In light of the complexity and elusiveness of LOAD pathogenesis, new approaches are needed to boost the probability of identifying causal genes and pathways. Recently, we have leveraged the molecular network structure that is reflected in genotypic and gene expression data to uncover biologically meaningful gene modules involved in the development of complex disease (Chen et al., 2008; Emilsson et al., 2008). Targeting such causal networks in ways that restore them to a normal state has been proposed as a path to treat disease (Schadt et al., 2009), but this potential has never been realized for LOAD. However, the complexity of these networks makes it difficult to distinguish the causal from correlated disease effects or how the causal regulators propagate their effects. To better address this, we constructed molecular networks based on whole-genome gene expression profiling and genotyping data in 1647 autopsied brain tissues from hundreds of LOAD patients and non-demented subjects. We identified numerous modules of distinct functional categories and cellular specificity, many showing a massive remodeling effect in the LOAD brain. Next we applied an integrative network-based approach
