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      Differential and spatial expression meta-analysis of genes identified in genome-wide association studies of depression

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          Abstract

          Major depressive disorder (MDD) is the most prevalent psychiatric disorder worldwide and affects individuals of all ages. It causes significant psychosocial impairments and is a major cause of disability. A recent consortium study identified 102 genetic variants and 269 genes associated with depression. To provide targets for future depression research, we prioritized these recently identified genes using expression data. We examined the differential expression of these genes in three studies that profiled gene expression of MDD cases and controls across multiple brain regions. In addition, we integrated anatomical expression information to determine which brain regions and transcriptomic cell types highly express the candidate genes. We highlight 12 of the 269 genes with the most consistent differential expression: MANEA, UBE2M, CKB, ITPR3, SPRY2, SAMD5, TMEM106B, ZC3H7B, LST1, ASXL3, ZNF184 and HSPA1A. The majority of these top genes were found to have sex-specific differential expression. We place greater emphasis on ZNF184 as it is the top gene in a more conservative analysis of the 269. Specifically, the differential expression of ZNF184 was strongest in subcortical regions in males and females. Anatomically, our results suggest the importance of the dorsal lateral geniculate nucleus, cholinergic, monoaminergic and enteric neurons. These findings provide a guide for targeted experiments to advance our understanding of the genetic underpinnings of depression.

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          Proteomics. Tissue-based map of the human proteome.

          Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
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            voom: precision weights unlock linear model analysis tools for RNA-seq read counts

            New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.
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              Biological Insights From 108 Schizophrenia-Associated Genetic Loci

              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.
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                Author and article information

                Contributors
                leon.french@utoronto.ca
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                4 January 2021
                4 January 2021
                2021
                : 11
                : 8
                Affiliations
                [1 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Institute for Medical Science, , University of Toronto, ; Toronto, Canada
                [2 ]GRID grid.155956.b, ISNI 0000 0000 8793 5925, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, ; Toronto, Canada
                [3 ]GRID grid.155956.b, ISNI 0000 0000 8793 5925, Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, ; Toronto, ON Canada
                [4 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Psychiatry, Faculty of Medicine, , University of Toronto, ; Toronto, ON Canada
                [5 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Pharmacology and Toxicology, , University of Toronto, ; Toronto, ON Canada
                Author information
                http://orcid.org/0000-0001-5832-6570
                http://orcid.org/0000-0002-9409-4583
                Article
                1127
                10.1038/s41398-020-01127-3
                7791035
                33414381
                c672341b-e014-4a3a-af63-7bd9b16ee717
                © 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
                : 27 March 2020
                : 11 November 2020
                : 27 November 2020
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Clinical Psychology & Psychiatry
                depression,biomarkers,genomics
                Clinical Psychology & Psychiatry
                depression, biomarkers, genomics

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