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Chunk #20 — RESULTS — Bayesian Networks and the Immune Module as an Effector in LOAD

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Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease.
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study (Webster et al., 2009). The methodology to identify cis and trans eSNPs is detailed in Extended Experimental Procedures, while Table S1 lists all cis and trans acting eSNPs detected in the present study at FDR of 10%. There was between 70 and 80% sharing of cis eSNP-transcripts between different brain regions and 37% overlapped all brain regions (Figure S2A). Importantly, we find a variable and often strong enrichment of brain eSNPs in many of the LOAD-associated modules compared to all probes on the array, suggesting the possibility that these variants determine the differential connectivity observed in LOAD. For instance, in the PFC region (Figure 4C) there were five modules showing significant enrichment for cis eSNPs including the unfolded protein (3.8 fold, P=3.8e-81), nerve myelination (2.5 fold, P=2.9e-40), immune function (2.2 fold, P=4.3e-30), GABA metabolism (2.7 fold, P=2.3e-13) and extracellular matrix (1.6 fold, P=2.3e-07) modules (Figure 4C). The enrichment of cis eSNPs in the differentially connected LOAD modules in the VC and CB regions is shown in Figure S2B. For the present study, however, a particular attention was paid to the cis eSNPs for their applicability as priors in the construction of Bayesian networks (Extended Experimental Procedures and schematic Figure