Institute of Human Genetics, University of Lübeck, 23562 Lübeck, Germany.
Institute of Human Genetics, Christian-Albrechts-Universität, 24105 Kiel, Germany.
Cold Spring Harb Mol Case Stud. 2022 Apr 28;8(3). doi: 10.1101/mcs.a006196. Print 2022 Apr.
The increase in sequencing capacity, reduction in costs, and national and international coordinated efforts have led to the widespread introduction of next-generation sequencing (NGS) technologies in patient care. More generally, human genetics and genomic medicine are gaining importance for more and more patients. Some communities are already discussing the prospect of sequencing each individual's genome at time of birth. Together with digital health records, this shall enable individualized treatments and preventive measures, so-called precision medicine. A central step in this process is the identification of disease causal mutations or variant combinations that make us more susceptible for diseases. Although various technological advances have improved the identification of genetic alterations, the interpretation and ranking of the identified variants remains a major challenge. Based on our knowledge of molecular processes or previously identified disease variants, we can identify potentially functional genetic variants and, using different lines of evidence, we are sometimes able to demonstrate their pathogenicity directly. However, the vast majority of variants are classified as variants of uncertain clinical significance (VUSs) with not enough experimental evidence to determine their pathogenicity. In these cases, computational methods may be used to improve the prioritization and an increasing toolbox of experimental methods is emerging that can be used to assay the molecular effects of VUSs. Here, we discuss how computational and experimental methods can be used to create catalogs of variant effects for a variety of molecular and cellular phenotypes. We discuss the prospects of integrating large-scale functional data with machine learning and clinical knowledge for the development of accurate pathogenicity predictions for clinical applications.
测序能力的提高、成本的降低，以及国家和国际间的协调努力，使得下一代测序（NGS）技术在患者治疗中得到了广泛的应用。更广泛地说，人类遗传学和基因组医学对于越来越多的患者变得越来越重要。一些社区已经在讨论在出生时对每个人的基因组进行测序的前景。与数字健康记录相结合，这将能够实现个体化治疗和预防措施，也就是所谓的精准医学。这一过程的一个核心步骤是确定使我们更容易患上疾病的疾病因果突变或变体组合。尽管各种技术进步提高了遗传改变的识别能力，但对鉴定出的变体的解释和排序仍然是一个主要挑战。基于我们对分子过程或先前鉴定的疾病变体的了解，我们可以识别出潜在的功能遗传变体，并使用不同的证据线，有时能够直接证明它们的致病性。然而，绝大多数变体被归类为临床意义不确定的变体（VUSs），没有足够的实验证据来确定它们的致病性。在这些情况下，可以使用计算方法来提高优先级，并且出现了越来越多的实验方法工具箱，可以用于检测 VUSs 的分子效应。在这里，我们讨论了如何使用计算和实验方法来创建各种分子和细胞表型的变体效应目录。我们讨论了将大规模功能数据与机器学习和临床知识相结合，为临床应用开发准确致病性预测的前景。
