Genes Underlying Inheritance Linked Disorders
Introduction
The past decade has witnessed dramatic advances in genome sequencing and a substantial shift in the number of genome wide association studies (GWAS). These efforts have expanded considerably our knowledge on the sequential variations in Human DNA and their consequences on the human biology. Complex genetic disorders often involve products of multiple genes acting cooperatively. Nevertheless, pinpointing the decisive elements of such disease pathways is still a challenge. Recently, network biology has proven its use in identifying candidate genes associated with a disease based on the simple observation that proteins translated by phenotypically related genes tend to interact, so called guilt-by-association principle. Here, we present GUILD (Genes Underlying Inheritance Linked Disorders), a network-based prioritization framework to unveil genes associated with a disease phenotype (disease-genes). In GUILD, we exploit several communication mechanisms between disease-genes emerging from the topology of the interaction network. We used three sources of gene-phenotypic association to specify nodes involved in a disorder (including Online Mendelian Inheritance in Man database)previously published data sets. Analyses on multiple human disease phenotypes demonstrated that the methods proposed in GUILD effectively prioritize genes even when the linkage information is not known a priori. We compared the algorithms we have developed with state-of-the-art prioritization methods such as PageRank with priors, Functional-Flow, Random walk with restart and Network propagation. We also tested the robustness of the approaches proving the effect of the network properties and the independence with the number of original genes/proteins associated with the function or phenotype. Finally, we applied GUILD to prioritize genes in the case of Alzheimer Disease.
