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Genome-wide association study for genetic variants related with maximal voluntary ventilation reveals two novel genomic signals associated with lung function

Authors
Suh, YujinLee, Chaeyoung
Issue Date
Nov-2017
Publisher
LIPPINCOTT WILLIAMS & WILKINS
Keywords
fragile histidine triad; genome-wide association study; maximal voluntary ventilation; single nucleotide variant; spirometry parameter
Citation
MEDICINE, v.96, no.44
Journal Title
MEDICINE
Volume
96
Number
44
URI
http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/6210
DOI
10.1097/MD.0000000000008530
ISSN
0025-7974
Abstract
Genome-wide association studies (GWAS) for spirometry parameters have been limited to forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and their ratio. This study examined to identify genetic variants associated with maximal voluntary ventilation (MVV), an important spirometry parameter presenting inspiratory muscle strength.A total of 8842 Korean subjects participated in the Korean Association REsource Consortium were used to identify nucleotide variants associated with MVV and other spirometry parameters through a GWAS. Genetic associations were determined by employing a mixed model that can control background polygenic effects.The analysis revealed 3 nucleotide variants associated with MVV (P<5x10(-8)). One (rs1496255) was also associated with FVC and FEV1. The other 2 variants were identified only for MVV and located in the genes of LOC102724340 (rs41434646) and FHIT (rs9833533). In particular, FHIT represses transcriptional activity of -catenin, a critical protein for growth of skeletal muscle, and thus might have influenced the level of MVV.The current study revealed 2 novel nucleotide variants as genetic association signals for MVV. The association signals were suggested specific for neuromuscular diseases with a restrictive ventilatory impairment. Further studies are required to understand underlying mechanisms for their influence to restrictive lung diseases.
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College of Natural Sciences (Department of Bioinformatics & Life Science)
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