Aging and Environmental Exposures Alter Tissue-Specific DNA Methylation Dependent upon CpG Island Context
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{"title"=>"Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CPG island context", "type"=>"journal", "authors"=>[{"first_name"=>"Brock C.", "last_name"=>"Christensen", "scopus_author_id"=>"8577632600"}, {"first_name"=>"E. Andres", "last_name"=>"Houseman", "scopus_author_id"=>"8335321500"}, {"first_name"=>"Carmen J.", "last_name"=>"Marsit", "scopus_author_id"=>"6602980785"}, {"first_name"=>"Shichun", "last_name"=>"Zheng", "scopus_author_id"=>"7403146685"}, {"first_name"=>"Margaret R.", "last_name"=>"Wrensch", "scopus_author_id"=>"55553735754"}, {"first_name"=>"Joseph L.", "last_name"=>"Wiemels", "scopus_author_id"=>"7004014285"}, {"first_name"=>"Heather H.", "last_name"=>"Nelson", "scopus_author_id"=>"7402584225"}, {"first_name"=>"Margaret R.", "last_name"=>"Karagas", "scopus_author_id"=>"7003779589"}, {"first_name"=>"James F.", "last_name"=>"Padbury", "scopus_author_id"=>"7004714928"}, {"first_name"=>"Raphael", "last_name"=>"Bueno", "scopus_author_id"=>"7006034260"}, {"first_name"=>"David J.", "last_name"=>"Sugarbaker", "scopus_author_id"=>"35418035600"}, {"first_name"=>"Ru Fang", "last_name"=>"Yeh", "scopus_author_id"=>"7006852463"}, {"first_name"=>"John K.", "last_name"=>"Wiencke", "scopus_author_id"=>"7006038504"}, {"first_name"=>"Karl T.", "last_name"=>"Kelsey", "scopus_author_id"=>"7102336677"}], "year"=>2009, "source"=>"PLoS Genetics", "identifiers"=>{"issn"=>"15537390", "scopus"=>"2-s2.0-70149099610", "sgr"=>"70149099610", "pui"=>"355245417", "isbn"=>"10.1371/journal.pgen.1000602", "pmid"=>"19680444", "doi"=>"10.1371/journal.pgen.1000602"}, "id"=>"c0e47a77-9245-3c88-a945-5123222debdf", "abstract"=>"<title>Author Summary</title> <p>The causes and extent of tissue-specific interindividual variation in human epigenomes are underappreciated and, hence, poorly characterized. We surveyed over 200 carefully annotated human tissue samples from ten anatosites at 1,413 CpGs for methylation alterations to appraise the nature of phenotypically, and hence potentially clinically important epigenomic alterations. Within tissue types, across individuals, we found variation in methylation that was significantly related to aging and environmental exposures such as tobacco smoking. Individual variation in age- and exposure-related methylation may significantly contribute to increased susceptibility to several diseases. As the NIH–funded HapMap project is critically contributing to annotating the human reference genome defining normal genetic variability, our work raises key issues to consider in the construction of reference epigenomes. It is well recognized that understanding genetic variation is essential to understanding disease. Our work, and the known interplay of epigenetics and genetics, makes it equally clear that a more complete characterization of epigenetic variation and its sources must be accomplished to reach the goal of a complete understanding of disease. Additional research is absolutely necessary to define the mechanisms controlling epigenomic variation. We have begun to lay the foundations for essential normal tissue controls for comparison to diseased tissue, which will allow the identification of the most crucial disease-related alterations and provide more robust targets for novel treatments.</p>", "link"=>"http://www.mendeley.com/research/aging-environmental-exposures-alter-tissuespecific-dna-methylation-dependent-upon-cpg-island-context", "reader_count"=>435, "reader_count_by_academic_status"=>{"Unspecified"=>6, "Professor > Associate Professor"=>29, "Researcher"=>102, "Student > Doctoral Student"=>25, "Student > Ph. D. 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Student"=>117, "Student > Postgraduate"=>24, "Other"=>12, "Student > Master"=>50, "Student > Bachelor"=>43, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>4, "Professor"=>19}, "reader_count_by_subject_area"=>{"Unspecified"=>24, "Agricultural and Biological Sciences"=>219, "Veterinary Science and Veterinary Medicine"=>2, "Chemistry"=>1, "Computer Science"=>12, "Earth and Planetary Sciences"=>1, "Engineering"=>2, "Environmental Science"=>3, "Biochemistry, Genetics and Molecular Biology"=>70, "Nursing and Health Professions"=>1, "Materials Science"=>2, "Mathematics"=>1, "Medicine and Dentistry"=>69, "Neuroscience"=>9, "Sports and Recreations"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>4, "Physics and Astronomy"=>2, "Psychology"=>7, "Social Sciences"=>4, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>69}, "Social Sciences"=>{"Social