2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Shared mechanisms across the major psychiatric and neurodegenerative diseases

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Several common psychiatric and neurodegenerative diseases share epidemiologic risk; however, whether they share pathophysiology is unclear and is the focus of our investigation. Using 25 GWAS results and LD score regression, we find eight significant genetic correlations between psychiatric and neurodegenerative diseases. We integrate the GWAS results with human brain transcriptomes ( n = 888) and proteomes ( n = 722) to identify cis- and trans- transcripts and proteins that are consistent with a pleiotropic or causal role in each disease, referred to as causal proteins for brevity. Within each disease group, we find many distinct and shared causal proteins. Remarkably, 30% (13 of 42) of the neurodegenerative disease causal proteins are shared with psychiatric disorders. Furthermore, we find 2.6-fold more protein-protein interactions among the psychiatric and neurodegenerative causal proteins than expected by chance. Together, our findings suggest these psychiatric and neurodegenerative diseases have shared genetic and molecular pathophysiology, which has important ramifications for early treatment and therapeutic development.

          Abstract

          Studying the shared genetic etiology of disease can help improve diagnosis and treatment. Here, the authors find evidence for shared genetic and molecular pathophysiology between several common psychiatric and neurodegenerative diseases using results of 25 GWAS and large-scale human brain transcriptomic and proteomic sequencing.

          Related collections

          Most cited references101

          • Record: found
          • Abstract: found
          • Article: not found

          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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.
                Bookmark

                Author and article information

                Contributors
                thomas.wingo@emory.edu
                aliza.wingo@emory.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                26 July 2022
                26 July 2022
                2022
                : 13
                : 4314
                Affiliations
                [1 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Goizueta Alzheimer’s Disease Center, , Emory University School of Medicine, ; Atlanta, GA USA
                [2 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Neurology, , Emory University School of Medicine, ; Atlanta, GA USA
                [3 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Human Genetics, , Emory University School of Medicine, ; Atlanta, GA USA
                [4 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Cell Biology, , Emory University School of Medicine, ; Atlanta, GA USA
                [5 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Psychiatry, , Emory University School of Medicine, ; Atlanta, GA USA
                [6 ]GRID grid.240684.c, ISNI 0000 0001 0705 3621, Rush Alzheimer’s Disease Center, , Rush University Medical Center, ; Chicago, IL USA
                [7 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Biochemistry, , Emory University School of Medicine, ; Atlanta, GA USA
                [8 ]Veterans Affairs Atlanta Health Care System, Decatur, GA USA
                Author information
                http://orcid.org/0000-0002-7679-6282
                http://orcid.org/0000-0003-3480-3234
                http://orcid.org/0000-0002-2114-5271
                http://orcid.org/0000-0002-4507-624X
                http://orcid.org/0000-0002-3153-502X
                http://orcid.org/0000-0002-6360-6726
                Article
                31873
                10.1038/s41467-022-31873-5
                9325708
                35882878
                6cb3e636-a399-4534-8a89-a6ff6b15773c
                © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022

                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
                : 5 October 2021
                : 7 July 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100006812, Center for Integrated Healthcare, U.S. Department of Veterans Affairs (VISN 2 Center for Integrated Healthcare);
                Award ID: I01 BX003853
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000738, U.S. Department of Veterans Affairs (Department of Veterans Affairs);
                Award ID: IK4 BX005219
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                depression,post-traumatic stress disorder,alzheimer's disease
                Uncategorized
                depression, post-traumatic stress disorder, alzheimer's disease

                Comments

                Comment on this article