|Title||A novel statistical method for interpreting the pathogenicity of rare variants.|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Wang, J, Liu, H, Bertrand, RElaine, Sarrion-Perdigones, A, Gonzalez, Y, Venken, KJT, Chen, R|
|Date Published||2021 01|
|Keywords||Alleles, Databases, Genetic, Gene Frequency, Genetic Variation, Humans, Virulence|
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.
|Alternate Journal||Genet Med|
|PubMed Central ID||PMC7796914|
|Grant List||S10 OD023469 / OD / NIH HHS / United States |
R01 EY022356 / EY / NEI NIH HHS / United States
P30 EY002520 / EY / NEI NIH HHS / United States
R01 EY018571 / EY / NEI NIH HHS / United States