DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization

We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called DADA, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that DADA outperforms existing methods in prioritizing candidate disease genes.These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. DADA is implemented in Matlab and is freely available at http://compbio.case.edu/dada/ webcite.Identification of disease-associated genes is an important step toward enhancing our understanding of the cellular mechanisms that drive human diseases, with profound applications in modeling, diagnosis, prognosis, and therapeutic intervention [1]. Genome-wide linkage and association studies (GWAS) in healthy and affected populations identify chromosomal regions potentially containing hundreds of candidate genes possibly associated with genetic diseases [2]. Investigation of these candidates using experimental methods is an expensive task, thus not always a feasible option. Consequently, comp