Publications by authors named "Tova Fuller"

Until recently, advances in understanding the genetic architecture of psychiatric disorders have been impeded by a historic, and often mandated, commitment to the use of traditional, and unvalidated, categorical diagnoses in isolation as the relevant phenotype. Such studies typically required lengthy structured interviews to delineate differences in the character and duration of behavioral symptomatology amongst disorders that were thought to be etiologic, and they were often underpowered as a result. Increasing acceptance of the fact that co-morbidity in psychiatric disorders is the rule rather than the exception has led to alternative designs in which shared dimensional symptomatology is analyzed as a quantitative trait and to association analyses in which combined polygenic risk scores are computationally compared across multiple traditional categorical diagnoses to identify both distinct and unique genetic and environmental elements.

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Unlabelled: Introduction The number of civilians killed in Iraq following the 2003 invasion has proven difficult to measure and contentious in recent years. The release of the Wikileaks War Logs (WL) has created the potential to conduct a sensitivity analysis of the commonly-cited Iraq Body Count's (IBC's) tally, which is based on press, government, and other public sources. Hypothesis The 66,000 deaths reported in the Wikileaks War Logs are mostly the same events as those previously reported in the press and elsewhere as tallied by iraqbodycount.

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Despite large-scale genome-wide association studies (GWAS), the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls.

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Background: Since human brain tissue is often unavailable for transcriptional profiling studies, blood expression data is frequently used as a substitute. The underlying hypothesis in such studies is that genes expressed in brain tissue leave a transcriptional footprint in blood. We tested this hypothesis by relating three human brain expression data sets (from cortex, cerebellum and caudate nucleus) to two large human blood expression data sets (comprised of 1463 individuals).

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Weighted gene coexpression network analysis (WGCNA) has been applied to many important studies since its introduction in 2005. WGCNA can be used as a data exploratory tool or as a gene screening method; WGCNA can also be used as a tool to generate testable hypothesis for validation in independent data sets. In this article, we review key concepts of WGCNA and some of its applications in gene expression analysis of oncology, brain function, and protein interaction data.

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Background: We performed gene expression profiling of the amygdala and hippocampus taken from inbred mouse strains C57BL/6J and A/J. The selected brain areas are implicated in neurobehavioral traits while these mouse strains are known to differ widely in behavior. Consequently, we hypothesized that comparing gene expression profiles for specific brain regions in these strains might provide insight into the molecular mechanisms of human neuropsychiatric traits.

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Background: Amyotrophic Lateral Sclerosis (ALS) is a lethal disorder characterized by progressive degeneration of motor neurons in the brain and spinal cord. Diagnosis is mainly based on clinical symptoms, and there is currently no therapy to stop the disease or slow its progression. Since access to spinal cord tissue is not possible at disease onset, we investigated changes in gene expression profiles in whole blood of ALS patients.

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Background: Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.

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Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes-a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F(2) mouse inter-cross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules.

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