A genetic algorithm approach to active subnetwork search applied to GWAS data

Abstract

An active subnetwork is a group of interconnected genes that show condition-specific differences. It has been observed that the gene products that have alterations associated with a disease of interest, incline to be part of the subnetworks among the overall interaction network. Hence, the integration of the interaction data with the genotypic data underlying disease states facilitates the separation of the subnetworks perturbed in a given disorder from the rest of the network. In the literature, active subnetwork search is used to discover disease related regulatory pathways, dysregulated genes, functional modules, cancer markers, to classify diseases, and to predict response to treatment. In this study, a genetic algorithm based method is developed for active subnetwork search and applied to WTCCC Rheumatoid Arthritis genome-wide association study dataset. The relevance of the identified subnetworks against the disease is compared in terms of biological pathways. Our results show that the proposed method works well in detecting the significant RA associated subnetworks, and it is also applicable to recognize subnetworks of other complex diseases.

Evaluation of Sub-Network search programs in epilepsy-related GWAS dataset

“…In these PPI networks, the active module search aims to find disease-related subnetworks that contain most of the highly affected nodes (proteins) and their interaction partners with medium effect on the disease [2]. The active subnetwork search problem requires two main inputs, i) protein-protein interaction network, ii) node scores of proteins that indicate the statistical significance of a protein for the disease under investigation [3]- [5]. Most methods often use undirected graphs of protein-protein interaction networks and the node scores are used as the weight of the nodes.…”