COMPUTATIONAL GENE KNOCKOUT REVEALS TRANSDISEASE–TRANSGENE ASSOCIATION STRUCTURE

Abstract

Genome-wide association studies for a variety of diseases are identifying increasing numbers of candidate genes. Now we are confronted with the fact that some genes are common candidates across diseases. Thus there is a strong need to develop a hypothesis formulation methodology to comprehend multifaceted associations between genes and diseases. We have developed a computational method for building transdisease-transgene association structure. By introducing the basic rationale underlying the gene knockout approach as an information processing procedure to a network constructed on the basis of hyperlinks between disease and gene pages listed in the Online Mendelian Inheritance in Man (OMIM) database, relations of genes with diseases are computationally quantified. We did successively eliminate gene pages (called "computational gene knockout" in this paper) expected to contribute to metabolic syndrome, and catalogued each association with various disease pages. We thereby apply a co-clustering method to the gene-disease relations to obtain an association structure by classifying diseases and genes simultaneously. Observing an association structure between over 100 diseases and their related genes, we then found that the structure revealed gene classes that were commonly associated with diseases as well as gene classes that were selectively associated with a specific disease class.