Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

Motivation: Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. Results: We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype

Mining high-level brain imaging genetic associations

Indiana University-Purdue University Indianapolis (IUPUI)Imaging genetics is an emerging research field in neurodegenerative diseases. It studies the influence of genetic variants on brain structure and function. Genome-wide association studies (GWAS) of brain imaging has identified a few independent risk loci for individual imaging quantitative trait (iQT), which however display only modest effect size and explain limited heritability. This thesis focuses on mining high-level imaging genetic associations and their applications on neurodegenerative diseases. This thesis first presents a novel network-based GWAS framework for identifying functional modules, by employing a two-step strategy in a top-down manner. It first integrates tissue-specific network with GWAS of corresponding phenotype in regression models in addition to classification, to re-prioritize genome-wide associations. Then it detects densely connected and disease-relevant modules based on interactions among top reprioritizations. The discovered modules hold both phenotypical specificity and densely interaction. We applied it to an amygdala imaging genetics analysis in the study of Alzheimer's disease (AD). The proposed framework effectively detects densely interacted modules; and the reprioritizations achieve highest concordance with AD genes. We then present an extension of the above framework, named GWAS top-neighbor-based (tnGWAS); and compare it with previous approaches. This tnGWAS extracts densely connected modules from top GWAS findings, based on the hypothesis that relevant modules consist of top GWAS findings and their close neighbors. It is applied to a hippocampus imaging genetics analysis in AD research, and yields the densest interactions among top candidate genes. Experimental results demonstrate that precise context does help explore collective effects of genes with functional interactions specific to the studied phenotype. In the second part, a novel imaging genetic enrichment analysis (IGEA) paradigm is proposed for discovering complex associations among genetic modules and brain circuits. In addition to genetic modules, brain regions of interest also grouped to play role. We expand the scope of one-dimensional enrichment analysis into imaging genetics. This framework jointly considers meaningful gene sets (GS) and brain circuits (BC), and examines whether given GS-BC module is enriched in gene-iQT findings. We conduct the proof-of-concept study and demonstrate its performance by applying to a brain-wide imaging genetics study of AD

Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

System-Level Analysis of Alzheimer\u27s Disease Prioritizes Candidate Genes for Neurodegeneration.

Alzheimer\u27s disease (AD) is a debilitating neurodegenerative disorder. Since the advent of the genome-wide association study (GWAS) we have come to understand much about the genes involved in AD heritability and pathophysiology. Large case-control meta-GWAS studies have increased our ability to prioritize weaker effect alleles, while the recent development of network-based functional prediction has provided a mechanism by which we can use machine learning to reprioritize GWAS hits in the functional context of relevant brain tissues like the hippocampus and amygdala. In parallel with these developments, groups like the Alzheimer\u27s Disease Neuroimaging Initiative (ADNI) have compiled rich compendia of AD patient data including genotype and biomarker information, including derived volume measures for relevant structures like the hippocampus and the amygdala. In this study we wanted to identify genes involved in AD-related atrophy of these two structures, which are often critically impaired over the course of the disease. To do this we developed a combined score prioritization method which uses the cumulative distribution function of a gene\u27s functional and positional score, to prioritize top genes that not only segregate with disease status, but also with hippocampal and amygdalar atrophy. Our method identified a mix of genes that had previously been identified in AD GWAS including APOE, TOMM40, and NECTIN2(PVRL2) and several others that have not been identified in AD genetic studies, but play integral roles in AD-effected functional pathways including IQSEC1, PFN1, and PAK2. Our findings support the viability of our novel combined score as a method for prioritizing region- and even cell-specific AD risk genes

Spatiotemporal dynamics of the postnatal developing primate brain transcriptome.

Developmental changes in the temporal and spatial regulation of gene expression drive the emergence of normal mature brain function, while disruptions in these processes underlie many neurodevelopmental abnormalities. To solidify our foundational knowledge of such changes in a primate brain with an extended period of postnatal maturation like in human, we investigated the whole-genome transcriptional profiles of rhesus monkey brains from birth to adulthood. We found that gene expression dynamics are largest from birth through infancy, after which gene expression profiles transition to a relatively stable state by young adulthood. Biological pathway enrichment analysis revealed that genes more highly expressed at birth are associated with cell adhesion and neuron differentiation, while genes more highly expressed in juveniles and adults are associated with cell death. Neocortex showed significantly greater differential expression over time than subcortical structures, and this trend likely reflects the protracted postnatal development of the cortex. Using network analysis, we identified 27 co-expression modules containing genes with highly correlated expression patterns that are associated with specific brain regions, ages or both. In particular, one module with high expression in neonatal cortex and striatum that decreases during infancy and juvenile development was significantly enriched for autism spectrum disorder (ASD)-related genes. This network was enriched for genes associated with axon guidance and interneuron differentiation, consistent with a disruption in the formation of functional cortical circuitry in ASD

