Gene-level analysis of missense sensitivity in neurodevelopmental disorders

Thesis

Neurodevelopmental disorders (NDDs) pose a growing medical challenge—collectively, they are a significant cause of paediatric hospitalisations and result in debilitating health issues. With the advent of large-scale sequencing technologies, our understanding of the genetic basis of NDDs has greatly advanced. Rare protein-coding variants, namely protein-truncating variants (PTVs) and missense variants, are associated with thousands of rare, neurodevelopmental conditions. Characterizing the functional impact of such variants—whether they exert pathogenicity via loss-of-function (LOF) or gain-of-function (GOF) effects—is crucial to understanding disease mechanism and therapeutic development. While predicting the functional impact of PTVs is relatively straightforward, predicting missense variant impact is extremely challenging and remains a major hurdle in translational efforts.

In this thesis, we use bioinformatics tools to evaluate missense sensitivity and functional impact in NDDs. At the gene-level, missense sensitivity refers to a gene’s vulnerability to disruption by missense variation. Specifically, we aim to quantitatively define and apply missense sensitivity to understand variant impact. First, we develop a quantitative metric of missense sensitivity through computational modeling. Next, we compare the missense sensitivity metric to observed genomic and phenotypic data. Lastly, we apply missense sensitivity to characterize the functional and clinical impact of genetic variants. Using such data, we simultaneously compile a database of NDDs, disease-causing variants and their functional effects. Such insights, we hope, will fuel further efforts in identifying clinically relevant variants and allow for important translational efforts in the treatment of NDDs.

Authors: Kohani, S

• DOI: 10.5287/ora-r1gby1oa7
• Type of award: MSc by Research
• Level of award: Masters
• Awarding institution: University of Oxford
• Language: English