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随着时间的推移,与癫痫相关的基因和变异的临床显著变化:重新分析的意义。

Clinically significant changes in genes and variants associated with epilepsy over time: implications for re-analysis.

机构信息

Faculty of Medicine, University of Queensland, Brisbane, Australia.

Medical Genomics Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia.

出版信息

Sci Rep. 2024 Apr 2;14(1):7717. doi: 10.1038/s41598-024-57976-1.

DOI:10.1038/s41598-024-57976-1
PMID:38565608
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10987647/
Abstract

Despite the significant advances in understanding the genetic architecture of epilepsy, many patients do not receive a molecular diagnosis after genomic testing. Re-analysing existing genomic data has emerged as a potent method to increase diagnostic yields-providing the benefits of genomic-enabled medicine to more individuals afflicted with a range of different conditions. The primary drivers for these new diagnoses are the discovery of novel gene-disease and variants-disease relationships; however, most decisions to trigger re-analysis are based on the passage of time rather than the accumulation of new knowledge. To explore how our understanding of a specific condition changes and how this impacts re-analysis of genomic data from epilepsy patients, we developed Vigelint. This approach combines the information from PanelApp and ClinVar to characterise how the clinically relevant genes and causative variants available to laboratories change over time, and this approach to five clinical-grade epilepsy panels. Applying the Vigelint pipeline to these panels revealed highly variable patterns in new, clinically relevant knowledge becoming publicly available. This variability indicates that a more dynamic approach to re-analysis may benefit the diagnosis and treatment of epilepsy patients. Moreover, this work suggests that Vigelint can provide empirical data to guide more nuanced, condition-specific approaches to re-analysis.

摘要

尽管在理解癫痫的遗传结构方面取得了重大进展,但许多患者在基因组测试后仍未得到分子诊断。重新分析现有基因组数据已成为一种增加诊断效果的有效方法,为更多患有各种不同疾病的个体带来了基因组医学的益处。这些新诊断的主要驱动因素是发现新的基因-疾病和变异-疾病关系;然而,大多数触发重新分析的决定是基于时间的推移,而不是新知识的积累。为了探索我们对特定疾病的理解如何变化,以及这如何影响对癫痫患者基因组数据的重新分析,我们开发了 Vigelint。这种方法结合了 PanelApp 和 ClinVar 的信息,以描述实验室可用的临床相关基因和致病变异随时间的变化,以及这种方法对五个临床级别的癫痫面板。将 Vigelint 管道应用于这些面板揭示了新的、临床相关的知识公开可用的高度可变模式。这种可变性表明,更动态的重新分析方法可能有益于癫痫患者的诊断和治疗。此外,这项工作表明 Vigelint 可以提供经验数据,以指导更细致、针对特定疾病的重新分析方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbe/10987647/b39a8c33b087/41598_2024_57976_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbe/10987647/3c9886769a9d/41598_2024_57976_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbe/10987647/8dddbcb635db/41598_2024_57976_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbe/10987647/b39a8c33b087/41598_2024_57976_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbe/10987647/3c9886769a9d/41598_2024_57976_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbe/10987647/8dddbcb635db/41598_2024_57976_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6dbe/10987647/b39a8c33b087/41598_2024_57976_Fig3_HTML.jpg

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