In order to better understand how schizophrenia risk genetics operate at the cognitive and neural systems level, our recent work under this project has focused on understanding the biological correlates of both polygenic illness risk and gene-by-environment interactions. Ongoing studies in these areas permit better understanding of heritable, trait-related abnormalities in schizophrenia, of the underlying molecular biology responsible for such abnormalities, and of strategies for resolving some of the illness heterogeneity that makes biological research so challenging. Schizophrenia is highly heritable, but its genetic architecture is exceptionally complex. Recent gains in delineating a host of diverse genetic markers that each show small statistical illness association effects in very large cohorts present major challenges and opportunities to understanding molecular pathways to system-level dysfunction in schizophrenia. By generating metrics of cumulative schizophrenia risk burden that combine information from schizophrenia risk polymorphisms across the genome, we have been able to test important hypotheses about illness-linked neuroimaging phenotypes. For instance, following a series of MRI experiments in individuals with schizophrenia in which we have established that hippocampal activity measured during memory encoding may be abnormal in patients and also affected in their relatives, we have shown in collaborative work that cumulative, polygenic risk burden for schizophrenia predicts variability in this measurement in healthy individuals (Chen et al, 2018). We have also shown effects of polygenic risk for late-onset Alzheimers disease on this same measure of hippocampal engagement (Xiao et al, 2017), highlighting the complex molecular underpinnings of these neuroimaging signals. This and similar experimentation incorporating positron emission tomographic measurements ongoing in the Branch provide important validation of target neurophysiological phenotypes that may arise from etiological genetic variation. In conjunction with genetic factors underlying risk for schizophrenia, environmental influences have been identified as illness risk factors in epidemiological and twin studies, and delineating gene-environment interactions is likely crucial to better understanding the causes of schizophrenia. Recent research has highlighted two environmental variables that show association with schizophrenia: urbanicity and early life complications. For instance, we have demonstrated that whether an individual was raised in an urban environment significantly modifies effects of dopamine-related genes on how the prefrontal cortex responds to working memory demands (Reed et al, 2018). We have further replicated this finding in two additional datasets and posit that urban upbringing may alter brain function in a way that meaningfully intersects with dopaminergic neurogenetic mechanisms. Other work suggests that the predictive strength of most strongly associated loci from the recent schizophrenia genome-wide association study is greatly amplified when there is a history of clinically significant obstetrical complications (Ursini et al, 2018). These data suggest that traditional univariate approaches must be buttressed by gene-by-environment experimentation to more fully elaborate genetic risk architecture in schizophrenia.

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Project End
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Budget End
Support Year
7
Fiscal Year
2019
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Name
U.S. National Institute of Mental Health
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Chen, Qiang; Ursini, Gianluca; Romer, Adrienne L et al. (2018) Schizophrenia polygenic risk score predicts mnemonic hippocampal activity. Brain 141:1218-1228
Reed, Jessica L; D'Ambrosio, Enrico; Marenco, Stefano et al. (2018) Interaction of childhood urbanicity and variation in dopamine genes alters adult prefrontal function as measured by functional magnetic resonance imaging (fMRI). PLoS One 13:e0195189
Ursini, Gianluca; Punzi, Giovanna; Chen, Qiang et al. (2018) Convergence of placenta biology and genetic risk for schizophrenia. Nat Med 24:792-801
Xiao, Ena; Chen, Qiang; Goldman, Aaron L et al. (2017) Late-Onset Alzheimer's Disease Polygenic Risk Profile Score Predicts Hippocampal Function. Biol Psychiatry Cogn Neurosci Neuroimaging 2:673-679
Reed, Jessica L; Gallagher, Natalie M; Sullivan, Marie et al. (2017) Sex differences in verbal working memory performance emerge at very high loads of common neuroimaging tasks. Brain Cogn 113:56-64
Gregory, Michael D; Kippenhan, J Shane; Eisenberg, Daniel P et al. (2017) Neanderthal-Derived Genetic Variation Shapes Modern Human Cranium and Brain. Sci Rep 7:6308
Dickinson, Dwight; Pratt, Danielle N; Giangrande, Evan J et al. (2017) Attacking Heterogeneity in Schizophrenia by Deriving Clinical Subgroups From Widely Available Symptom Data. Schizophr Bull :
Jabbi, Mbemba; Cropp, Brett; Nash, Tiffany et al. (2017) BDNF Val66Met polymorphism tunes frontolimbic circuitry during affective contextual learning. Neuroimage 162:373-383
Marenco, Stefano; Meyer, Christian; Kuo, Susan et al. (2016) Prefrontal GABA Levels Measured With Magnetic Resonance Spectroscopy in Patients With Psychosis and Unaffected Siblings. Am J Psychiatry 173:527-34
Masdeu, Joseph C; Dalmau, Josep; Berman, Karen F (2016) NMDA Receptor Internalization by Autoantibodies: A Reversible Mechanism Underlying Psychosis? Trends Neurosci 39:300-310

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