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

      DNA methylation as a predictor of fetal alcohol spectrum disorder

      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.



          Fetal alcohol spectrum disorder (FASD) is a developmental disorder that manifests through a range of cognitive, adaptive, physiological, and neurobiological deficits resulting from prenatal alcohol exposure. Although the North American prevalence is currently estimated at 2–5%, FASD has proven difficult to identify in the absence of the overt physical features characteristic of fetal alcohol syndrome. As interventions may have the greatest impact at an early age, accurate biomarkers are needed to identify children at risk for FASD. Building on our previous work identifying distinct DNA methylation patterns in children and adolescents with FASD, we have attempted to validate these associations in a different clinical cohort and to use our DNA methylation signature to develop a possible epigenetic predictor of FASD.


          Genome-wide DNA methylation patterns were analyzed using the Illumina HumanMethylation450 array in the buccal epithelial cells of a cohort of 48 individuals aged 3.5–18 (24 FASD cases, 24 controls). The DNA methylation predictor of FASD was built using a stochastic gradient boosting model on our previously published dataset FASD cases and controls (GSE80261). The predictor was tested on the current dataset and an independent dataset of 48 autism spectrum disorder cases and 48 controls (GSE50759).


          We validated findings from our previous study that identified a DNA methylation signature of FASD, replicating the altered DNA methylation levels of 161/648 CpGs in this independent cohort, which may represent a robust signature of FASD in the epigenome. We also generated a predictive model of FASD using machine learning in a subset of our previously published cohort of 179 samples (83 FASD cases, 96 controls), which was tested in this novel cohort of 48 samples and resulted in a moderately accurate predictor of FASD status. Upon testing the algorithm in an independent cohort of individuals with autism spectrum disorder, we did not detect any bias towards autism, sex, age, or ethnicity.


          These findings further support the association of FASD with distinct DNA methylation patterns, while providing a possible entry point towards the development of epigenetic biomarkers of FASD.

          Electronic supplementary material

          The online version of this article (10.1186/s13148-018-0439-6) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references 70

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

          Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

          The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.
            • Record: found
            • Abstract: found
            • Article: not found

            The sva package for removing batch effects and other unwanted variation in high-throughput experiments.

            Heterogeneity and latent variables are now widely recognized as major sources of bias and variability in high-throughput experiments. The most well-known source of latent variation in genomic experiments are batch effects-when samples are processed on different days, in different groups or by different people. However, there are also a large number of other variables that may have a major impact on high-throughput measurements. Here we describe the sva package for identifying, estimating and removing unwanted sources of variation in high-throughput experiments. The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function.
              • Record: found
              • Abstract: found
              • Article: not found

              Persistent epigenetic differences associated with prenatal exposure to famine in humans.

              Extensive epidemiologic studies have suggested that adult disease risk is associated with adverse environmental conditions early in development. Although the mechanisms behind these relationships are unclear, an involvement of epigenetic dysregulation has been hypothesized. Here we show that individuals who were prenatally exposed to famine during the Dutch Hunger Winter in 1944-45 had, 6 decades later, less DNA methylation of the imprinted IGF2 gene compared with their unexposed, same-sex siblings. The association was specific for periconceptional exposure, reinforcing that very early mammalian development is a crucial period for establishing and maintaining epigenetic marks. These data are the first to contribute empirical support for the hypothesis that early-life environmental conditions can cause epigenetic changes in humans that persist throughout life.

                Author and article information

                Clin Epigenetics
                Clin Epigenetics
                Clinical Epigenetics
                BioMed Central (London )
                12 January 2018
                12 January 2018
                : 10
                [1 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, British Columbia Children’s Hospital Research Institute, , University of British Columbia, ; Vancouver, British Columbia Canada
                [2 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Department of Cellular and Physiological Sciences, Life Sciences Institute, , University of British Columbia, ; Vancouver, British Columbia Canada
                [3 ]ISNI 0000 0004 1936 9609, GRID grid.21613.37, Department of Pediatrics and Child Health, Faculty of Medicine, , University of Manitoba, ; Winnipeg, Manitoba Canada
                [4 ]ISNI 0000 0004 1936 9609, GRID grid.21613.37, Department of Biochemistry and Medical Genetics, Faculty of Medicine, , University of Manitoba, ; Winnipeg, Manitoba Canada
                [5 ]ISNI 0000 0004 1936 8331, GRID grid.410356.5, Department of Biomedical and Molecular Sciences, Centre for Neuroscience Studies, , Queen’s University, ; Kingston, Ontario Canada
                [6 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Michael Smith Laboratories, , University of British Columbia, ; Vancouver, British Columnbia Canada
                [7 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Human Early Learning Partnership, , University of British Columbia, ; Vancouver, British Columbia Canada
                [8 ]ISNI 0000 0001 2288 9830, GRID grid.17091.3e, Department of Psychiatry, , University of British Columbia, ; Vancouver, British Columbia Canada
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

                Funded by: FundRef, Networks of Centres of Excellence of Canada;
                Award ID: NeuroDevNet
                Award Recipient :
                Funded by: Fondation Brain Canada and NeuroDevNet(CA)
                Award ID: Developmental Neurosciences Research Training Award
                Award Recipient :
                Funded by: FundRef, National Institutes of Health;
                Award ID: R37 AA007789
                Award Recipient :
                Funded by: FundRef, National Institute on Alcohol Abuse and Alcoholism;
                Award ID: RO1 AA022460
                Award Recipient :
                Funded by: Canadian Foundation for Fetal Alcohol Research
                Funded by: Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (CA)
                Award ID: Discovery Grant
                Award Recipient :
                Custom metadata
                © The Author(s) 2018


                Comment on this article