Sciences"=>4}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>7}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>24}, "Environmental Science"=>{"Environmental Science"=>3}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>4}, "Engineering"=>{"Engineering"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>9}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>219}, "Computer Science"=>{"Computer Science"=>12}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>70}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"Colombia"=>2, "Hong Kong"=>2, "United States"=>11, "Uruguay"=>1, "United Kingdom"=>5, "Switzerland"=>1, "Iceland"=>1, "Spain"=>5, "New Zealand"=>1, "Canada"=>4, "Netherlands"=>5, "China"=>1, "Brazil"=>1, "Mexico"=>1, "Italy"=>1, "Israel"=>1, "Slovenia"=>2, "Germany"=>2}, "group_count"=>18}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/888088"], "description"=>"<p>Methylation average β is yellow for unmethylated and blue for methylated. Methylation profile classes are stacked in rows separated by red lines, and class height corresponds to the number of samples in each class. Class methylation at each CpG locus is a mean of methylation for all samples within a class. (A) Methylation profile classes significantly differentiate all normal tissue types (n = 217, <i>P</i><0.0001). (B) Methylation profile classes from RPMM of adult bloods, class membership is significantly associated with age (<i>P</i><0.005, n = 30). (C) Methylation profile classes from RPMM of lung tissue samples (n = 53). (D) Methylation profile classes from RPMM of pleural samples (n = 18).</p>", "links"=>[], "tags"=>["partitioning", "models", "methylation", "profiles"], "article_id"=>558528, "categories"=>["Virology", "Molecular Biology", "Genetics", "Medicine"], "users"=>["Brock C. Christensen", "E. Andres Houseman", "Carmen J. Marsit", "Shichun Zheng", "Margaret R. Wrensch", "Joseph L. Wiemels", "Heather H. Nelson", "Margaret R. Karagas", "James F. Padbury", "Raphael Bueno", "David J. Sugarbaker", "Ru-Fang Yeh", "John K. Wiencke", "Karl T. Kelsey"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000602.g002", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Recursive_partitioning_mixture_models_RPMM_of_methylation_profiles_in_normal_tissuses_/558528", "title"=>"Recursive partitioning mixture models (RPMM) of methylation profiles in normal tissuses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-14 02:22:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/888628"], "description"=>"*<p>Permutation test for association between tissue type and methylation profile class <b><i>P</i></b><b><0.0001.</b></p>", "links"=>[], "tags"=>["partitioned", "model-derived", "cpg", "methylation"], "article_id"=>559061, "categories"=>["Virology", "Molecular Biology", "Genetics", "Medicine"], "users"=>["Brock C. Christensen", "E. Andres Houseman", "Carmen J. Marsit", "Shichun Zheng", "Margaret R. Wrensch", "Joseph L. Wiemels", "Heather H. Nelson", "Margaret R. Karagas", "James F. Padbury", "Raphael Bueno", "David J. Sugarbaker", "Ru-Fang Yeh", "John K. Wiencke", "Karl T. Kelsey"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000602.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Recursively_partitioned_mixture_model_derived_CpG_methylation_profile_class_membership_significantly_predicts_tissue_type_/559061", "title"=>"Recursively partitioned mixture model-derived CpG methylation profile class membership significantly predicts tissue type.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-08-14 02:31:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/888350"], "description"=>"<p>Each plot displays the gene transcription start site and surrounding CpGs are indicated by tick marks where the array CpG tick mark has a filled circle on top, and the sequenced CpGs are surrounded by a box. (A) Distribution of mean bisulfite pyrosequencing percent methylation for <i>RARA</i>_P176 and five downstream CpGs (79 bases total) stratified by known exposure to asbestos in DNA from pleural samples (n = 16) confirms the observation from array results of increased methylation at this locus among individuals with a known asbestos exposure (<i>P</i> = 0.10). (B) Mean bisulfite pyrosequencing percent methylation of <i>DNMT3B</i>_P352 and two downstream CpGs (30 bases total) in adult bloods run on the array and an independent set of controls plotted versus age (<i>P</i> = 0.03, rho = ––0.18, n = 112). (C) Mean bisulfite pyrosequencing percent methylation of <i>LIF</i>_P383 and two downstream CpGs (41 bases total) in adult bloods run on the array and an independent set of controls plotted versus smoking packyears (<i>P</i> = 0.01, rho = 0.24, n = 112). (D) Mean bisulfite pyrosequencing percent methylation of <i>FZD9</i>_E458 and five downstream CpGs (25 bases total) in adult bloods run on the array and an independent set of controls plotted versus age (<i>P</i> = 7.0E-04, rho = 0.29, n = 112).