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A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

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

Isabelle B Cooperstein¹, Shruti Marwaha^2,3, Alistair Ward^1,4

¹Department of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA.

Genome Medicine

|October 22, 2025

Summary

Optimized parameters significantly improve variant prioritization for rare disease diagnosis using Exomiser and Genomiser tools. These evidence-based recommendations enhance diagnostic yield from exome and genome sequencing data.

Keywords:

Diagnosis Exome sequencing Exomiser Genome sequencing Genomiser HPO Parameter optimization Phenotype Rare disease Variant prioritization

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Area of Science:

Genomics
Bioinformatics
Rare disease diagnostics

Background:

Exome sequencing (ES) and genome sequencing (GS) are crucial for identifying diagnostic variants in rare diseases.
Variant prioritization remains a challenge, hindering efficient interpretation of genetic data.
The Exomiser/Genomiser suite is widely used but lacks data-driven optimization guidelines.

Purpose of the Study:

To provide optimized parameters and practical recommendations for Exomiser and Genomiser tools.
To improve diagnostic variant prioritization in rare disease cases.
To propose alternative workflows for complex cases where diagnostic variants may be missed.

Main Methods:

Analysis of 386 diagnosed probands from the Undiagnosed Diseases Network (UDN).
Systematic evaluation of parameters affecting tool performance: gene:phenotype data, pathogenicity predictors, phenotype terms, and family variant data.
Assessment of coding and noncoding variants.

Main Results:

Parameter optimization substantially improved Exomiser performance for GS (49.7% to 85.5%) and ES (67.3% to 88.2%) coding variants.
Genomiser performance for noncoding variants improved from 15.0% to 40.0% in top 10 rankings.
Refinement strategies, including p-value thresholds, were explored.

Conclusions:

An evidence-based framework for variant prioritization using Exomiser and Genomiser in ES/GS data.
Recommendations implemented in the Mosaic platform to enhance diagnostic yield for undiagnosed participants.
Highlights the need for tracking solved cases to benchmark bioinformatics tools.