Prioritizing disease-causing genes based on network diffusion and rank concordance

Disease-causing genes prioritization is very important for understanding mechanisms of diseases and biomedical applications, such as drug design. Previous studies have shown that promising candidate genes are mostly ranked according to their relatedness to known disease genes or closely related disease processes. Therefore, a dangling gene (isolated gene) with no edges in the network can not be effectively prioritized. These approaches tend to prioritize those genes that are highly connected in the PPI network and perform poorly when they are applied to loosely connected disease genes. Motivated by this observation, we propose a new disease-causing genes prioritization method that based on network diffusion and rank concordance (NDRC). The method is evaluated by leave-one-out cross validation on 1931 diseases in which at least one gene is known to be involved, and it is able to rank the true causal gene first in 849 of all 2542 cases, and as the experimental results suggest that NDRC significantly outperforms other existing methods such as RWR, VAVIEN, DADA and PRINCE on identifying loosely connected disease genes and which successfully put dangling genes as potential candidate disease genes. Furthermore, we apply NDRC method to study two representative diseases, Meckel syndrome 1 and Peroxisome biogenesis disorder 1A (Zellweger). Our study has also found that the complex disease-causing genes are divided into several modules that are closely associated with different disease phenotype.