Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany.
Institute of Neurogenomics, Helmholtz Zentrum München, Munich, Germany.
Nat Rev Neurol. 2024 Feb;20(2):114-126. doi: 10.1038/s41582-023-00909-9. Epub 2024 Jan 3.
The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype-phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.
全外显子组和基因组测序能力彻底改变了罕见运动障碍的分子检测,基因组测序正在成为这些异质疾病常规诊断工作流程的一个组成部分。然而,解释正在产生的大量基因组变异信息仍然具有很大的挑战性。在这篇观点文章中,我们概述了罕见运动障碍患者遗传诊断的多维策略。我们研究了为帮助准确确定致病变异优先级而开发的生物信息学工具和计算指标。此外,我们还强调了旨在促进发现新的基因型-表型关系的社区驱动的数据共享和病例匹配平台。最后,我们考虑了如何通过整合基因组、表观基因组、转录组和/或蛋白质组谱,来优化诊断成功率,从而实现对变异影响的更全面评估。综上所述,我们所讨论的方法为深入了解罕见运动障碍的遗传基础提供了途径。