Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Hum Mutat. 2021 Nov;42(11):1503-1517. doi: 10.1002/humu.24269. Epub 2021 Aug 15.
Prioritizing causal variants is one major challenge for the clinical application of sequencing data. Prompted by the observation that 74.3% of missense pathogenic variants locate in protein domains, we developed an approach named domain damage index (DDI). DDI identifies protein domains depleted of rare missense variations in the general population, which can be further used as a metric to prioritize variants. DDI is significantly correlated with phylogenetic conservation, variant-level metrics, and reported pathogenicity. DDI achieved great performance for distinguishing pathogenic variants from benign ones in three benchmark datasets. The combination of DDI with the other two best approaches improved the performance of each individual method considerably, suggesting DDI provides a powerful and complementary way of variant prioritization.
优先考虑因果变异是测序数据临床应用的主要挑战之一。受观察到 74.3%的错义致病性变异位于蛋白质结构域的启发,我们开发了一种名为结构域损伤指数(DDI)的方法。DDI 识别在普通人群中罕见错义变异缺失的蛋白质结构域,可进一步用作优先考虑变异的指标。DDI 与系统发育保守性、变异水平指标和报道的致病性显著相关。DDI 在三个基准数据集上实现了从良性变异中区分致病性变异的优异性能。DDI 与另外两种最佳方法相结合,大大提高了每种方法的性能,表明 DDI 提供了一种强大而互补的变异优先级排序方法。