Population Health and Immunity Division, the Walter and Eliza Hall Institute of Medical Research, Parkville 3052, VIC, Australia; Department of Medical Biology, The University of Melbourne, Melbourne 3010, VIC, Australia; Mathematics and Statistics, Murdoch University, Murdoch 6150, WA, Australia.
Population Health and Immunity Division, the Walter and Eliza Hall Institute of Medical Research, Parkville 3052, VIC, Australia; Department of Medical Biology, The University of Melbourne, Melbourne 3010, VIC, Australia; Epilepsy Research Centre, Department of Medicine, The University of Melbourne, Austin Health, Heidelberg 3084, VIC, Australia.
Am J Hum Genet. 2018 Dec 6;103(6):858-873. doi: 10.1016/j.ajhg.2018.10.015. Epub 2018 Nov 29.
Repeat expansions cause more than 30 inherited disorders, predominantly neurogenetic. These can present with overlapping clinical phenotypes, making molecular diagnosis challenging. Single-gene or small-panel PCR-based methods can help to identify the precise genetic cause, but they can be slow and costly and often yield no result. Researchers are increasingly performing genomic analysis via whole-exome and whole-genome sequencing (WES and WGS) to diagnose genetic disorders. However, until recently, analysis protocols could not identify repeat expansions in these datasets. We developed exSTRa (expanded short tandem repeat algorithm), a method that uses either WES or WGS to identify repeat expansions. Performance of exSTRa was assessed in a simulation study. In addition, four retrospective cohorts of individuals with eleven different known repeat-expansion disorders were analyzed with exSTRa. We assessed results by comparing the findings to known disease status. Performance was also compared to three other analysis methods (ExpansionHunter, STRetch, and TREDPARSE), which were developed specifically for WGS data. Expansions in the assessed STR loci were successfully identified in WES and WGS datasets by all four methods with high specificity and sensitivity. Overall, exSTRa demonstrated more robust and superior performance for WES data than did the other three methods. We demonstrate that exSTRa can be effectively utilized as a screening tool for detecting repeat expansions in WES and WGS data, although the best performance would be produced by consensus calling, wherein at least two out of the four currently available screening methods call an expansion.
重复扩展导致 30 多种遗传性疾病，主要是神经遗传疾病。这些疾病可能具有重叠的临床表型，使分子诊断具有挑战性。基于单基因或小面板 PCR 的方法可以帮助确定精确的遗传原因，但它们可能速度较慢且成本较高，并且通常没有结果。研究人员越来越多地通过全外显子组和全基因组测序（WES 和 WGS）进行基因组分析，以诊断遗传疾病。然而，直到最近，分析协议都无法在这些数据集中识别重复扩展。我们开发了 exSTRa（扩展短串联重复算法），这是一种使用 WES 或 WGS 来识别重复扩展的方法。在模拟研究中评估了 exSTRa 的性能。此外，还使用 exSTRa 分析了四个具有十一种不同已知重复扩展疾病的回顾性队列。我们通过将结果与已知疾病状态进行比较来评估结果。还将性能与专门为 WGS 数据开发的三种其他分析方法（ExpansionHunter、STRetch 和 TREDPARSE）进行了比较。四种方法均成功地在 WES 和 WGS 数据集中识别了所评估 STR 基因座的扩展，具有很高的特异性和敏感性。总体而言，与其他三种方法相比，exSTRa 在 WES 数据中表现出更强大和优越的性能。我们证明，exSTRa 可以有效地用作检测 WES 和 WGS 数据中重复扩展的筛选工具，尽管最佳性能将通过共识调用产生，其中至少两种当前可用的筛选方法中的四种调用扩展。