</p>", "links"=>[], "tags"=>["pyrosequencing", "confirmation", "methylation", "alterations", "observed"], "article_id"=>558790, "categories"=>["Virology", "Molecular Biology", "Genetics", "Medicine"], "users"=>["Brock C. Christensen", "E. Andres Houseman", "Carmen J. Marsit", "Shichun Zheng", "Margaret R. Wrensch", "Joseph L. Wiemels", "Heather H. Nelson", "Margaret R. Karagas", "James F. Padbury", "Raphael Bueno", "David J. Sugarbaker", "Ru-Fang Yeh", "John K. Wiencke", "Karl T. Kelsey"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000602.g004", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bisulfite_pyrosequencing_confirmation_of_age_and_environmental_exposure_8211_related_methylation_alterations_observed_in_array_results_/558790", "title"=>"Bisulfite pyrosequencing confirmation of age and environmental exposure–related methylation alterations observed in array results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-14 02:26:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/887943"], "description"=>"<p>Normal tissue sample average beta values were subjected to unsupervised hierarchical clustering based on Manhattan distance and average linkage. Each column represents a sample and each row represents a CpG locus (500 most variable autosomal loci). Above the heatmap, colors indicate tissue type as in key. In the heat map blue = average β of one, or methylated, and yellow = average β of zero, or unmethylated.</p>", "links"=>[], "tags"=>["clustering", "beta"], "article_id"=>558383, "categories"=>["Virology", "Molecular Biology", "Genetics", "Medicine"], "users"=>["Brock C. Christensen", "E. Andres Houseman", "Carmen J. Marsit", "Shichun Zheng", "Margaret R. Wrensch", "Joseph L. Wiemels", "Heather H. Nelson", "Margaret R. Karagas", "James F. Padbury", "Raphael Bueno", "David J. Sugarbaker", "Ru-Fang Yeh", "John K. Wiencke", "Karl T. Kelsey"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000602.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unsupervised_clustering_of_average_beta_values_in_normal_human_tissues_/558383", "title"=>"Unsupervised clustering of average beta values in normal human tissues.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-14 02:19:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/888581"], "description"=>"*<p>False discovery rate corrected <i>P</i>-value.</p>a<p>All samples with age range >0.</p>b<p>Two samples.</p>c<p>One sample missing age data.</p>", "links"=>[], "tags"=>["cpg", "locus-by-locus", "methylation"], "article_id"=>559024, "categories"=>["Virology", "Molecular Biology", "Genetics", "Medicine"], "users"=>["Brock C. Christensen", "E. Andres Houseman", "Carmen J. Marsit", "Shichun Zheng", "Margaret R. Wrensch", "Joseph L. Wiemels", "Heather H. Nelson", "Margaret R. Karagas", "James F. Padbury", "Raphael Bueno", "David J. Sugarbaker", "Ru-Fang Yeh", "John K. Wiencke", "Karl T. Kelsey"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000602.t004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_statistics_of_global_CpG_locus_by_locus_age_vs_methylation_association_tests_/559024", "title"=>"Summary statistics of global CpG locus-by-locus age vs. methylation association tests.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-08-14 02:30:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/888242"], "description"=>"<p>For (A–F), the top plot is the estimate of mean regression coefficients for age associated methylation (by decade), and its 95% confidence interval from GEE for each CpG RPMM class. The middle plot is of CpGs clustered with RPMM into eight classes for each group of samples. The bottom plot indicates the CpG island status for each locus (where magenta = CpG island locus, green = non-CpG island locus). (A) Estimates of class-specific age-associated methylation among all solid tissues (n = 119), RPMM clustering of CpGs, and CpG island status. Age-associated methylation is significantly increased among classes with a high prevalence of CpG-island loci (<i>P</i> = 2.3E-08). (B) Estimates of class-specific age-associated methylation among blood samples (n = 29), RPMM clustering of CpGs, and CpG island status. Age-associated methylation is significantly decreased among classes with a high prevalence of non-CpG-island loci (<i>P</i> = 6.3E-06). (C) Estimates of class-specific age associated