Dopamine perturbation of gene co-expression networks reveals differential response in schizophrenia for translational machinery.

The dopaminergic hypothesis of schizophrenia (SZ) postulates that positive symptoms of SZ, in particular psychosis, are due to disturbed neurotransmission via the dopamine (DA) receptor D2 (DRD2). However, DA is a reactive molecule that yields various oxidative species, and thus has important non-receptor-mediated effects, with empirical evidence of cellular toxicity and neurodegeneration. Here we examine non-receptor-mediated effects of DA on gene co-expression networks and its potential role in SZ pathology. Transcriptomic profiles were measured by RNA-seq in B-cell transformed lymphoblastoid cell lines from 514 SZ cases and 690 controls, both before and after exposure to DA ex vivo (100 μM). Gene co-expression modules were identified using Weighted Gene Co-expression Network Analysis for both baseline and DA-stimulated conditions, with each module characterized for biological function and tested for association with SZ status and SNPs from a genome-wide panel. We identified seven co-expression modules under baseline, of which six were preserved in DA-stimulated data. One module shows significantly increased association with SZ after DA perturbation (baseline: P = 0.023; DA-stimulated: P = 7.8 × 10-5; ΔAIC = -10.5) and is highly enriched for genes related to ribosomal proteins and translation (FDR = 4 × 10-141), mitochondrial oxidative phosphorylation, and neurodegeneration. SNP association testing revealed tentative QTLs underlying module co-expression, notably at FASTKD2 (top P = 2.8 × 10-6), a gene involved in mitochondrial translation. These results substantiate the role of translational machinery in SZ pathogenesis, providing insights into a possible dopaminergic mechanism disrupting mitochondrial function, and demonstrates the utility of disease-relevant functional perturbation in the study of complex genetic etiologies

Estimation of Gaussian Networks and Modular Brain Functional Networks

University of Minnesota Ph.D. dissertation. November 2017. Major: Biostatistics. Advisor: Wei Pan. 1 computer file (PDF); vii, 72 pages.Graphical models are intuitive tools to demonstrate dependence relation between variables of interest. Both undirected and directed graphical models are widely used in many applications, such as reconstructing gene expression/co-expression networks and brain functional networks. A popular model for undirected graphs is the Gaussian graphical model, where conditional independence can be inferred from the absence of an edge in the graph. Another approach for estimating undirected graphs does not depend on the distribution of the data. Instead, the resulting network is constructed through transformation of empirical sample correlation and node connectivity. The estimated network connectivity measures can be used as a secondary phenotype for association tests with genotypes. Finally, new methods have been proposed to estimate directed Gaussian graphs. The direction of an edge allows easier interpretation of causal relation between nodes in the graph. We first aim to estimate multiple Gaussian graphs in the presence of sample heterogeneity, where the independent samples may come from different and unknown populations or distributions. We embed in the framework of a Gaussian mixture model one of two recently proposed methods for estimating multiple precision matrices in Gaussian graphical models. Secondly, we adapt a weighted gene co-expression network analysis (WGCNA) framework to resting-state fMRI (rs-fMRI) data to identify modular structures in brain functional networks. We propose applying a new adaptive test built on the proportional odds model (POM) that can be applied to a high-dimensional setting, where the number of variables (p) can exceed the sample size (n) in addition to the usual p < n setting. Finally, we implemented a new method for estimating directed acyclic graph (DAG) as an R package, and demonstrated its use via application to a real data set and simulation studies

Semiparametric Bayes conditional graphical models for imaging genetics applications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135205/1/sta4119_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135205/2/sta4119.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135205/3/sta4119-sup-0002-Supplementary2.pd

IMAGING GENOMICS

Imaging genomics is an emerging research field, where integrative analysis of imaging and omics data is performed to provide new insights into the phenotypic characteristics and genetic mechanisms of normal and/or disordered biological structures and functions, and to impact the development of new diagnostic, therapeutic and preventive approaches. The Imaging Genomics Session at PSB 2017 aims to encourage discussion on fundamental concepts, new methods and innovative applications in this young and rapidly evolving field

5HTTLPR Polymorphism, stressful events, neuropsychological performance and brain connectivity in eating disorders

