Flanagan Sarah E, Patch Ann-Marie, Ellard Sian
Institute of Biomedical and Clinical Science, Peninsula Medical School, University of Exeter, Exeter, United Kingdom.
Genet Test Mol Biomarkers. 2010 Aug;14(4):533-7. doi: 10.1089/gtmb.2010.0036.
The interpretation of novel missense variants is a challenge with increasing numbers of such variants being identified and a responsibility to report the findings in the context of all available scientific evidence. Various in silico bioinformatic tools have been developed that predict the likely pathogenicity of missense variants; however, their utility within the diagnostic setting requires further investigation.
The aim of our study was to test the predictive value of two of these tools, sorting intolerant from tolerant (SIFT) and polymorphism phenotyping (PolyPhen), in a set of 141 missense variants (131 pathogenic, 8 benign) identified in the ABCC8, GCK, and KCNJ11 genes.
Sixty-six of the mutations caused a gain of protein function, while 67 were loss-of-function mutations. The evolutionary conservation at each residue was also investigated using multiple sequence alignments from the UCSC genome browser.
The sensitivity of SIFT and PolyPhen was reasonably high (69% and 68%, respectively), but their specificity was low (13% and 16%). Both programs were significantly better at predicting loss-of-function mutations than gain-of-function mutations (SIFT, p = 0.001; PolyPhen, p < or = 0.0001). The most reliable method for assessing the likely pathogenicity of a missense variant was to investigate the degree of conservation at the affected residue. Eighty-eight percent of the mutations affected highly conserved residues, while all of the benign variants occurred at residues that were polymorphic across multiple species.
Although SIFT and PolyPhen may be useful in prioritizing changes that are likely to cause a loss of protein function, their low specificity means that their predictions should be interpreted with caution and further evidence to support/refute pathogenicity should be sought before reporting novel missense changes.
随着新发现的错义变异数量不断增加，对其进行解读成为一项挑战，并且有责任在所有现有科学证据的背景下报告研究结果。已经开发了各种计算机生物信息学工具来预测错义变异的可能致病性；然而，它们在诊断环境中的实用性需要进一步研究。
我们研究的目的是在一组在ABCC8、GCK和KCNJ11基因中鉴定出的141个错义变异（131个致病性变异，8个良性变异）中测试其中两种工具，即从耐受中筛选不耐受（SIFT）和多态性表型分析（PolyPhen）的预测价值。
其中66个突变导致蛋白质功能获得，而67个是功能丧失突变。还使用加州大学圣克鲁兹分校基因组浏览器的多序列比对研究了每个残基的进化保守性。
SIFT和PolyPhen的敏感性相当高（分别为69%和68%），但它们的特异性较低（分别为13%和16%）。这两个程序在预测功能丧失突变方面比功能获得突变要好得多（SIFT，p = 0.001；PolyPhen，p≤0.0001）。评估错义变异可能致病性的最可靠方法是研究受影响残基的保守程度。88%的突变影响高度保守的残基，而所有良性变异都发生在多个物种中具有多态性的残基上。
尽管SIFT和PolyPhen可能有助于对错义变异进行优先级排序，这些变异可能导致蛋白质功能丧失，但其低特异性意味着对其预测应谨慎解读，在报告新的错义变异之前应寻求更多支持/反驳致病性的证据。
