Integration of GWAS and microarray data to understand the disease mechanism

Abstract

In order to understand the genetic basis of diseases, wetlab scientists conduct different kinds of experiments. Two such experiments are i. Genome wide association (GWA) studies that generate a list of Single Nucleotide Polymorphisms (SNPs) related with diseases; ii. Microarray experiments that generate a list of candidate genes associated with disease. However, the systematic integration of the results of these experiments requires programming skills. In this paper, we developed an automated tool to find out the overlaps between the upstream regions of the candidate genes (obtained from microarray experiments) and a set of SNPs (obtained from GWA studies). As part of this tool, the functional information of a SNP is also checked for an overlapping SNP to identify a causal SNP. Particularly in the SNP functionalization step, we focused on whether an overlapping SNP is found on the transcrption factor binding site or whether a miRNA targets that site. We tested our tool on aneurysm GWA study and microarray datasets.