Moreover, ABACUS is particularly suited for pathway analysis, given its ability of simultaneously considering common and rare variants and different direction of genotype effects.Īn R package is available here A rule-based model of insulin signalling pathway We consider biological pathways as the preferred definition of SNP-sets, since studying the cumulative variation of SNPs mapping on genes in the same pathway (interacting genes) might fill in part the missing heritability and guide mechanistic studies helping uncovering the underlying disease pathways. ABACUS is robust to the concurrent presence of SNPs with protective and detrimental effects and of common and rare variants moreover it is powerful even when few SNPs in the SNP-set are associated with the phenotype.ĪBACUS first requires the definition of the SNP-sets, such as pathways, genes or genomic regions encoding a priori information on the potential point effects of the SNPs in each subset. Applied to a whole SNP dataset, ABACUS gives as output a list of SNP-sets associated with the disease and, for each SNP-set, the list of significant SNPs. In general, a combination of rare and common variants influencing the genotype with a protective or detrimental effect is likely to contribute to the disease, an ideal method should be robust to different MAF, to different direction of genotype effects and to the number of associated SNPs within the SNP-set being analyzed.ĪBACUS, Algorithm based on a BivAriate CUmulative Statistic, is designed to identify SNPs significantly associated with a disease within predefined sets of SNPs such as pathways or genomic regions. In the last years, both sequencing and microarray have been widely used to search for relations between genetic variations and predisposition to complex pathologies such as diabetes or neurological disorders. ABACUS: an entropy based cumulative bivariate statistic robust to rare variants and different direction of genotype effect.
#Tutorials on netsimk software
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