Jones Edward G, Landstrom Andrew P
Department of Pediatrics, Baylor College of Medicine.
Department of Pediatrics, Division of Cardiology, Duke University School of Medicine;
J Vis Exp. 2019 Jan 16(143). doi: 10.3791/58907.
Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. While this has led to increased identification of likely pathogenic mutations associated with genetic syndromes, it has also dramatically increased the number of incidentally found genetic variants of unknown significance (VUS). Determining the clinical significance of these variants is a major challenge for both scientists and clinicians. An approach to assist in determining the likelihood of pathogenicity is signal-to-noise analysis at the protein sequence level. This protocol describes a method for amino acid-level signal-to-noise analysis that leverages variant frequency at each amino acid position of the protein with known protein topology to identify areas of the primary sequence with elevated likelihood of pathologic variation (relative to population "background" variation). This method can identify amino acid residue location "hotspots" of high pathologic signal, which can be used to refine the diagnostic weight of VUSs such as those identified by next generation genetic testing.
新一代基因测序在成本和速度方面的进步引发了临床全外显子组和全基因组检测的激增。虽然这使得与遗传综合征相关的可能致病突变的识别有所增加，但也显著增加了偶然发现的意义未明的基因变异（VUS）的数量。确定这些变异的临床意义对科学家和临床医生来说都是一项重大挑战。一种有助于确定致病性可能性的方法是在蛋白质序列水平进行信号噪声分析。本方案描述了一种氨基酸水平信号噪声分析方法，该方法利用已知蛋白质拓扑结构的蛋白质每个氨基酸位置的变异频率，来识别一级序列中病理变异可能性升高的区域（相对于群体“背景”变异）。这种方法可以识别高病理信号的氨基酸残基位置“热点”，可用于完善VUS的诊断权重，如下一代基因检测所识别的那些VUS。
