6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Methylome-wide association study provides evidence of particulate matter air pollution-associated DNA methylation

      research-article
      a , * , a , a , b , a , c , a , a , d , d , e , a , f , g , h , i , j , i , k , i , f , l , m , n , o , o , p , q , r , a , s , t , u , v , w , x , y , e , a , z
      Environment international
      Particulate matter, DNA methylation, Epigenetics, Air pollution, Epigenome-wide association study

      Read this article at

      Bookmark
          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.

          Abstract

          Background:

          DNA methylation (DNAm) may contribute to processes that underlie associations between air pollution and poor health. Therefore, our objective was to evaluate associations between DNAm and ambient concentrations of particulate matter (PM) ≤2.5, ≤10, and 2.5–10 μm in diameter (PM 2.5; PM 10; PM 2.5–10).

          Methods:

          We conducted a methylome-wide association study among twelve cohort- and race/ethnicity-stratified subpopulations from the Women’s Health Initiative and the Atherosclerosis Risk in Communities study ( n = 8397; mean age: 61.5 years; 83% female; 45% African American; 9% Hispanic/Latino American). We averaged geocoded address-specific estimates of daily and monthly mean PM concentrations over 2, 7, 28, and 365 days and 1 and 12 months before exams at which we measured leukocyte DNAm in whole blood. We estimated subpopulation-specific, DNAm-PM associations at approximately 485,000 Cytosine-phosphate-Guanine (CpG) sites in multi-level, linear, mixed-effects models. We combined subpopulation- and site-specific estimates in fixed-effects, inverse variance-weighted meta-analyses, then for associations that exceeded methylome-wide significance and were not heterogeneous across subpopulations ( P < 1.0 × 10 −7; P Cochran’s Q > 0.10), we characterized associations using publicly accessible genomic databases and attempted replication in the Cooperative Health Research in the Region of Augsburg (KORA) study.

          Results:

          Analyses identified significant DNAm-PM associations at three CpG sites. Twenty-eight-day mean PM 10 was positively associated with DNAm at cg19004594 (chromosome 20; MATN4; P = 3.33 × 10 −8). One-month mean PM 10 and PM 2.5–10 were positively associated with DNAm at cg24102420 (chromosome 10; ARPP21; P = 5.84 × 10 −8) and inversely associated with DNAm at cg12124767 (chromosome 7; CFTR; P = 9.86 × 10 −8). The PM-sensitive CpG sites mapped to neurological, pulmonary, endocrine, and cardiovascular disease-related genes, but DNAm at those sites was not associated with gene expression in blood cells and did not replicate in KORA.

          Conclusions:

          Ambient PM concentrations were associated with DNAm at genomic regions potentially related to poor health among racially, ethnically and environmentally diverse populations of U.S. women and men. Further investigation is warranted to uncover mechanisms through which PM-induced epigenomic changes may cause disease.

          Related collections

          Most cited references89

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          An Integrated Encyclopedia of DNA Elements in the Human Genome

          Summary The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found
            Is Open Access

            The Genotype-Tissue Expression (GTEx) project.

            Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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/.
                Bookmark

                Author and article information

                Journal
                7807270
                22115
                Environ Int
                Environ Int
                Environment international
                0160-4120
                1873-6750
                7 August 2019
                14 June 2019
                November 2019
                01 November 2020
                : 132
                : 104723
                Affiliations
                [a ]Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
                [b ]Department of Community and Family Medicine, Duke University School of Medicine, Durham, NC, USA
                [c ]Geisinger Health System, Danville, PA, USA
                [d ]Division of Epidemiology, Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, USA
                [e ]Laboratory of Environmental Epigenetics, Departments of Environmental Health Sciences and Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
                [f ]Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
                [g ]Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
                [h ]Environmental Public Health Division, National Health and Environmental Effects Research Laboratory, 104 Mason Farm Rd, Chapel Hill, NC, USA
                [i ]Institute of Epidemiology, Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, Neuherberg, Germany
                [j ]Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, Neuherberg, Germany
                [k ]Environmental Science Center, University of Augsburg, Augsburg, Germany
                [l ]Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
                [m ]Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
                [n ]Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
                [o ]Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
                [p ]Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
                [q ]Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
                [r ]Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
                [s ]Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, NC, USA
                [t ]Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
                [u ]Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
                [v ]Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
                [w ]Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
                [x ]Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University Chicago, Evanston, IL, USA
                [y ]Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University Chicago, Evanston, IL, USA
                [z ]Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
                Author notes
                [* ]Corresponding author at: 123 W. Franklin St., Chapel Hill, NC 27516, USA., rahgonda@ 123456unc.edu (R. Gondalia).
                Article
                NIHMS1532552
                10.1016/j.envint.2019.03.071
                6754789
                31208937
                a999a521-8442-41ca-92c6-0335f9bb1ca9

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/BY-NC-ND/4.0/).

                History
                Categories
                Article

                particulate matter,dna methylation,epigenetics,air pollution,epigenome-wide association study

                Comments

                Comment on this article