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      Association between DNA methylation levels in brain tissue and late-life depression in community-based participants

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          Abstract

          Objective

          Major depressive disorder (MDD) arises from a combination of genetic and environmental risk factors and DNA methylation is one of the molecular mechanisms through which these factors can manifest. However, little is known about the epigenetic signature of MDD in brain tissue. This study aimed to investigate associations between brain tissue-based DNA methylation and late-life MDD.

          Methods

          We performed a brain epigenome-wide association study (EWAS) of late-life MDD in 608 participants from the Religious Order Study and the Rush Memory and Aging Project (ROS/MAP) using DNA methylation profiles of the dorsal lateral prefrontal cortex generated using the Illumina HumanMethylation450 Beadchip array. We also conducted an EWAS of MDD in each sex separately.

          Results

          We found epigenome-wide significant associations between brain tissue-based DNA methylation and late-life MDD. The most significant and robust association was found with altered methylation levels in the YOD1 locus (cg25594636, p value = 2.55 × 10 −11; cg03899372, p value = 3.12 × 10 −09; cg12796440, p value = 1.51 × 10 −08, cg23982678, p value = 7.94 × 10 −08). Analysis of differentially methylated regions ( p value = 5.06 × 10 −10) further confirmed this locus. Other significant loci include UGT8 (cg18921206, p value = 1.75 × 10 −08), FNDC3B (cg20367479, p value = 4.97 × 10 −08) and SLIT2 (cg10946669, p value = 8.01 × 10 −08). Notably, brain tissue-based methylation levels were strongly associated with late-life MDD in men more than in women.

          Conclusions

          We identified altered methylation in the YOD1, UGT8, FNDC3B, and SLIT2 loci as new epigenetic factors associated with late-life MDD. Furthermore, our study highlights the sex-specific molecular heterogeneity of MDD.

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          Most cited references24

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          Religious Orders Study and Rush Memory and Aging Project.

          The Religious Orders Study and Rush Memory and Aging Project are both ongoing longitudinal clinical-pathologic cohort studies of aging and Alzheimer's disease (AD).
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            De novo identification of differentially methylated regions in the human genome

            Background The identification and characterisation of differentially methylated regions (DMRs) between phenotypes in the human genome is of prime interest in epigenetics. We present a novel method, DMRcate, that fits replicated methylation measurements from the Illumina HM450K BeadChip (or 450K array) spatially across the genome using a Gaussian kernel. DMRcate identifies and ranks the most differentially methylated regions across the genome based on tunable kernel smoothing of the differential methylation (DM) signal. The method is agnostic to both genomic annotation and local change in the direction of the DM signal, removes the bias incurred from irregularly spaced methylation sites, and assigns significance to each DMR called via comparison to a null model. Results We show that, for both simulated and real data, the predictive performance of DMRcate is superior to those of Bumphunter and Probe Lasso, and commensurate with that of comb-p. For the real data, we validate all array-derived DMRs from the candidate methods on a suite of DMRs derived from whole-genome bisulfite sequencing called from the same DNA samples, using two separate phenotype comparisons. Conclusions The agglomeration of genomically localised individual methylation sites into discrete DMRs is currently best served by a combination of DM-signal smoothing and subsequent threshold specification. The findings also suggest the design of the 450K array shows preference for CpG sites that are more likely to be differentially methylated, but its overall coverage does not adequately reflect the depth and complexity of methylation signatures afforded by sequencing. For the convenience of the research community we have created a user-friendly R software package called DMRcate, downloadable from Bioconductor and compatible with existing preprocessing packages, which allows others to apply the same DMR-finding method on 450K array data. Electronic supplementary material The online version of this article (doi:10.1186/1756-8935-8-6) contains supplementary material, which is available to authorized users.
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              missMethyl: an R package for analyzing data from Illumina's HumanMethylation450 platform.

              DNA methylation is one of the most commonly studied epigenetic modifications due to its role in both disease and development. The Illumina HumanMethylation450 BeadChip is a cost-effective way to profile >450 000 CpGs across the human genome, making it a popular platform for profiling DNA methylation. Here we introduce missMethyl, an R package with a suite of tools for performing normalization, removal of unwanted variation in differential methylation analysis, differential variability testing and gene set analysis for the 450K array.

                Author and article information

                Contributors
                mpepste@emory.edu
                thomas.wingo@emory.edu
                aliza.wingo@emory.edu
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                30 July 2020
                30 July 2020
                2020
                : 10
                : 262
                Affiliations
                [1 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Epidemiology and Gangarosa Department of Environmental Health, Rollins School of Public Health, , Emory University, ; Atlanta, GA USA
                [2 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Human Genetics, , Emory University, ; Atlanta, GA USA
                [3 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Neurology, , Emory University School of Medicine, ; Atlanta, GA USA
                [4 ]GRID grid.66859.34, Cell Circuits Program, Broad Institute, ; Cambridge, MA USA
                [5 ]GRID grid.239585.0, ISNI 0000 0001 2285 2675, Center for Translational and Computational Neuroimmunology, Department of Neurology, , Columbia University Medical Center, ; New York, NY 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.414026.5, ISNI 0000 0004 0419 4084, Division of Mental Health, Atlanta VA Medical Center, ; Decatur, GA USA
                [8 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Psychiatry, , Emory University School of Medicine, ; Atlanta, GA USA
                Author information
                http://orcid.org/0000-0002-6005-417X
                http://orcid.org/0000-0002-8057-2505
                http://orcid.org/0000-0001-9647-9738
                http://orcid.org/0000-0002-7679-6282
                http://orcid.org/0000-0002-6360-6726
                Article
                948
                10.1038/s41398-020-00948-6
                7393126
                32733030
                4f1f92a8-7d18-4a67-978e-57b3e3afe2f7
                © The Author(s) 2020

                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
                : 21 April 2020
                : 6 July 2020
                : 14 July 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: HU 2731/1-1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000066, U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS);
                Award ID: P30ES019776
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: T32 NS007480
                Award ID: P30AG10161, R01AG15819, R01AG17917, R01AG16042, R01AG36042, U01AG61356
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | National Institutes of Health (NIH)
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Clinical Psychology & Psychiatry
                depression,genetics
                Clinical Psychology & Psychiatry
                depression, genetics

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