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：
在过去的十年里，基因检测已成为孟德尔疾病的重要病因诊断工具，包括儿科神经系统疾病。基因诊断对疾病管理和治疗有相当大的影响；然而，许多病例在应用标准诊断测序技术后仍未确诊.这篇综述讨论了在这些基因组感冒病例中提高分子诊断率的各种方法。我们讨论要考虑的扩展分析方法，非孟德尔继承模型，马赛克，双重/多重诊断，定期重新分析，人工智能工具，和深层表型，除了整合各种组学方法以改善变体优先级。最后,新颖的基因组技术，包括长读数测序，人工长读数测序，和光学基因组作图进行了讨论。总之,为了提高诊断率，必须进行更全面的分子分析和对未解决的病例进行及时的重新分析。此外,由于技术的限制，我们目前对人类基因组的理解仍然有限。现在有新技术可以改善这些限制中的一些限制，并且可以更准确地捕获所有人类基因组变异。最后,我们建议更常规地实施高分子量DNA提取方法,该方法与使用这些新基因组方法的能力和/或最佳受益于这些新基因组方法的能力相一致.
