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      Genome-wide association study identifies glutamate ionotropic receptor GRIA4 as a risk gene for comorbid nicotine dependence and major depression

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          Abstract

          Smoking and major depression frequently co-occur, at least in part due to shared genetic risk. However, the nature of the shared genetic basis is poorly understood. To detect genetic risk variants for comorbid nicotine dependence (ND) and major depression (MD), we conducted genome-wide association study (GWAS) in two samples of African-American participants (Yale-Penn 1 and 2) using linear mixed model, followed by meta-analysis. 3724 nicotine-exposed subjects were analyzed: 2596 from Yale-Penn-1 and 1128 from Yale-Penn-2. Continuous measures ( Fagerström Test for Nicotine Dependence (FTND) scores and DSM-IV MD criteria) rather than disorder status were used to maximize the power of the GWAS. Genotypes were ascertained using the Illumina HumanOmni1-Quad array (Yale-Penn-1 sample) or the Illumina HumanCore Exome array (Yale-Penn-2 sample), followed by imputation based on the 1000 Genomes reference panel. An intronic variant at the GRIA4 locus, rs68081839, was significantly associated with ND–MD comorbidity ( β = 0.69 [95% CI, 0.43–0.89], P = 1.53 × 10 −8). GRIA4 encodes an AMPA-sensitive glutamate receptor that mediates fast excitatory synaptic transmission and neuroplasticity. Conditional analyses revealed that the association was explained jointly by both traits. Enrichment analysis showed that the top risk genes and genes co-expressed with GRIA4 are enriched in cell adhesion, calcium ion binding, and synapses. They also have enriched expression in the brain and they have been implicated in the risk for other neuropsychiatric disorders. Further research is needed to determine the replicability of these findings and to identify the biological mechanisms through which genetic risk for each condition is conveyed.

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            PLINK: a tool set for whole-genome association and population-based linkage analyses.

            Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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              A global reference for human genetic variation

              The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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                Author and article information

                Contributors
                +1 (203) 494-6326x3590 , joel.gelernter@yale.edu
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                4 October 2018
                4 October 2018
                2018
                : 8
                : 208
                Affiliations
                [1 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Psychiatry, , Yale University School of Medicine, ; New Haven, CT USA
                [2 ]ISNI 0000000121901201, GRID grid.83440.3b, Molecular Psychiatry Laboratory, Division of Psychiatry, , University College London, ; London, UK
                [3 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Neuroscience, , Yale University School of Medicine, ; New Haven, CT USA
                [4 ]Clinical Neurosciences Division, VA National Center for PTSD, VA CT Healthcare System, West Haven, CT USA
                [5 ]ISNI 0000 0004 0367 5222, GRID grid.475010.7, Department of Medicine (Biomedical Genetics), , Boston University School of Medicine, ; Boston, MA USA
                [6 ]ISNI 0000 0004 0367 5222, GRID grid.475010.7, Department of Neurology, , Boston University School of Medicine, ; Boston, MA USA
                [7 ]ISNI 0000 0004 0367 5222, GRID grid.475010.7, Department of Ophthalmology, , Boston University School of Medicine, ; Boston, MA USA
                [8 ]ISNI 0000 0004 0367 5222, GRID grid.475010.7, Department of Genetics and Genomics, , Boston University School of Medicine, ; Boston, MA USA
                [9 ]ISNI 0000 0004 1936 7558, GRID grid.189504.1, Department of Epidemiology and Biostatistics, , Boston University School of Public Health, ; Boston, MA USA
                [10 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Psychiatry, , University of Pennsylvania Perelman School of Medicine, ; Philadelphia, PA USA
                [11 ]ISNI 0000 0004 0420 350X, GRID grid.410355.6, VISN 4 MIRECC, , Crescenz VA Medical Center, ; Philadelphia, PA USA
                [12 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Genetics, , Yale University School of Medicine, ; New Haven, CT USA
                [13 ]Department of Psychiatry, VA CT Healthcare Center, West Haven, CT USA
                Author information
                http://orcid.org/0000-0002-7694-6391
                http://orcid.org/0000-0002-1018-0450
                http://orcid.org/0000-0002-4067-1859
                Article
                258
                10.1038/s41398-018-0258-8
                6172277
                30287806
                0550ec0f-c510-44fb-9c61-94425306c2f6
                © The Author(s) 2018

                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
                : 11 December 2017
                : 21 February 2018
                : 11 May 2018
                Funding
                Funded by: VA Connecticut Healthcare Center
                Funded by: National Center for Post Traumatic Stress Disorder
                Funded by: Philadelphia VA MIRECCS
                Categories
                Article
                Custom metadata
                © The Author(s) 2018

                Clinical Psychology & Psychiatry
                Clinical Psychology & Psychiatry

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