Clinical and genetic heterogeneity as well as influence of environmental factors have hampered identification of the genetic factors which are involved in episodic diseases such as migraine, episodic ataxia and epilepsy. The study of rare, but clearly genetically determined subtypes, may help to unravel the pathogenesis of the more common forms. Recently, different types of mutation in the brain-specific P/Q type calcium channel α 1A subunit gene (CACNA1A) on chromosome 19p13 were shown to be involved in three human disorders: familial hemiplegic migraine (FHM), episodic ataxia type 2 (EA2), and chronic spinocerebellar ataxia type 6 (SCA6). In addition, evidence is accumulating that the same gene is also involved in the common forms of migraine with and without aura. In the tottering and leaner mouse, which are characterised by epilepsy and ataxia, similar mutations were identified in the mouse homologue of the calcium channel α 1A subunit gene. These findings add to the growing list of episodic (and now also chronic) neurological disorders, which are caused by inherited abnormalities of voltage-dependent ion channels. The findings in migraine illustrate that rare, but monogenic variants of a disorder, may be successfully used to identify candidate genes for the more common, but genetically more complex, forms.

We studied aura symptoms in 83 patients from 6 unrelated families suffering from familial hemiplegic migraine. Fifty-five of the patients reported symptoms that allowed us to categorize them as basilar migraine (BM) patients, in accordance with the International Headache Society (IHS) criteria. In a control group of 33 patients suffering from migraine with aura and 33 patients suffering from migraine without aura, 9 patients complained of vertigo, and only one patient of diplopia during one of her attacks. None of these control patients fulfilled the IHS criteria for BM. We suggest that familial hemiplegic migraine and BM may share certain pathophysiologic mechanisms, which may consist of a (genetically determined) disturbance of basilar artery blood flow.

Other cerebral ion channel genes remain candidate genes for AHC.

Background DNA methylation (DNAm) based predictors hold great promise to serve as clinical tools for health interventions and disease management. While these algorithms often have high prediction accuracy and are associated with many disease-related phenotypes, the reliability of their performance remains to be determined. We therefore conducted a systematic evaluation across 101 different data processing strategies that preprocess and normalize DNAm data and assessed how each analytical strategy affects the reliability and prediction accuracy of 41 DNAm-based predictors. Results Our analyses were conducted in a large EPIC DNAm sample of the Jackson Heart Study (N=2,053) that included 146 pairs of technical replicate samples. By estimating the average absolute agreement between replicate pairs, we show that 32 out of 41 predictors (78%) demonstrate excellent test-retest reliability when appropriate data processing and normalization steps are implemented. Across all pairs of predictors, we find a moderate correlation in performance across analytical strategies (mean rho=0.40, SD=0.27), highlighting significant heterogeneity in performance across algorithms within a choice of an analytical pipeline. (Un)successful removal of technical variation furthermore significantly impacts downstream phenotypic association analysis, such as all-cause mortality risk associations. Conclusions We show that DNAm-based algorithms are sensitive to technical variation. The right choice of data processing and normalization pipeline is important to achieve reproducible estimates and improve prediction accuracy in downstream phenotypic association analyses. For each of the 41 DNAm predictors, we report its test-retest reliability and provide the best performing analytical strategy as a guideline for the research community. As DNAm-based predictors become more and more widely used, both for research purposes as well as for clinic applications, our work helps improve their performance and standardize their implementation.