Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-04-06

AUTHORS

Jinyan Huang, Ling Bai, Bowen Cui, Liang Wu, Liwen Wang, Zhiyin An, Shulin Ruan, Yue Yu, Xianyang Zhang, Jun Chen

ABSTRACT

BackgroundEpigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data.ResultsIn this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts.ConclusionsCovariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended. More... »

PAGES

88

References to SciGraph publications

  • 2016-09-30. Identification of DNA methylation changes associated with disease progression in subchondral bone with site-matched cartilage in knee osteoarthritis in SCIENTIFIC REPORTS
  • 2014-08-17. Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci in NATURE NEUROSCIENCE
  • 2015-08-19. Age-related profiling of DNA methylation in CD8+ T cells reveals changes in immune response and transcriptional regulator genes in SCIENTIFIC REPORTS
  • 2014-08-17. Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease in NATURE NEUROSCIENCE
  • 2017-05-26. Fast and robust adjustment of cell mixtures in epigenome-wide association studies with SmartSVA in BMC GENOMICS
  • 2017-06-13. A phase II, multicentre trial of decitabine in higher-risk chronic myelomonocytic leukemia in LEUKEMIA
  • 2016-02-16. Oestrogen receptor β regulates epigenetic patterns at specific genomic loci through interaction with thymine DNA glycosylase in EPIGENETICS & CHROMATIN
  • 2013-09-24. Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia in GENOME BIOLOGY
  • 2010-11-30. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis in BMC BIOINFORMATICS
  • 2016-06-29. DNA methylation signature of human fetal alcohol spectrum disorder in EPIGENETICS & CHROMATIN
  • 2019-06-04. A practical guide to methods controlling false discoveries in computational biology in GENOME BIOLOGY
  • 2017-04-05. Interaction between prenatal pesticide exposure and a common polymorphism in the PON1 gene on DNA methylation in genes associated with cardio-metabolic disease risk—an exploratory study in CLINICAL EPIGENETICS
  • 2013-02-12. DNA methylation: roles in mammalian development in NATURE REVIEWS GENETICS
  • 2017-01-04. Placental mitochondrial DNA and CYP1A1 gene methylation as molecular signatures for tobacco smoke exposure in pregnant women and the relevance for birth weight in JOURNAL OF TRANSLATIONAL MEDICINE
  • 2014-11-15. Carcinogenic polycyclic aromatic hydrocarbons induce CYP1A1 in human cells via a p53-dependent mechanism in ARCHIVES OF TOXICOLOGY
  • 2010-09-14. Tackling the widespread and critical impact of batch effects in high-throughput data in NATURE REVIEWS GENETICS
  • 2009-01-08. Filtering for increased power for microarray data analysis in BMC BIOINFORMATICS
  • 2015-10-16. DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies in CLINICAL EPIGENETICS
  • 2012-09-05. An integrated encyclopedia of DNA elements in the human genome in NATURE
  • 2016. ggplot2, Elegant Graphics for Data Analysis in NONE
  • 2016-05-30. Data-driven hypothesis weighting increases detection power in genome-scale multiple testing in NATURE METHODS
  • 2013-06-12. Epigenetics in clinical practice: the examples of azacitidine and decitabine in myelodysplasia and acute myeloid leukemia in LEUKEMIA
  • 2016-05-03. An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies in GENOME BIOLOGY
  • 2011-05-18. Regulation of X-chromosome inactivation by the X-inactivation centre in NATURE REVIEWS GENETICS
  • 2014-06-19. Non-specific filtering of beta-distributed data in BMC BIOINFORMATICS
  • 2014-09-19. Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study in BMC BIOINFORMATICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13059-020-02001-7

    DOI

    http://dx.doi.org/10.1186/s13059-020-02001-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1126182170

