A Fast and Flexible Framework for Network-Assisted Genomic Association
Daniel E. Carlin,
Samson H. Fong,
Yue Qin,
Tongqiu Jia,
Justin K. Huang,
Bokan Bao,
Chao Zhang,
Trey Ideker
Affiliations
Daniel E. Carlin
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Corresponding author
Samson H. Fong
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
Yue Qin
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
Tongqiu Jia
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
Justin K. Huang
Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
Bokan Bao
Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
Chao Zhang
Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
Trey Ideker
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
Summary: We present an accessible, fast, and customizable network propagation system for pathway boosting and interpretation of genome-wide association studies. This system—NAGA (Network Assisted Genomic Association)—taps the NDEx biological network resource to gain access to thousands of protein networks and select those most relevant and performative for a specific association study. The method works efficiently, completing genome-wide analysis in under 5 minutes on a modern laptop computer. We show that NAGA recovers many known disease genes from analysis of schizophrenia genetic data, and it substantially boosts associations with previously unappreciated genes such as amyloid beta precursor. On this and seven other gene-disease association tasks, NAGA outperforms conventional approaches in recovery of known disease genes and replicability of results. Protein interactions associated with disease are visualized and annotated in Cytoscape, which, in addition to standard programmatic interfaces, allows for downstream analysis. : Biological Sciences; Genomics; Bioinformatics Subject Areas: Biological Sciences, Genomics, Bioinformatics
