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全基因组 DNA 甲基化分析在孟德尔神经发育障碍中的诊断效用。

Diagnostic Utility of Genome-Wide DNA Methylation Analysis in Mendelian Neurodevelopmental Disorders.

机构信息

Department of Pathology and Laboratory Medicine, Western University, London, ON N6A 3K7, Canada.

Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, London, ON N6A 5W9, Canada.

出版信息

Int J Mol Sci. 2020 Dec 6;21(23):9303. doi: 10.3390/ijms21239303.

Abstract

Mendelian neurodevelopmental disorders customarily present with complex and overlapping symptoms, complicating the clinical diagnosis. Individuals with a growing number of the so-called rare disorders exhibit unique, disorder-specific DNA methylation patterns, consequent to the underlying gene defects. Besides providing insights to the pathophysiology and molecular biology of these disorders, we can use these epigenetic patterns as functional biomarkers for the screening and diagnosis of these conditions. This review summarizes our current understanding of DNA methylation episignatures in rare disorders and describes the underlying technology and analytical approaches. We discuss the computational parameters, including statistical and machine learning methods, used for the screening and classification of genetic variants of uncertain clinical significance. Describing the rationale and principles applied to the specific computational models that are used to develop and adapt the DNA methylation episignatures for the diagnosis of rare disorders, we highlight the opportunities and challenges in this emerging branch of diagnostic medicine.

摘要

孟德尔神经发育障碍通常表现出复杂且重叠的症状,这使得临床诊断变得复杂。越来越多的所谓罕见疾病患者表现出独特的、特定于疾病的 DNA 甲基化模式,这是由潜在的基因缺陷引起的。除了为这些疾病的病理生理学和分子生物学提供见解外,我们还可以将这些表观遗传模式用作这些疾病的筛查和诊断的功能生物标志物。本综述总结了我们目前对罕见疾病中 DNA 甲基化外显子标记的理解,并描述了潜在的技术和分析方法。我们讨论了用于筛选和分类具有不确定临床意义的遗传变异的计算参数,包括统计和机器学习方法。描述应用于特定计算模型的原理和原则,这些模型用于开发和调整 DNA 甲基化外显子标记以诊断罕见疾病,我们强调了这一新兴诊断医学分支的机遇和挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a585/7730976/c653715ea16d/ijms-21-09303-g001.jpg

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