax. Because the majority of patients with folliculin mutations have radiographic evidence of pulmonary cysts, folliculin has been hypothesized to contribute to the development of emphysema. To determine whether folliculin sequence variants are risk factors for severe COPD, we genotyped seven previously reported Birt-Hogg-Dubé or familial spontaneous pneumothorax associated folliculin mutations in 152 severe COPD probands participating in the Boston Early-Onset COPD Study. We performed bidirectional resequencing of all 14 folliculin exons in a subset of 41 probands and subsequently genotyped four identified variants in an independent sample of 345 COPD subjects from the National Emphysema Treatment Trial (cases) and 420 male smokers with normal lung function from the Normative Aging Study (controls). Results: None of the seven previously reported Birt-Hogg-Dubé or familial spontaneous pneumothorax mutations were observed in the 152 severe, early-onset COPD probands. Exon resequencing identified 31 variants, including two non-synonymous polymorphisms and two common non-coding polymorphisms. No significant association was observed for any of these four variants with presence of COPD or emphysema-related phenotypes. Conclusion: Genetic variation in folliculin does not appear to be a major risk factor for severe COPD. These data suggest that familial spontaneous pneumothorax and COPD have distinct genetic causes, despite some overlap in radiographic characteristics.