Complex genetic disorders often involve products of multiple genes acting 
cooperatively. Hence, the pathophenotype is the outcome of the perturbations in 
the underlying pathways, where gene products cooperate through various 
mechanisms such as protein-protein interactions. Pinpointing the decisive 
elements of such disease pathways is still challenging. Over the last years, 
computational approaches exploiting interaction network topology have been 
successfully applied to prioritize individual genes involved in diseases. 
Although linkage intervals provide a list of disease-gene candidates, recent 
genome-wide studies demonstrate that genes not associated with any known linkage 
interval may also contribute to the disease phenotype. Network based 
prioritization methods help highlighting such associations. Still, there is a 
need for robust methods that capture the interplay among disease-associated 
genes mediated by the topology of the network. Here, we propose a genome-wide 
network-based prioritization framework named GUILD. This framework implements 
four network-based disease-gene prioritization algorithms. We analyze the 
performance of these algorithms in dozens of disease phenotypes. The algorithms 
in GUILD are compared to state-of-the-art network topology based algorithms for 
prioritization of genes. As a proof of principle, we investigate top-ranking 
genes in Alzheimer's disease (AD), diabetes and AIDS using disease-gene 
associations from various sources. We show that GUILD is able to significantly 
highlight disease-gene associations that are not used a priori. Our findings 
suggest that GUILD helps to identify genes implicated in the pathology of human 
disorders independent of the loci associated with the disorders.