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/32252795


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/05", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Environmental Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Aging", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "CpG Islands", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "DNA Methylation", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Data Interpretation, Statistical", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Epigenome", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Epigenomics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Lupus Erythematosus, Systemic", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Phenotype", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Smoking", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China", 
              "id": "http://www.grid.ac/institutes/grid.412277.5", 
              "name": [
                "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Huang", 
            "givenName": "Jinyan", 
            "id": "sg:person.01363744764.18", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01363744764.18"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China", 
              "id": "http://www.grid.ac/institutes/grid.412277.5", 
              "name": [
                "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bai", 
            "givenName": "Ling", 
            "id": "sg:person.011017774303.50", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011017774303.50"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China", 
              "id": "http://www.grid.ac/institutes/grid.412277.5", 
              "name": [
                "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cui", 
            "givenName": "Bowen", 
            "id": "sg:person.07425033303.37", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07425033303.37"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China", 
              "id": "http://www.grid.ac/institutes/grid.412277.5", 
              "name": [
                "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wu", 
            "givenName": "Liang", 
            "id": "sg:person.014161707567.81", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014161707567.81"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of General Surgery, Rui-Jin Hospital, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China", 
              "id": "http://www.grid.ac/institutes/grid.412277.5", 
              "name": [
                "Department of General Surgery, Rui-Jin Hospital, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wang", 
            "givenName": "Liwen", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China", 
              "id": "http://www.grid.ac/institutes/grid.412277.5", 
              "name": [
                "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "An", 
            "givenName": "Zhiyin", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China", 
              "id": "http://www.grid.ac/institutes/grid.412277.5", 
              "name": [
                "State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ruan", 
            "givenName": "Shulin", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Digital Health Sciences, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA", 
              "id": "http://www.grid.ac/institutes/grid.66875.3a", 
              "name": [
                "Division of Digital Health Sciences, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Yu", 
            "givenName": "Yue", 
            "id": "sg:person.011565206334.42", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011565206334.42"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Statistics, Texas A&M University, Blocker 449D, 77843, College Station, TX, USA", 
              "id": "http://www.grid.ac/institutes/grid.264756.4", 
              "name": [
                "Department of Statistics, Texas A&M University, Blocker 449D, 77843, College Station, TX, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhang", 
            "givenName": "Xianyang", 
            "id": "sg:person.013166044574.14", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013166044574.14"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Biomedical Statistics and Informatics, Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA", 
              "id": "http://www.grid.ac/institutes/grid.66875.3a", 
              "name": [
                "Division of Biomedical Statistics and Informatics, Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Chen", 
            "givenName": "Jun", 
            "id": "sg:person.01023412627.52", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023412627.52"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/nn.3786", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037762791", 
              "https://doi.org/10.1038/nn.3786"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-15-312", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017248116", 
              "https://doi.org/10.1186/1471-2105-15-312"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-24277-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028525626", 
              "https://doi.org/10.1007/978-3-319-24277-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13072-016-0055-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020685274", 
              "https://doi.org/10.1186/s13072-016-0055-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-11-587", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025448148", 
              "https://doi.org/10.1186/1471-2105-11-587"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-15-199", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021310489", 
              "https://doi.org/10.1186/1471-2105-15-199"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13072-016-0074-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050956810", 
              "https://doi.org/10.1186/s13072-016-0074-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13059-016-0935-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014176746", 
              "https://doi.org/10.1186/s13059-016-0935-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg2825", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037809833", 
              "https://doi.org/10.1038/nrg2825"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.3885", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026367943", 
              "https://doi.org/10.1038/nmeth.3885"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg3354", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016511715", 
              "https://doi.org/10.1038/nrg3354"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature11247", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029065430", 
              "https://doi.org/10.1038/nature11247"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-10-11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022886012", 
              "https://doi.org/10.1186/1471-2105-10-11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep13107", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045248336", 
              "https://doi.org/10.1038/srep13107"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/leu.2017.186", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085998914", 
              "https://doi.org/10.1038/leu.2017.186"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg2987", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038478288", 
              "https://doi.org/10.1038/nrg2987"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nn.3782", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015134701", 
              "https://doi.org/10.1038/nn.3782"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/leu.2013.173", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047612708", 
              "https://doi.org/10.1038/leu.2013.173"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12967-016-1113-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012167400", 
              "https://doi.org/10.1186/s12967-016-1113-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2013-14-9-r105", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031523910", 
              "https://doi.org/10.1186/gb-2013-14-9-r105"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep34460", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048488091", 
              "https://doi.org/10.1038/srep34460"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00204-014-1409-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041015293", 
              "https://doi.org/10.1007/s00204-014-1409-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-017-3808-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085598099", 
              "https://doi.org/10.1186/s12864-017-3808-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13148-015-0148-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045280957", 
              "https://doi.org/10.1186/s13148-015-0148-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13059-019-1716-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1116652806", 
              "https://doi.org/10.1186/s13059-019-1716-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13148-017-0336-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084612603", 
              "https://doi.org/10.1186/s13148-017-0336-4"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2020-04-06", 
        "datePublishedReg": "2020-04-06", 
        "description": "BackgroundEpigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data.ResultsIn this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts.ConclusionsCovariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.", 
        "genre": "article", 
        "id": "sg:pub.10.1186/s13059-020-02001-7", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.8348956", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.8368115", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.7705535", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1023439", 
            "issn": [
              "1474-760X", 
              "1465-6906"
            ], 
            "name": "Genome Biology", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "21"
          }
        ], 
        "keywords": [
          "FDR control methods", 
          "sparse signals", 
          "control method", 
          "false discovery rate control", 
          "auxiliary covariates", 
          "EWAS data", 
          "detection power", 
          "informative covariates", 
          "multiple hypothesis testing", 
          "multiple testing methods", 
          "statistical covariates", 
          "hypothesis testing", 
          "real datasets", 
          "null hypothesis", 
          "omnibus test", 
          "specific dataset", 
          "rate control", 
          "association testing", 
          "power", 
          "covariates", 
          "signals", 
          "multiple testing correction", 
          "dataset", 
          "weighting", 
          "applications", 
          "correction", 
          "biological covariates", 
          "biological context", 
          "variance", 
          "performance", 
          "data", 
          "ST procedure", 
          "procedure", 
          "EWAS", 
          "control", 
          "informativeness", 
          "testing methods", 
          "likelihood", 
          "association studies", 
          "CAMT", 
          "context", 
          "study", 
          "testing", 
          "contrast", 
          "hypothesis", 
          "test", 
          "median", 
          "marks", 
          "findings", 
          "outcomes", 
          "exposure", 
          "association", 
          "epigenetic marks", 
          "method", 
          "ResultsIn", 
          "methylation"
        ], 
        "name": "Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing", 
        "pagination": "88", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1126182170"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s13059-020-02001-7"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "32252795"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s13059-020-02001-7", 
          "https://app.dimensions.ai/details/publication/pub.1126182170"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-10-01T06:48", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_874.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1186/s13059-020-02001-7"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s13059-020-02001-7'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s13059-020-02001-7'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13059-020-02001-7'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13059-020-02001-7'