Systems Approaches For Gene and Drug Discovery in Alzheimer’s Disease

Alzheimer’s Disease (AD) is a devastating neurodegenerative disorder affecting all tissues and cell types of brain leading to emotional dysregulation and cognitive dysfunction. From genome-wide association studies (GWAS), to date we have identified forty-two genome-wide significant genes for AD that influence overall disease risk or endophenotypes, including neuroimaging and gene expression profiles. Nevertheless, the currently known AD genes do not account for a significant proportion of the heritability of disease risk, implying the existence of many weak-effect variants in potentially thousands of genes as drivers of AD outcomes. This genetic architecture, composed of many small effects, is partly due to the complexity of molecular interaction networks, where the influence of individual genetic variants is attenuated by overlapping molecular pathways that are tuned by evolution for robustness. Thus, overcoming the limitations of GWAS for dissecting the mechanisms of AD requires methods to identify disease pathways that are enriched for weak genetic effects. Network-based functional prediction (NBFP) methods use machine learning in gene interaction networks to robustly learn pathways containing risk genes, which augments the raw statistical signal from GWAS with biological prior knowledge encoded in a tissue-specific gene network. NBFP methods have several benefits, including their robustness to statistical noise over raw GWAS statistics and they enable nomination of functionally relevant candidate genes that do not themselves carry risk polymorphisms. However, most NBFP methods are currently limited to single tissues, which is not optimal for complex disorders like AD that involve many functionally distinct cell types and brain regions. Moreover, there are now multiple studies of AD endophenotypes, including brain-region specific gene expression and whole-brain neuroimaging, both paired with genotypes, and single-cell gene expression data. Thus, there is an additional need for integrative tools that combine disparate data sources to nominate candidate genes for distinct pathophysiological processes. In this work, we developed new methods to rank candidate genes based on multiple disease-relevant networks and to combine gene rankings arising from multiple sources, including NBFP, imaging GWAS, and gene expression. In our first study, we applied NBFP to systematically rank AD-risk genes in the hippocampus and amygdala and developed a novel combined scoring method to integrate these scores with GWAS associations for low hippocampal and amygdalar volume in patients with AD. Our method nominated a novel set of region-specific candidate genes primarily involved in maintaining the stability of the synapse and regulating excitotoxicity. In our second study, we developed a multi-network-based functional prediction (mNBFP) to allow multiple source networks. Using three brain-cell-specific networks, our mNBFP approach outperformed single-network approaches in training performance and achieved high concordance with recently published AD-GWAS associations. In our third and final study we integrated our multi-network NBFP and combined scoring approaches with single-cell gene expression and the Library of Integrated Network-based Cellular Signatures (LINCS) database to identify potential drug repositioning candidates for AD. We identified the protein AP1B1 as having strong potential to target an early-AD gene expression signature, which may yield a novel mechanism for early therapeutic intervention

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders.

Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology

Finding the pathology of major depression through effects on gene interaction networks

The disease signature of major depressive disorder is distributed across multiple physical scales and investigative specialties, including genes, cells and brain regions. No single mechanism or pathway currently implicated in depression can reproduce its diverse clinical presentation, which compounds the difficulty in finding consistently disrupted molecular functions. We confront these key roadblocks to depression research - multi-scale and multi-factor pathology - by conducting parallel investigations at the levels of genes, neurons and brain regions, using transcriptome networks to identify collective patterns of dysfunction. Our findings highlight how the collusion of multi-system deficits can form a broad-based, yet variable pathology behind the depressed phenotype. For instance, in a variant of the classic lethality-centrality relationship, we show that in neuropsychiatric disorders including major depression, differentially expressed genes are pushed out to the periphery of gene networks. At the level of cellular function, we develop a molecular signature of depression based on cross-species analysis of human and mouse microarrays from depression-affected areas, and show that these genes form a tight module related to oligodendrocyte function and neuronal growth/structure. At the level of brain-region communication, we find a set of genes and hormones associated with the loss of feedback between the amygdala and anterior cingulate cortex, based on a novel assay of interregional expression synchronization termed "gene coordination". These results indicate that in the absence of a single pathology, depression may be created by dysynergistic effects among genes, cell-types and brain regions, in what we term the "floodgate" model of depression. Beyond our specific biological findings, these studies indicate that gene interaction networks are a coherent framework in which to understand the faint expression changes found in depression and complex neuropsychiatric disorders