Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Genet Med. 2013 Jan;15(1):36-44. doi: 10.1038/gim.2012.112. Epub 2012 Sep 20.
Next-generation sequencing has transformed genetic research and is poised to revolutionize clinical diagnosis. However, the vast amount of data and inevitable discovery of incidental findings require novel analytic approaches. We therefore implemented for the first time a strategy that utilizes an a priori structured framework and a conservative threshold for selecting clinically relevant incidental findings.
We categorized 2,016 genes linked with Mendelian diseases into "bins" based on clinical utility and validity, and used a computational algorithm to analyze 80 whole-genome sequences in order to explore the use of such an approach in a simulated real-world setting.
The algorithm effectively reduced the number of variants requiring human review and identified incidental variants with likely clinical relevance. Incorporation of the Human Gene Mutation Database improved the yield for missense mutations but also revealed that a substantial proportion of purported disease-causing mutations were misleading.
This approach is adaptable to any clinically relevant bin structure, scalable to the demands of a clinical laboratory workflow, and flexible with respect to advances in genomics. We anticipate that application of this strategy will facilitate pretest informed consent, laboratory analysis, and posttest return of results in a clinical context.
下一代测序技术改变了遗传研究，并有望彻底改变临床诊断。然而，大量的数据和不可避免的偶然发现需要新的分析方法。因此，我们首次实施了一种策略，该策略利用了先验的结构化框架和选择临床相关偶然发现的保守阈值。
我们根据临床效用和有效性将 2016 个与孟德尔疾病相关的基因分类到“箱”中，并使用计算算法分析 80 个全基因组序列，以探索在模拟的真实环境中使用这种方法的情况。
该算法有效地减少了需要人工审查的变异数量，并确定了具有潜在临床相关性的偶然变异。纳入人类基因突变数据库提高了错义突变的检出率，但也表明，很大一部分所谓的致病突变是误导性的。
这种方法适用于任何临床相关的箱结构，可以根据临床实验室工作流程的需求进行扩展，并灵活适应基因组学的进步。我们预计，该策略的应用将有助于在临床环境中进行检测前知情同意、实验室分析和检测后结果的返回。
