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A genome wide association study for lung function in the Korean population using an exome array

Cited 1 time in Web of Science Cited 1 time in Scopus
Authors

Lee, Kyu-Sun; Kim, Kun Hee; Oh, Yeon-Mok; Han, Buhm; Kim, Woo Jin

Issue Date
2021-03
Publisher
대한내과학회
Citation
The Korean Journal of Internal Medicine, Vol.36, pp.S142-+
Abstract
Background/Aims: Lung function is an objective indicator of diagnosis and prognosis of respiratory diseases. Many common genetic variants have been associated with lung function in multiple ethnic populations. We looked for coding variants associated with forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC) in the Korean general population. Methods: We carried out exome array analysis and lung function measurements of the FEV1 and FEV1/FVC in 7,524 individuals of the Korean population. We evaluated single variants with minor allele frequency greater than 0.5%. We performed look-ups for candidate coding variants associations in the UK Biobank, SpiroMeta, and CHARGE consortia. Results: We identified coding variants in the SMIM29 (C6orfu) (p = 1.2 x 10(-5)) and HMGA1 locus on chromosome 6p21, the GIT2 = 6.5 x 10(-5)) locus on chromosome 12q24, and the ARHGEF40 = 9.9 x 10(-5)) locus on chromosome 14q11 as having a significant association with lung function (FEV1). We also confirmed a previously reported association with lung function and chronic obstructive pulmonary disease in the FAM13A (p = 4.54 x 10(-6)) locus on chromosome 4q22, in TNXB (p = 1.30 x 10(-6)) and in AGER (p =1.09 x 10(-8)) locus on chromosome 6p21. Conclusions: Our exome array analysis identified that several protein coding variants were associated with lung function in the Korean population. Common coding variants in SMIM29 (C6orf1), HMGA1, GIT2, FAM13A, TNXB, AGER and low-frequency variant in ARHGEF40 potentially affect lung function, which warrant further study.
ISSN
1226-3303
URI
https://hdl.handle.net/10371/191480
DOI
https://doi.org/10.3904/kjim.2019.204
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  • College of Medicine
  • Department of Medicine
Research Area Bioinformatics, Computational Biology, Genomics, Human Leukocyte Antigen, Statistical Genetics

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