A novel algorithm, AlphaMissense, has been shown to have an improved ability to predict the pathogenicity of rare missense genetic variants. However, it is not known whether AlphaMissense improves the ability of gene-based testing to identify disease-causing genes. Using whole-exome sequencing data from the UK Biobank, we compared gene-based association analysis strategies including sets of deleterious variants: predicted loss-of-function (pLoF) variants only, pLoF plus AlphaMissense pathogenic variants, pLoF with missense variants predicted to be deleterious by any of five commonly utilized annotation methods (Missense (1/5)) or only variants predicted to be deleterious by all five methods (Missense (5/5)). We measured performance to identify 519 previously identified positive control genes, which can cause Mendelian diseases, or are the targets of successfully developed medicines. These strategies identified 850k pLoF variants and 5 million deleterious missense variants, including 22k likely pathogenic missense variants identified exclusively by AlphaMissense. The gene-based association tests found 608 significant gene associations (at P<1.25x10-7) across 24 common traits and diseases. Compared to pLOFs plus Missense (5/5), tests using pLoFs and AlphaMissense variants found slightly more significant gene-disease and gene-trait associations, albeit with a marginally lower proportion of positive control genes. Nevertheless, their overall performance was similar. Merging AlphaMissense with Missense (5/5), whether through their intersection or union, did not yield any further enhancement in performance. In summary, employing AlphaMissense to select deleterious variants for gene-based testing did not improve the ability to identify genes that are known to cause disease.

中文翻译：

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AlphaMissense 识别致病基因的性能

一种新的算法 AlphaMissense 已被证明能够提高预测罕见错义遗传变异致病性的能力。然而，尚不清楚 AlphaMissense 是否提高了基于基因的测试识别致病基因的能力。使用英国生物银行的全外显子组测序数据，我们比较了基于基因的关联分析策略，包括一组有害变异：仅预测功能丧失（pLoF）变异，pLoF加AlphaMissense致病变异，pLoF与预测为错义变异的pLoF。五种常用注释方法中的任何一种（错义（1/5））都是有害的，或者只有所有五种方法都预测为有害的变体（错义（5/5））。我们测量了性能，以确定 519 个先前识别的阳性对照基因，这些基因可能导致孟德尔疾病，或者是成功开发药物的目标。这些策略识别出 850k pLoF 变异和 500 万个有害错义变异，其中包括由 AlphaMissense 独家识别的 22k 可能致病错义变异。基于基因的关联测试在 24 种常见性状和疾病中发现了 608 个显着的基因关联（P<1.25x10-7）。与 pLOFs 加 Missense (5/5) 相比，使用 pLoFs 和 AlphaMissense 变体的测试发现基因-疾病和基因-性状关联稍微更显着，尽管阳性对照基因的比例略低。尽管如此，他们的整体表现还是相似的。将 AlphaMissense 与 Missense (5/5) 合并，无论是通过它们的交集还是并集，都没有产生性能的任何进一步增强。总之，使用 AlphaMissense 选择有害变异进行基于基因的测试并没有提高识别已知导致疾病的基因的能力。