Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap
TL;DR: Autism, schizophrenia, and bipolar disorder share global gene expression patterns, characterized by astrocyte activation and disrupted synaptic processes, which provide a systems-level view of the neurobiological architecture of major neuropsychiatric illness and demonstrate pathways of molecular convergence and specificity.
Abstract: Recent large-scale studies have identified multiple genetic risk factors for mental illness and indicate a complex, polygenic, and pleiotropic genetic architecture for neuropsychiatric disease. However, little is known about how genetic variants yield brain dysfunction or pathology. We use transcriptomic profiling as an unbiased, quantitative readout of molecular phenotypes across 5 major psychiatric disorders, including autism (ASD), schizophrenia (SCZ), bipolar disorder (BD), depression (MDD), and alcoholism (AAD), compared with carefully matched controls. We identify a clear pattern of shared and distinct gene-expression perturbations across these conditions, identifying neuronal gene co-expression modules downregulated across ASD, SCZ, and BD, and astrocyte related modules most prominently upregulated in ASD and SCZ. Remarkably, the degree of sharing of transcriptional dysregulation was strongly related to polygenic (SNP-based) overlap across disorders, indicating a significant genetic component. These findings provide a systems-level view of the neurobiological architecture of major neuropsychiatric illness and demonstrate pathways of molecular convergence and specificity.
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TL;DR: Regulation of the hippo signaling pathway was commonly associated with ASD, SCZ, BD and OCD, implicating neural development and neuronal maintenance as key in neuropsychiatric disorders.
59 citations
Douglas M. Ruderfer1, Ayman H. Fanous2, Ayman H. Fanous3, Ayman H. Fanous4, Stephan Ripke5, Stephan Ripke6, Andrew McQuillin7, Richard Amdur3, Pablo V. Gejman8, Michael Conlon O'Donovan9, Ole A. Andreassen10, Srdjan Djurovic10, Christina M. Hultman11, John R. Kelsoe12, John R. Kelsoe3, Stéphane Jamain13, Stéphane Jamain14, Mikael Landén15, Mikael Landén11, Marion Leboyer14, Marion Leboyer13, Vishwajit L. Nimgaonkar16, John I. Nurnberger17, Jordan W. Smoller6, Nicholas John Craddock9, Aiden Corvin18, Patrick Sullivan19, Peter Holmans9, Pamela Sklar1, Kenneth S. Kendler2 •
Icahn School of Medicine at Mount Sinai1, Virginia Commonwealth University2, Veterans Health Administration3, Georgetown University4, Broad Institute5, Harvard University6, University College London7, NorthShore University HealthSystem8, Cardiff University9, Oslo University Hospital10, Karolinska Institutet11, University of California, San Diego12, University of Paris13, French Institute of Health and Medical Research14, University of Gothenburg15, University of Pittsburgh16, Indiana University17, Trinity College, Dublin18, University of North Carolina at Chapel Hill19
TL;DR: In this paper, the authors performed a combined genome-wide association study (GWAS) of 19 779 bipolar disorder (BP) and schizophrenia (SCZ) cases versus 19 423 controls, in addition to a direct comparison GWAS of 7129 SCZ cases versus 9252 BP cases.
Abstract: Bipolar disorder and schizophrenia are two often severe disorders with high heritabilities. Recent studies have demonstrated a large overlap of genetic risk loci between these disorders but diagnostic and molecular distinctions still remain. Here, we perform a combined genome-wide association study (GWAS) of 19 779 bipolar disorder (BP) and schizophrenia (SCZ) cases versus 19 423 controls, in addition to a direct comparison GWAS of 7129 SCZ cases versus 9252 BP cases. In our case-control analysis, we identify five previously identified regions reaching genome-wide significance (CACNA1C, IFI44L, MHC, TRANK1 and MAD1L1) and a novel locus near PIK3C2A. We create a polygenic risk score that is significantly different between BP and SCZ and show a significant correlation between a BP polygenic risk score and the clinical dimension of mania in SCZ patients. Our results indicate that first, combining diseases with similar genetic risk profiles improves power to detect shared risk loci and second, that future direct comparisons of BP and SCZ are likely to identify loci with significant differential effects. Identifying these loci should aid in the fundamental understanding of how these diseases differ biologically. These findings also indicate that combining clinical symptom dimensions and polygenic signatures could provide additional information that may someday be used clinically.
48 citations
30 Mar 2019
TL;DR: The results provide a manageable list of anchors from which to investigate epigenetic mechanism or gene-gene interaction on the development of neuropsychiatric disorders and for developing a measurement matrix for disease risk to potentially develop a novel taxonomy for precision medicine.
Abstract: Background. Genetic correlation and pleiotropic effects among psychiatric disorders have been reported. This study aimed to identify specific common genetic variants shared between five adult psychiatric disorders: schizophrenia, bipolar, major depressive disorder, attention deficit-hyperactivity disorder, and autism spectrum disorder.
Methods. A combined p-value of about 8 million single nucleotide polymorphisms (SNPs) was calculated in an equivalent sample of 151,672 cases and 284,444 controls of European ancestry from published data based on the latest genome-wide association studies of five major psychiatric disorder. SNPs that achieved genome-wide significance (P<5x10-08) were mapped to loci and genomic regions for further investigation; gene annotation and clustering were performed to understand the biological process and molecular function of the loci identified. We also examined CNVs and performed expression quantitative trait loci analysis for SNPs by genomic region.
