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提高儿科神经遗传学中基因组冷案分子诊断的方法。

Methods to Improve Molecular Diagnosis in Genomic Cold Cases in Pediatric Neurology.

机构信息

Center for Statistical Genetics, Sergievsky Center, Department of Neurology, Columbia University Medical Center, New York, NY 10032, USA.

出版信息

Genes (Basel). 2022 Feb 11;13(2):333. doi: 10.3390/genes13020333.

DOI:10.3390/genes13020333
PMID:35205378
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8871714/
Abstract

During the last decade, genetic testing has emerged as an important etiological diagnostic tool for Mendelian diseases, including pediatric neurological conditions. A genetic diagnosis has a considerable impact on disease management and treatment; however, many cases remain undiagnosed after applying standard diagnostic sequencing techniques. This review discusses various methods to improve the molecular diagnostic rates in these genomic cold cases. We discuss extended analysis methods to consider, non-Mendelian inheritance models, mosaicism, dual/multiple diagnoses, periodic re-analysis, artificial intelligence tools, and deep phenotyping, in addition to integrating various omics methods to improve variant prioritization. Last, novel genomic technologies, including long-read sequencing, artificial long-read sequencing, and optical genome mapping are discussed. In conclusion, a more comprehensive molecular analysis and a timely re-analysis of unsolved cases are imperative to improve diagnostic rates. In addition, our current understanding of the human genome is still limited due to restrictions in technologies. Novel technologies are now available that improve upon some of these limitations and can capture all human genomic variation more accurately. Last, we recommend a more routine implementation of high molecular weight DNA extraction methods that is coherent with the ability to use and/or optimally benefit from these novel genomic methods.

摘要

在过去的十年中,基因检测已成为孟德尔疾病(包括儿科神经疾病)的重要病因诊断工具。基因诊断对疾病管理和治疗有重大影响;然而,在应用标准诊断测序技术后,许多病例仍未得到诊断。本综述讨论了各种方法来提高这些基因组冷案的分子诊断率。我们讨论了扩展分析方法,以考虑非孟德尔遗传模式、嵌合体、双重/多重诊断、定期重新分析、人工智能工具和深度表型分析,以及整合各种组学方法以改善变异优先级。最后,讨论了新型基因组技术,包括长读测序、人工长读测序和光学基因组图谱。总之,更全面的分子分析和及时重新分析未解决的病例对于提高诊断率至关重要。此外,由于技术限制,我们目前对人类基因组的理解仍然有限。现在有一些新的技术可以改进其中的一些限制,并更准确地捕捉所有人类基因组变异。最后,我们建议更常规地采用与使用和/或最佳受益于这些新型基因组方法的能力一致的高分子量 DNA 提取方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3b3/8871714/8c1ad9cae7f9/genes-13-00333-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3b3/8871714/18069a39860e/genes-13-00333-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3b3/8871714/8c1ad9cae7f9/genes-13-00333-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3b3/8871714/18069a39860e/genes-13-00333-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3b3/8871714/8c1ad9cae7f9/genes-13-00333-g002.jpg

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