WGCNA of human brain tissue has been valuable in the identification of cell-type specific networks that are shared among distinct brain regions and in assigning cell-type classification to predicted protein function (Oldham et al., 2008). Oldham and colleagues demonstrated the ability of a network approach to provide discrete insight into the brain transcriptome. Similar network-based approaches have been used to identify gene co-expression modules that are associated with other psychiatric disorders, such as bipolar disorders and schizophrenia (Chen et al., 2013; Torkamani et al., 2010), as well as alcohol and other drug of abuse disorders (Farris et al., 2015a; Ponomarev et al., 2012; Zhang et al., 2014a). Although co-expression networks for alcohol-related phenotypes are still emerging, it is likely that RNA-seq studies will offer greater insight into drug-induced transcriptional changes (Farris and Mayfield, 2014). For instance, RNA-seq profiling of human postmortem prefrontal cortex revealed that gene network structure was significantly correlated with lifetime alcohol consumption in addition to an overall loss in network structure (Farris et al., 2015a; Farris and Mayfield, 2014). Intermodular correlations were found to be significantly higher
