Motivation: Although genome-wide association studies (GWAS) have found many common genetic variants associated with human diseases, it remains a challenge to elucidate the functional links between associated variants and complex traits.
Results: We developed a package called eResponseNet by implementing and extending the existing ResponseNet algorithm for prioritizing candidate disease genes through cellular pathways. Using type II diabetes (T2D) as a study case, we demonstrate that eResponseNet outperforms currently available approaches in prioritizing candidate disease genes. More importantly, the package is instrumental in revealing cellular pathways underlying disease-associated genetic variations.
Availability: The eResponseNet package is freely downloadable at http://hanlab.genetics.ac.cn/eResponseNet.
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.