Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
Graduate School of Biomedical Sciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
Hum Genet. 2022 Oct;141(10):1549-1577. doi: 10.1007/s00439-022-02457-6. Epub 2022 Apr 30.
Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.
评估疾病驱动基因中发现的变异的影响为个性化治疗机会打开了大门。临床关联和实验室实验只能描述所有可用变异的一小部分，而将大多数变异归类为意义不明的变异（VUS）。计算方法通过大规模提供即时估计来弥补这一差距，这些估计通常基于物种之间的大量遗传差异。尽管人们担心这些方法在个体受试者中可能缺乏可靠性，但它们在队列中的大量实际应用表明，当在适当的规模和背景下使用时，它们已经很有帮助，并且在基因组解释中发挥了作用。在这篇综述中，我们旨在深入了解这些变异效应预测方法的训练和验证，并举例说明具有代表性的实验和临床应用类型。使用尚未公布的各种数据集进行的客观性能评估表明了每种方法的优缺点。这些结果表明，谨慎使用计算方法变异影响预测器对于解决基因组解释挑战至关重要。
