在原发性免疫缺陷(PID)患者的临床遗传测试中,区分致病性变异和非致病性变异仍然是一个主要挑战。大多数现有的突变致病性预测工具将所有突变视为同质实体,忽略不同基因特征的差异,并对不同疾病的基因使用相同的模型。在这项研究中,我们开发了单核苷酸变异(SNV)致病性预测工具,PID的变体影响预测器(VIPPID;https://mylab。shinyapps.io/VIPPID/),它是为PID基因量身定制的,并为每个最普遍的PID已知基因使用了特定的模型。它采用了条件推断森林模型,并利用了SNV的85个特征的信息以及来自20个现有预测工具的分数。VIPPID的评估表明,它具有优于非特定常规工具的性能(曲线下面积=0.91)。此外,我们还表明,基因特异性模型优于非基因特异性模型.我们的研究表明,疾病特异性和基因特异性模型可以提高SNV致病性预测性能。这一观察结果支持了这样一种观点,即模型中突变的每个特征都可以被潜在地使用,在一个新的算法中,研究编码蛋白质的特性和功能。
Distinguishing pathogenic variants from non-pathogenic ones remains a major challenge in clinical genetic testing of primary immunodeficiency (PID) patients. Most of the existing mutation pathogenicity prediction tools treat all mutations as homogeneous entities, ignoring the differences in characteristics of different genes, and use the same model for genes in different diseases. In this study, we developed a single nucleotide variant (SNV) pathogenicity prediction tool, Variant Impact Predictor for PIDs (VIPPID; https://mylab.shinyapps.io/VIPPID/), which was tailored for PIDs genes and used a specific model for each of the most prevalent PID known genes. It employed a Conditional Inference Forest model and utilized information of 85 features of SNVs and scores from 20 existing prediction tools. Evaluation of VIPPID showed that it had superior performance (area under the curve = 0.91) over non-specific conventional tools. In addition, we also showed that the gene-specific model outperformed the non-gene-specific models. Our study demonstrated that disease-specific and gene-specific models can improve SNV pathogenicity prediction performance. This observation supports the notion that each feature of mutations in the model can be potentially used, in a new algorithm, to investigate the characteristics and function of the encoded proteins.