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

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.

SUBMITTER: Juang JM

PROVIDER: S-EPMC3902491 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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

Juang Jyh-Ming Jimmy JM Lu Tzu-Pin TP Lai Liang-Chuan LC Hsueh Chia-Hsiang CH Liu Yen-Bin YB Tsai Chia-Ti CT Lin Lian-Yu LY Yu Chih-Chieh CC Hwang Juey-Jen JJ Chiang Fu-Tien FT Yeh Sherri Shih-Fan SS Chen Wen-Pin WP Chuang Eric Y EY Lai Ling-Ping LP Lin Jiunn-Lee JL

Scientific reports 20140127

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 ...[more]

PMID: 24463578

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Project description:Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. Despite the widespread availability of genome-wide data, existing methods to analyze genetic data still primarily focus on marginal association models, which fall short of fully capturing the polygenic nature of complex traits and elucidating biological causal mechanisms. Here we present a computationally efficient causal inference framework for genome-wide detection of putative causal variants underlying genetic associations. Our approach utilizes summary statistics from potentially overlapping studies as input, constructs in silico knockoff copies of summary statistics as negative controls to attenuate confounding effects induced by linkage disequilibrium, and employs efficient ultrahigh-dimensional sparse regression to jointly model all genetic variants across the genome. Our method is computationally efficient, requiring less than 15 minutes on a single CPU to analyze genome-wide summary statistics. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD) we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline via marginal association testing. The identified putative causal variants achieve state-of-the-art agreement with massively parallel reporter assays and CRISPR-Cas9 experiments. Additionally, we applied the method to a retrospective analysis of large-scale genome-wide association studies (GWAS) summary statistics from 2013 to 2022. Results reveal the method's capacity to robustly discover additional loci for polygenic traits beyond conventional GWAS and pinpoint potential causal variants underpinning each locus (on average, 22.7% more loci and 78.7% fewer proxy variants), contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses. We are making the discoveries and software freely available to the community and anticipate that routine end-to-end in silico identification of putative causal genetic variants will become an important tool that will facilitate downstream functional experiments and future research into disease etiology, as well as the exploration of novel therapeutic avenues.

Project description:BackgroundBrugada syndrome (BrS) is a severe inherited arrhythmia syndrome that can be unmasked by fever.MethodsA multicentre clinical analysis was performed in 261 patients diagnosed with fever-induced BrS, including 198 (75.9%) and 27 (10.3%) patients who received next-generation genetic sequencing and epicardial arrhythmogenic substrate (AS) mapping, respectively.FindingsIn fever-induced BrS patients, pathogenic or likely pathogenic (P/LP) SCN5A variant carriers developed fever-induced BrS at a younger age, and more often in females and those of Caucasian descent. They exhibited significant electrophysical abnormalities, including a larger epicardial AS area, and more prolonged abnormal epicardial electrograms. During a median follow-up of 50.5 months (quartiles 32.5-81.5 months) after the diagnosis, major cardiac events (MCE) occurred in 27 (14.4%) patients. Patients with P/LP SCN5A variants had a higher ratio of MCE compared with the rest. Additionally, history of syncope, QRS duration, and Tpe interval could also predict an increased risk for future MCE according to univariate analysis. Multivariate analysis indicated that only P/LP SCN5A variants were independent significant predictors of MCE. Computational structural modelling showed that most variants are destabilizing, suggesting that Nav1.5 structure destabilization caused by SCN5A missense variants may contribute to fever-induced BrS.InterpretationIn our cohort, P/LP SCN5A variant carriers with fever-induced BrS are more prevalent among patients of Caucasian descent, females, and younger patients. These patients exhibit aggressive electrophysiological abnormalities and worse outcome, which warrants closer monitoring and more urgent management of fever.FundingThe current work was supported by the National Natural Science Foundation Project of China (Nos. 82270332 & 81670304), The Fundamental Research Funds for the Central Universities of China - Independent Research Project of Wuhan University (No. 2042022kf1217) from China; the National Institutes of Health of USA [NIH R56 (HL47678), NIH R01 (HL138103), and NIH R01 (HL152201)], the W. W. Smith Charitable Trust and the Wistar and Martha Morris Fund, Sharpe-Strumia Research Foundation, the American Heart Association Postdoctoral Fellowship (20POST35220002) from United States; the Netherlands CardioVascular Research Initiative: the Dutch Heart Foundation, Dutch Federation of University Medical Centers, the Netherlands Organization for Health Research and Development, and the Royal Netherlands Academy of Sciences (PREDICT2) from the Netherlands.

Dataset Information

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

Publications

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

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