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Abstract
Gene-set analysis has been proposed as a powerful tool to deal with the highly polygenic architecture of complex traits, as well as with the small effect sizes typically found in GWAS studies for complex traits. We developed a tool, Joint Association of Genetic variants (JAG), which can be applied to Genome Wide Association (GWA) data and tests for the joint effect of all single nucleotide polymorphisms (SNPs) located in a user-specified set of genes or biological pathway. JAG assigns SNPs to genes and incorporates self-contained and/or competitive tests for gene-set analysis. JAG uses permutation to evaluate gene-set significance, which implicitly controls for linkage disequilibrium, sample size, gene size, the number of SNPs per gene and the number of genes in the gene-set. We conducted a power analysis using the Wellcome Trust Case Control Consortium (WTCCC) Crohn’s disease data set and show that JAG correctly identifies validated gene-sets for Crohn’s disease and has more power than currently available tools for gene-set analysis. JAG is a powerful, novel tool for gene-set analysis, and can be freely downloaded from the CTG Lab website.
Highlights
The advent of genome-wide association (GWA) analysis has resulted in the identification of a large number of human disease genes and disease-related genetic variants for several traits such as type-2 diabetes, macular degeneration and Crohn’s disease [1,2,3,4]
We removed the single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) 5% and 3321 SNPs that were assigned to chromosome 0 by Wellcome Trust Case Control Consortium (WTCCC)
We showed that using a standard dataset Joint Association of Genetic variants (JAG) correctly identifies validated gene-sets and has more power than currently available tools for gene-set analysis
Summary
The advent of genome-wide association (GWA) analysis has resulted in the identification of a large number of human disease genes and disease-related genetic variants for several traits such as type-2 diabetes, macular degeneration and Crohn’s disease [1,2,3,4]. Several software tools for gene-set analysis have been proposed, such as GATES [16], ALIGATOR [12], the set-based test in PLINK [17], GENGEN [18] and GRASS [19], and see the review by Wang et al [20] These tools differ on several aspects such as the type of input data required (raw data versus summary statistics), using a self-contained or competitive test and the actual alternative hypothesis being tested [20]. Available tools for gene-set analysis do not necessarily include both self-contained and/or competitive tests, do not always accommodate the use of custom gene-sets and some of them do not optimally make use of the multivariate evidence of association of all genetic variants in a gene-set. We show that JAG correctly identifies previously validated gene-sets for Crohn’s disease and is more powerful than any of the other tools tested
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