Department of Pediatrics, Division of Pediatric Cardiology (LMK, SK, SC, SLA, MEM-M, LEP, MBR, LAP, APL), Duke University School of Medicine, Durham, NC.
Department of Pediatrics, Section of Pediatric Cardiology, Baylor College of Medicine, Houston, TX (H.J.T.).
Circ Genom Precis Med. 2023 Aug;16(4):317-327. doi: 10.1161/CIRCGEN.122.003911. Epub 2023 Jul 6.
With genetic testing advancements, the burden of incidentally identified cardiac disease-associated gene variants is rising. These variants may carry a risk of sudden cardiac death, highlighting the need for accurate diagnostic interpretation. We sought to identify pathogenic hotspots in sudden cardiac death-associated genes using amino acid-level signal-to-noise (S:N) analysis and develop a web-based precision medicine tool, , to improve variant evaluation.
The minor allele frequency of putatively pathogenic variants was derived from cohort-based cardiomyopathy and channelopathy studies in the literature. We normalized disease-associated minor allele frequencies to rare variants in an ostensibly healthy population (Genome Aggregation Database) to calculate amino acid-level S:N. Amino acids with S:N above the gene-specific threshold were defined as hotspots. was built using JavaScript ES6 and using open-source JavaScript library ReactJS, web development framework Next.js, and JavaScript runtime NodeJS. We validated the ability of to identify pathogenic variants using variants from ClinVar and individuals clinically evaluated at the Duke University Hospitals with cardiac genetic testing.
We developed as an internet-based tool for S:N-based variant hotspots. Upon validation, a higher proportion of ClinVar likely pathogenic/pathogenic variants localized to hotspots (43.1%) than likely benign/benign variants (17.8%; 0.0001). Further, 75.3% of ClinVar variants reclassified to likely pathogenic/pathogenic were in hotspots, compared with 41.3% of those reclassified as variants of uncertain significance (0.0001) and 23.4% of those reclassified as likely benign/benign (<0.0001). Of the clinical cohort variants, 73.1% of likely pathogenic/pathogenic were in hotspots, compared with 0.0% of likely benign/benign (0.01).
reliably identifies disease-susceptible amino acid residues to evaluate variants by searching amino acid-specific S:N ratios.
随着基因检测技术的进步，偶然发现的与心脏疾病相关的基因突变的负担正在增加。这些突变可能会增加心源性猝死的风险，这凸显了准确诊断解释的必要性。我们试图使用基于氨基酸的信号与噪声（S:N）分析来识别与心源性猝死相关基因中的致病热点，并开发了一个基于网络的精准医学工具 ，以改善变异评估。
从文献中的基于队列的心肌病和通道病研究中得出潜在致病性变异的次要等位基因频率。我们将与疾病相关的罕见等位基因频率归一化为表型健康人群中的罕见变异（基因组聚集数据库），以计算氨基酸水平的 S:N。S:N 高于基因特异性阈值的氨基酸被定义为热点。 使用 JavaScript ES6 构建了 ，并使用了开源 JavaScript 库 ReactJS、Web 开发框架 Next.js 和 JavaScript 运行时 NodeJS。我们使用 ClinVar 中的变体和在杜克大学医院接受心脏基因检测的临床评估个体来验证 识别致病性变体的能力。
我们开发了 作为一种基于互联网的 S:N 变异热点工具。经过验证，ClinVar 中更大概率致病性/致病性变异位于 热点（43.1%）的比例高于更大概率良性/良性变异（17.8%；0.0001）。此外，75.3%的 ClinVar 变体重新分类为致病性/致病性变体位于热点，而 41.3%的变体重新分类为不确定意义的变体（0.0001）和 23.4%的变体重新分类为更大概率良性/良性（<0.0001）。在临床队列变体中，73.1%的更大概率致病性/致病性变体位于热点，而 0.0%的更大概率良性/良性变体（0.01）。
通过搜索基于氨基酸的 S:N 比值， 可靠地鉴定出易感疾病的氨基酸残基，以评估变体。
