Analysis of structural variants (SVs) along with smaller single nucleotide variants (SNVs) can improve diagnostic yield of whole genome sequencing in rare disease cases by as much as 15 percent, but analyzing SVs is fraught with challenge. In a study involving 56 rare genetic disease cases with causal SVs, Fabric GEM – Fabric Genomics’ AI gene-ranking algorithm – provided a unified approach to analyzing SVs and SNVs in a single step, correctly identifying 96 percent of these SVs. Moreover, GEM identified the causal SV in the top five list of all candidates (SVs and SNVs) 93 percent of the time, requiring review of fewer than five candidates per case. These results hold promise for patients seeking answers to undiagnosed diseases.
The study was conducted in conjunction with Rady Children’s Institute for Genomic Medicine, the University of Utah, and the Utah Center for Genetic Discovery.
Fabric GEM is currently optimized for analyzing SVs from whole genome data, giving physicians the most comprehensive genetic test possible.