Increasing phenotypic annotation improves the diagnostic rate of exome sequencing in a rare neuromuscular disorder. Thompson, R., Papakonstantinou Ntalis, A., Beltran, S., Tapf, A., de Paula Estephan, E., Polavarapu, K., ’t Hoen, P. A. C., Missier, P., & Lochmuller, H. Human Mutation, 2019. ![Increasing phenotypic annotation improves the diagnostic rate of exome sequencing in a rare neuromuscular disorder [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Abstract 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.
@article{thompson_increasing_2019,
title = {Increasing phenotypic annotation improves the diagnostic rate of exome sequencing in a rare neuromuscular disorder},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/humu.23792},
doi = {10.1002/humu.23792},
abstract = {Abstract 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.},
journal = {Human Mutation},
author = {Thompson, Rachel and Papakonstantinou Ntalis, Anastasios and Beltran, Sergi and Tapf, Ana and de Paula Estephan, Eduardo and Polavarapu, Kiran and ’t Hoen, Peter A. C. and Missier, Paolo and Lochmuller, Hanns},
year = {2019},
keywords = {congenital myasthenic syndromes, deep phenotyping, diagnosis, exome sequencing, Exomiser, human phenotype ontology, variant prioritization},
}
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