Refining structural variants using high-throughput sequencing in New Zealanders with neurodevelopmental disorders

Abstract

Neurodevelopmental disorders (NDDs) are a diverse group of conditions accounting for approximately 73% of childhood disability in New Zealand. Genetic variants, including structural variants (SVs), explain at least 60% of NDD cases. However, the role of SVs in NDDs is likely under-appreciated due to challenges in detecting them. Until recently, clinical SV detection techniques (such as cytogenetic banding and array comparative genomic hybridisation) have been limited by resolution (e.g.>2Mb) or the classes of SV detected (e.g. copy number variants) and thus are unable to accurately identify the full spectrum of SVs. However, whole genome sequencing (WGS) has the potential to detect all classes of SVs to base pair resolution. This thesis aimed to identify causative SVs using WGS in a cohort of seven New Zealanders with undiagnosed NDDs who carry a SV of unknown significance. To accurately identify SVs from WGS reads, the suitability of saliva as a DNA source was evaluated. This evaluation revealed that sequence reads from non-human DNA (such as food and bacteria) could align to the human reference genome causing false-positive variant calls. Importantly, incorporating a bacterial decoy sequence or increasing the minimum seed length to 25 prevented the alignment of 43.66% and 99.39% of non-human reads to the human reference genome on average, respectively. A WGS SV identification and annotation pipeline was subsequently developed and applied to detect all types and sizes of SV. In all cases, the workflow refined SV breakpoints implicating novel gene disruptions and destabilisations of genomic spatial interactions cryptic to standard clinical techniques. These results could potentially explain all or part of the clinical phenotype for each case. PLXNA4 was implicated as a novel disease gene, resulting in the design of an assay to determine the functional impact of PLXNA4 disruption using monocytes as a proxy for the brain. This thesis demonstrates how SV detection from WGS can identify novel pathogenic mechanisms cryptic to standard clinical techniques in patients with undiagnosed NDDs. These findings further support WGS as a powerful diagnostic tool for rare disorders, where an early and accurate diagnosis could significantly improve health outcomes for patients and their families.