EyeG2P: an automated variant filtering approach improves efficiency of diagnostic genomic testing for inherited ophthalmic disorders.

PurposeThe widespread adoption of genomic testing for individuals with ophthalmic disorders has increased demand on diagnostic genomic services for these conditions. Moreover, the clinical utility of a molecular diagnosis for individuals with inherited ophthalmic disorders is increasingly placing pressure on the speed and accuracy of genomic testing. MethodsWe created EyeG2P, a publically available resource to assist diagnostic filtering of genomic datasets for ophthalmic conditions, utilising the Ensembl Variant Effect Predictor. We assessed the sensitivity of EyeG2P for 1234 individuals with a broad range of conditions, who had previously received a confirmed molecular diagnosis through routine genomic diagnostic approaches. For a prospective cohort of 83 individuals, we also assessed the precision of EyeG2P in comparision to routine genomic diagnostic approaches. ResultsWe observed that EyeG2P had a 99.5% sensitivity for genomic variants previously identified as a molecular diagnosis for 1234 individuals. EyeG2P enabled a significant increase in precision in comparison to routine testing strategies (p<0.001), with an increased precision in variant analysis of 35% per individual, on average. ConclusionAutomated filtering of genomic variants through EyeG2P can increase the efficiency of diagnostic testing for individuals with a broad range of inherited ophthalmic disorders.