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AbstractIn1899, psychiatrist Emil Krapaelin introduced a separation between schizophrenia (SZ) and bipolar disorder (BD), formerly “dementia praecox” and“manic-depressive disorder”, which came to be known as the Krapaelinian dichotomy, and has prevailed over the past century (Kraepelin, 1904). Although Emil Krapaelin postulated that these are distinct entities, multiple converging lines of evidence suggest that SZ and BD have a shared etiology: (1) first-degree relatives of a SZ-affected individual are at higher risk for BD than the general population, and vice versa (Lichtenstein et al., 2009), (2) recent work from genome-wide association studies (GWAS) and rare variant studies revealed that SZ and BD share common risk genes, suggesting that these disorders share a set of molecular substrates, and (3) second-generation antipsychotics exhibit effectiveness in ameliorating psychosis and mania (Buckley, 2008). SZ and BD are highly heritable mental illnesses with a lifetime prevalence near 1%. Onset typically occurs in late adolescence to early adulthood. Their etiology is complex and multi-factorial. SZ and BD are among the leading causes of disability around the globe(Global Burden of Disease Study 2013 Collaborators, 2015). There isa constellation of symptoms related toSZ, including hallucinations (e.g., auditory, olfactory, visual), delusions (e.g., persecutions, grandiosity), thought disturbances, affective flattening, and anhedonia. SZ and BD have clinical resemblances like psychosis, though this is more widely recognized as a hallmark of SZ. The core feature of BD is extreme changes in mood ranging from periods of mania followed by severe depression, which is also referred to as “switching”. Drugs for treating SZ and BD have changed very little over the past 50 years, and those that are used today are not always effective and can elicit severe side effects. SZ and BD research is evolving rapidly but our understanding of these disorders is still in its infancy. One of the major advances in the field has been the “big data” revolution. Technological advances have been a critical driving force of this revolution, including emergence of DNA microarray chips for high-throughput genome-wide genotyping and gene expression profiling. These technologies became widely adopted in psychiatry and led to a proliferation of genome-and transcriptome-wide studies in psychiatry to aid in the discovery of novel genes and pathways related to mental illness. Despite SZ and BD having a strong genetic basis, identifying susceptibility genes was a significant challenge. Combining data across laboratories became a fundamental strategy to overcome inherent weaknesses with statistical power and methodological biases, which has proven be to a fruitful strategy for GWAS (Cross Disorder Group of the Psychiatric Genomics Consortium, 2013; Ripke et al., 2014; Ruderfer et al., 2013; Sklar et al., 2011). Yet, a robust methodological and statistical framework for analyzing combined collections of gene expression data has been lacking in psychiatry. Microarray studies of SZ and BD suffered from low statistical power and drawbacks that affected their reproducibility (Draghici, Khatri, Eklund, & Szallasi, 2006; Evans, Watson, & Akil, 2003). Combining gene expression data from numerous sources and addressing methodological issues may help to uncover reliable molecular associations. Even though the relevance of gene expression to physiology is not always clear, gene expression abnormalities in mental illness can provide fundamental insight into gene regulatory networks in brain and peripheral tissues, and provide a framework for interpreting genomics data. Integrating findings between GWAS and gene expression studies has the potential to elucidate the etiological overlap of SZ and BD. Moreover, gene expression signatures of mental illnesses may have biomarker utility and set up a foundation for identifying better drug targets. Data sharing is now a common place. Although microarrays are gradually being replaced by more sensitive and precise technologies such as next-generation sequencing, data harmonizing will be a pervasive issue unless dealt with now.In this dissertation, I present two review papers describing the current state of SZ and BD genetics research followed by three primary research studies that I performed to answer these prevailing questions: (1) what are the genes, pathways, and regulatory elements that relate to risk for SZ and BD, and are these similar or different across disorders? (2) what genes and pathways are abnormally expressed in SZ and BD, and might these differences converge with genomic evidence? (3) can differences between SZ and BD reflected in gene expression profiles offer biomarker utility and a basis for developing disorder-specific classifiers?My primary hypothesis for this work is SZ and BD exhibit overlapping abnormalities across pathways related to neurodevelopment, neurotransmission, and immunity/cellular response to stressors; furthermore, these abnormalities are relevant for pathophysiology. My dissertation work encapsulates the development of methodologies and computational tools to analyze large “poly-omics” data sets, i.e., jointly analyzing genomic, epigenomic, and transcriptomic data to identify abnormalities gene expression regulation and molecular substrates that are common between and unique to SZ and BD. My work uncovered convergent evidence of dysregulation among genes, pathways, and regulatory molecules associated with SZ and BD. Major outcomes of this thesis may help to lay the groundwork for causal inference of the effect of genetic variants on cellular phenotypes, biological sub-typing of mental illness through gene expression profiling, and rational drug design.
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