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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.