GCORE-sib: An efficient gene-gene interaction tool for genome-wide association studies based on discordant sib pairs

Genetics of migraine: Delineation of contemporary understanding of the genetic underpinning of migraine

Migraine is a disabling episodic brain disorder with an increased familial relative risk, an increased concordance in monozygotic twins, and an estimated heritability of approximately 50%. Various genetic approaches have been applied to identify genetic factors conferring migraine risk. Initially, candidate gene associations studies (CGAS) have been performed that test DNA variants in genes prioritized based on presumed a priori knowledge of migraine pathophysiology. More recently, genome-wide association studies (GWAS) are applied that test genetic variants, single-nucleotide polymorphisms (SNPs), in a hypothesis-free manner. To date, GWAS have identified ~40 genetic loci associated with migraine. New GWAS data, which are expected to come out soon, will reveal over 100 loci. Also, large-scale GWAS, which have appeared for many traits over the last decade, have enabled studying the overlap in genetic architecture between migraine and its comorbid disorders. Importantly, other genetic factors that cannot be identified by a GWAS approach also confer risk for migraine. First steps have been taken to determine the contribution of these mechanisms by investigating mitochondrial DNA and epigenetic mechanisms. In addition to typical epigenetic mechanisms, that is, DNA methylation and histone modifications, also RNA-based mechanisms regulating gene silencing and activation have recently gotten attention. Regardless, until now, most relevant genetic discoveries related to migraine still come from investigating monogenetic syndromes with migraine as a prominent part of the phenotype. Experimental studies on these syndromes have expanded our knowledge on the mechanisms underlying migraine pathophysiology. It can be envisaged that when all (epi)genetic and phenotypic data on the common and rare forms of migraine will be integrated, this will help to unravel the biological mechanisms for migraine, which will likely guide decision-making in clinical practice in the future.

Exploring gene-gene interaction in family-based data with an unsupervised machine learning method: EPISFA

Gene-gene interaction (G × G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single-nucleotide polymorphisms. The current tools for exploring G × G were often developed for case-control designs with less considerations for their applications in families. Family-based studies are robust against bias led from population stratification in genetic studies and helpful in understanding G × G. We proposed a new algorithm epistasis sparse factor analysis (EPISFA) and epistasis sparse factor analysis for linkage disequilibrium (EPISFA-LD) based on unsupervised machine learning to screen G × G. Extensive simulations were performed to compare EPISFA/EPISFA-LD with a classical family-based algorithm FAM-MDR (family-based multifactor dimensionality reduction). The results showed that EPISFA/EPISFA-LD is a tool of both high power and computational efficiency that could be applied in family designs and is applicable within high-dimensionality datasets. Finally, we applied EPISFA/EPISFA-LD to a real dataset drawn from the Fangshan/family-based Ischemic Stroke Study in China. Five pairs of G × G were discovered by EPISFA/EPISFA-LD, including three pairs verified by other algorithms (FAM-MDR and logistic), and an additional two pairs uniquely identified by EPISFA/EPISFA-LD only. The results from EPISFA might offer new insights for understanding the genetic etiology of complex diseases. EPISFA/EPISFA-LD was implemented in R. All relevant source code as well as simulated data could be freely downloaded from https://github.com/doublexism/episfa.