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GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort.

ABSTRACT: BACKGROUND:Large sample GWAS is needed to identify genetic factors associated with depression. This study used genome-wide genotypic and phenotypic data from the COPDGene study to identify genetic risk factors for depression. METHODS:Data were from 9716 COPDGene subjects with ?10 pack-year history. Depression was defined as antidepressant use and/or a HADS depression subscale score ?8. Non-Hispanic White (6576) and African-American (3140) subsets were analyzed. A GWAS pipeline identified SNPs associated with depression in each group. Network analysis software analyzed gene interactions through common biological pathways, genetic interactions, and tissue-specific gene expression. RESULTS:The mean age was 59.4 years (SD 9.0) with 46.5% female subjects. Depression was in 24.7% of the NHW group (1622) and 12.5% of the AA group (391). No SNPs had genome-wide significance. One of the top SNPs, rs12036147 (p?=?1.28?×?10-6), is near CHRM3. Another SNP was near MDGA2 (rs17118176, p?=?3.52?×?10-6). Top genes formed networks for synaptic transmission with a statistically significant level of more co-expression in brain than other tissues, particularly in the basal ganglia (p?=?1.00?×?10-4). LIMITATIONS:Limitations included a depression definition based on antidepressant use and a limited HADS score subgroup, which could increase false negatives in depressed patients not on antidepressants. Antidepressants used for smoking cessation in non-depressed patients could lead to false positives. CONCLUSIONS:Systems biology analysis identified statistically significant pathways whereby multiple genes influence depression. The gene set pathway analysis and COPDGene data can help investigate depression in future studies.

SUBMITTER: Heinzman JT

PROVIDER: S-EPMC6186181 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort.

Heinzman Jonathan T JT Hoth Karin F KF Cho Michael H MH Sakornsakolpat Phuwanat P Regan Elizabeth A EA Make Barry J BJ Kinney Gregory L GL Wamboldt Frederick S FS Holm Kristen E KE Bormann Nicholas N Robles Julian J Kim Victor V Iyer Anand S AS Silverman Edwin K EK Crapo James D JD Han Shizhong S Potash James B JB Shinozaki Gen G

Journal of affective disorders 20180907

<h4>Background</h4>Large sample GWAS is needed to identify genetic factors associated with depression. This study used genome-wide genotypic and phenotypic data from the COPDGene study to identify genetic risk factors for depression.<h4>Methods</h4>Data were from 9716 COPDGene subjects with ≥10 pack-year history. Depression was defined as antidepressant use and/or a HADS depression subscale score ≥8. Non-Hispanic White (6576) and African-American (3140) subsets were analyzed. A GWAS pipeline ide ...[more]

PMID: 30219690

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Project description:RATIONALE:A significant proportion of smokers have lung function impairment characterized by a reduced FEV(1) with a preserved FEV(1)/FVC ratio. These smokers are a poorly characterized group due to their systematic exclusion from chronic obstructive pulmonary disease (COPD) studies. OBJECTIVES:To characterize the clinical, functional, and radiographic features of Global Initiative for Chronic Obstructive Lung Disease (GOLD)-Unclassified (FEV(1)/FVC ≥ 0.7 and FEV(1) < 80% predicted) and lower limits of normal (LLN)-unclassified (FEV(1)/FVC ≥ LLN and FEV(1) < LLN) subjects compared to smokers with normal lung function and subjects with COPD. METHODS:Data from the first 2,500 subjects enrolled in the COPDGene study were analyzed. All subjects had 10 or more pack-years of smoking and were between the ages of 45 and 80 years. Multivariate regression models were constructed to determine the clinical and radiological variables associated with GOLD-Unclassified (GOLD-U) and LLN-Unclassified status. Separate multivariate regressions were performed in the subgroups of subjects with complete radiologic measurement variables available. MEASUREMENTS AND MAIN RESULTS:GOLD-U smokers account for 9% of smokers in COPDGene and have increased body mass index (BMI), a disproportionately reduced total lung capacity, and a higher proportion of nonwhite subjects and subjects with diabetes. GOLD-U subjects exhibit increased airway wall thickness compared to smoking control subjects and decreased gas trapping and bronchodilator responsiveness compared to subjects with COPD. When LLN criteria were used to define the "unclassified" group, African American subjects were no longer overrepresented. Both GOLD-U and LLN-Unclassified subjects demonstrated a wide range of lung function impairment, BMI, and percentage of total lung emphysema. CONCLUSIONS:Subjects with reduced FEV(1) and a preserved FEV(1)/FVC ratio are a heterogeneous group with significant symptoms and functional limitation who likely have a variety of underlying etiologies beyond increased BMI. Clinical trial registered with www.clinicaltrials.gov (NCT000608764).

Project description:BackgroundRisk factor identification is a proven strategy in advancing treatments and preventive therapy for many chronic conditions. Quantifying the impact of those risk factors on health outcomes can consolidate and focus efforts on individuals with specific high-risk profiles. Using multiple risk factors and longitudinal outcomes in 2 independent cohorts, we developed and validated a risk score model to predict mortality in current and former cigarette smokers.MethodsWe obtained extensive data on current and former smokers from the COPD Genetic Epidemiology (COPDGene®) study at enrollment. Based on physician input and model goodness-of-fit measures, a subset of variables was selected to fit final Weibull survival models separately for men and women. Coefficients and predictors were translated into a point system, allowing for easy computation of mortality risk scores and probabilities. We then used the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) cohort for external validation of our model.ResultsOf 9867 COPDGene participants with standard baseline data, 17.6% died over 10 years of follow-up, and 9074 of these participants had the full set of baseline predictors (standard plus 6-minute walk distance and computed tomography variables) available for full model fits. The average age of participants in the cohort was 60 for both men and women, and the average predicted 10-year mortality risk was 18% for women and 25% for men. Model time-integrated area under the receiver operating characteristic curve statistics demonstrated good predictive model accuracy (0.797 average), validated in the external cohort (0.756 average). Risk of mortality was impacted most by 6-minute walk distance, forced expiratory volume in 1 second and age, for both men and women.ConclusionsCurrent and former smokers exhibited a wide range of mortality risk over a 10- year period. Our models can identify higher risk individuals who can be targeted for interventions to reduce risk of mortality, for participants with or without chronic obstructive pulmonary disease (COPD) using current Global initiative for obstructive Lung Disease (GOLD) criteria.

Dataset Information

GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort.

Publications

GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort.

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