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Network Theory for Data-Driven Epistasis Networks

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Book cover Epistasis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1253))

Abstract

One of the challenges of understanding the genetic basis of complex phenotypes is explaining variability not attributable to individual genes. While most existing methods that investigate variant mutations or differential gene expression focus on individual effects, a complex system of gene interactions (epistasis) and pathways is likely needed to explain phenotypic variation. Herein, we examine methods for treating the interactions in these biological data sets as edges in a network model of the phenotype and review relevant network theory methods for analyzing network structure and identifying important genes. In particular, we review methods for detecting community structure, describing the statistical properties of networks, and computing network centrality of genes that may reveal insights missed by individual genetic effects. We also discuss available tools to facilitate the construction and visualization of epistasis networks of GWAS data.

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Tulsa, 800 S. Tucker Drive, Rayzor Hall 2145, Tulsa, OK, 74104, USA

    Caleb A. Lareau & Brett A. McKinney Ph.D.

  2. Tandy School of Computer Science, University of Tulsa, 800 S. Tucker Drive, Rayzor Hall 2145, Tulsa, OK, 74104, USA

    Brett A. McKinney Ph.D.

  3. Laureate Institute for Brain Research, Tulsa, OK, USA

    Brett A. McKinney Ph.D.

Authors
  1. Caleb A. Lareau
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  2. Brett A. McKinney Ph.D.
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Corresponding author

Correspondence to Brett A. McKinney Ph.D. .

Editor information

Editors and Affiliations

  1. Department of Genetics, Institute of Quantitative Biomedical Sciences, Geisel School of Medicine, Hanover, New Hampshire, USA

    Jason H. Moore

  2. Department of Genetics, Institute of Quantitative Biomedical Sciences, Geisel School of Medicine, Hanover, New Hampshire, USA

    Scott M. Williams

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© 2015 Springer Science+Business Media New York

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Lareau, C.A., McKinney, B.A. (2015). Network Theory for Data-Driven Epistasis Networks. In: Moore, J., Williams, S. (eds) Epistasis. Methods in Molecular Biology, vol 1253. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2155-3_15

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  • DOI: https://doi.org/10.1007/978-1-4939-2155-3_15

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2154-6

  • Online ISBN: 978-1-4939-2155-3

  • eBook Packages: Springer Protocols

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