A novel statistical method for interpreting the pathogenicity of rare variants

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.

Automated summary

To achieve personalized treatment for patients, accurate molecular diagnosis and interpretation of genetic variants impact on gene function is crucial. A new statistical test combining patient cohort sequencing data with a normal control population database has been developed to identify likely benign variants, showing promising results in filtering low frequency variants in the normal population.