A systematic method for mapping multiple loci: an application to construct a genetic network for rheumatoid arthritis.
ABSTRACT The advent of high-throughput single nucleotide polymorphisms (SNPs) omics technologies has brought tremendous genetic data. Systematic evaluation of the genome-wide SNPs is expected to provide breakthroughs in the understanding of complex diseases. In this study, we developed a new systematic method for mapping multiple loci and applied the proposed method to construct a genetic network for rheumatoid arthritis (RA) via analysis of 746 multiplex families genotyped with more than five thousands of genome-wide SNPs. We successfully identified 41 significant SNPs relevant to RA, 25 associated genes and a number of important SNP-SNP interactions (SNP patterns). Many findings (loci, genes and interactions) have experimental support from previous studies while novel findings may define unknown genetic pathways for this complex disease. Finally, we constructed a genetic network by integrating the results from this analysis with the rapidly accumulated knowledge in biomedical domains, which gave us a more detailed insight onto the RA etiology. The results suggest that the proposed systematic method is powerful when applied to genome-wide association studies. Integrating the analysis of high-throughput SNP data with knowledge-based SNP functional annotation offers a promising way to reversely engineer the underlying genetic networks for complex human diseases.
Article: Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements.[show abstract] [hide abstract]
ABSTRACT: With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases. The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8) (p approximately 0), desmin (DES) (p = 2.71 x 10(-6)) and enolase 1 (ENO1) (p = 4.19 x 10(-5))], while two novel hub genes [RNA binding motif protein 9 (RBM9) (p = 1.50 x 10(-4)) and ribosomal protein L30 (RPL30) (p = 1.50 x 10(-4))] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO) based analysis of the colon cancer-specific gene network and the sub-network that consisted of three-way gene interactions suggested that tumourigenesis in colon cancer resulted from dysfunction in protein biosynthesis and categories associated with ribonucleoprotein complex which are well supported by multiple lines of experimental evidence. This study demonstrated that IL8, DES and ENO1 act as the central elements in colon cancer susceptibility, and protein biosynthesis and the ribosome-associated function categories largely account for the colon cancer tumuorigenesis. Thus, the newly developed relevancy-based networking approach offers a powerful means to reverse-engineer the disease-specific network, a promising tool for systematic dissection of complex diseases.BMC Systems Biology 09/2008; 2:72. · 3.15 Impact Factor