We constructed both multi-tissue and single tissue co-expression networks from the top one-third (n=13,193) of the most variably expressed genes in each tissue and condition. We computed the module differential connectivity (MDC) in LOAD brains as: δΩ(x,y)=∑i=1N−1∑j=i+1Nkijx∑i=1N−1∑j=i+1Nkijy, where, kij is the connectivity between two genes i and j in a given network, and assessed the statistical significance through the FDR method. We constructed causal probabilistic Bayesian networks from individual co-expression modules and used brain cis eSNPs as priors to infer directionality between nodes (see Figure S3). For this, we identified 11,318 unique cis eSNPs-transcripts at FDR of 10% (Extended Experimental Procedures), all listed in Table S1. The Bayesian inference allowed us to compute the causal regulators of the differential connectivity in individual modules by examining the number of N-hob downstream nodes.
