The identification and characterization of gene coexpression modules represents a new approach for annotating gene function in the human brain and generating hypotheses related to human disease through the principle of guilt by association. Guilt by association implies that the expression levels of genes with the strongest evidence of membership for the same module are probably driven by the same underlying factors. Seen in this context, our described module memberships (Supplementary Tables 3-6) are rich sources of new hypotheses for thousands of genes expressed in the human brain (Supplementary Note). The consistency of gene coexpression network architecture may also be leveraged to identify new candidate disease genes through differential network analysis15. By establishing a baseline network for comparison with disease, differences in the strength of module membership can be assessed for all genes relative to all identified modules. In principle, such an approach offers several advantages over the conventional methods used to study neurological or neuropsychiatric disorders. First, differential network analysis can suggest dysregulation at the level of modules, which may implicate specific cell types or functional pathways. Although pre-existing
