Genes · 2025
Gene-Based Burden Testing of Rare Variants in Hemiplegic Migraine: A Computational Approach to Uncover the Genetic Architecture of a Rare Brain Disorder.
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Alfayyadh Mohammed M, Maksemous Neven, Sutherland Heidi G, Lea Rodney A, Griffiths Lyn R
Аннотация
BACKGROUND: HM is a rare, severe form of migraine with aura, characterised by motor weakness and strongly influenced by genetic factors affecting the brain. While pathogenic variants in CACNA1A, ATP1A2, and SCN1A genes have been implicated in familial HM, approximately 75% of cases lack known pathogenic variants in these genes, suggesting a more complex genetic basis.
METHODS: To advance our understanding of HM, we applied a variant prioritisation approach using whole-exome sequencing (WES) data from patients referred for HM diagnosis (n = 184) and utilised PathVar, a bioinformatics pipeline designed to identify pathogenic variants. Our analysis incorporated two strategies for association testing: (1) PathVar-identified single nucleotide variants (SNVs) and (2) PathVar SNVs combined with missense and rare variants. Principal component analysis (PCA) was performed to adjust for ancestral and other unknown differences between cases and controls.
RESULTS: Our results reveal a sequential reduction in the number of genes significantly associated with HM, from 20 in the first strategy to 11 in the second, which highlights the unique contribution of PathVar SNVs to the genetic architecture of HM. PathVar SNVs were more distinctive in the case cohort, suggesting a closer link to the functional changes underlying HM compared to controls. Notably, novel genes, such as SLC38A10, GCOM1, and NXPH2, which were previously not implicated in HM, are now associated with the disorder, advancing our understanding of its genetic basis.
CONCLUSIONS: By prioritising PathVar SNVs, we identified a broader set of genes potentially contributing to HM. Given that HM is a rare condition, our findings, utilising a sample size of 184, represent a unique contribution to the field. This iterative analysis demonstrates that integrating diverse variant schemes provides a more comprehensive view of the genetic factors driving HM.
METHODS: To advance our understanding of HM, we applied a variant prioritisation approach using whole-exome sequencing (WES) data from patients referred for HM diagnosis (n = 184) and utilised PathVar, a bioinformatics pipeline designed to identify pathogenic variants. Our analysis incorporated two strategies for association testing: (1) PathVar-identified single nucleotide variants (SNVs) and (2) PathVar SNVs combined with missense and rare variants. Principal component analysis (PCA) was performed to adjust for ancestral and other unknown differences between cases and controls.
RESULTS: Our results reveal a sequential reduction in the number of genes significantly associated with HM, from 20 in the first strategy to 11 in the second, which highlights the unique contribution of PathVar SNVs to the genetic architecture of HM. PathVar SNVs were more distinctive in the case cohort, suggesting a closer link to the functional changes underlying HM compared to controls. Notably, novel genes, such as SLC38A10, GCOM1, and NXPH2, which were previously not implicated in HM, are now associated with the disorder, advancing our understanding of its genetic basis.
CONCLUSIONS: By prioritising PathVar SNVs, we identified a broader set of genes potentially contributing to HM. Given that HM is a rare condition, our findings, utilising a sample size of 184, represent a unique contribution to the field. This iterative analysis demonstrates that integrating diverse variant schemes provides a more comprehensive view of the genetic factors driving HM.