Zhang, David; (2022) Improving the genetic diagnosis of Mendelian disorders using RNA-sequencing. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Providing patients with Mendelian disorders a genetic diagnosis improves the management of symptoms, informs genetic counselling and provides opportunities for therapeutic intervention. The advent of next generation sequencing technologies have greatly improved our ability to identify gene-disease associations. Despite these advances, most patients still leave the clinic without a genetic diagnosis. Although whole genome sequencing can capture genome-wide genetic variation, accurate interpretation of these variants remains a major challenge. In this thesis, I develop and apply methods that use transcriptomics to improve variant interpretation and consequently, diagnostic yield. Using publicly available RNA-seq data across 43 different human tissues, I improved the annotation of the majority of known, disease-causing genes. The detected novel exons were more depleted for genetic variation in humans than expected by chance, suggestive of their functional importance. In addition, a subset were shown to be potentially protein-coding. The novel annotation is publicly released through the resource, vizER, which enables the querying and visualisation of genes for evidence of their reannotation. I developed the R/Bioconductor package, dasper, which integrates junction and coverage data from RNA-sequencing to improve the detection of aberrant splicing events. Benchmarking analysis demonstrated that dasper detects pathogenic splicing events more accurately than existing approaches, as well as harnesses both publicly available and in-house RNA-sequencing data effectively as controls. dasper is designed for diagnostics, providing a rank-based report of how aberrant each splicing event looks, as well as including visualization functions to facilitate interpretation. RNA-sequencing was applied to fibroblasts derived from a cohort of patients with suspected mitochondrial disorders, who remained unsolved after whole exome sequencing. Using this approach, a genetic diagnosis was confirmed for 1 patient and candidate genes were discovered for a remaining third. Beyond diagnosis, the potential of RNA-sequencing to improve our understanding of disease pathogenesis was explored in three ways; deriving the mechanism of splicing disruptions, detection of perturbed pathways downstream of the pathogenic variant and elucidating genetic modifiers that influence phenotypic variability.

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