Abstract Introduction. Low functioning variants of 5HTTLPR have been associated to an increased risk of depression in subjects who experienced stressful events, to altered cognitive functioning and decisional processes, and functional and structural neural patterns. Contrasting evidence is available up to now in Eating Disorders (ED), and no study has evaluated the polymorphism effect on brain connectivity according to graph theory in Anorexia Nervosa (AN). Methods. We recruited up to 735 patients with life-time history of AN or bulimia nervosa (BN) according to DSM-IV criteria and up to 241 healthy controls (HC) for the assessment of the association between 5HTTLPR polymorphism and ED. We merged our Biobank data from BIO.Ve.D.A. and meta-analyzed 22 former studies. Patients underwent a structured diagnostic interview for present or life-time ED, an interview for presence and severity of stressful events, Edinburgh Handedness Inventory, Wisconsin Card Sorting Test, Trail A making test, Trail B making test, Iowa Gambling Task, Cognitive Bias Task, psychopathology rating scales for ED and general symptoms. Finally patients with AN and HCs underwent a Magnetic Resonance; their brains’ connectivity integration and segregation measures were then measured with Graph Analysis Toolbox, according to 5HTTLPR polymorpshim. Results. Our results from a meta-analysis including data from BIO.Ve.D.A. and 22 previous studies, suggest that 5HTTLPR polymorphism does not have a role per se in determing ED onset. However it may moderate the effect of SEs in increasing the risk of ED onset, and the influence of SEs on ED severity, anxious, depressive and obsessive symptoms. When we tested both a multiplicative and an additive model, which is considered to be more representative of a real-world gene by environment interaction, such a 5HTTLPR by SE interaction was not confirmed instead. S allele was associated with worse performance at Cognitive Bias Task and Trail Making B, and with increased ED psychopathology, general psychopathology, anxious, depressive, and obsessive symptoms. Finally S allele was associated with decreased segregation measures at brain connectivity analysis according to graph theory compared with L allele in AN; this was an opposite association compared with healthy controls who had higher modularity associated with S allele instead. Conclusions. 5HTTLPR polymorphism does not seem to be a causal factor of ED per se, but it seems to play a role in moderating the role of stressful events in increasing ED risk. Such a moderation however did not reflect a gene by environment interaction according to either a multiplicative or additive model. S allele was associated with higher psychopathology scores, and worse neuropsychological functions in AN, and with a disrupted segregation measures of brain signal connectivity compared to HCs

FAM222A encodes a protein which accumulates in plaques in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by amyloid plaques and progressive cerebral atrophy. Here, we report FAM222A as a putative brain atrophy susceptibility gene. Our cross-phenotype association analysis of imaging genetics indicates a potential link between FAM222A and AD-related regional brain atrophy. The protein encoded by FAM222A is predominantly expressed in the CNS and is increased in brains of patients with AD and in an AD mouse model. It accumulates within amyloid deposits, physically interacts with amyloid-β (Aβ) via its N-terminal Aβ binding domain, and facilitates Aβ aggregation. Intracerebroventricular infusion or forced expression of this protein exacerbates neuroinflammation and cognitive dysfunction in an AD mouse model whereas ablation of this protein suppresses the formation of amyloid deposits, neuroinflammation and cognitive deficits in the AD mouse model. Our data support the pathological relevance of protein encoded by FAM222A in AD

Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases

Mapping perturbed molecular circuits that underlie complex diseases remains a great challenge. We developed a comprehensive resource of 394 cell type– and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity among transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) showed that disease-associated genetic variants—including variants that do not reach genome-wide significance—often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues. Our resource opens the door to systematic analysis of regulatory programs across hundreds of human cell types and tissue

Computational analysis of innate and adaptive immune responses

Both innate and adaptive immune processes rely on the activation of differentiated haematopoietic stem cell lineages to affect an appropriate response to pathogens. This thesis employs a largely network biology focused approach to better understand the specificity of immune cell responses in two distinct cases of pathogenic challenge. In the context of adaptive immunity, I studied the transcriptional responses of T cells during Graft-versus-Host Disease (GvHD). GvHD represents one of the major complications to arise following allogeneic hematopoietic stem cell transplantation and yet why only particular organs are damaged as a result of this pathology is still unclear. To investigate whether key GvHD transcriptional signatures seen in effector CD8+ T cells compared to naïve T cells are triggered in target organs or the secondary lymphoid organs, a module-based association test was developed to combine the output of gene clustering algorithms with that of differential expression analysis. This methodology significantly aided the identification of skin specific effector T cell transcriptional programs believed to drive murine GvHD pathogenesis at this site. Turning to the innate immune response, I investigated the transcriptional profiles of resting and activated macrophages in the setting of Tuberculosis (TB), the second leading cause of death from infectious disease worldwide. Regression-based analyses and clustering of macrophage expression data provided insight into the variations in gene expression profiles of naïve macrophages compared to those infected with Mycobacterium tuberculosis (MTB) or a vaccine strain of mycobacteria (BCG). The availability of genotype data as part of the macrophage dataset facilitated an expression quantitative trait loci (eQTL) study which highlighted a novel association between the cytoskeleton gene BCAR1 and TB risk, together with a previously undescribed trans-eQTL module specific to MTB infected macrophages. Potential genetic variants impacting expression of the aforementioned GvHD specific T cell transcriptional signatures were additionally investigated using external trans-eQTL datasets

