medRxivGenetic & Genomic MedicinePreprint
Systematic identification of disease-causing promoter and untranslated region variants in 8,040 undiagnosed individuals with rare disease
Alexandra C Martin-GearyiD, Alexander J. M. BlakesiD, Ruebena DawesiD, Scott D. FindlayiD, Jenny LordiD, Susan WalkeriD, Jonathan Talbot-Martin, Nechama Wieder, Elston N. D’SouzaiD, Maria Fernandes, Sarah Hilton, Nayana LahiriiD, Christopher Campbell, Sarah Jenkinson, Christian G. DeGoede, Emily R Anderson, Christopher B. BurgeiD, Stephan SandersiD, Jamie M. EllingfordiD, Diana BaralleiD, Siddharth BankaiD, Nicola WhiffiniD
Abstract
BackgroundBoth promoters and untranslated regions (UTRs) have critical regulatory roles, yet variants in these regions are largely excluded from clinical genetic testing due to difficulty in interpreting pathogenicity. The extent to which these regions may harbour diagnoses for individuals with rare disease is currently unknown. MethodsWe present a framework for the identification and annotation of potentially deleterious proximal promoter and UTR variants in known dominant disease genes. We use this framework to annotate de novo variants (DNVs) in 8,040 undiagnosed individuals in the Genomics England 100,000 genomes project, which were subject to strict region-based filtering, clinical review, and validation studies where possible. In addition, we performed region and variant annotation-based burden testing in 7,862 unrelated probands against matched unaffected controls. ResultsWe prioritised eleven DNVs and identified an additional variant overlapping one of the eleven. Ten of these twelve variants (82%) are in genes that are a strong match to the individuals phenotype and six had not previously been identified. Through burden testing, we did not observe a significant enrichment of potentially deleterious promoter and/or UTR variants in individuals with rare disease collectively across any of our region or variant annotations. ConclusionsOverall, we demonstrate the value of screening promoters and UTRs to uncover additional diagnoses for previously undiagnosed individuals with rare disease and provide a framework for doing so without dramatically increasing interpretation burden.
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 5
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