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      Epigenome-wide association study of alcohol use disorder in five brain regions

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          Abstract

          Alcohol use disorder (AUD) is closely linked to the brain regions forming the neurocircuitry of addiction. Postmortem human brain tissue enables the direct study of the molecular pathomechanisms of AUD. This study aims to identify these mechanisms by examining differential DNA-methylation between cases with severe AUD ( n = 53) and controls ( n = 58) using a brain-region-specific approach, in which sample sizes ranged between 46 and 94. Samples of the anterior cingulate cortex (ACC), Brodmann Area 9 (BA9), caudate nucleus (CN), ventral striatum (VS), and putamen (PUT) were investigated. DNA-methylation levels were determined using the Illumina HumanMethylationEPIC Beadchip. Epigenome-wide association analyses were carried out to identify differentially methylated CpG-sites and regions between cases and controls in each brain region. Weighted correlation network analysis (WGCNA), gene-set, and GWAS-enrichment analyses were performed. Two differentially methylated CpG-sites were associated with AUD in the CN, and 18 in VS ( q < 0.05). No epigenome-wide significant CpG-sites were found in BA9, ACC, or PUT. Differentially methylated regions associated with AUD case-/control status ( q < 0.05) were found in the CN ( n = 6), VS ( n = 18), and ACC ( n = 1). In the VS, the WGCNA-module showing the strongest association with AUD was enriched for immune-related pathways. This study is the first to analyze methylation differences between AUD cases and controls in multiple brain regions and consists of the largest sample to date. Several novel CpG-sites and regions implicated in AUD were identified, providing a first basis to explore epigenetic correlates of AUD.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            WGCNA: an R package for weighted correlation network analysis

            Background 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, e.g. 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. Results 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. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              METAL: fast and efficient meta-analysis of genomewide association scans

              Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu
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                Author and article information

                Contributors
                rainer.spanagel@zi-mannheim.de
                Journal
                Neuropsychopharmacology
                Neuropsychopharmacology
                Neuropsychopharmacology
                Springer International Publishing (Cham )
                0893-133X
                1740-634X
                13 November 2021
                13 November 2021
                March 2022
                : 47
                : 4
                : 832-839
                Affiliations
                [1 ]GRID grid.413757.3, ISNI 0000 0004 0477 2235, Department of Genetic Epidemiology in Psychiatry, , Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, ; Mannheim, Germany
                [2 ]GRID grid.413757.3, ISNI 0000 0004 0477 2235, Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, ; Mannheim, Germany
                [3 ]GRID grid.10388.32, ISNI 0000 0001 2240 3300, Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, ; Bonn, Germany
                [4 ]GRID grid.410718.b, ISNI 0000 0001 0262 7331, Department of Child and Adolescent Psychiatry, , University Hospital Essen, University of Duisburg-Essen, ; Essen, Germany
                [5 ]GRID grid.413757.3, ISNI 0000 0004 0477 2235, Center for Innovative Psychiatric and Psychotherapeutic Research, Biobank, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, ; Mannheim, Germany
                Author information
                http://orcid.org/0000-0003-4867-9465
                http://orcid.org/0000-0003-1080-4339
                http://orcid.org/0000-0002-7989-5833
                http://orcid.org/0000-0003-1057-465X
                http://orcid.org/0000-0002-8770-2464
                http://orcid.org/0000-0002-5236-6149
                http://orcid.org/0000-0002-1571-1468
                Article
                1228
                10.1038/s41386-021-01228-7
                8882178
                34775485
                cf9b2424-bb8e-4e78-b21f-33b0305679d2
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 July 2021
                : 5 October 2021
                : 21 October 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research);
                Award ID: 031L0190A
                Award ID: 01ZX01909
                Award ID: 01ZX01909
                Award ID: 01ZX01909
                Award ID: 031L0190A
                Award ID: 01ZX01909
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: 402170461
                Award Recipient :
                Categories
                Article
                Custom metadata
                © American College of Neuropsychopharmacology 2022

                Pharmacology & Pharmaceutical medicine
                epigenetics and behaviour,dna methylation,addiction
                Pharmacology & Pharmaceutical medicine
                epigenetics and behaviour, dna methylation, addiction

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