A Novel Statistical Method for Interpreting the Pathogenicity of Rare Variants

Authors

Jun Wang
Hehe Liu
Renae Elaine Bertrand
Alejandro Sarrion-Perdigones
Yezabel Gonzalez
Koen J T Venken
Rui Chen

Publication Date

1-1-2021

Journal

Genetics in Medicine

DOI

0.1038/s41436-020-00948-3

PMID

32884132

PMCID

PMC7796914

PubMedCentral® Posted Date

3-4-2021

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes

Keywords

Alleles, Databases, Genetic, Gene Frequency, Genetic Variation, Humans, Virulence, variant interpretation, allele frequency, mendelian diseases, statistical test, clinical genomics

Abstract

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.

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