Multi-objective prioritization of genes for high-throughput functional assays towards improved clinical variant classification
Multi-objective prioritization of genes for high-throughput functional assays towards improved clinical variant classification
复制标题
用于高通量功能测定的基因多目标优先级排序，以改进临床变异分类
DOI：
10.1142/9789811270611_0030
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发表时间：
2022
影响因子：
--
通讯作者：
V. Pejaver
中科院分区：
文献类型：
--
作者：
Yile Chen;Shantanu Jain;Daniel Zeiberg;L. Iakoucheva;S. Mooney;P. Radivojac;V. Pejaver
遗传变异的准确解释对于临床可操作性至关重要。然而，大多数变体的意义仍然不确定。变异效应的多重检测（MAVE）可以帮助在整个基因的尺度上为不确定意义的变异（VUS）提供功能证据。尽管从临床角度来看，对用于此类测定的基因进行系统的优先级排序已经引起了极大的兴趣，但现有策略很少强调这种动机。在这里，我们提出了三个目标来量化每个满足特定临床目标的基因的重要性：（1）可移动性分数，以优先考虑具有最多VUS移动到非VUS类别的基因，（2）校正分数，以优先考虑具有可重新分类的最多致病性和/或良性变体的基因，以及（3）不确定性分数，以优先考虑具有VUS的基因，对于所述基因，用于临床分类的变体致病性预测因子表现出最大的不确定性。我们证明，现有的方法是次优的，考虑到这些明确的临床目标。我们还提出了一个组合加权得分，同时优化这三个目标，并找到最佳的权重，以改善现有的方法。我们的策略通常会比现有的知识驱动和数据驱动策略产生更好的性能，并产生与临床相关的基因集。我们的工作对系统性的努力有影响，这些努力旨在在预测器开发、实验和临床翻译之间进行协调。
The accurate interpretation of genetic variants is essential for clinical actionability. However, a majority of variants remain of uncertain significance. Multiplexed assays of variant effects (MAVEs), can help provide functional evidence for variants of uncertain significance (VUS) at the scale of entire genes. Although the systematic prioritization of genes for such assays has been of great interest from the clinical perspective, existing strategies have rarely emphasized this motivation. Here, we propose three objectives for quantifying the importance of genes each satisfying a specific clinical goal: (1) Movability scores to prioritize genes with the most VUS moving to non-VUS categories, (2) Correction scores to prioritize genes with the most pathogenic and/or benign variants that could be reclassified, and (3) Uncertainty scores to prioritize genes with VUS for which variant pathogenicity predictors used in clinical classification exhibit the greatest uncertainty. We demonstrate that existing approaches are sub-optimal when considering these explicit clinical objectives. We also propose a combined weighted score that optimizes the three objectives simultaneously and finds optimal weights to improve over existing approaches. Our strategy generally results in better performance than existing knowledge-driven and data-driven strategies and yields gene sets that are clinically relevant. Our work has implications for systematic efforts that aim to iterate between predictor development, experimentation and translation to the clinic.
DOI：
10.1136/ebmh.11.4.102
发表时间：
2008-10
期刊：
Evidence Based Mental Health
影响因子：
--
作者：
P. Cochat;L. Vaucoret;J. Sarles
通讯作者：
P. Cochat;L. Vaucoret;J. Sarles