We confirmed and expanded on previous findings of sexual dimorphism in gene expression and exon usage, including several disease-related genes. These findings offer possible transcriptional mechanisms underlying sex differences in the incidence, prevalence, and severity of many disorders. We also demonstrated how the dataset can be used to profile trajectories of genes associated with specific neurobiological categories or disorders, many of which would not likely be evident in the transcriptomes of commonly studied model systems. Coupled with analysis of co-expressed genes in the dataset, these provide information about when and where these genes are expressed in the brain, which can help infer their function. Our data can enhance genome-wide association and linkage studies by narrowing the focus to the candidate genes that are specifically expressed during development or restricted to a specific region known to be preferentially affected.
