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      Genome-wide DNA methylation association study of recent and cumulative marijuana use in middle aged adults

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          Abstract

          Marijuana is a widely used psychoactive substance in the US and medical and recreational legalization has risen over the past decade. Despite the growing number of individuals using marijuana, studies investigating the association between epigenetic factors and recent and cumulative marijuana use remain limited. We therefore investigated the association between recent and cumulative marijuana use and DNA methylation levels. Participants from the Coronary Artery Risk Development in Young Adults Study with whole blood collected at examination years (Y) 15 and Y20 were randomly selected to undergo DNA methylation profiling at both timepoints using the Illumina MethylationEPIC BeadChip. Recent use of marijuana was queried at each examination and used to estimate cumulative marijuana use from Y0 to Y15 and Y20. At Y15 ( n = 1023), we observed 22 and 31 methylation markers associated (FDR P ≤ 0.05) with recent and cumulative marijuana use and 132 and 16 methylation markers at Y20 ( n = 883), respectively. We replicated 8 previously reported methylation markers associated with marijuana use. We further identified 640 cis-meQTLs and 198 DMRs associated with recent and cumulative use at Y15 and Y20. Differentially methylated genes were statistically overrepresented in pathways relating to cellular proliferation, hormone signaling, and infections as well as schizophrenia, bipolar disorder, and substance-related disorders. We identified numerous methylation markers, pathways, and diseases associated with recent and cumulative marijuana use in middle-aged adults, providing additional insight into the association between marijuana use and the epigenome. These results provide novel insights into the role marijuana has on the epigenome and related health conditions.

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          The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019

          Abstract The GWAS Catalog delivers a high-quality curated collection of all published genome-wide association studies enabling investigations to identify causal variants, understand disease mechanisms, and establish targets for novel therapies. The scope of the Catalog has also expanded to targeted and exome arrays with 1000 new associations added for these technologies. As of September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait associations from 3567 publications. New content includes 284 full P-value summary statistics datasets for genome-wide and new targeted array studies, representing 6 × 109 individual variant-trait statistics. In the last 12 months, the Catalog's user interface was accessed by ∼90000 unique users who viewed >1 million pages. We have improved data access with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database. Summary statistics provision is supported by a new format proposed as a community standard for summary statistics data representation. This format was derived from our experience in standardizing heterogeneous submissions, mapping formats and in harmonizing content. Availability: https://www.ebi.ac.uk/gwas/.
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            WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs

            Abstract WebGestalt is a popular tool for the interpretation of gene lists derived from large scale -omics studies. In the 2019 update, WebGestalt supports 12 organisms, 342 gene identifiers and 155 175 functional categories, as well as user-uploaded functional databases. To address the growing and unique need for phosphoproteomics data interpretation, we have implemented phosphosite set analysis to identify important kinases from phosphoproteomics data. We have completely redesigned result visualizations and user interfaces to improve user-friendliness and to provide multiple types of interactive and publication-ready figures. To facilitate comprehension of the enrichment results, we have implemented two methods to reduce redundancy between enriched gene sets. We introduced a web API for other applications to get data programmatically from the WebGestalt server or pass data to WebGestalt for analysis. We also wrapped the core computation into an R package called WebGestaltR for users to perform analysis locally or in third party workflows. WebGestalt can be freely accessed at http://www.webgestalt.org.
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              Next-generation genotype imputation service and methods.

              Genotype imputation is a key component of genetic association studies, where it increases power, facilitates meta-analysis, and aids interpretation of signals. Genotype imputation is computationally demanding and, with current tools, typically requires access to a high-performance computing cluster and to a reference panel of sequenced genomes. Here we describe improvements to imputation machinery that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools. We also describe a new web-based service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity.
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                Author and article information

                Contributors
                drew.nannini1@northwestern.edu
                Journal
                Mol Psychiatry
                Mol Psychiatry
                Molecular Psychiatry
                Nature Publishing Group UK (London )
                1359-4184
                1476-5578
                31 May 2023
                31 May 2023
                2023
                : 28
                : 6
                : 2572-2582
                Affiliations
                [1 ]Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, ( https://ror.org/000e0be47) Chicago, IL USA
                [2 ]Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, ( https://ror.org/017zqws13) Minneapolis, MN USA
                [3 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, University of California at San Francisco School of Medicine, ; San Francisco, CA USA
                [4 ]Department of Medicine, Northwestern University Feinberg School of Medicine, ( https://ror.org/000e0be47) Chicago, IL USA
                Author information
                http://orcid.org/0000-0002-2333-7920
                http://orcid.org/0000-0002-2006-7320
                http://orcid.org/0000-0002-9094-9011
                http://orcid.org/0000-0002-7232-0543
                http://orcid.org/0000-0002-6327-2439
                Article
                2106
                10.1038/s41380-023-02106-y
                10611566
                37258616
                b01e6219-42bf-4029-9954-64c91bb8bf69
                © The Author(s) 2023

                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
                : 3 November 2022
                : 24 April 2023
                : 3 May 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000049, U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging);
                Award ID: R21AG068955
                Award ID: R21AG063370
                Award ID: R21AG068955
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000050, U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI);
                Award ID: HHSN268201800004I
                Award ID: HHSN268201800003I
                Award ID: HHSN268201800005I
                Award ID: HHSN268201800007I
                Award ID: HHSN268201800006I
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
                Funded by: U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
                Funded by: U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
                Funded by: U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
                Funded by: FundRef https://doi.org/10.13039/100000968, American Heart Association (American Heart Association, Inc.);
                Award ID: 14SFRN20790000
                Award ID: 17SFRN33700278
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Molecular medicine
                genetics,biomarkers,psychiatric disorders
                Molecular medicine
                genetics, biomarkers, psychiatric disorders

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