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印度未确诊疾病项目中全外显子测序数据的重新分析:提高诊断率并结束诊断之旅。

Reanalysis of Exome Sequencing Data in the Indian Undiagnosed Diseases Program: Improving Diagnostic Yield and Ending Diagnostic Odyssey.

作者信息

Garg Neha, Lakshmi Pragna, Singh Suzena M, Kulshreshta Samarth, Ranganath Prajnya, Moirangthem Amita, Dalal Ashwin, Gahlot Aakanksha, Puri Ratna Dua

机构信息

Institute of Medical Genetics and Genomics, Sir Ganga Ram Hospital, New Delhi, India.

Diagnostics Division, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, India.

出版信息

Clin Genet. 2025 Jun;107(6):620-635. doi: 10.1111/cge.14694. Epub 2025 Jan 13.

Abstract

In 2021, the Indian Undiagnosed Diseases Program was initiated for patients without a definite diagnosis despite extensive evaluation in four participating sites. Between February 2021 and March 2023, a total of 88 patients were recruited and underwent deep phenotyping. A uniform methodology for data re-analysis was implemented as the first step prior to conducting additional genomic testing. The largest cohort was of 38 patients with neurodevelopmental disorders (NDD). A genetic diagnosis was achieved in 24 of the 88 patients (27.2%), including 7 cases within the NDD cohort. Factors contributing to the increased diagnostic yield included: (a) identification of a novel disease association in DAAM2 gene, and (b) limitations of the standard analysis pipeline, particularly for synonymous variants in SELENOI and KIAA0753 genes, non-frameshift variant in GLRX5 gene, low-coverage variant in GJC2 gene, large deletions in PCNT and PHKG2 genes, and intronic variants in VPS33B and FBN1. Improved phenotyping led to a diagnosis in three cases, while genomic variants missed in the previous bioinformatics analysis were identified in 12 cases. The study also contributed to the development of enhanced bioinformatics scripts for variant prioritization and more refined literature search for novel disease associations. It highlights the importance of incorporating data reanalysis into clinical workflows before pursuing advanced diagnostic tests, particularly in resource-limited settings where healthcare expenses are often borne out of pocket.

摘要

2021年,印度未确诊疾病项目启动,面向在四个参与地点经过广泛评估仍未明确诊断的患者。2021年2月至2023年3月期间,共招募了88名患者并进行了深度表型分析。在进行额外的基因检测之前,首先实施了统一的数据重新分析方法。最大的队列是38名神经发育障碍(NDD)患者。88名患者中有24名(27.2%)获得了基因诊断,其中NDD队列中有7例。诊断率提高的因素包括:(a)在DAAM2基因中发现了一种新的疾病关联,以及(b)标准分析流程的局限性,特别是对于SELENOI和KIAA0753基因中的同义变异、GLRX5基因中的非移码变异、GJC2基因中的低覆盖变异、PCNT和PHKG2基因中的大片段缺失以及VPS33B和FBN1基因中的内含子变异。改进的表型分析在三例中得出了诊断结果,同时在12例中发现了先前生物信息学分析遗漏的基因变异。该研究还为开发用于变异优先级排序的增强型生物信息学脚本以及更精确地搜索新疾病关联的文献做出了贡献。它强调了在进行先进诊断测试之前将数据重新分析纳入临床工作流程的重要性,特别是在医疗费用往往需自掏腰包的资源有限的环境中。

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