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Synapse elimination and learning rules co-regulated by MHC class I H2-Db. Nature [Epub ahead of print
, 2014
"... The formation of precise connections between retina and LGN involves the activity-dependent elimination of some synapses, with strengthening and retention of others. Here we show that the MHC Class I (MHCI) molecule H2-Db is necessary and sufficient for synapse elimination in the retinogeniculate sy ..."
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The formation of precise connections between retina and LGN involves the activity-dependent elimination of some synapses, with strengthening and retention of others. Here we show that the MHC Class I (MHCI) molecule H2-Db is necessary and sufficient for synapse elimination in the retinogeniculate system. In mice lacking both H2-Kb and H2-Db (KbDb−/−) despite intact retinal activity and basal synaptic transmission, the developmentally-regulated decrease in functional convergence of retinal ganglion cell synaptic inputs to LGN neurons fails and eye-specific layers do not form. Neuronal expression of just H2-Db in KbDb−/ − mice rescues both synapse elimination and eye specific segregation despite a compromised immune system. When patterns of stimulation mimicking endogenous retinal waves are used to probe synaptic learning rules at retinogeniculate synapses, LTP is intact but LTD is impaired in KbDb−/ − mice. This change is due to an increase in Ca2+ permeable AMPA receptors. Restoring H2-Db to KbDb−/ − neurons renders AMPA receptors Ca2+ impermeable and rescues LTD. These observations reveal an MHCI mediated link between developmental synapse pruning and balanced synaptic learning rules enabling both LTD and LTP, and demonstrate a direct requirement for H2-Db in functional
Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation
, 2014
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"... JEPEG: a summary statistics based tool for gene-level joint test-ing of functional variants ..."
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JEPEG: a summary statistics based tool for gene-level joint test-ing of functional variants
Genome analysis JEPEG: a summary statistics based tool for gene-level joint testing of functional variants
"... Motivation: Gene expression is influenced by variants commonly known as expression quantitative trait loci (eQTL). On the basis of this fact, researchers proposed to use eQTL/functional information univariately for prioritizing single nucleotide polymorphisms (SNPs) signals from genome-wide asso-cia ..."
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Motivation: Gene expression is influenced by variants commonly known as expression quantitative trait loci (eQTL). On the basis of this fact, researchers proposed to use eQTL/functional information univariately for prioritizing single nucleotide polymorphisms (SNPs) signals from genome-wide asso-ciation studies (GWAS). However, most genes are influenced by multiple eQTLs which, thus, jointly affect any downstream phenotype. Therefore, when compared with the univariate prioritization ap-proach, a joint modeling of eQTL action on phenotypes has the potential to substantially increase sig-nal detection power. Nonetheless, a joint eQTL analysis is impeded by (i) not measuring all eQTLs in a gene and/or (ii) lack of access to individual genotypes. Results: We propose joint effect on phenotype of eQTL/functional SNPs associated with a gene (JEPEG), a novel software tool which uses only GWAS summary statistics to (i) impute the sum-mary statistics at unmeasured eQTLs and (ii) test for the joint effect of all measured and imputed eQTLs in a gene. We illustrate the behavior/performance of the developed tool by analysing the GWAS meta-analysis summary statistics from the Psychiatric Genomics Consortium Stage 1 and the Genetic Consortium for Anorexia Nervosa.
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"... Synaptic, transcriptional, and chromatin genes disrupted in autism A full list of authors and affiliations appears at the end of the article. Summary The genetic architecture of autism spectrum disorder involves the interplay of common and rare variation and their impact on hundreds of genes. Using ..."
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Synaptic, transcriptional, and chromatin genes disrupted in autism A full list of authors and affiliations appears at the end of the article. Summary The genetic architecture of autism spectrum disorder involves the interplay of common and rare variation and their impact on hundreds of genes. Using exome sequencing, analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, and a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5 % of autistic subjects. Many of the genes implicated encode proteins for synaptic, transcriptional, and chromatin remodeling pathways. These include voltage-gated ion channels regulating propagation of action potentials, pacemaking, and excitability-transcription coupling, as well as histone-modifying enzymes and chromatin remodelers, prominently histone post-translational modifications involving lysine methylation/demethylation. Features of subjects with autism spectrum disorder (ASD) include compromised social communication and interaction. Because the bulk of risk arises from de novo and inherited genetic variation1-10, characterizing which genes are involved informs on ASD neurobiology and on what makes us social beings. Whole-exome sequencing (WES) studies have proved fruitful in uncovering risk-conferring variation, especially by enumerating de novo variation, which is sufficiently rare that recurrent mutations in a gene provide strong causal evidence. De novo loss-of-function (LoF) single-nucleotide variants (SNV) or insertion/deletion (indel) variants11-15 are found Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms *Correspondence should be addressed to J.D.B.
