Person:

Hersh, Craig

Background: Pulmonary function decline is a major contributor to morbidity and mortality among smokers. Post bronchodilator FEV1 and FEV1/FVC ratio are considered the standard assessment of airflow obstruction. We performed a genome-wide association study (GWAS) in 9919 current and former smokers in the COPDGene study (6659 non-Hispanic Whites [NHW] and 3260 African Americans [AA]) to identify associations with spirometric measures (post-bronchodilator FEV1 and FEV1/FVC). We also conducted meta-analysis of FEV1 and FEV1/FVC GWAS in the COPDGene, ECLIPSE, and GenKOLS cohorts (total n = 13,532). Results: Among NHW in the COPDGene cohort, both measures of pulmonary function were significantly associated with SNPs at the 15q25 locus [containing CHRNA3/5, AGPHD1, IREB2, CHRNB4] (lowest p-value = 2.17 × 10−11), and FEV1/FVC was associated with a genomic region on chromosome 4 [upstream of HHIP] (lowest p-value = 5.94 × 10−10); both regions have been previously associated with COPD. For the meta-analysis, in addition to confirming associations to the regions near CHRNA3/5 and HHIP, genome-wide significant associations were identified for FEV1 on chromosome 1 [TGFB2] (p-value = 8.99 × 10−9), 9 [DBH] (p-value = 9.69 × 10−9) and 19 [CYP2A6/7] (p-value = 3.49 × 10−8) and for FEV1/FVC on chromosome 1 [TGFB2] (p-value = 8.99 × 10−9), 4 [FAM13A] (p-value = 3.88 × 10−12), 11 [MMP3/12] (p-value = 3.29 × 10−10) and 14 [RIN3] (p-value = 5.64 × 10−9). Conclusions: In a large genome-wide association study of lung function in smokers, we found genome-wide significant associations at several previously described loci with lung function or COPD. We additionally identified a novel genome-wide significant locus with FEV1 on chromosome 9 [DBH] in a meta-analysis of three study populations. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0299-4) contains supplementary material, which is available to authorized users.

Background: Despite inhaled medications that decrease exacerbation risk, some COPD patients experience frequent exacerbations. We determined prospective risk factors for exacerbations among subjects in the COPDGene Study taking inhaled medications. Methods: 2113 COPD subjects were categorized into four medication use patterns: triple therapy with tiotropium (TIO) plus long-acting beta-agonist/inhaled-corticosteroid (ICS ± LABA), tiotropium alone, ICS ± LABA, and short-acting bronchodilators. Self-reported exacerbations were recorded in telephone and web-based longitudinal follow-up surveys. Associations with exacerbations were determined within each medication group using four separate logistic regression models. A head-to-head analysis compared exacerbation risk among subjects using tiotropium vs. ICS ± LABA. Results: In separate logistic regression models, the presence of gastroesophageal reflux, female gender, and higher scores on the St. George’s Respiratory Questionnaire were significant predictors of exacerbator status within multiple medication groups (reflux: OR 1.62–2.75; female gender: OR 1.53 - OR 1.90; SGRQ: OR 1.02–1.03). Subjects taking either ICS ± LABA or tiotropium had similar baseline characteristics, allowing comparison between these two groups. In the head-to-head comparison, tiotropium users showed a trend towards lower rates of exacerbations (OR = 0.69 [95 % CI 0.45, 1.06], p = 0.09) compared with ICS ± LABA users, especially in subjects without comorbid asthma (OR = 0.56 [95 % CI 0.31, 1.00], p = 0.05). Conclusions: Each common COPD medication usage group showed unique risk factor patterns associated with increased risk of exacerbations, which may help clinicians identify subjects at risk. Compared to similar subjects using ICS ± LABA, those taking tiotropium showed a trend towards reduced exacerbation risk, especially in subjects without asthma. Trial registration ClinicalTrials.gov NCT00608764, first received 1/28/2008. Electronic supplementary material The online version of this article (doi:10.1186/s12890-016-0191-7) contains supplementary material, which is available to authorized users.

Background: Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Results: Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. Conclusions: We demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0517-y) contains supplementary material, which is available to authorized users.