2014 ACMG: User-Friendly Genomic Results: Leveraging a Novel Approach that has the Potential to Decrease Turn-Around Time and Preserve Opportunities for Novel Discoveries

The identification of causal variants in exome testing continues to rely on appropriate filtering of the tens of thousands of variants identified in an affected individual. Protocols typically apply hard filters to exclude variants least likely to be disease-causing. Sensitivity suffers when criteria are too strict, potentially missing novel candidate genes or cases that expand the phenotypic spectrum of known diseases. Conversely, relaxation of filtering criteria may result in overwhelming numbers of candidates, impeding diagnosis. We designed a knowledge-based ranking system that combines clinical and curated phenotype information as inputs into genotype-based analytics (Figure 1). This system increases the likelihood that causal findings are highly ranked while preserving the ability to detect novel genes/variants.