Schizophrenia is a debilitating neurodevelopmental disorder that typically onsets during the transition from late adolescence to early adulthood and is characterized by substantial heterogeneity in both etiology and clinical presentation (1). Genetic factors contribute substantially to the disorder, with heritability estimates in the range of 70-85% (1; 2). While onset of schizophrenia typically coincides with the later stages of adolescent neurodevelopment, most contributing risk factors, including genetic variation (2; 3), hypoxia (4), and maternal infection (5), exert effects earlier in life. Social, functional, and cognitive decline often occur well before the emergence of psychotic symptoms (more than 10 years), which in turn appear months to years before a diagnosis is given (1). As such, it is clear that the mechanisms by which risk is instantiated and at least some aspects of phenotypic affection begin well before individuals are diagnosed.
One of the biggest challenges in developing a prevention strategy for schizophrenia is identifying mechanisms by which individuals become phenotypically affected. In other words, what do risk-conferring genes do in the context of neurodevelopment to increase risk for overt expression of psychosis? There are many components of this question, including: 1) How do thousands of genes converge (or not) to differentially affect various endophenotypes and/or aspects of schizophrenia? 2) How do genetic risk factors impact earlier phases of disease development? 3) Through what neurobiological mechanisms might these genetic risk factors be expressed? The goal of this dissertation is to provide 1insights into these three interrelated questions to help elucidate the mechanisms intervening between known risk-conferring genetic variation for schizophrenia and disease expression.
To expand the ways in which we can investigate genotype-phenotype relationships, in Study 1, we applied machine learning – a statistical approach used in other fields but relatively novel to psychiatric genetics – alongside commonly employed methods to connect risk-conferring genotypes with cognitive endophenotypes for schizophrenia in two cohorts of subjects. The first sample was used for discovery (N = 739) and the second for external validation (N= 364). We found that random forest models (and only those among the methods tested) could be used to generate generalizable models that highlighted molecular pathways known to associate with cognition broadly, as well as cognitive impairment in schizophrenia. This suggested that identifying multivariate patterns of genotypic variation may be a useful tool for genotype-phenotype mapping, ostensibly because these relationships can be interactive. This has important implications for the architecture of genetic risk broadly, as well as for delineating risk profiles that can account for clinical heterogeneity.
In Study 2, we tested whether genotypic variation associated with schizophrenia risk could prospectively predict the transition from a prodromal phase characterized by subthreshold psychotic symptoms to onset of a full-blown psychotic disorder. Using a North American cohort of subjects with a prodromal risk syndrome and matched controls (N= 601), a polygenic risk score reflecting the aggregated influences of thousands of risk variants was found to be associated with prodromal risk status (i.e., higher in at risk as compared to control individuals), conversion to psychosis (i.e., higher among those who converted relative to those at risk who did not), and baseline symptoms, establishing the viability of inherited genetic risk in the pathophysiology underlying this transition.
In Study 3, we sought to elucidate genomic mechanisms that could influence risk for onset of psychosis by perturbing cortical maturation. Individuals who convert to psychosis show accelerated cortical thinning on repeated magnetic resonance imaging scans leading up to full disease expression. This accelerated rate of cortical thinning is in turn predicted by baseline levels of pro-inflammatory cytokines, suggesting a role for inflammatory signaling in cortical development among those with a prodromal risk syndrome. We extended this work by identifying patterns of regulatory gene expression (specifically, microRNAs) correlated with both cortical thinning and levels of pro-inflammatory cytokines that target molecular pathways within immune cells known to regulate inflammatory responses. These signaling cascades are candidate mechanisms by which cortical maturation could go awry, promoting conversion to psychosis.
Taken together, this work provides insights into the structure of risk-associated genotypic effects, their prospective predictive power, and potential molecular mechanisms by which they can manifest. Better capturing the structure of genetic risk can bolster the viability of genomic risk for clinical prediction and prevention. Advances in delineating the pathophysiological processes that drive the transition to full-blown psychosis provide the next step in identifying entry points for preventative intervention. Future work connecting distal risk factors to these more proximate mechanisms will be critical for building towards a strategy of early identification and prevention.
|Advisor:||Cannon, Tyrone D.|
|School Location:||United States -- Connecticut|
|Source:||DAI-B 79/05(E), Dissertation Abstracts International|
|Subjects:||Psychobiology, Genetics, Clinical psychology|
|Keywords:||Cognition, Cortical Development, Genetics, Polygenic Risk, Psychosis, Schizophrenia|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be