Results. We find that 6,293 SNPs mapped to 336 loci shared by the three adult psychiatric disorders, 1,108 variants at 73 loci shared by the childhood disorders, and 713 variants at 47 genes shared by all five disorders at genome-wide significance (P<5x10-08). Of the 2,583 SNPs at the extended major histocompatibility complex identified for three adult disorders, none of them were associated with childhood disorders; and SNPs shared by all five disorders were located in regions that have been identified as containing copy number variation associated with autism and had largely neurodevelopmental functions.
Conclusion. We show a number of specific SNPs associated with psychiatric disorders of childhood or adult-onset, illustrating not only genetic heterogeneity across these disorders but also developmental genes shared by them all. These results provide a manageable list of anchors from which to investigate epigenetic mechanism or gene-gene interaction on the development of neuropsychiatric disorders and for developing a measurement matrix for disease risk to potentially develop a novel taxonomy for precision medicine.
10 citations
TL;DR: Analysis of global SC indicated that BN-related cortical thinning preferentially occurred in regions with high global connectivity and it was shown that individuals’ contribution to global SC at the group level were significantly related to EDE-Q score.
Abstract: Bulimia nervosa (BN) is a serious psychiatric illness defined by preoccupation with weight and shape, episodic binge-eating and compensatory behaviors. Although diagnosed BN has been associated with diffuse grey matter volume reductions, characterization of brain structure alterations in women with a range of BN symptoms has yet to be made. This study examined whether changes in cortical thickness (CT) scaled with BN symptom severity in a sample of 33 adult women (n = 10 BN; n = 5 EDNOS-BN). Our second objective was to assess global structural connectivity (SC) of CT and to determine if individual differences in global SC relate to BN symptom severity. We used the validated Eating Disorder Examination Questionnaire (EDE-Q; Fairburn & Beglin, 1994) as a continuous measure of BN symptom severity. Increased EDE-Q score was negatively related to global CT and local CT in the left middle frontal gyrus, right superior frontal gyrus and bilateral orbitofrontal cortex (OFC) and temporoparietal regions. Moreover, analysis of global SC indicated that BN-related cortical thinning preferentially occurred in regions with high global connectivity. Finally, we showed that individuals' contribution to global SC at the group level were significantly related to EDE-Q score, where increased EDE-Q score correlated with reduced connectivity of the left OFC and middle temporal cortex and increased connectivity of the right superior parietal lobule. Our findings offer novel insight into CT alterations in BN and further suggest that the combination of CT and structural connectivity measures may be sensitive to individual differences in BN symptom severity.
1 citations
References
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TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
22,147 citations
TL;DR: The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis that includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software.
Abstract: Correlation networks are increasingly being used in bioinformatics applications For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets These methods have been successfully applied in various biological contexts, eg cancer, mouse genetics, yeast genetics, and analysis of brain imaging data While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software Along with the R package we also present R software tutorials While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings The WGCNA package provides R functions for weighted correlation network analysis, eg co-expression network analysis of gene expression data The R package along with its source code and additional material are freely available at http://wwwgeneticsuclaedu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA
14,243 citations
TL;DR: The metafor package provides functions for conducting meta-analyses in R and includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models.
Abstract: The metafor package provides functions for conducting meta-analyses in R. The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. Meta-regression analyses with continuous and categorical moderators can be conducted in this way. Functions for the Mantel-Haenszel and Peto's one-step method for meta-analyses of 2 x 2 table data are also available. Finally, the package provides various plot functions (for example, for forest, funnel, and radial plots) and functions for assessing the model fit, for obtaining case diagnostics, and for tests of publication bias.
11,237 citations
Teri A. Manolio1, Francis S. Collins1, Nancy J. Cox2, David Goldstein3, Lucia A. Hindorff1, David J. Hunter4, Mark I. McCarthy5, Erin M. Ramos1, Lon R. Cardon6, Aravinda Chakravarti7, Judy H. Cho8, Alan E. Guttmacher1, Augustine Kong9, Leonid Kruglyak10, Leonid Kruglyak11, Elaine R. Mardis12, Charles N. Rotimi1, Montgomery Slatkin13, David Valle7, Alice S. Whittemore14, Michael Boehnke15, Andrew G. Clark16, Evan E. Eichler17, Greg Gibson18, Jonathan L. Haines19, Trudy F. C. Mackay20, Steven A. McCarroll4, Peter M. Visscher21 •
National Institutes of Health1, University of Chicago2, Duke University3, Harvard University4, University of Oxford5, GlaxoSmithKline6, Johns Hopkins University7, Yale University8, deCODE genetics9, Princeton University10, Howard Hughes Medical Institute11, Washington University in St. Louis12, University of California, Berkeley13, Stanford University14, University of Michigan15, Cornell University16, University of Washington17, University of Queensland18, Vanderbilt University19, North Carolina State University20, QIMR Berghofer Medical Research Institute21
TL;DR: This paper examined potential sources of missing heritability and proposed research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
Abstract: Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
7,797 citations
TL;DR: Associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses.
Abstract: Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.
6,809 citations
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