The evolution of next-generation sequencing technologies has facilitated the detection of causal genetic variants in diseases previously undiagnosed at a molecular level. However, in genome sequencing studies, the identification of disease genes among a candidate gene list is often difficult because of the large number of apparently damaging (but usually neutral) variants. A number of variant prioritization tools have been developed to help detect disease-causal sites. However, the results may be misleading as many variants scored as damaging by these tools are often tolerated, and there are inconsistencies in prediction results among the different variant-level prediction tools. Recently, studies have indicated that understanding gene properties might improve detection of genes liable to have associated disease variation and that this information improves molecular diagnostics. The purpose of this systematic review is to evaluate how understanding gene-specific properties might improve filtering strategies in clinical sequence data to prioritize potential disease variants. Improved understanding of the 'disease genome', which includes coding, noncoding and regulatory variation, might help resolve difficult cases. This review provides a comprehensive assessment of existing gene-level approaches, the relationships between measures of gene-pathogenicity and how use of these prediction tools can be developed for molecular diagnostics.

中文翻译：

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特定基因的度量标准，可帮助鉴定疾病基因，以便在患者基因组中进行分子诊断：系统综述。

下一代测序技术的发展促进了以前在分子水平上无法诊断的疾病中因果遗传变异的检测。但是，在基因组测序研究中，由于存在大量明显具有破坏性（但通常是中性）的变体，因此很难在候选基因列表中鉴定疾病基因。已经开发了许多变量优先级排序工具来帮助检测疾病致病部位。但是，这些结果可能会误导人们，因为通常会容忍许多被这些工具评为损坏的变体，并且不同变体级别的预测工具之间的预测结果不一致。最近，研究表明，了解基因特性可能会改善容易引起相关疾病变异的基因的检测，并且该信息可以改善分子诊断。本系统综述的目的是评估对基因特异性属性的了解如何改善临床序列数据中的过滤策略，从而优先考虑潜在的疾病变异。更好地理解“疾病基因组”，包括编码，非编码和调控变异，可能有助于解决疑难病例。这篇综述提供了对现有基因水平方法，基因致病性测度之间的关系以及如何开发这些预测工具进行分子诊断的全面评估。本系统综述的目的是评估对基因特异性属性的了解如何改善临床序列数据中的过滤策略，从而优先考虑潜在的疾病变异。更好地理解“疾病基因组”，包括编码，非编码和调控变异，可能有助于解决疑难病例。这篇综述提供了对现有基因水平方法，基因致病性测度之间的关系以及如何开发这些预测工具进行分子诊断的全面评估。本系统综述的目的是评估对基因特异性属性的了解如何改善临床序列数据中的过滤策略，从而优先考虑潜在的疾病变异。更好地理解“疾病基因组”，包括编码，非编码和调控变异，可能有助于解决疑难病例。这篇综述提供了对现有基因水平方法，基因致病性测度之间的关系以及如何开发这些预测工具进行分子诊断的全面评估。