Automated application of novel gene-disease associations to scale reanalysis of undiagnosed patients

Abstract

A pressing challenge for genomic medicine services is to increase the diagnostic rate of molecular testing. Reanalysis of genomic data can increase the diagnostic yield of molecular testing for rare diseases by 5.9-47% and novel gene-disease associations are often cited as the catalyst for significant findings. However, clinical services lack adequate resources to conduct routine reanalysis for unresolved cases. To determine whether an automated application could lead to new diagnoses and streamline routine reanalysis, we developed TierUp. TierUp identifies new gene-disease associations with implications for unresolved rare disease cases recruited to the 100,000 Genomes Project. TierUp streams data from the public PanelApp database to enable routine, up-to-date reanalyses. When applied to 948 undiagnosed rare disease cases, TierUp highlighted 410 high and moderate impact variants in under 77 minutes, reducing the burden of variants for review with this reanalysis strategy by 99%. Ongoing variant interpretation has produced five follow-up clinical reports, including a molecular diagnosis of a rare form of spondylometaphyseal dysplasia. We recommend that clinical services leverage bioinformatics expertise to develop automated reanalysis tools. Additionally, we highlight the need for studies focused on the ethical, legal and health economics considerations raised by automated reanalysis tools.