Neurobiological Foundations Of Stability And Flexibility

In order to adapt to changing and uncertain environments, humans and other organisms must balance stability and flexibility in learning and behavior. Stability is necessary to learn environmental regularities and support ongoing behavior, while flexibility is necessary when beliefs need to be revised or behavioral strategies need to be changed. Adjusting the balance between stability and flexibility must often be based on endogenously generated decisions that are informed by information from the environment but not dictated explicitly. This dissertation examines the neurobiological bases of such endogenous flexibility, focusing in particular on the role of prefrontally-mediated cognitive control processes and the neuromodulatory actions of dopaminergic and noradrenergic systems. In the first study (Chapter 2), we examined the role of frontostriatal circuits in instructed reinforcement learning. In this paradigm, inaccurate instructions are given prior to trial-and-error learning, leading to bias in learning and choice. Abandoning the instructions thus necessitates flexibility. We utilized transcranial direct current stimulation over dorsolateral prefrontal cortex to try to establish a causal role for this area in this bias. We also assayed two genes, the COMT Val158Met genetic polymorphism and the DAT1/SLC6A3 variable number tandem repeat, which affect prefrontal and striatal dopamine, respectively. The results support the role of prefrontal cortex in biasing learning, and provide further evidence that individual differences in the balance between prefrontal and striatal dopamine may be particularly important in the tradeoff between stability and flexibility. In the second study (Chapter 3), we assess the neurobiological mechanisms of stability and flexibility in the context of exploration, utilizing fMRI to examine dynamic changes in functional brain networks associated with exploratory choices. We then relate those changes to changes in norepinephrine activity, as measured indirectly via pupil diameter. We find tentative support for the hypothesis that increased norepinephrine activity around exploration facilitates the reorganization of functional brain networks, potentially providing a substrate for flexible exploratory states. Together, this work provides further support for the framework that stability and flexibility entail both costs and benefits, and that optimizing the balance between the two involves interactions of learning and cognitive control systems under the influence of catecholamines

NEW STATISTICAL METHODS FOR HIGH-DIMENSIONAL DATA WITH COMPLEX STRUCTURES

The overwhelming advances in biomedical technology facilitate the availability of high-dimensional biomedical data with complex and organized structures. However, due to the obscured true signals by substantial false-positive noises and the high dimensionality, the statistical inference is challenging with the critical issue of research reproducibility and replicability. Hence, motivated by these urgent needs, this dissertation is devoted to statistical approaches in understanding the latent structures among biomedical objects, as well as improving statistical power and reducing false-positive errors in statistical inference. The first objective of this dissertation is motivated by the group-level brain connectome analysis in neuropsychiatric research with the goal of exhibiting the connectivity abnormality between clinical groups. In Chapter 2, we develop a likelihood-based adaptive dense subgraph discovery (ADSD) procedure to identify connectomic subnetworks (subgraphs) that are systematically associated with brain disorders. We propose the statistical inference procedure leveraging graph properties and combinatorics. We validate the proposed method by a brain fMRI study for schizophrenia research and synthetic data under various settings. In Chapter 3, we are interested in assessing the genetic effects on brain structural imaging with spatial specificity. In contrast to the inference on individual SNP-voxel pairs, we focus on the systematic associations between genetic and imaging measurements, which assists the understanding of a polygenic and pleiotropic association structure. Based on voxel-wise genome-wide association analysis (vGWAS), we characterize the polygenic and pleiotropic SNP-voxel association structure using imaging-genetics dense bi-cliques (IGDBs). We develop the estimation procedure and statistical inference framework on the IGDBs with computationally efficient algorithms. We demonstrate the performance of the proposed approach using imaging-genetics data from the human connectome project (HCP). Chapter 4 carries the analysis of gene co-expression network (GCN) in examining the gene-gene interactions and learning the underlying complex yet highly organized gene regulatory mechanisms. We propose the interconnected community network (ICN) structure that allows the interactions between genes from different communities, which relaxes the constraint of most existing GCN analysis approaches. We develop a computational package to detect the ICN structure based on graph norm shrinkage. The application of ICN detection is illustrated using an RNA-seq data from The Cancer Genome Atlas (TCGA) Acute Myeloid Leukemia (AML) study