+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      DNA methylation mediates the effect of maternal smoking on offspring birthweight: a birth cohort study of multi-ethnic US mother–newborn pairs


      Read this article at

          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.



          Maternal smoking affects more than half a million pregnancies each year in the US and is known to result in fetal growth restriction as measured by lower birthweight and its associated long-term consequences. Maternal smoking also has been linked to altered fetal DNA methylation (DNAm). However, what remains largely unexplored is whether these DNAm alterations are merely markers of smoking exposure or if they also have implications for health outcomes. This study tested the hypothesis that fetal DNAm mediates the effect of maternal smoking on newborn birthweight.


          This study included mother–newborn pairs from a US predominantly urban, low-income multi-ethnic birth cohort. DNAm in cord blood were determined using the Illumina Infinium MethylationEPIC BeadChip. After standard quality control and normalization procedures, an epigenome-wide association study (EWAS) of maternal smoking was performed using linear regression models, controlling for maternal age, education, race, parity, pre-pregnancy body mass index, alcohol consumption, gestational age, maternal pregestational/gestational diabetes, child sex, cord blood cell compositions and batch effects. To quantify the degree to which cord DNAm mediates the smoking-birthweight association, the VanderWeele-Vansteelandt approach for single mediator and structural equational model for multiple mediators were used, adjusting for pertinent covariates.


          The study included 954 mother–newborn pairs. Among mothers, 165 (17.3%) ever smoked before or during pregnancy. Newborns with smoking exposure had on average 258 g lower birthweight than newborns without exposure ( P < 0.001). Using a false discovery rate (FDR) < 0.05 as the significance cutoff, the EWAS identified 38 differentially methylated CpG sites associated with maternal smoking. Of those, 17 CpG sites were mapped to previously reported genes: GFI1, AHRR, CYP1A1, and CNTNAP2; 8 of those, located in the first three genes, were Bonferroni significantly associated with newborn birthweight and mediated the smoking-birthweight association. The combined mediation effect of the three genes explained 67.8% of the smoking-birthweight association.


          Our study not only lends further support that maternal smoking alters fetal DNAm in a multiethnic population, but also suggests that fetal DNAm substantially mediates the maternal smoking–birthweight association. Our findings, if further validated, indicate that DNAm modification is likely an important pathway by which maternal smoking impairs fetal growth and, perhaps, even long-term health outcomes.

          Related collections

          Most cited references37

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

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

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

              Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays.

              The recently released Infinium HumanMethylation450 array (the '450k' array) provides a high-throughput assay to quantify DNA methylation (DNAm) at ∼450 000 loci across a range of genomic features. Although less comprehensive than high-throughput sequencing-based techniques, this product is more cost-effective and promises to be the most widely used DNAm high-throughput measurement technology over the next several years. Here we describe a suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. The software is structured to easily adapt to future versions of the technology. We include methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. We show how our software provides a powerful and flexible development platform for future methods. We also illustrate how our methods empower the technology to make discoveries previously thought to be possible only with sequencing-based methods. http://bioconductor.org/packages/release/bioc/html/minfi.html. khansen@jhsph.edu; rafa@jimmy.harvard.edu Supplementary data are available at Bioinformatics online.

                Author and article information

                Clin Epigenetics
                Clin Epigenetics
                Clinical Epigenetics
                BioMed Central (London )
                4 March 2021
                4 March 2021
                : 13
                : 47
                [1 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Computer Science, Whiting School of Engineering, , Johns Hopkins University, ; Baltimore, MD USA
                [2 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Center On the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, , Johns Hopkins University Bloomberg School of Public Health, ; Baltimore, MD USA
                [3 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Biostatistics, , Johns Hopkins University Bloomberg School of Public Health, ; Baltimore, MD 21205 USA
                [4 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Civil and Systems Engineering, Whiting School of Engineering, , Johns Hopkins University, ; Baltimore, MD USA
                [5 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Pediatrics, , Johns Hopkins University School of Medicine, ; Baltimore, MD 21205 USA
                [6 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Epidemiology, T.H. Chan School of Public Health, , Harvard University, ; Boston, MA USA
                [7 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biostatistics, T.H. Chan School of Public Health, , Harvard University, ; Boston, MA USA
                Author information
                © 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.

                : 28 September 2020
                : 15 February 2021
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 2R01HD041702
                Award ID: R01ES031272
                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: R01HD086013
                Award ID: R01HD098232
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: R21AI154233
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000066, National Institute of Environmental Health Sciences;
                Award ID: R01ES031272
                Award Recipient :
                Custom metadata
                © The Author(s) 2021

                maternal smoking,low birthweight,dna methylation,epigenome-wide association study,mediation analysis


                Comment on this article