Genomics England Research Consortium,

bioRxiv2020

New pathogenic variants and insights into pathogenic mechanisms in GRK1-related Oguchi disease.

PurposeBiallelic mutations in G-Protein coupled receptor kinase 1 (GRK1) cause Oguchi disease, a rare subtype of congenital stationary night blindness (CSNB). The purpose of this study was to identify pathogenic GRK1 variants and use in-depth bioinformatic analyses to evaluate how their impact on protein structure could lead to pathogenicity. MethodsPatients genomic DNA was sequenced by whole genome, whole exome or focused exome sequencing. Pathogenic variants, published and novel, were compared to nondisease associated missense variants. The impact of GRK1 missense variants at the protein level were then predicted using a series of computational tools. ResultsWe identified eleven previously unpublished cases with biallelic pathogenic GRK1 variants, including seven novel variants, and reviewed all GRK1 pathogenic variants. Further structure-based scoring revealed a hotspot for missense variants in the kinase domain. Additionally, to aid future clinical interpretation, we identified the bioinformatics tools best able to differentiate pathogenic from non-pathogenic variants. ConclusionWe identified new GRK1 pathogenic variants in Oguchi disease patients and investigated how disease-causing variants may impede protein function, giving new insights into the mechanisms of pathogenicity. All pathogenic GRK1 variants described to date have been collated into a Leiden Open Variation Database (http://dna2.leeds.ac.uk/GRK1_LOVD/genes/GRK1).

genetics↗

bioRxiv2019

Functional and in-silico interrogation of rare genomic variants impacting RNA splicing for the diagnosis of genomic disorders

PurposeTo develop a comprehensive analysis framework to identify pre-messenger RNA splicing mutations in the context of rare disease.\n\nMethodsWe assessed variants of uncertain significance through six in-silico prioritization strategies. Firstly, 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.\n\nResultsWe 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.\n\nConclusionWe 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.\n\nKey HighlightsO_LIWe establish an in-silico and functional analysis framework for the incorporation of splice variant assessment into diagnostic testing that is applicable across monogenic disorders.\nC_LIO_LIAfter assessment of six distinct variant prioritization strategies, we concluded that SpliceAI was the best method to accurately identify genomic variation disrupting normal pre-mRNA splicing. We determined this through (i) functional assessment of novel variants of uncertain significance described in this study, and (ii) calculation of sensitivity and specificity for prioritization strategies to distinguish known splicing mutations from common variants in the general population.\nC_LIO_LIWe describe novel disease-causing variants with support from cell based functional assays which underpin autosomal recessive, autosomal dominant and X-linked Mendelian disorders. This includes variants which are deeply intronic, within the nearby splice region of canonical splice sites and variants which activate cryptic splice sites within the protein-coding regions of genes.\nC_LIO_LIWe integrated the best performing variant prioritization strategy alongside clinical diagnostic testing for 2783 individuals referred to a well-established targeted gene panel test available through the UK National Health Service. We show that integration of such strategies will increase accuracy and clarity of diagnostic reporting, including the identification of variants which could provide new diagnoses and new carrier findings for referred individuals.\nC_LIO_LIFunctional assessment is essential for accurate clinical assessment of variants disrupting pre-mRNA splicing. We show through cell based functional assessments that variants impacting splicing may have complex impacts on pre-mRNA splicing, which may cause multiple interpretable consequences according to ACMG guidelines.\nC_LI

genomics↗