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      Gestational Perfluoroalkyl Substance Exposure and DNA Methylation at Birth and 12 Years of Age: A Longitudinal Epigenome-Wide Association Study


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          DNA methylation alterations may underlie associations between gestational perfluoroalkyl substances (PFAS) exposure and later-life health outcomes. To the best of our knowledge, no longitudinal studies have examined the associations between gestational PFAS and DNA methylation.


          We examined associations of gestational PFAS exposure with longitudinal DNA methylation measures at birth and in adolescence using the Health Outcomes and Measures of the Environment (HOME) Study (2003–2006; Cincinnati, Ohio).


          We quantified serum concentrations of perfluorooctanoate (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoate (PFNA), and perfluorohexane sulfonate (PFHxS) in mothers during pregnancy. We measured DNA methylation in cord blood ( n = 266 ) and peripheral leukocytes at 12 years of age ( n = 160 ) using the Illumina HumanMethylation EPIC BeadChip. We analyzed associations between log 2 -transformed PFAS concentrations and repeated DNA methylation measures using linear regression with generalized estimating equations. We included interaction terms between children’s age and gestational PFAS. We performed Gene Ontology enrichment analysis to identify molecular pathways. We used Project Viva (1999–2002; Boston, Massachusetts) to replicate significant associations.


          After adjusting for covariates, 435 cytosine–guanine dinucleotide (CpG) sites were associated with PFAS (false discovery rate, q < 0.05 ). Specifically, we identified 2 CpGs for PFOS, 12 for PFOA, 8 for PFHxS, and 413 for PFNA; none overlapped. Among these, 2 CpGs for PFOA and 4 for PFNA were replicated in Project Viva. Some of the PFAS-associated CpG sites annotated to gene regions related to cancers, cognitive health, cardiovascular disease, and kidney function. We found little evidence that the associations between PFAS and DNA methylation differed by children’s age.


          In these longitudinal data, PFAS biomarkers were associated with differences in several CpGs at birth and at 12 years of age in or near genes linked to some PFAS-associated health outcomes. Future studies should examine whether DNA methylation mediates associations between gestational PFAS exposure and health. https://doi.org/10.1289/EHP10118

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          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.
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            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/.
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              Statistical significance for genomewide studies.

              With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.

                Author and article information

                Environ Health Perspect
                Environ Health Perspect
                Environmental Health Perspectives
                Environmental Health Perspectives
                10 March 2022
                March 2022
                : 130
                : 3
                : 037005
                [ 1 ]Department of Epidemiology, Brown University School of Public Health , Providence, Rhode Island, USA
                [ 2 ]Department of Biostatistics, Brown University School of Public Health , Providence, Rhode Island, USA
                [ 3 ]Department of Laboratory Medicine and Pathology, Brown University , Providence, Rhode Island, USA
                [ 4 ]Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School , Boston, Massachusetts, USA
                [ 5 ]Department of Environmental & Public Health Sciences, University of Cincinnati College of Medicine , Cincinnati, Ohio, USA
                [ 6 ]Department of Environmental Health and Engineering, Johns Hopkins University Bloomberg School of Public Health , Baltimore, Maryland, USA
                [ 7 ]Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine , Philadelphia, Pennsylvania, USA
                [ 8 ]Faculty of Health Sciences, Simon Fraser University , Burnaby, British Columbia, Canada
                [ 9 ]Department of Radiology, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine , Cincinnati, Ohio, USA
                [ 10 ]Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine , Cincinnati, Ohio, USA
                [ 11 ]Diabetes Unit, Massachusetts General Hospital , Boston, Massachusetts, USA
                [ 12 ]Department of Epidemiology, Berkeley School of Public Health, University of California , Berkeley, California, USA
                [ 13 ]Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University , New York, New York, USA
                Author notes
                Address correspondence to Yun Liu, Department of Epidemiology, Brown University School of Public Health, Box G-S121-2, Providence, RI 02912 USA. Email: yun_liu@ 123456brown.edu
                Author information

                EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted.

                : 10 August 2021
                : 21 February 2022
                : 23 February 2022

                Public health
                Public health


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