     

    This table displays all metadata directly associated to this object as RDF triples.

    341 TRIPLES      21 PREDICATES      118 URIs      83 LITERALS      17 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s13059-020-02001-7 schema:about N4f49f9b66d9c4c3395bd83674b9ea999
    2 N641a8adb48db4a3792e094c354e6dcf8
    3 N76286c63fee84b18a3cc5bfc1f455585
    4 N7bc4a532c9d24d0b81e91abca65889a9
    5 N81a591ec3c4c4f139db55b167f2c00e7
    6 N8ee72663d716481bb5597a7c56835c2b
    7 N9250d12f7f8542cc85e6c3e03fe205eb
    8 N942e4226c8714d049747159cea74fe24
    9 N9849694398374c7ba5118e33626695a7
    10 Nea5b8bdfd77e47a285ad966e8bab6ba0
    11 anzsrc-for:05
    12 anzsrc-for:06
    13 anzsrc-for:08
    14 schema:author N4b42759ccb54499ba509d63894b1639d
    15 schema:citation sg:pub.10.1007/978-3-319-24277-4
    16 sg:pub.10.1007/s00204-014-1409-1
    17 sg:pub.10.1038/leu.2013.173
    18 sg:pub.10.1038/leu.2017.186
    19 sg:pub.10.1038/nature11247
    20 sg:pub.10.1038/nmeth.3885
    21 sg:pub.10.1038/nn.3782
    22 sg:pub.10.1038/nn.3786
    23 sg:pub.10.1038/nrg2825
    24 sg:pub.10.1038/nrg2987
    25 sg:pub.10.1038/nrg3354
    26 sg:pub.10.1038/srep13107
    27 sg:pub.10.1038/srep34460
    28 sg:pub.10.1186/1471-2105-10-11
    29 sg:pub.10.1186/1471-2105-11-587
    30 sg:pub.10.1186/1471-2105-15-199
    31 sg:pub.10.1186/1471-2105-15-312
    32 sg:pub.10.1186/gb-2013-14-9-r105
    33 sg:pub.10.1186/s12864-017-3808-1
    34 sg:pub.10.1186/s12967-016-1113-4
    35 sg:pub.10.1186/s13059-016-0935-y
    36 sg:pub.10.1186/s13059-019-1716-1
    37 sg:pub.10.1186/s13072-016-0055-7
    38 sg:pub.10.1186/s13072-016-0074-4
    39 sg:pub.10.1186/s13148-015-0148-3
    40 sg:pub.10.1186/s13148-017-0336-4
    41 schema:datePublished 2020-04-06
    42 schema:datePublishedReg 2020-04-06
    43 schema:description BackgroundEpigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data.ResultsIn this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts.ConclusionsCovariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.
    44 schema:genre article
    45 schema:isAccessibleForFree true
    46 schema:isPartOf N16baaaa2d6f94164b1cd667f3b50522e
    47 N738f34f038124c7f8ce408d2092bbffe
    48 sg:journal.1023439
    49 schema:keywords CAMT
    50 EWAS
    51 EWAS data
    52 FDR control methods
    53 ResultsIn
    54 ST procedure
    55 applications
    56 association
    57 association studies
    58 association testing
    59 auxiliary covariates
    60 biological context
    61 biological covariates
    62 context
    63 contrast
    64 control
    65 control method
    66 correction
    67 covariates
    68 data
    69 dataset
    70 detection power
    71 epigenetic marks
    72 exposure
    73 false discovery rate control
    74 findings
    75 hypothesis
    76 hypothesis testing
    77 informative covariates
    78 informativeness
    79 likelihood
    80 marks
    81 median
    82 method
    83 methylation
    84 multiple hypothesis testing
    85 multiple testing correction
    86 multiple testing methods
    87 null hypothesis
    88 omnibus test
    89 outcomes
    90 performance
    91 power
    92 procedure
