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iScience. 2019 Jun 28;16:155-161. doi: 10.1016/j.isci.2019.05.025. Epub 2019 May 24.

A Fast and Flexible Framework for Network-Assisted Genomic Association.

Author information

1
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA. Electronic address: carlin.daniel@gmail.com.
2
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.
3
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.
4
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
5
Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA.
6
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.

Abstract

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.

KEYWORDS:

Bioinformatics; Biological Sciences; Genomics

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