Phenotype-aware prioritisation of rare Mendelian disease variants

A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices. Next-generation sequencing technologies have made achieving a molecular diagnosis for a rare genetic disorder more and more feasible and, in turn, have enabled a more personalised clinical management of the affected patients and their families.Identifying the one or two variants that are responsible for a certain disease phenotype from the millions identified by sequencing can be time-consuming and expensive.Numerous phenotype-aware variant prioritisation (VP) software tools now exist to help semi-automate the molecular diagnosis process for rare diseases.Although many of the published VP tools have many limitations, show a lack of maintenance, and become soon unfit for usage, several are up-to-date and demonstrate an impressive capacity in prioritising molecular diagnoses when tested on real patient data.Adopting phenotype-aware VP software tools in diagnostics settings can efficiently assist the multidisciplinary teams of clinicians and scientists in reporting genetic diagnoses for rare disease.