Poster ID
P-41
Poster Title
Re-analysis of National Whole Genome Sequencing (WGS) Enables New Mitochondrial Disease Diagnoses
Authors
Maureen Mulia: Department of Genomic Medicine, University of Cambridge, Cambridge, UK
Ji-Ming Yang: Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
Katherine Schon: Department of Genomic Medicine, University of Cambridge, Cambridge, UK
Ji-Ming Yang: Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
Katherine Schon: Department of Genomic Medicine, University of Cambridge, Cambridge, UK
Abstract
Background: National genomic initiatives have generated large-scale WGS datasets that remain an underused resource for rare disease diagnosis. As analytical methods evolve, re-analysis can identify clinically relevant variants missed by earlier pipelines without additional sequencing. Mitochondrial DNA (mtDNA) variants remain challenging to detect because of heteroplasmy and the limited sensitivity of conventional WGS pipeline. This study evaluated whether an mtDNA-specific analysis pipeline could improve diagnostic yield across multiple rare disease cohort in the UK NHS Genomic Medicine Service (GMS) dataset.
Methods: WGS data from the NHS GMS rare disease cohort were analysed within the Genomics England (GEL) Trusted Research Environment using mtDNA-specific pipeline, MitoHPC. Variants passing quality control underwent clinical annotation, heteroplasmy assessment, phenotype correlation and expert review to identify pathogenic and likely pathogenic mtDNA variants overlooked by the original analytical workflow.
Results: A total of 1,471 participants were re-analysed, including 676 with likely inherited metabolic disorders and 795 with other neuromuscular conditions. Re-analysis identified five previously unresolved cases with clinically significant mtDNA findings, comprising two participants who received new molecular diagnoses and three participants with variants warranting further clinical evaluation because they may represent potential diagnoses. Additionally, the pipeline identified three previously established mtDNA diagnoses, demonstrating concordance with existing clinical diagnoses and supporting the analytical sensitivity of the workflow.
Impact: Iterative re-analysis of existing WGS data improved diagnostic yield without additional sequencing, demonstrating the continued clinical value of national genomic datasets. Implementing specialised mtDNA analysis within the secure GEL Trusted Research Environment enables reproducible analysis while maximising the utility of genomic resources. These findings illustrate how interoperable genomic infrastructure supports scalable data discovery and aligns with GA4GH's vision of reusable, clinically actionable genomic data.
Methods: WGS data from the NHS GMS rare disease cohort were analysed within the Genomics England (GEL) Trusted Research Environment using mtDNA-specific pipeline, MitoHPC. Variants passing quality control underwent clinical annotation, heteroplasmy assessment, phenotype correlation and expert review to identify pathogenic and likely pathogenic mtDNA variants overlooked by the original analytical workflow.
Results: A total of 1,471 participants were re-analysed, including 676 with likely inherited metabolic disorders and 795 with other neuromuscular conditions. Re-analysis identified five previously unresolved cases with clinically significant mtDNA findings, comprising two participants who received new molecular diagnoses and three participants with variants warranting further clinical evaluation because they may represent potential diagnoses. Additionally, the pipeline identified three previously established mtDNA diagnoses, demonstrating concordance with existing clinical diagnoses and supporting the analytical sensitivity of the workflow.
Impact: Iterative re-analysis of existing WGS data improved diagnostic yield without additional sequencing, demonstrating the continued clinical value of national genomic datasets. Implementing specialised mtDNA analysis within the secure GEL Trusted Research Environment enables reproducible analysis while maximising the utility of genomic resources. These findings illustrate how interoperable genomic infrastructure supports scalable data discovery and aligns with GA4GH's vision of reusable, clinically actionable genomic data.