Data-driven modelling of mutational hotspots and in-silico predictors in hypertrophic cardiomyopathy
Data-driven modelling of mutational hotspots and in-silico predictors in hypertrophic cardiomyopathy
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肥厚型心肌病突变热点和计算机预测因子的数据驱动建模
DOI：
10.1101/826164
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Waring A
中科院分区：
文献类型：
--
作者：
Waring A
虽然罕见的错义变异的潜在的一些孟德尔疾病已注意到集群在特定区域的蛋白质，这一信息可能是未充分利用时，评估致病性的基因或变异。在病例对照研究中，我们分别介绍了两种快速关联检验和预测建模的方法，即联合收割机变异负荷和氨基酸残基聚类。我们发现，在肥厚型心肌病的模拟和实验34基因组中，ChronterBurden增加了识别错义变异驱动的疾病基因的统计能力。然后，我们证明thatGAM可以被用来应用ACMG标准PM1和PP3定量，并解决了广泛的致病性之间的不确定的意义的变体的潜力。一个R包可用于关联测试，使用Pathogenicity_by_Position，一个网络应用程序可用于错义变异风险预测，使用GAM对六个肌节基因进行预测。总之，氨基酸残基位置信息的列入提高了基因和罕见变异致病性interpretation.Author SummaryTwo统计方法的准确性已经开发，利用信号在残基位置的错义变体。第一个是一个快速的关联方法，测试的联合假设的一个多余的罕见的变异和罕见的变异聚类。当罕见的错义变体聚集在蛋白质的离散致病区域时，该方法，即CherterBurden，是强大的。它可以应用于外显子组扫描，以发现新的孟德尔疾病基因，这可能无法通过经典的负担测试。第二种方法是一个统计模型的罕见的误解变量解释。通过在我们的大型病例对照数据集上进行训练，与genericin silicopredictors相比，它提供了上级预测性能。该方法代表了一种数据驱动的定量方法，应用ACMG变体解释指南中的热点和硅预测标准。
Although rare missense variants underlying a number of Mendelian diseases have been noted to cluster in specific regions of proteins, this information may be underutilized when evaluating the pathogenicity of a gene or variant. We introduceClusterBurdenandGAMs, two methods for rapid association testing and predictive modelling, respectively, that combine variant burden and amino-acid residue clustering, in case-control studies. We show thatClusterBurdenincreases statistical power to identify disease genes driven by missense variants, in simulated and experimental 34-gene panel for hypertrophic cardiomyopathy. We then demonstrate thatGAMscan be used to apply the ACMG criteria PM1 and PP3 quantitatively, and resolve a wide range of pathogenicity potential amongst variants of uncertain significance. An R package is available for association testing usingClusterBurden, and a web application (Pathogenicity_by_Position)is available for missense variant risk prediction using GAMs for six sarcomeric genes. In conclusion, the inclusion of amino-acid residue positional information enhances the accuracy of gene and rare variant pathogenicity interpretation.Author SummaryTwo statistical methods have been developed that utilize signal in the residue position of missense variants. The first is a rapid association method that tests the joint hypothesis of an excess of rare-variants and rare-variant clustering. The method,ClusterBurden, is powerful when rare-missense variants cluster in discrete pathogenic regions of the protein. It can be applied to exome-scans to discover novel Mendelian disease-genes, that may not be identified by classic burden testing. The second method is a statistical model for rare-missense variant interpretation. It provides superior predictive performance compared to genericin silicopredictors by training on our large case-control dataset. The method represents a data-driven quantitative approach to apply hotspot andin-silicoprediction criteria from the ACMG variant interpretation guidelines.
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影响因子：
3.7
作者：
Persyn E;Karakachoff M;Le Scouarnec S;Le Clézio C;Campion D;Consortium FE;Schott JJ;Redon R;Bellanger L;Dina C
通讯作者：
Dina C
DOI：
10.1056/nejmsr1406261
发表时间：
2015-06-04
期刊：
The New England journal of medicine
影响因子：
--
作者：
Rehm HL;Berg JS;Brooks LD;Bustamante CD;Evans JP;Landrum MJ;Ledbetter DH;Maglott DR;Martin CL;Nussbaum RL;Plon SE;Ramos EM;Sherry ST;Watson MS;ClinGen
通讯作者：
ClinGen
影响因子：
9.8
作者：
Guo, Michael H.;Plummer, Lacey;Lippincott, Margaret F.
通讯作者：
Lippincott, Margaret F.
影响因子：
158.5
作者：
WATKINS, H;ROSENZWEIG, A;SEIDMAN, JG
通讯作者：
SEIDMAN, JG
影响因子：
24
作者：
Mogensen, J;Murphy, RT;McKenna, WJ
通讯作者：
McKenna, WJ