The accuracy of computer-based diagnostic tools for the identification of concurrent genetic disorders

The increasing use of next-generation sequencing, especially clinical exome sequencing, has revealed that individuals having two coexisting genetic conditions are not uncommon occurrences. This pilot study evaluates the efficacy of two methodologically distinct computational differential diagnosis generating tools-FindZebra and SimulConsult-in identifying multiple genetic conditions in a single patient. Clinical query terms were generated for each of 15 monogenic disorders that were effective in resulting in the top 10 list of differential diagnoses for each of the 15 monogenic conditions when entered into these bioinformatics tools. Then, the terms of over 125 pairings of these conditions were entered using each tool and the resulting list of diagnoses evaluated to determine how often both diagnoses of a pair were represented in that list. Neither tool was successful in identifying both members of a pair of conditions in greater than 40% of test cases. Disorder detection sensitivity was not homogeneous within a tool, with each tool favoring the identification of a subset of genetic conditions. In view of recent exome sequencing data showing an unexpectedly high prevalence of coexistent monogenic conditions, the results from this pilot study highlight a need for the development of computational tools designed to effectively generate differential diagnoses with consideration of the possibility of coexisting conditions.