We present a novel statistical framework for meta-analysis of differential gene co-expression. In contrast to standard methods, which identify genes that are over or under expressed in disease vs controls, differential co-expression identifies gene pairs with correlated expression profiles specific to one state. We apply our differential co-expression meta-analysis method to identify genes specifically mis-expressed in blood-derived cells of systemic lupus erythematosus (SLE) patients. The resulting network is strongly enriched for genes genetically associated with SLE, and effectively identifies gene modules known to play important roles in SLE etiology, such as increased type 1 interferon response and response to wounding. Our results also strongly support previous preliminary studies suggesting a role for dysregulation of neutrophil extracellular trap formation in SLE. Strikingly, two of the gene modules we identify contain SLE-associated transcription factors that have binding sites significantly enriched in the promoter regions of their respective gene modules, suggesting a possible mechanism underlying the mis-expression of the modules. Thus, our general method is capable of identifying specific dysregulated gene expression programs, as opposed to large global responses. We anticipate that methods such as ours will be more and more useful as gene expression monitoring becomes increasingly common in clinical settings.