Neuropsychiatric disorders are behavioral conditions marked by intellectual, social, or emotional deficits that can be linked to diseases of the nervous system. Autism spectrum disorder (ASD), schizophrenia (SCZ), bipolar disorder (BP), major depressive disorder (MDD), and attention deficit and hyperactivity disorder (ADHD) are common, heritable diseases each with a prevalence exceeding 1% of the population, none of which can be characterized by discernable anatomical or neurological pathologies. Genetic association studies have identified mutations in hundreds of genes that contribute to risk for at least one of these disorders, and have shown that a substantial fraction of the genetic liability is shared between many of these neuropsychiatric diseases. It has long been hoped that with enough genetic evidence we will identify the biological pathways, developmental time points, and brain regions that, when disrupted, give rise to neuropsychiatric disorders. However, the cellular and functional complexity of the human brain, as well as the genetic complexity of neuropsychiatric disease, make it difficult to search for such convergence.
In this thesis, I investigate global and local transcriptional regulation within and across 12 regions of the human brain in order to investigate the regional specificity of neuropsychiatric disorders. I develop novel bioinformatics methods – ranging from data processing to network construction – to identify whether the transcriptional regulation of a set of genes is shared or specific. I hypothesize that local, region-specific transcriptional regulation corresponds directly to cell types and processes that are specific to, or far more prevalent in, a given region; that cross-regional transcriptional regulation corresponds to cell types that show little heterogeneity across brain regions; and that genetic disruption of region-specific transcriptional programs results in regional susceptibility. I use a systems-biology approach to summarize transcriptional regulation into reproducibly co-expressed gene sets (“co-expression modules”), which can be analyzed statistically to identify common functions, pathways, and cell types. I then integrate data from genetic association studies to ascertain gene sets conferring outsized risk for neuropsychiatric disorders, thereby implicating the corresponding pathways for further investigation in disease etiology. Finally, I use the network structure itself to investigate the genetic architecture of ASD and SCZ in terms of omnigenics and network polygenics.
Chapter 1 presents the biological background for the studies and summarizes some of the major studies of neuropsychiatric disorders along with their principal methods and conclusions. In chapter 2, utilizing my multi-regional co-expression approach, I identify 12 brain-wide, 114 region-specific, and 50 cross-regional co-expression modules. Nearly 40% of expressed genes fall into brain-wide modules and correspond to major cell classes and conserved biological processes, while region-specific modules comprise 25% of expressed genes and correspond to region-specific cell types. The detailed study in chapter 3 demonstrates that neuropsychiatric risk concentrates in both brain wide and multi-regional modules, implicating major core cell types in disease etiology but not region-specific susceptibility. Chapter 4 presents a new and more general framework for defining genetic networks. Using this framework, I show that the network pattern of ASD-associated rare loss-of-function mutations, as well as the large number of significant targets for trans master regulators in BP and SCZ, support a classical polygenic architecture with thousands of directly causal genes. These results suggest that a nontrivial component of risk for neuropsychiatric disease comes from the global polygenic disruption of neuronal function and neuronal maturation.