This is a novel web-based resource to check for pathway (or GO terms) associations to diseases (or any other trait) in genome-wide association analysis (GWAS) with SNPs or CNVs. Searching directly for the association of pathways (or other types of functional modules) to phenotypic traits or diseases, instead of just analysing SNPs independently, constitutes a novel perspective in polymorphisms' analysis inspired in systems biology. As in other fields (e.g. microarray analysis) gene set-based analysis has demonstrated its effectiveness in providing a testing framework much more powerful than the conventional one-gene-at-a-time testing scheme. There is a great interest among the scientific community in strategies that increase the statistical power to find associations in GWAS. To our knowledge, this is the only web server offering this possibility of analysis.

Genome-wide association studies (GWAS) use dense maps of markers (SNPs and, in some cases, CNVs) along the genome to look for allele-frequency differences between cases (e.g. individuals with a certain disease or trait) and controls. A significant frequency difference is taken as an indication of the presence of functional DNA-sequence variants related to the disease or trait in question within the genomic region corresponding to the markers [1]. Despite the high resolution available today to interrogate the genomes (e.g., more that 2 million features in the Affymetrix 6.0 chip), the necessary adjustments for multiple testing correction has limited the success of its application in real studies. Thus, only consortia analysing large cohorts were successful in finding clear and reproducible results [2]. Under the philosophy of Gene Set Analysis (GSA), modules of functionally-related genes are considered the ultimate operational units in the cell. Accordingly, GSA methods aim to test the activity of such modules instead of testing the activity of individual genes [3]. The extrapolation of this concept to the study of complex traits by GWAS, where mutations in different genes can globally affect a pathway making it difficult to find detectable associations in individual genes, is straightforward. As proven recently, GSA approaches can detect pathways significantly associated to the trait studied by taking into account small but coordinate associations of their gene components, thus providing an enormous increase in the power testing over the conventional approaches [4]. The program inputs a lists of polymorphisms with the corresponding value of association in the format of the popular plink program [5] (although tab-delimited format, with SNP or CNV identifiers and the corresponding association values, is also accepted). The application then labels the SNPs or CNVs with the KEGG pathways or the GO term corresponding to the neighbour genes. Finally, it uses a GSA test [6] to check for modules showing significant association to the trait studied. The significant terms along with the corresponding p-values are listed in the output.