PathVar: A Customisable NGS Variant Calling Algorithm Implicates Novel Candidate Genes and Pathways in Hemiplegic Migraine

• Published in: Clinical genetics Vol. 107; no. 2; pp. 157 - 168
• Main Authors: Alfayyadh, Mohammed M., Maksemous, Neven, Sutherland, Heidi G., Lea, Rodney A., Griffiths, Lyn R.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.02.2025
• Subjects: Algorithms; annotation; Bioinformatics; Computational Biology - methods; Exome Sequencing; Genetic Association Studies - methods; Genetic Predisposition to Disease; genetic variants; Genetic Variation; Genomic analysis; Genomics; Headache; hemiplegic migraine; High-Throughput Nucleotide Sequencing - methods; Humans; Migraine; Migraine Disorders - genetics; Migraine with Aura - genetics; Original; Pathogenicity; PathVar; Software; Whole genome sequencing; annotation; genetic variants; PathVar; hemiplegic migraine; bioinformatics
• ISSN: 0009-9163; 1399-0004; 1399-0004
• DOI: 10.1111/cge.14625

ABSTRACT The exponential growth of next‐generation sequencing (NGS) data requires innovative bioinformatics approaches to unravel the genetic underpinnings of diseases. Hemiplegic migraine (HM), a debilitating neurological disorder with a genetic basis, is one such condition that warrants further investigation. Notably, the genetic heterogeneity of HM is underscored by the fact that approximately two‐thirds of patients lack pathogenic variants in the known causal ion channel genes. In this context, we have developed PathVar, a novel bioinformatics algorithm that harnesses publicly available tools and software for pathogenic variant discovery in NGS data. PathVar integrates a suite of tools, including HaplotypeCaller from the Genome Analysis Toolkit (GATK) for variant calling, Variant Effect Predictor (VEP) and ANNOVAR for variant annotation, and TAPES for assigning the American College of Medical Genetics and Genomics (ACMG) pathogenicity labels. Applying PathVar to whole exome sequencing data from 184 HM patients, we detected 648 variants that are probably pathogenic in multiple patients. Moreover, we have identified several candidate genes for HM, many of which cluster around the Rho GTPases pathway. Future research can leverage PathVar to generate high quality, candidate pathogenic variants, which may enhance our understanding of HM and other complex diseases. PathVar, a customisable next‐generation sequencing (NGS) variant calling algorithm, has been employed to investigate genes potentially implicated in hemiplegic migraine (HM). This approach enabled the identification of several candidate genes associated with HM, including SLC38A10, AMPD1, APC2, PADI3, FRAS1, SETX, PRAG1 and GSE1.