methylation among pleural tissues (n = 18), RPMM clustering of CpGs, and CpG island status. Age-associated methylation is significantly increased among classes with a high prevalence of CpG-island loci (<i>P</i> = 2.3E-08). (D) Estimates of class-specific age-associated methylation among lung tissues (n = 52), RPMM clustering of CpGs, and CpG island status. (E) Estimates of class-specific age-associated methylation among brain tissues (n = 11), RPMM clustering of CpGs, and CpG island status. Age-associated methylation is significantly increased for the predominantly CpG island loci in class 3, and significantly decreased among classes with a high prevalence of non-CpG-island loci (<i>P</i> = 7.0E-04). (F) Estimates of class-specific age associated methylation among head and neck tissues (n = 10), RPMM clustering of CpGs, and CpG island status. Age-associated methylation is significantly decreased among classes with a high prevalence of non-CpG-island loci (<i>P</i> = 5.2E-08).</p>", "links"=>[], "tags"=>["correlations", "methylation", "alterations", "cpg"], "article_id"=>558685, "categories"=>["Virology", "Molecular Biology", "Genetics", "Medicine"], "users"=>["Brock C. Christensen", "E. Andres Houseman", "Carmen J. Marsit", "Shichun Zheng", "Margaret R. Wrensch", "Joseph L. Wiemels", "Heather H. Nelson", "Margaret R. Karagas", "James F. Padbury", "Raphael Bueno", "David J. Sugarbaker", "Ru-Fang Yeh", "John K. Wiencke", "Karl T. Kelsey"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000602.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_direction_of_correlations_for_age_associated_methylation_alterations_differ_dependent_upon_CpG_island_status_/558685", "title"=>"The direction of correlations for age associated methylation alterations differ dependent upon CpG island status.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-08-14 02:24:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/888718"], "description"=>"<p>Overall misclassification error: 8.8% (<i>P</i><0.0001)</p>", "links"=>[], "tags"=>["cpg", "methylation-based", "forests", "classification"], "article_id"=>559159, "categories"=>["Virology", "Molecular Biology", "Genetics", "Medicine"], "users"=>["Brock C. Christensen", "E. Andres Houseman", "Carmen J. Marsit", "Shichun Zheng", "Margaret R. Wrensch", "Joseph L. Wiemels", "Heather H. Nelson", "Margaret R. Karagas", "James F. Padbury", "Raphael Bueno", "David J. Sugarbaker", "Ru-Fang Yeh", "John K. Wiencke", "Karl T. Kelsey"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000602.t002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Normal_tissue_CpG_methylation_based_random_forests_classification_confusion_matrix_/559159", "title"=>"Normal tissue CpG methylation-based random forests classification confusion matrix.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-08-14 02:32:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/888675"], "description"=>"a<p>All samples with age range ≠ 0.</p>b<p>Decreased methylation with increasing age.</p>c<p>One sample.</p>d<p>Two samples missing age data.</p>*<p>CpG loci tested, with no age association: <i>ESR1</i>_E298, <i>IGF2</i>_P1036, <i>MGMT</i>_P272, <i>SFRP1</i>_E398 & P157, <i>TERT</i>_E20, <i>WRN</i>_E57.</p>", "links"=>[], "tags"=>["age-related", "methylation", "alterations", "cpg", "loci"], "article_id"=>559108, "categories"=>["Virology", "Molecular Biology", "Genetics", "Medicine"], "users"=>["Brock C. Christensen", "E. Andres Houseman", "Carmen J. Marsit", "Shichun Zheng", "Margaret R. Wrensch", "Joseph L. Wiemels", "Heather H. Nelson", "Margaret R. Karagas", "James F. Padbury", "Raphael Bueno", "David J. Sugarbaker", "Ru-Fang Yeh", "John K. Wiencke", "Karl T. Kelsey"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000602.t003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_age_related_methylation_alterations_from_selected_CpG_loci_in_normal_tissues_/559108", "title"=>"Significant age-related methylation alterations from selected CpG loci in normal tissues.