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X. Cui, Q. Sha, S. Zhang and H.-S. Chen, “A Combinatorial Approach for Detecting Gene-Gene Interaction Using Multiple Traits of Genetic Analysis Workshop 16 Rheumatoid Arthritis Data,” BMC Proceedings, Vol. 3, Suppl. 7, 2009, p. S43.

has been cited by the following article:

• TITLE: Combining Gene-Phenotype Association Matrix with KEGG Pathways to Mine Gene Modules Using Data Set in GAW17

  AUTHORS: Hua Lin, Yang Zheng, Ping Zhou

  KEYWORDS: Gene Modules; KEGG Pathways; Biclusters

  JOURNAL NAME: Engineering, Vol.5 No.10B, November 1, 2013

  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.