An optimized variant prioritization process for rare disease diagnostics: recommendations for Exomiser and Genomiser

Abstract

PurposeWhole-exome sequencing (WES) and whole-genome sequencing (WGS) are increasingly used as standard genetic tests to identify the diagnostic variants in rare disease cases. However, prioritizing these variants to reduce the time and burden of manual interpretation by clinical teams remains a significant challenge. The Exomiser/Genomiser software suite is the most widely adopted open-source software for prioritizing coding and non-coding variants. Despite its ubiquitous use, limited data-driven guidelines currently exist to optimize its performance for diagnostic variant prioritization. Based on detailed analyses of Undiagnosed Diseases Network (UDN) probands, this study presents optimized parameters and practical recommendations for deploying the Exomiser and Genomiser tools. We also highlight scenarios where diagnostic variants may be missed and propose alternative workflows to improve diagnostic success in such complex cases. MethodsWe analyzed 386 diagnosed probands from the UDN, including cases with coding and non-coding diagnostic variants. We systematically evaluated how tool performance was affected by key parameters, including gene:phenotype association data, variant pathogenicity predictors, phenotype term quality and quantity, and the inclusion and accuracy of family variant data. ResultsParameter optimization significantly improved Exomisers performance over default parameters. For WGS data, the percentage of coding diagnostic variants ranked within the top ten candidates increased from 49.7% to 85.5%, and for WES, from 67.3% to 88.2%. For non-coding variants prioritized with Genomiser, the top ten rankings improved from 15.0% to 40.0%. We also explored refinement strategies for Exomiser outputs, including using p-value thresholds and flagging genes that are frequently ranked in the top 30 candidates but rarely associated with diagnoses. ConclusionThis study provides an evidence-based framework for variant prioritization in WES and WGS data using Exomiser and Genomiser. These recommendations have been implemented in the Mosaic platform to support the ongoing analysis of undiagnosed UDN participants and provide efficient, scalable reanalysis to improve diagnostic yield. Our work also highlights the importance of tracking solved cases and diagnostic variants that can be used to benchmark bioinformatics tools.