Next-generation sequencing (NGS) is an incredibly useful tool for genetic disease diagnosis. However, the most commonly used bioinformatics methods for analyzing sequence reads insufficiently discriminate genomic regions with extensive sequence identity, such as gene families and pseudogenes, complicating diagnostics. This problem has been recognized for specific genes, including many involved in human disease, and diagnostic labs must perform additional costly steps to guarantee accurate diagnosis in these cases. Here we report a new data analysis method based on the comparison of read depth between highly homologous regions to identify misalignment. Analyzing six clinically important genes—CYP21A2, GBA, HBA1/2, PMS2, and SMN1—each exhibiting misalignment issues related to homology, we show that our technique can correctly identify potential misalignment events and be used to make appropriate calls. Combined with long-range PCR and/or MLPA orthogonal testing, our clinical laboratory can improve variant calling with minimal additional cost. We propose an accurate and cost-efficient NGS testing procedure that will benefit disease diagnostics, carrier screening, and research-based population studies.

下一代测序（NGS）是用于遗传疾病诊断的极为有用的工具。然而，最常用的用于分析序列的生物信息学方法无法充分区分具有广泛序列同一性的基因组区域，例如基因家族和假基因，从而使诊断变得复杂。对于特定基因，包括许多与人类疾病有关的特定基因，已经认识到了这个问题，诊断实验室必须执行其他昂贵的步骤，以保证在这些情况下的准确诊断。在这里，我们报告了一种基于高度同源区域之间的读取深度比较以识别错位的新数据分析方法。分析六个临床重要基因CYP21A2，GBA，HBA1 / 2，PMS2和SMN1-每个都表现出与同源性有关的错位问题，我们证明了我们的技术可以正确地识别潜在的错位事件，并可以用来进行适当的调用。结合远程PCR和/或MLPA正交测试，我们的临床实验室可以以最小的额外成本改善变异检测。我们提出了一种准确且具有成本效益的NGS测试程序，该程序将有益于疾病诊断，携带者筛查和基于研究的人群研究。