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Analysis Name and Accession
Name: A Genome-Wide Association Meta-Analysis Of Airway Obstruction In Cohorts Of European Ancestry-NoAsthma
Accession: pha003917.1

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Analysis Description
The Cohorts for Heart and Aging Research in Genomic Epimiology (CHARGE) consortium was formed to facilitate genome-wide association study meta-analyses and replication among large, well-phenotyped cohort studies. CHARGE has incorporated many genomic and phenotypic data from other cohorts, depending on the phenotype. The basic design was estimation of SNP effects on the outcome of airflow obstruction (a key pathophysiologic characteristic of chronic obstructive pulmonary disease, COPD) in 15 population-based cohorts with 32,875 subjects of European ancestry. The discovery meta-analysis was conducted in those with airflow obstruction defined as FEV1 and FEV1/FVC both less than the lower limit of normal, with unaffected subjects defined as FEV1, FVC, and FEV1/FVC all above the lower limit of normal. Logistic regression models were adjusted for current and former smoking, pack-years of smoking, age, sex, standing height, center/cohort as needed, and principal components for genetic ancestry (as needed). Genome-wide imputation and analysis were performed by cohort investigators, and genome-wide and regional meta-analyses were performed using METAL software. FIve discovery analyses were performed - all cohorts (including ever and never smokers), ever smokers, never smokers, asthma-free participants, and those with FEV1 < 65% predicted (more severe airflow obstruction).
Analysis Methods
Each study independently implemented a predefined GWAS analysis plan. Each contributing cohort had its own GWAS platform, variant calling algorithm, call rate filter, HWE filter, MAF filter and analytic software (ProbABEL, SNPTEST, GenABEL, GWAF, PLINK, MACH2QTL, R-scripts). For the FEV1 trait, association with SNP genotypes used a 1 df additive model adjusted for covariates. Study-specific interaction estimates (β) and standard errors were combined by fixed effects inverse variance weighted meta-analysis using METAL
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