William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, UK.
UCL Institute of Ophthalmology, University College London, London, EC1V 9EL, UK.
Trends Genet. 2022 Dec;38(12):1271-1283. doi: 10.1016/j.tig.2022.07.002. Epub 2022 Aug 4.
A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices.
从罕见孟德尔疾病的测序数据分析中进行分子诊断，对患者及其家属的管理有重大影响。已经开发了许多针对患者表型的变异优先级（VP）工具，以帮助实现这一过程的自动化，并缩短诊断过程，但关于真实患者数据的性能统计数据有限。在这里，我们使用来自 134 名具有分子诊断的个体的全外显子、视网膜疾病数据集，确定、评估和比较所有最新的、免费的、可通过编程访问的工具的性能。所有工具都能够将大约三分之二的遗传诊断识别为排名最高的候选诊断，其中 LIRICAL 的总体性能最佳。最后，我们讨论了克服挑战，因为即使采用当前最先进的实践，仍有大多数病例无法诊断。
