贝叶斯建模预测 、 及 变异体恶性高热易感性和致病性。
Bayesian modeling to predict malignant hyperthermia susceptibility and pathogenicity of , and variants.
机构信息
Department of Anesthesia, Riley Hospital for Children at Indiana University Health, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
The North American Malignant Hyperthermia Registry of the Malignant Hyperthermia Association of the United States (MHAUS), Department of Nurse Anesthesia, University of Pittsburgh, Pittsburgh, PA 15261, USA.
出版信息
Pharmacogenomics. 2019 Sep;20(14):989-1003. doi: 10.2217/pgs-2019-0055.
Identify variants in , and , and predict malignant hyperthermia (MH) pathogenicity using Bayesian statistics in individuals clinically treated as MH susceptible (MHS). Whole exome sequencing including , and performed on 64 subjects with: MHS; suspected MH event or first-degree relative; and MH negative. Variant pathogenicity was estimated using analysis, allele frequency and prior data to calculate Bayesian posterior probabilities. Bayesian statistics predicted variant p.Thr1009Lys and variants p.Ser1728Phe and p.Leu4824Pro are likely pathogenic, and novel variant p.Met187Thr has uncertain significance. Nearly a third of MHS subjects had only benign variants. Bayesian method provides new approach to predict MH pathogenicity of genetic variants.
在临床上被诊断为恶性高热易感性（MHS）的个体中，通过贝叶斯统计方法，对 和 中的变异进行鉴定，并预测其恶性高热（MH）的致病性。对 64 名具有以下特征的个体进行全外显子组测序，包括 MHS、疑似 MH 事件或一级亲属以及 MH 阴性：进行全外显子组测序。使用 分析、等位基因频率和先验数据来估计变异的致病性，以计算贝叶斯后验概率。贝叶斯统计学预测 变异 p.Thr1009Lys 和 变异 p.Ser1728Phe 和 p.Leu4824Pro 可能具有致病性，而新型 变异 p.Met187Thr 则具有不确定的意义。近三分之一的 MHS 受试者仅存在良性变异。贝叶斯方法为预测遗传变异的 MH 致病性提供了新的方法。
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