A network-based approach to prioritize results from genome-wide association studies

Nirmala Akula, Ancha Baranova, Donald Seto, Jeffrey Solka, Michael A. Nalls, Andrew Singleton, Luigi Ferrucci, Toshiko Tanaka, Stefania Bandinelli, Yoon Shin Cho, Young Jin Kim, Jong Young Lee, Bok Ghee Han, Francis J. McMahon

Research output: Contribution to journalArticle

Abstract

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call 'trait prioritized sub-networks.' As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn's disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn's disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses.

Original languageEnglish (US)
Article numbere24220
JournalPLoS One
Volume6
Issue number9
DOIs
StatePublished - 2011
Externally publishedYes

Fingerprint

Genome-Wide Association Study
Miners
Genes
Association reactions
Interfaces (computer)
Crohn disease
Crohn Disease
Multifactorial Inheritance
protein-protein interactions
Proteins
interleukins
genome-wide association study
Interleukins
Protein Biosynthesis
nucleic acids
translation (genetics)
B-lymphocytes
T-Cell Antigen Receptor
signal transduction
Signal transduction

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Akula, N., Baranova, A., Seto, D., Solka, J., Nalls, M. A., Singleton, A., ... McMahon, F. J. (2011). A network-based approach to prioritize results from genome-wide association studies. PLoS One, 6(9), [e24220]. https://doi.org/10.1371/journal.pone.0024220

A network-based approach to prioritize results from genome-wide association studies. / Akula, Nirmala; Baranova, Ancha; Seto, Donald; Solka, Jeffrey; Nalls, Michael A.; Singleton, Andrew; Ferrucci, Luigi; Tanaka, Toshiko; Bandinelli, Stefania; Cho, Yoon Shin; Kim, Young Jin; Lee, Jong Young; Han, Bok Ghee; McMahon, Francis J.

In: PLoS One, Vol. 6, No. 9, e24220, 2011.

Research output: Contribution to journalArticle

Akula, N, Baranova, A, Seto, D, Solka, J, Nalls, MA, Singleton, A, Ferrucci, L, Tanaka, T, Bandinelli, S, Cho, YS, Kim, YJ, Lee, JY, Han, BG & McMahon, FJ 2011, 'A network-based approach to prioritize results from genome-wide association studies', PLoS One, vol. 6, no. 9, e24220. https://doi.org/10.1371/journal.pone.0024220
Akula N, Baranova A, Seto D, Solka J, Nalls MA, Singleton A et al. A network-based approach to prioritize results from genome-wide association studies. PLoS One. 2011;6(9). e24220. https://doi.org/10.1371/journal.pone.0024220
Akula, Nirmala ; Baranova, Ancha ; Seto, Donald ; Solka, Jeffrey ; Nalls, Michael A. ; Singleton, Andrew ; Ferrucci, Luigi ; Tanaka, Toshiko ; Bandinelli, Stefania ; Cho, Yoon Shin ; Kim, Young Jin ; Lee, Jong Young ; Han, Bok Ghee ; McMahon, Francis J. / A network-based approach to prioritize results from genome-wide association studies. In: PLoS One. 2011 ; Vol. 6, No. 9.
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