Briefings in bioinformatics
JGM, Computational Biology, Genetic Diseases, Inborn, Humans, Phenotype
Brief Bioinform. 2022;23(5)
This study was supported by the National Institutes of Health (NIH) grants 1R24OD011883, U54 HG006370, and NIH, National Institute of Child Health and Human Development 1R01HD103805-01.
Yuan et al. recently described an independent evaluation of several phenotype-driven gene prioritization methods for Mendelian disease on two separate, clinical datasets. Although they attempted to use default settings for each tool, we describe three key differences from those we currently recommend for our Exomiser and PhenIX tools. These influence how variant frequency, quality and predicted pathogenicity are used for filtering and prioritization. We propose that these differences account for much of the discrepancy in performance between that reported by them (15-26% diagnoses ranked top by Exomiser) and previously published reports by us and others (72-77%). On a set of 161 singleton samples, we show using these settings increases performance from 34% to 72% and suggest a reassessment of Exomiser and PhenIX on their datasets using these would show a similar uplift.
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases. Brief Bioinform. 2022;23(5)
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.