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Purpose: To develop a comprehensive analysis framework to identify mutations impacting pre-messenger RNA splicing in the context of rare disease. Methods: We assessed 'variants of uncertain significance' through six in-silico prioritization strategies. First, through comparison to functional analyses, we determined the precise effect on splicing of variants identified through clinical multi-disciplinary meetings. Next, we calculated the sensitivity of in-silico prioritization strategies to distinguish known splicing mutations from common variation (>2% in allele frequency in gnomAD) within relevant disease genes. These approaches defined an accurate in-silico strategy for variant prioritization, which we retrospectively applied to a large cohort of 2783 individuals who had previously received genomic testing for rare genomic disorders. We assessed the clinical impact of such prioritization strategies alongside routine diagnostic testing strategies. Results: We identified 21 variants that potentially impacted splicing, and used cell based splicing assays to identify those variants which disrupted normal splicing. These findings underpinned new molecular diagnoses for 14 individuals. This process established that the use of pre-defined thresholds from a machine learning splice prediction algorithm, SpliceAI, was the most efficient method for variant prioritization, with a positive predictive value of 86%. We analysed 1,346,744 variants identified through diagnostic testing for 2783 individuals, and observed that splicing variant prioritization strategies would improve clarity in clinical analysis for 15% of the individuals surveyed. Prioritized variants could provide new molecular diagnoses or provide additional support for molecular diagnosis for up to 81 individuals within our cohort. Conclusion: We present an in-silico and functional analysis framework for the assessment of variants impacting pre-messenger RNA splicing which is applicable across monogenic disorders. Incorporation of these strategies improves clarity in diagnostic reporting, increases diagnostic yield and, with the advent of targeted treatment strategies, can directly alter patient clinical management.

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