Research Review: Polygenic methods and their
"... Background: Despite evidence from twin and family studies for an important contribution of genetic factors to both childhood and adult onset psychiatric disorders, identifying robustly associated specific DNA variants has proved challenging. In the pregenomics era the genetic architecture (number, f ..."
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Background: Despite evidence from twin and family studies for an important contribution of genetic factors to both childhood and adult onset psychiatric disorders, identifying robustly associated specific DNA variants has proved challenging. In the pregenomics era the genetic architecture (number, frequency and effect size of risk variants) of complex genetic disorders was unknown. Empirical evidence for the genetic architecture of psychiatric disorders is emerging from the genetic studies of the last 5 years. Methods and scope: We review the methods investigating the polygenic nature of complex disorders. We provide mini-guides to genomic profile (or polygenic) risk scoring and to estimation of variance (or heritability) from common SNPs; a glossary of key terms is also provided. We review results of applications of the methods to psychiatric disorders and related traits and consider how these methods inform on missing heritability, hidden heritability and still-missing heritability. Findings: Genome-wide genotyping and sequencing studies are providing evidence that psychiatric disorders are truly polygenic, that is they have a genetic architecture of many genetic variants, including risk variants that are both common and rare in the population. Sample sizes published to date are mostly underpowered to detect effect sizes of the magnitude presented by nature, and these effect sizes may be constrained by the biological validity of the diagnostic constructs. Conclusions: Increasing the sample size for genome wide association studies of psychiatric disorders will lead to the identification of more associated genetic variants, as already found for schizophrenia. These loci provide the starting point of
Biological Insights From 108 Schizophrenia-Associated Genetic Loci
"... A full list of authors and affiliations appears at the end of the article. Summary 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 ..."
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A full list of authors and affiliations appears at the end of the article. Summary 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 novel insights into aetiology, but associations at DRD2 and multiple 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 play important roles in immunity, providing support for the hypothesized link between the immune system and schizophrenia. Schizophrenia has a life time risk of around 1%, and is associated with substantial morbidity and mortality as well as personal and societal costs.1-3 Although pharmacological treatments are available for schizophrenia, their efficacy is poor for many patients.4 All available antipsychotic drugs are thought to exert their main therapeutic effects through blockade of Correspondence and requests for materials should be addressed to Michael O’Donovan: odonovanmc@cardiff.ac.uk or
IN
, 2015
"... The influence of DNA methylation on gene expression involved in the etiology and treatment of psychiatric disorders ..."
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The influence of DNA methylation on gene expression involved in the etiology and treatment of psychiatric disorders
miRNAs in NMDA receptor-dependent synaptic plasticity and psychiatric disorders
, 2016
"... Abstract The identification and functional delineation of miRNAs (a class of small non-coding RNAs) have added a new layer of complexity to our understanding of the molecular mechanisms underlying synaptic plasticity. Genome-wide association studies in conjunction with investigations in cellular an ..."
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Abstract The identification and functional delineation of miRNAs (a class of small non-coding RNAs) have added a new layer of complexity to our understanding of the molecular mechanisms underlying synaptic plasticity. Genome-wide association studies in conjunction with investigations in cellular and animal models, moreover, provide evidence that miRNAs are involved in psychiatric disorders. In the present review, we examine the current knowledge about the roles played by miRNAs in NMDA (N-methyl-D-aspartate) receptor-dependent synaptic plasticity and psychiatric disorders.
MAGMA: Generalized Gene-Set Analysis of GWAS Data
"... Abstract By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Sta ..."
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Abstract By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and geneset analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn's Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn's Disease data was found to be considerably faster as well. Author Summary Gene and gene-set analysis are statistical methods for analysing multiple genetic markers simultaneously to determine their joint effect. These methods can be used when the effects of individual markers is too weak to detect, which is a common problem when studying polygenic traits. Moreover, gene-set analysis can provide additional insight into functional and biological mechanisms underlying the genetic component of a trait. Although a number of methods for gene and gene-set analysis are available however, they generally suffer from various statistical issues and can be very time-consuming to run. We have therefore PLOS Computational Biology |
