Increasing phenotypic annotation improves the diagnostic rate of exome sequencing in a rare neuromuscular disorder

Phenotype-based filtering and prioritization contribute to the interpretation of genetic variants detected in exome sequencing. However, it is currently unclear how extensive this phenotypic annotation should be. In this study, we compare methods for incorporating phenotype into the interpretation process and assess the extent to which phenotypic annotation aids prioritization of the correct variant. Using a cohort of 29 patients with congenital myasthenic syndromes with causative variants in known or newly discovered disease genes, exome data and the Human Phenotype Ontology (HPO)-coded phenotypic profiles, we show that gene-list filters created from phenotypic annotations perform similarly to curated disease-gene virtual panels. We use Exomiser, a prioritization tool incorporating phenotypic comparisons, to rank candidate variants while varying phenotypic annotation. Analyzing 3,712 combinations, we show that increasing phenotypic annotation improved prioritization of the causative variant, from 62% ranked first on variant alone to 90% with seven HPO annotations. We conclude that any HPO-based phenotypic annotation aids variant discovery and that annotation with over five terms is recommended in our context. Although focused on a constrained cohort, this provides real-world validation of the utility of phenotypic annotation for variant prioritization. Further research is needed to extend this concept to other diseases and more diverse cohorts. Data was analyzed using the RD‐Connect Genome‐Phenome Analysis Platform developed under the 2012‐2018 FP7‐funded project RD‐ Connect (Grant Agreement No.: 305444). R. T., S. B., A. P. N., P. tH., and H. L. received support from the EU FP7 projects 305444 (RD‐ Connect) and 305121 (NeurOmics). S. B. received support from the EU FP7 project 313010 (BBMRI‐LPC). S. B. and P. tH. received support from the EU Horizon 2020 project 779257 (Solve‐RD). S. B., P. tH., and H. L. received support from the EU Horizon 2020 project 825575 (EJP‐RD). H. L. is supported by a project grant of the Canadian Institutes of Health Research (CIHR PJT 162265)