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-08-14 02:31:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/439912", "https://ndownloader.figshare.com/files/439942", "https://ndownloader.figshare.com/files/439967", "https://ndownloader.figshare.com/files/439985", "https://ndownloader.figshare.com/files/440007", "https://ndownloader.figshare.com/files/440046", "https://ndownloader.figshare.com/files/440075", "https://ndownloader.figshare.com/files/440136", "https://ndownloader.figshare.com/files/440238"], "description"=>"<div><p>Epigenetic control of gene transcription is critical for normal human development and cellular differentiation. While alterations of epigenetic marks such as DNA methylation have been linked to cancers and many other human diseases, interindividual epigenetic variations in normal tissues due to aging, environmental factors, or innate susceptibility are poorly characterized. The plasticity, tissue-specific nature, and variability of gene expression are related to epigenomic states that vary across individuals. Thus, population-based investigations are needed to further our understanding of the fundamental dynamics of normal individual epigenomes. We analyzed 217 non-pathologic human tissues from 10 anatomic sites at 1,413 autosomal CpG loci associated with 773 genes to investigate tissue-specific differences in DNA methylation and to discern how aging and exposures contribute to normal variation in methylation. Methylation profile classes derived from unsupervised modeling were significantly associated with age (<em>P</em><0.0001) and were significant predictors of tissue origin (<em>P</em><0.0001). In solid tissues (n = 119) we found striking, highly significant CpG island–dependent correlations between age and methylation; loci in CpG islands gained methylation with age, loci not in CpG islands lost methylation with age (<em>P</em><0.001), and this pattern was consistent across tissues and in an analysis of blood-derived DNA. Our data clearly demonstrate age- and exposure-related differences in tissue-specific methylation and significant age-associated methylation patterns which are CpG island context-dependent. This work provides novel insight into the role of aging and the environment in susceptibility to diseases such as cancer and critically informs the field of epigenomics by providing evidence of epigenetic dysregulation by age-related methylation alterations. Collectively we reveal key issues to consider both in the construction of reference and disease-related epigenomes and in the interpretation of potentially pathologically important alterations.</p></div>", "links"=>[], "tags"=>["aging", "exposures", "tissue-specific", "dna", "methylation", "cpg"], "article_id"=>146721, "categories"=>["Cancer", "Molecular Biology", "Genetics", "Medicine"], "users"=>["Brock C. Christensen", "E. Andres Houseman", "Carmen J. Marsit", "Shichun Zheng", "Margaret R. Wrensch", "Joseph L. Wiemels", "Heather H. Nelson", "Margaret R. Karagas", "James F. Padbury", "Raphael Bueno", "David J. Sugarbaker", "Ru-Fang Yeh", "John K. Wiencke", "Karl T. Kelsey"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1000602.s001", "https://dx.doi.org/10.1371/journal.pgen.1000602.s002", "https://dx.doi.org/10.1371/journal.pgen.1000602.s003", "https://dx.doi.org/10.1371/journal.pgen.1000602.s004", "https://dx.doi.org/10.1371/journal.pgen.1000602.s005", "https://dx.doi.org/10.1371/journal.pgen.1000602.s006", "https://dx.doi.org/10.1371/journal.pgen.1000602.s007", "https://dx.doi.org/10.1371/journal.pgen.1000602.s008", "https://dx.doi.org/10.1371/journal.pgen.1000602.s009"], "stats"=>{"downloads"=>17, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Aging_and_Environmental_Exposures_Alter_Tissue_Specific_DNA_Methylation_Dependent_upon_CpG_Island_Context/146721", "title"=>"Aging and Environmental Exposures Alter Tissue-Specific DNA Methylation Dependent upon CpG Island Context", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-08-14 01:52:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/888531"], "description"=>"a<p>Tissue n is for samples with available age data.</p>b<p>Estimate is per decade (∼n CpGs altered/decade; positive indicates methylation with age, negative for loss of methylation with age).</p>", "links"=>[], "tags"=>["age-related", "methylation", "coefficients", "cpg"], "article_id"=>558975, "categories"=>["Virology", "Molecular Biology", "Genetics", "Medicine"], "users"=>["Brock C. Christensen", "E. Andres Houseman", "Carmen J. Marsit", "Shichun Zheng", "Margaret R. Wrensch", "Joseph L. Wiemels", "Heather H. Nelson", "Margaret R. Karagas", "James F. Padbury", "Raphael Bueno", "David J. Sugarbaker", "Ru-Fang Yeh", "John K. Wiencke", "Karl T. Kelsey"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1000602.t005", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_direction_of_age_related_methylation_coefficients_is_dependent_on_CpG_island_status_/558975", "title"=>"The direction of age-related methylation coefficients is dependent on CpG island status.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-08-14 02:29:35"}

PMC Usage Stats | Further Information

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Relative Metric

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