Visual Assessment of Chest Computed Tomographic Images Is Independently Useful for Genetic Association Analysis in Studies of Chronic Obstructive Pulmonary Disease
Year: 2016
Type: article
Abstract: RATIONALE: Automated analysis of computed tomographic (CT) lung images for epidemiologic and genetic association studies is increasingly common, but little is known about the utility of visual versus semiautomated emphysema and airway assessments for genetic association studies. OBJECTIVES: Assess the relative utility of visual versus semiautomated emphysema and airway assessments for genetic association studies. METHODS: A standardized inspection protocol was used to visually assess chest CT images for 1,540 non-Hispanic white subjects within the COPDGene Study for the presence and severity of radiographic features representing airway wall thickness and emphysema. A genome-wide association study (GWAS) was performed, and two sets of candidate single-nucleotide polymorphisms with a higher prior likelihood of association were specified a priori for separate analysis. For each visual CT examination feature, a corresponding semiautomated CT feature(s) was identified for comparison in the same subjects. MEASUREMENTS AND MAIN RESULTS: ). In the a priori-specified set of 19 previously identified GWAS loci, 7 and 8 loci were associated with airway measures or emphysema measures, respectively. In the a priori-specified candidate gene set, 13 of 196 candidate genes harbored a nearby single-nucleotide polymorphism significantly associated with an emphysema phenotype. Visual CT examination associations were robust to adjustment for semiautomated correlates in many cases. CONCLUSIONS: Standardized visual assessments of emphysema and airway disease are significantly associated with genetic loci previously associated with chronic obstructive pulmonary disease susceptibility or semiautomated CT examination phenotypes in GWAS. Visual CT measures of emphysema and airways disease offer independent information for genetic association studies in relation to standard semiautomated measures.
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Predictive Modeling with Clinical and Genomic Data in COPD, G2168838001, (2 of 2) Genetic Epidemiology of COPD, Metabolic profiles of COPD phenotypes, G5258357286, (1 of 2) Genetic Epidemiology of COPD, (2 of 2) Genetic Epidemiology of COPD, G5855967250, Using Integrative Genomics To Identify and Characterize Emphysema-Associated eQTL, (1 of 2) Genetic Epidemiology of COPD, G8354759825, Genetics of Smoke-Altered LTA4H in COPD, G9000091268, G9000228787, Colorado Biomedical Informatics Training Program
OpenAlex ID: W2530750666