12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      An epigenetic association analysis of childhood trauma in psychosis reveals possible overlap with methylation changes associated with PTSD

      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

          Patients with a severe mental disorder report significantly higher levels of childhood trauma (CT) than healthy individuals. Studies have suggested that CT may affect brain plasticity through epigenetic mechanisms and contribute to developing various psychiatric disorders. We performed a blood-based epigenome-wide association study using the Childhood Trauma Questionnaire-short form in 602 patients with a current severe mental illness, investigating DNA methylation association separately for five trauma subtypes and the total trauma score. The median trauma score was set as the predefined cutoff for determining whether the trauma was present or not. Additionally, we compared our genome-wide results with methylation probes annotated to candidate genes previously associated with CT. Of the patients, 83.2% reported CT above the cutoff in one or more trauma subtypes, and emotional neglect was the trauma subtype most frequently reported. We identified one significant differently methylated position associated with the gene TANGO6 for physical neglect. Seventeen differentially methylated regions (DMRs) were associated with different trauma categories. Several of these DMRs were annotated to genes previously associated with neuropsychiatric disorders such as post-traumatic stress disorder and cognitive impairments. Our results support a biomolecular association between CT and severe mental disorders. Genes that were previously identified as differentially methylated in CT-exposed subjects with and without psychosis did not show methylation differences in our analysis. We discuss this inconsistency, the relevance of our findings, and the limitations of our study.

          Related collections

          Most cited references76

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

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

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Adjusting batch effects in microarray expression data using empirical Bayes methods.

              Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
                Bookmark

                Author and article information

                Contributors
                solveig.lokhammer@uib.no
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                30 April 2022
                30 April 2022
                2022
                : 12
                : 177
                Affiliations
                [1 ]GRID grid.7914.b, ISNI 0000 0004 1936 7443, NORMENT, Department of Clinical Science, , University of Bergen, ; Bergen, Norway
                [2 ]GRID grid.412008.f, ISNI 0000 0000 9753 1393, Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, , Haukeland University Hospital, ; Bergen, Norway
                [3 ]GRID grid.5510.1, ISNI 0000 0004 1936 8921, NORMENT, Division of Mental Health and Addiction, , Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, ; Oslo, Norway
                [4 ]GRID grid.459157.b, ISNI 0000 0004 0389 7802, Department of Mental Health Research and Development, , Division of Mental Health and Addiction, Vestre Viken Hospital Trust, ; Oslo, Norway
                [5 ]GRID grid.504188.0, ISNI 0000 0004 0460 5461, Norwegian Centre for Violence and Traumatic Stress Studies, ; Oslo, Norway
                [6 ]GRID grid.412008.f, ISNI 0000 0000 9753 1393, Bergen Center for Brain Plasticity, , Haukeland University Hospital, ; Bergen, Norway
                Author information
                http://orcid.org/0000-0001-8049-7106
                http://orcid.org/0000-0002-4461-3568
                http://orcid.org/0000-0002-9783-548X
                Article
                1936
                10.1038/s41398-022-01936-8
                9061740
                35501310
                564dce56-6cc0-4510-907c-1147cab22d1f
                © The Author(s) 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
                : 24 September 2021
                : 12 April 2022
                : 13 April 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100005036, Universitetet i Bergen (University of Bergen);
                Funded by: FundRef https://doi.org/10.13039/501100005416, Norges Forskningsråd (Research Council of Norway);
                Funded by: FundRef https://doi.org/10.13039/100007793, Stiftelsen Kristian Gerhard Jebsen (Kristian Gerhard Jebsen Foundation);
                Funded by: The South-Eastern Norway Health Authority
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Clinical Psychology & Psychiatry
                epigenetics in the nervous system,psychiatric disorders

                Comments

                Comment on this article