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Characterization of Genetic Networks Associated with Alzheimer’s Disease

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Part of the Methods in Molecular Biology book series (MIMB,volume 1303)

Abstract

At the molecular level, the genetics of complex disease such as Alzheimer’s disease (AD) manifests itself as series of alterations in the molecular interactions in pathways and networks that define biological processes underlying the pathophysiological states of disease. While large-scale genome-wide association (GWA) studies of late-onset alzheimer’s disease (LOAD) have uncovered prominent genomic regions linked to the disease, the cause for the vast majority of LOAD cases still remains unknown. Increasingly available large-scale genomic and genetic data related to LOAD has made it possible to comprehensively uncover the mechanisms causally lined to LOAD in a completely data-driven manner. Here we review the various aspects of systems/network biology approaches and methodology in constructing genetic networks associated with AD from large sampling of postmortem brain tissues. We describe in detail a multiscale network modeling approach (MNMA) that integrates interaction and causal gene networks to analyze large-scale DNA, gene expression and pathophysiological data from multiple post-mortem brain regions of LOAD patients as well non-demented normal controls. MNMA first employs weighted gene co-expression network analysis (WGCNA) to construct multi-tissue networks that simultaneously capture intra-tissue and inter-tissue gene–gene interactions and then quantifies the change in connectivity among highly co-expressed genes in LOAD with respect to the normal state. Co-expressed gene modules are then rank ordered by relevance to pathophysiological traits and enrichment of genes differentially expressed in LOAD. Causal regulatory relationships among the genes in each module are then determined by a Bayesian network inference framework that is used to formally integrate genetic and gene expression information. MNMA has uncovered a massive remodeling of network structures in LOAD and identified novel subnetworks and key regulators that are causally linked to LOAD. In the end, we will outline the challenges in systems/network approaches to LOAD.

Key words

  • Alzheimer’s disease
  • Late-onset alzheimer’s disease
  • LOAD
  • Gene co-expression network
  • Bayesian network
  • Causal regulator
  • Multiscale network modeling
  • MNMA

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Acknowledgements

This work was supported in part by the National Institutes of Health (NIH)/National Institute on Aging (NIA) Award R01AG046170 (to B.Z. and J.Z.); NIH/National Institute of Mental Health (NIMH) Award R21MH097156-01A1 (to B.Z.); NIH/National Cancer Institute (NCI) Award R01CA163772 (to B.Z. and J.Z..); and NIH/National Institute of Allergy and Infectious Diseases (NIAID) Award U01AI111598-01 (to B.Z. and J.Z.).

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Correspondence to Bin Zhang Ph.D. .

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Zhang, B., Tran, L., Emilsson, V., Zhu, J. (2016). Characterization of Genetic Networks Associated with Alzheimer’s Disease. In: Castrillo, J., Oliver, S. (eds) Systems Biology of Alzheimer's Disease. Methods in Molecular Biology, vol 1303. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2627-5_28

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  • DOI: https://doi.org/10.1007/978-1-4939-2627-5_28

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2626-8

  • Online ISBN: 978-1-4939-2627-5

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