Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease

Bin Zhang, Chris Gaiteri, Liviu Gabriel Bodea, Zhi Wang, Joshua McElwee, Alexei A. Podtelezhnikov, Chunsheng Zhang, Tao Xie, Linh Tran, Radu Dobrin, Eugene Fluder, Bruce Clurman, Stacey Melquist, Manikandan Narayanan, Christine Suver, Hardik Shah, Milind Mahajan, Tammy Gillis, Jayalakshmi Mysore, Marcy E. MacDonaldJohn R. Lamb, David A. Bennett, Cliona Molony, David J. Stone, Vilmundur Gudnason, Amanda J. Myers, Eric E. Schadt, Harald Neumann, Jun Zhu, Valur Emilsson

Research output: Contribution to journalArticlepeer-review

1168 Scopus citations


The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer's disease (LOAD), we constructed gene-regulatory networks in 1,647 postmortem brain tissues from LOAD patients and nondemented subjects, and we demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune- and microglia-specific module that is dominated by genes involved in pathogen phagocytosis, contains TYROBP as a key regulator, and is upregulated in LOAD. Mouse microglia cells overexpressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a framework to test models of disease mechanisms underlying LOAD.

Original languageEnglish
Pages (from-to)707-720
Number of pages14
Issue number3
StatePublished - 25 Apr 2013


Dive into the research topics of 'Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease'. Together they form a unique fingerprint.

Cite this