An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

Abstract

Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients’ sequencing data and their phenotypes encoded with Human Phenotype Ontology (HPO) terms. It prioritizes variants by leveraging information on variant frequency, predicted pathogenicity, and gene-phenotype associations derived from human diseases, model organisms, and protein–protein interactions. Early published releases of Exomiser were able to prioritize disease-causative variants as top candidates in up to 97% of simulated whole-exomes. The size of the tested real patient datasets published so far are very limited. Here, we present the latest Exomiser version 12.0.1 with many new features. We assessed the performance using a set of 134 whole-exomes from patients with a range of rare retinal diseases and known molecular diagnosis. Using default settings, Exomiser ranked the correct diagnosed variants as the top candidate in 74% of the dataset and top 5 in 94%; not using the patients’ HPO profiles (i.e., variant-only analysis) decreased the performance to 3% and 27%, respectively. In conclusion, Exomiser is an effective support tool for rare Mendelian phenotype-driven variant prioritization.

Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.

“…Fig. 3B), in agreement with previous reports [52]. These data show the importance of including all types of cases and causal variants in benchmarking to avoid overestimation of diagnostic performance in real-world clinical applications.…”

Biallelic P4HTM variants associated with HIDEA syndrome and mitochondrial respiratory chain complex I deficiency

“…Access to panel-based genetic testing and next-generation sequencing of both mitochondrial and nuclear genomes has greatly increased the potential to achieve a molecular genetic diagnosis. However, owing to the number of candidate sequence variations often yielded when multiple genes are sequenced, rigorous phenotyping is still important for the interpretation of genetic variants [ 3 , 4 ].…”

IGenomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes

“…Primarily retrospective studies suggested higher rates of relevant results, and we replicate similar success to these studies in our observed concordance among previously diagnosed cases. 7,30,38 Importantly, the vast majority of our patients were undiagnosed when entering the study. This allowed us to establish the utility of computationally assisted interpretation among prospective diverse rare disease patients, on a scale far beyond any previously assessed rare disease cohort.…”

“…Multiple machine-learning tools have emerged to balance the variant/locus characteristics in an attempt to systematically extract optimal candidate prioritization. 7 The integration of such tools in rare disease molecular analyses has been demonstrated by several centers primarily for small, selected cohorts. [8][9][10][11][12] The universal feature is the patient's phenotype coded through human phenotype ontology (HPO) terms as a basis for prioritization, followed by the deployment of variable ranking algorithms.…”