Integrative analysis for finding genes and networks involved in diabetes and other complex diseases

Abstract

We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases.

Figure 1

The strategy used for the current study.

Figure 2

Protein interaction networks for predicted genetic interactions. (a) TNFA-D4S403, TNFA-D13S170 and TNFA-D2S177 are represented by one network, whereas TNFA-D1S229, TNFA-D16S287 and TNFA-D11S910 are represented by two or three networks. Color-code: red, genes from TNFA region; green and yellow, genes from interacting region; light grey, genes from other chromosomes. (b) Protein interaction networks involving D17S798. D17S798-D1S197, D17S798-D2P25 and D17S798-D5S429 are represented by four, three and two networks, respectively. Color-code: red, genes from D17S798-region; blue/green, genes from interacting region; light grey, genes from other chromosomes.

Figure 3

Significant functional modules (modules A-D). Straight lines represent validated protein-protein interactions, curved lines represent demonstrated genetic interactions (black bullets, predictive interactions; white bullets, protective interactions). Circles with gene names represent the gene encoding the protein of the interaction. Boxes are the marker regions shown to be involved in the genetic interactions and in which the genes are located.