Clinical and data-driven optimization of Genomiser for rare disease patients: experience from the Hong Kong Genome Project.

Abstract

Genomiser is a phenotype-driven tool that prioritizes coding and non-coding variants by relevance in rare disease diagnosis; yet comprehensive evaluation of its performance on real-life whole genome sequencing data is lacking. The Hong Kong Genome Project had initially incorporated Exomiser in the diagnostic pipeline. This study evaluated the feasibility of upgrading from Exomiser to Genomiser with three modifications: extension of the interval filter to include ±2000 bp from transcript boundaries, adjusting minor allele frequency (MAF) filter to 3%, and the inclusion of SpliceAI. A total of 985 patients with disclosed whole genome sequencing test results were included in this study, of which 207 positive cases (14 attributed to non-coding variants) were used for Genomiser parameter optimization by means of sensitivity evaluation. Under the default parameter setting, Genomiser achieved lower sensitivity compared to Exomiser (70.15% vs. 72.14%, top-3 candidates; 74.63% vs. 80.60%, top-5 candidates). Further investigation noted that this was attributed to non-coding variant noise influenced by Regulatory Mendelian Mutation (ReMM) scoring metrics. This issue was mitigated when a previously optimized ReMM score was applied as a filtering cut-off (ReMM = 0.963), improving Genomiser's sensitivity (92.54% vs. 89.55%, top-15 candidates). We further evaluated the optimized parameter in a cohort of 778 negative cases and detected 20 non-coding variants (2.6% added yield), with 5 validated to be disease-causing. Our proposed approach adheres to American College of Medical Genetics and Genomics/Association for Molecular Pathology and ClinGen variant interpretation guidelines to ensure interpretable results and integrates non-coding variant analysis into clinical pipelines.