Computational and experimental methods for classifying variants of unknown clinical significance.
Computational and experimental methods for classifying variants of unknown clinical significance.
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DOI：
10.1101/mcs.a006196
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发表时间：
2022-04
影响因子：
1.8
通讯作者：
Kircher M
中科院分区：
文献类型：
--
作者：
Spielmann M;Kircher M
测序能力的提高、成本的降低以及国家和国际的协调努力导致了下一代测序(NGS)技术在患者护理中的广泛引入。更广泛地说，人类遗传学和基因组医学对越来越多的患者来说变得越来越重要。一些社区已经在讨论对每个人出生时的基因组进行测序的前景。与数字健康记录一起，这将使个性化治疗和预防措施成为可能，即所谓的精准医学。在这一过程中的一个中心步骤是识别使我们更容易感染疾病的疾病原因突变或变异组合。尽管各种技术进步改善了对基因改变的识别，但对已识别的变异的解释和排序仍然是一个重大挑战。基于我们对分子过程或先前识别的疾病变异的知识，我们可以识别潜在的功能性遗传变异，并使用不同的证据线，有时我们能够直接证明它们的致病性。然而，绝大多数变异体被归类为临床意义不确定的变异体，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改进优先顺序，并且正在出现一个越来越多的实验方法工具箱，可以用于分析VUS的分子效应。在这里，我们讨论如何使用计算和实验方法来创建各种分子和细胞表型的不同效应目录。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的前景，以开发临床应用的准确致病预测。
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.
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影响因子：
14.9
作者：
Abugessaisa I;Ramilowski JA;Lizio M;Severin J;Hasegawa A;Harshbarger J;Kondo A;Noguchi S;Yip CW;Ooi JLC;Tagami M;Hori F;Agrawal S;Hon CC;Cardon M;Ikeda S;Ono H;Bono H;Kato M;Hashimoto K;Bonetti A;Kato M;Kobayashi N;Shin J;de Hoon M;Hayashizaki Y;Carninci P;Kawaji H;Kasukawa T
通讯作者：
Kasukawa T
影响因子：
56.9
作者：
Carninci, P;Kasukawa, T;Hayashizaki, Y
通讯作者：
Hayashizaki, Y
影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
影响因子：
12.3
作者：
Acuna-Hidalgo R;Veltman JA;Hoischen A
通讯作者：
Hoischen A
影响因子：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen