In silico variant effect predictions are available for nearly all missense variants but played a minimal role in clinical variant classification because they were deemed to provide only supporting evidence. Recently, the ClinGen Sequence Variant Interpretation (SVI) Working Group updated recommendations for variant effect prediction use. By analyzing control pathogenic and benign variants across all genes, they were able to compute evidence strength for predictor score intervals with some intervals generating moderate, strong, or even very strong evidence. However, this genome-wide approach could obscure heterogeneous predictor performance in different genes. We quantified the gene-by-gene performance of two top predictors, REVEL and BayesDel, by analyzing control variants in each predictor score interval in 3,668 disease-relevant genes. Approximately 10% of intervals had sufficient control variants for analysis, and ∼70% of these intervals exceeded the maximum number of incorrect predictions implied by the SVI recommendations. These trending discordant intervals arose owing to the divergence of the gene-specific distribution of predictions from the genome-wide distribution, suggesting that gene-specific calibration is needed in many cases. Approximately 22% of ClinVar missense variants of uncertain significance in genes we analyzed (REVEL = 100,629, BayesDel = 71,928) had predictions in trending discordant intervals. Thus, genome-wide calibrations could result in many variants receiving inappropriate evidence strength. To facilitate a review of the SVI’s calibrations, we developed a web application enabling visualization of gene-specific predictions and trending concordant and discordant intervals.

中文翻译：

---

全基因组数据上变异效应预测因子的校准掩盖了基因间的异质性性能


计算机变异效应预测几乎适用于所有错义变异，但在临床变异分类中的作用很小，因为它们被认为仅提供支持证据。最近，ClinGen 序列变异解释 （SVI） 工作组更新了变异效应预测使用的建议。通过分析所有基因的对照致病性和良性变异，他们能够计算预测因子评分区间的证据强度，其中一些区间产生中等、强烈甚至非常强的证据。然而，这种全基因组方法可能会掩盖不同基因中的异质预测性能。我们通过分析 3,668 个疾病相关基因中每个预测因子评分区间中的对照变异，量化了两个主要预测因子 REVEL 和 BayesDel 的逐基因性能。大约 10% 的区间具有足够的控制变体用于分析，并且这些区间中 ∼70% 超过了 SVI 建议隐含的最大错误预测数。这些趋势不一致的区间是由于预测的基因特异性分布与全基因组分布的差异而出现的，这表明在许多情况下需要基因特异性校准。在我们分析的基因中，大约 22% 的意义不确定的 ClinVar 错义变异 （REVEL = 100,629，BayesDel = 71,928） 在趋势不一致区间中具有预测。因此，全基因组校准可能导致许多变异获得不适当的证据强度。为了便于审查 SVI 的校准，我们开发了一个 Web 应用程序，可以可视化基因特异性预测和趋势一致和不一致区间。