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PubMed 24463578


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Title: Utilizing multiple in silico analyses to identify putative causal SCN5A variants in Brugada syndrome.

Authors: Jyh-Ming Jimmy Juang, Tzu-Pin Lu, Liang-Chuan Lai, Chia-Hsiang Hsueh, Yen-Bin Liu, Chia-Ti Tsai, Lian-Yu Lin, Chih-Chieh Yu, Juey-Jen Hwang, Fu-Tien Chiang, Sherri Shih-Fan Yeh, Wen-Pin Chen, Eric Y Chuang, Ling-Ping Lai, Jiunn-Lee Lin

Journal, date & volume: Sci Rep, 2014 , 4, 3850

PubMed link: http://www.ncbi.nlm.nih.gov/pubmed/24463578


Abstract
Brugada syndrome (BrS) is an inheritable sudden cardiac death disease mainly caused by SCN5A mutations. Traditional approaches can be costly and time-consuming if all candidate variants need to be validated through in vitro studies. Therefore, we developed a new approach by combining multiple in silico analyses to predict functional and structural changes of candidate SCN5A variants in BrS before conducting in vitro studies. Five SCN5A non-synonymous variants (1651G>A, 1776C>G, 1673A>G, 3269C>T and 3578G>A) were identified in 14 BrS patients using direct DNA sequencing. Several bioinformatics algorithms were applied and predicted that 1651G>A (A551T) and 1776C>G (N592K) were high-risk SCN5A variants (odds ratio 59.59 and 23.93). The results were validated by Mass spectrometry and in vitro electrophysiological assays. We concluded that integrating sequence-based information and secondary protein structures elements may help select highly potential variants in BrS before conducting time-consuming electrophysiological studies and two novel SCN5A mutations were validated.