An optimized variant prioritization process for rare disease diagnostics: recommendations for Exomiser and Genomiser.
Exome sequencing (ES) and genome sequencing (GS) are increasingly used as standard genetic tests to identify 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 noncoding 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.
We analyzed 386 diagnosed probands from the UDN, including cases with coding and noncoding 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.
Parameter optimization significantly improved Exomiser's performance over default parameters. For GS data, the percentage of coding diagnostic variants ranked within the top 10 candidates increased from 49.7% to 85.5%, and for ES, from 67.3% to 88.2%. For noncoding variants prioritized with Genomiser, the top 10 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.
This study provides an evidence-based framework for variant prioritization in ES and GS 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. Exomiser and Genomiser are available at https://github.com/exomiser/Exomiser/ .
We analyzed 386 diagnosed probands from the UDN, including cases with coding and noncoding 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.
Parameter optimization significantly improved Exomiser's performance over default parameters. For GS data, the percentage of coding diagnostic variants ranked within the top 10 candidates increased from 49.7% to 85.5%, and for ES, from 67.3% to 88.2%. For noncoding variants prioritized with Genomiser, the top 10 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.
This study provides an evidence-based framework for variant prioritization in ES and GS 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. Exomiser and Genomiser are available at https://github.com/exomiser/Exomiser/ .
Authors
Cooperstein Cooperstein, Marwaha Marwaha, Ward Ward, Kobren Kobren, Carter Carter, , Wheeler Wheeler, Marth Marth
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