Purpose

To achieve the ultimate goal of personalized treatment of patients, accurate molecular diagnosis and precise interpretation of the impact of genetic variants on gene function is essential. With sequencing cost becoming increasingly affordable, the accurate distinguishing of benign from pathogenic variants becomes the major bottleneck. Although large normal population sequence databases have become a key resource in filtering benign variants, they are not effective at filtering extremely rare variants.

Methods

To address this challenge, we developed a novel statistical test by combining sequencing data from a patient cohort with a normal control population database. By comparing the expected and observed allele frequency in the patient cohort, variants that are likely benign can be identified.

Results

The performance of this new method is evaluated on both simulated and real data sets coupled with experimental validation. As a result, we demonstrate this new test is well powered to identify benign variants, and is particularly effective for variants with low frequency in the normal population.

Conclusion

Overall, as a general test that can be applied to any type of variants in the context of all Mendelian diseases, our work provides a general framework for filtering benign variants with very low population allele frequency.

中文翻译：

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一种解释罕见变异致病性的新统计方法。

目的

要实现患者个体化治疗的最终目标，准确的分子诊断和准确解读基因变异对基因功能的影响至关重要。随着测序成本变得越来越实惠，准确区分良性和致病变异成为主要瓶颈。尽管大型正常人群序列数据库已成为过滤良性变异的关键资源，但它们在过滤极其罕见的变异方面并不有效。

方法

为了应对这一挑战，我们通过将来自患者队列的测序数据与正常对照人群数据库相结合，开发了一种新的统计测试。通过比较患者队列中预期和观察到的等位基因频率，可以识别可能是良性的变异。

结果

这种新方法的性能在模拟和真实数据集以及实验验证上进行了评估。因此，我们证明了这种新测试能够很好地识别良性变异，并且对于正常人群中频率较低的变异特别有效。

结论

总体而言，作为可以应用于所有孟德尔疾病背景下任何类型变异的通用测试，我们的工作为过滤具有非常低的群体等位基因频率的良性变异提供了一个通用框架。