Abstract
Currently, genome-wide association studies have been proved to be a powerful approach to identify risk loci. However, the molecular regulatory mechanisms of complex diseases are still not clearly understood. It is therefore important to consider the interplay between genetic factors and biological networks in elucidating the mechanisms of complex disease pathogenesis. In this paper, we first conducted a genome-wide association analysis by using the SNP genotype data and phenotype data provided by Genetic Analysis Workshop 17, in order to filter significant SNPs associated with the diseases. Second, we conducted a bioinformatics analysis of gene-phenotype association matrix to identify gene modules (biclusters). Third, we performed a KEGG enrichment test of genes involved in biclusters to find evidence to support their functional consensus. This method can be used for better understanding complex diseases.
Highlights
We first conducted a genome-wide association analysis by using the SNP genotype data and phenotype data provided by Genetic Analysis Workshop 17, in order to filter significant SNPs associated with the diseases
It is well known that genome-wide association studies (GWAS) have become an increasingly effective tool to identify genetic variation associated with the risk of complex disease
We suppose that a total of N (=3205) genes for the analyzed data are presented in KEGG pathways in which a set of genes in biclusters are significantly aggregated
Summary
It is well known that genome-wide association studies (GWAS) have become an increasingly effective tool to identify genetic variation associated with the risk of complex disease. In this case, univariate single-locus association analyses may not be the most appropriate strategy. GeneTrail (http://genetrail.bioinf.uni-sb.de/) and GSEA [4], are used to detect disease-related risk pathways or gene modules These methods integrate heterogeneous data to elucidate biological mechanisms, which is an essential and challenging problem in systems biology. A KEGG enrichment test of genes involved in biclusters was applied to find whether they were functional aggregated This method can be used for better understanding complex diseases
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