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Dysfunction in ion channels or processes involved in maintaining ionic homeostasis is thought to lower the threshold for cortical spreading depression (CSD), and plays a role in susceptibility to associated neurological disorders, including pathogenesis of a migraine. Rare pathogenic variants in specific ion channels have been implicated in monogenic migraine subtypes. In this study, we further examined the channelopathic nature of a migraine through the analysis of common genetic variants in three selected ion channel or transporter genes: SLC4A4, SLC1A3, and CHRNA4. Using the Agena MassARRAY platform, 28 single-nucleotide polymorphisms (SNPs) across the three candidate genes were genotyped in a case–control cohort comprised of 182 migraine cases and 179 matched controls. Initial results identified significant associations between migraine and rs3776578 (p = 0.04) and rs16903247 (p = 0.05) genotypes within the SLC1A3 gene, which encodes the EAAT1 glutamate transporter. These SNPs were subsequently genotyped in an independent cohort of 258 migraine cases and 290 controls using a high-resolution melt assay, and association testing supported the replication of initial findings—rs3776578 (p = 0.0041) and rs16903247 (p = 0.0127). The polymorphisms are in linkage disequilibrium and localise within a putative intronic enhancer region of SLC1A3. The minor alleles of both SNPs show a protective effect on migraine risk, which may be conferred via influencing the expression of SLC1A3. [ABSTRACT FROM AUTHOR]

Neuropeptides are mostly expressed in regions of the brain responsible for learning and memory and are centrally involved in cognitive pathways. The majority of neuropeptide research has been performed in animal models; with acknowledged differences between species, more research into the role of neuropeptides in humans is necessary to understand their contribution to higher cognitive function. In this study, we investigated the influence of genetic polymorphisms in neuropeptide genes on verbal learning and memory. Variants in genes encoding neuropeptides and neuropeptide receptors were tested for association with learning and memory measures using the Hopkins Verbal Learning Test—Revised (HVLT-R) in a healthy cohort of individuals (n = 597). The HVLT-R is a widely used task for verbal learning and memory assessment and provides five sub-scores: recall, delay, learning, retention, and discrimination. To determine the effect of candidate variants on learning and memory performance, genetic association analyses were performed for each HVLT-R sub-score with over 1300 genetic variants from 124 neuropeptide and neuropeptide receptor genes, genotyped on Illumina OmniExpress BeadChip arrays. This targeted analysis revealed numerous suggestive associations between HVLT-R test scores and neuropeptide and neuropeptide receptor gene variants; candidates include the SCG5, IGFR1, GALR1, OXTR, CCK, and VIPR1 genes. Further characterization of these genes and their variants will improve our understanding of the genetic contribution to learning and memory and provide insight into the importance of the neuropeptide network in humans. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]