Suzuki, Hidenobu Tokushima University Tokushima University Educator and Researcher Directory KAKEN Search Researchers
Kawata, Yoshiki Tokushima University Tokushima University Educator and Researcher Directory KAKEN Search Researchers
Niki, Noboru Tokushima University Tokushima University Educator and Researcher Directory KAKEN Search Researchers
Nakano, Yasutaka Shiga University of Medical Science
Kusumoto, Masahiko National Cancer Center Hospital
Kaneko, Masahiro Tokyo Health Service Association
single nucleotide polymorphism
longitudinal follow-up study
Chronic obstructive pulmonary disease (COPD) is predicted to become the third leading cause of death worldwide by 2030. Smoking is a well-known risk factor in the development of COPD. Association between COPD genes and smoking have been studied. This paper presents an association analysis of single nucleotide polymorphisms (SNPs) with a CT image-based phenotype of emphysema progression in heavy smokers. The emphysema progression was quantitatively represented by the annual increment of low attenuation volume (LAV) on CT scans for five years. 10 candidate SNPs were selected from 316 SNPs in 125 papers of genetic studies of COPD and lung cancer. The genotypes were determined by real-time polymerase chain reaction (PCR) using deoxyribonucleic acid (DNA) extracted from saliva samples. The association analysis was performed by Fisher's exact test and logistic regression analysis. This method was applied to a dataset with 144 participants (71 smokers, 61 past smokers, and 12 non-smokers). The results showed that the genotypes of rs3923564 and rs13180 SNPs were candidate SNPs associated with the CT image based-emphysema progression.
Proceedings of SPIE
Hidenobu Suzuki, Mikio Matsuhiro, Yoshiki Kawata, Noboru Niki, Issei Imoto, Yasutaka Nakano, Masahiko Kusumoto, and Masahiro Kaneko "Association analysis of SNPs with CT image-based phenotype of emphysema progression in heavy smokers", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142D (16 March 2020); https://doi.org/10.1117/12.2549431
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Science and Technology