The hidden burden of structural variants in neurodegenerative and neuromuscular disorders

Neurodegenerative and neuromuscular disorders are genetically heterogeneous, and many patients remain without a genetic diagnosis after multiple rounds of clinical gene panels or exome sequencing. These approaches often fail to detect structural variants (SVs), contributing to missed diagnoses. We investigated 286 individuals with neurodegenerative or neuromuscular diseases who previously received an uninformative report from diagnostic testing.

Short-read genome sequencing (srGS) was used for single nucleotide variant/indel, copy number variant, and SV calling with variant prioritisation in seqr. Findings were corroborated by long-read sequencing, orthogonal validation (PCR/MLPA/Sanger), transcriptomics, and proteomics. We also assessed the utility of Talos (an automated variant prioritisation tool) to find clinically relevant SVs.

Overall, 65/286 cases (22.7%) were solved; SVs accounted for 11/65 solved cases (16.9%); four of these would not be detectable with targeted panel or exome sequencing. We describe 10 illustrative diagnoses spanning diverse SV classes: an intronic interspersed duplication disrupting SPAST (SPG4); SVs supporting phenotype expansions associated with SPG11 (neuronal ceroid lipofuscinosis), heterozygous large in-frame deletions in TTN (distal arthrogryposis), and SPTAN1 (autosomal-dominant hereditary spastic paraplegia); an intergenic balanced translocation downstream of FOXG1; and a pathogenic SVA insertion in TAF1 detected as breakends in srGS. Talos prioritised seven of these 10 SV diagnoses.

Here, we show SVs contribute substantially to diagnoses in neurogenetic disease cohorts and can evade panel/exome pipelines due to intronic breakpoints, balanced rearrangements, microhomology, and partial-exon events. srGS combined with automated reanalysis can shorten diagnostic timelines and improve diagnostic equity through a single, genome-wide test, although the clinical interpretation of SVs remains a significant challenge. The authors acknowledge the individuals and families enrolled in our research program and recognise the rich contributions that families make towards finding genetic diagnoses and driving the research. The authors acknowledge the provision of computing and data resources provided by the Australian BioCommons Leadership Share (ABLeS) program.

This program is co-funded by Bioplatforms Australia (enabled by NCRIS), the National Computational Infrastructure and Pawsey Supercomputing Research Centre. This work was supported by resources provided by the Pawsey Supercomputing Research Centre’s Setonix Supercomputer (https://doi.org/10.48569/18sb-8s43), with funding from the Australian Government and the Government of Western Australia. Analysis was supported by the Centre for Population Genomics (Garvan Institute of Medical Research and Murdoch Children’s Research Institute).

This research was funded by grants from the Australian Medical Research Future Fund (APP2023357, APP2007681, APP2008820, APP2032931, APP2016030), Australian National Health and Medical Research Council—NHMRC (APP2009982), Hearts and Minds 1 (HM1), and the Margaret and Terry Orr Foundation. GR and DS are supported by Australian NHMRC Investigator Grants (APP2007769, APP2009732). This work also forms part of Australian BioCommons’ GUARDIANS program, which is enabled by NCRIS investment via Bioplatforms Australia.