USE OF STRUCTURAL ANALYSIS FOR CLINICAL INTERPRETATION OF GENE VARIATION

Abstract

Rare monogenic diseases affect around 300 millions worldwide at any given time. Although genetically and clinically heterogenous, rare diseases are frequently caused by pathogenic mutation in a single gene; in particular, missense and in-frame insertion and/or deletion (indel) variants. Differentiating the few truly pathogenic alterations from the background of functionally neutral variants is a challenge which has resulted in a translational bottleneck. Largely due to the absence of segregation and functional data, the majority of reported variants-of-interest remain clinically defined as variants of unknown significance (VUS). Since experimental validation of all variants is impractical, computational prediction tools have been designed to attempt to prioritise variants for downstream analysis. While accurate variant prioritisation can lead to fewer VUS and a higher rate of diagnosis, this has yet to reach clinical standard. The aim of this study was to i) design a protein-specific prediction model to improve missense variant prioritisation and ii) use integrative structural analysis to differentiate pathogenic from benign in-frame indel variants. Using a protein-specific approach to variant analysis, structural and clinical data were combined using machine learning to differentiate pathogenic from benign missense variants in a subset of genes. The resulting prediction model, ProSper (protein-specific variant interpreter), outperformed a number of widely-used or recently developed missense prediction tools for the majority of genes studied. For a group of in-frame indel variants that were classified as VUS in a lab, structural interpretation was used to show variant clustering and accurately differentiate pathogenic variants in a family of genes. Variant prioritisation and interpretation using these approaches in clinically relevant situations can improve rare disease diagnosis and patient management. In addition, structural analysis at the atomic-level can be used to assess a variant’s structural impact and provide data on the molecular mechanism of disease. This research adds to the body of evidence showing improved accuracy in variant prediction and prioritisation in groups of genes using a protein-specific approach compared to general predictors. Incorporating integrative structural analysis into the diagnostic framework can accelerate variant interpretation and minimise the rate of VUS leading to correct and timely diagnosis in more patients.

Date of Award	31 Dec 2021
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Graeme Black (Supervisor) & Simon Lovell (Supervisor)