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      Prenatal metal exposure, cord blood DNA methylation and persistence in childhood: an epigenome-wide association study of 12 metals

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          Abstract

          Background

          Prenatal exposure to essential and non-essential metals impacts birth and child health, including fetal growth and neurodevelopment. DNA methylation (DNAm) may be involved in pathways linking prenatal metal exposure and health. In the Project Viva cohort, we analyzed the extent to which metals (As, Ba, Cd, Cr, Cs, Cu, Hg, Mg, Mn, Pb, Se, and Zn) measured in maternal erythrocytes were associated with differentially methylated positions (DMPs) and regions (DMRs) in cord blood and tested if associations persisted in blood collected in mid-childhood. We measured metal concentrations in first-trimester maternal erythrocytes, and DNAm in cord blood ( N = 361) and mid-childhood blood ( N = 333, 6–10 years) with the Illumina HumanMethylation450 BeadChip. For each metal individually, we tested for DMPs using linear models (considered significant at FDR < 0.05), and for DMRs using comb-p (Sidak p < 0.05). Covariates included biologically relevant variables and estimated cell-type composition. We also performed sex-stratified analyses.

          Results

          Pb was associated with decreased methylation of cg20608990 ( CASP8) (FDR = 0.04), and Mn was associated with increased methylation of cg02042823 ( A2BP1) in cord blood (FDR = 9.73 × 10 –6). Both associations remained significant but attenuated in blood DNAm collected at mid-childhood ( p < 0.01). Two and nine Mn-associated DMPs were identified in male and female infants, respectively (FDR < 0.05), with two and six persisting in mid-childhood ( p < 0.05). All metals except Ba and Pb were associated with ≥ 1 DMR among all infants ( Sidak p < 0.05). Overlapping DMRs annotated to genes in the human leukocyte antigen (HLA) region were identified for Cr, Cs, Cu, Hg, Mg, and Mn.

          Conclusions

          Prenatal metal exposure is associated with DNAm, including DMRs annotated to genes involved in neurodevelopment. Future research is needed to determine if DNAm partially explains the relationship between prenatal metal exposures and health outcomes.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13148-021-01198-z.

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

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            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.
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              REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

              Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret. REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.
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                Author and article information

                Contributors
                andres.cardenas@berkeley.edu
                Journal
                Clin Epigenetics
                Clin Epigenetics
                Clinical Epigenetics
                BioMed Central (London )
                1868-7075
                1868-7083
                19 November 2021
                19 November 2021
                2021
                : 13
                : 208
                Affiliations
                [1 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Division of Environmental Health Sciences, School of Public Health, , University of California Berkeley, ; 2121 Berkeley Way, Room 5302, Berkeley, CA 94720 USA
                [2 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, , Harvard Medical School and Harvard Pilgrim Health Care Institute, ; Boston, MA USA
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biostatistics, Harvard T.H. Chan School of Public Health, , Harvard University, ; Boston, MA USA
                [4 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Environmental Health Sciences, Mailman School of Public Health, , Columbia University, ; New York City, NY USA
                [5 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Environmental Medicine and Public Health and Institute for Exposomic Research, , Icahn School of Medicine at Mount Sinai, ; NY New York City, USA
                [6 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Channing Division of Network Medicine, Department of Medicine, , Brigham and Women’s Hospital, ; Boston, MA USA
                [7 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Environmental Health, , Harvard T.H. Chan School of Public Health, Harvard University, ; Boston, MA USA
                [8 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard Medical School, ; Boston, MA USA
                [9 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Diabetes Unit, , Massachusetts General Hospital, ; Boston, MA USA
                [10 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Center for Computational Biology, , University of California, ; Berkeley, CA USA
                Author information
                http://orcid.org/0000-0003-0046-5767
                Article
                1198
                10.1186/s13148-021-01198-z
                8605513
                34798907
                cf1570c3-9cf2-471d-8982-d435c82b06a8
                © The Author(s) 2021

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 17 September 2021
                : 8 November 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;
                Award ID: R01ES031259
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100009633, Eunice Kennedy Shriver National Institute of Child Health and Human Development;
                Award ID: R01HD034568
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000052, NIH Office of the Director;
                Award ID: UH3OD023286
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Genetics
                dna methylation,ewas,manganese,metals,prenatal exposure
                Genetics
                dna methylation, ewas, manganese, metals, prenatal exposure

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