    93 rate control
    94 real datasets
    95 signals
    96 sparse signals
    97 specific dataset
    98 statistical covariates
    99 study
    100 test
    101 testing
    102 testing methods
    103 variance
    104 weighting
    105 schema:name Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing
    106 schema:pagination 88
    107 schema:productId N425c12a110324ccda4bdf60bb6e19a5d
    108 N4b2e5ddbcece469ab294835bbee8ce60
    109 Ne49f9994f7114b3fbff20d7001078011
    110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1126182170
    111 https://doi.org/10.1186/s13059-020-02001-7
    112 schema:sdDatePublished 2022-10-01T06:48
    113 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    114 schema:sdPublisher N79ef78b972ce482db6a8805a78f34d53
    115 schema:url https://doi.org/10.1186/s13059-020-02001-7
    116 sgo:license sg:explorer/license/
    117 sgo:sdDataset articles
    118 rdf:type schema:ScholarlyArticle
    119 N0985ec2739904a4b987b39cbb1be48e0 rdf:first sg:person.011017774303.50
    120 rdf:rest N7d8efb2009bf436a852646e94e339c2d
    121 N16baaaa2d6f94164b1cd667f3b50522e schema:issueNumber 1
    122 rdf:type schema:PublicationIssue
    123 N1f272ff5bccc4eceb926774240281807 rdf:first Na02d417daa0e413bbe4675027ae4deeb
    124 rdf:rest Ne8b6fc8af6a64f4f9401067b4f517fe8
    125 N21d36793970b4f81ba8bb23ed711cd73 rdf:first sg:person.011565206334.42
    126 rdf:rest Ncfbe82d4c4c242d3afc7719044e592ea
    127 N391692b96a324fe19a8075dab32fa6f2 rdf:first N465877dfde3f4c2bb2d56662fa9c1b51
    128 rdf:rest N1f272ff5bccc4eceb926774240281807
    129 N425c12a110324ccda4bdf60bb6e19a5d schema:name doi
    130 schema:value 10.1186/s13059-020-02001-7
    131 rdf:type schema:PropertyValue
    132 N465877dfde3f4c2bb2d56662fa9c1b51 schema:affiliation grid-institutes:grid.412277.5
    133 schema:familyName Wang
    134 schema:givenName Liwen
    135 rdf:type schema:Person
    136 N4b2e5ddbcece469ab294835bbee8ce60 schema:name pubmed_id
    137 schema:value 32252795
    138 rdf:type schema:PropertyValue
    139 N4b42759ccb54499ba509d63894b1639d rdf:first sg:person.01363744764.18
    140 rdf:rest N0985ec2739904a4b987b39cbb1be48e0
    141 N4f49f9b66d9c4c3395bd83674b9ea999 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    142 schema:name Aging
    143 rdf:type schema:DefinedTerm
    144 N641a8adb48db4a3792e094c354e6dcf8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    145 schema:name Epigenome
    146 rdf:type schema:DefinedTerm
    147 N738f34f038124c7f8ce408d2092bbffe schema:volumeNumber 21
    148 rdf:type schema:PublicationVolume
    149 N76286c63fee84b18a3cc5bfc1f455585 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    150 schema:name Data Interpretation, Statistical
    151 rdf:type schema:DefinedTerm
    152 N79ef78b972ce482db6a8805a78f34d53 schema:name Springer Nature - SN SciGraph project
    153 rdf:type schema:Organization
    154 N7bc4a532c9d24d0b81e91abca65889a9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    155 schema:name Phenotype
    156 rdf:type schema:DefinedTerm
    157 N7d8efb2009bf436a852646e94e339c2d rdf:first sg:person.07425033303.37
    158 rdf:rest Nef671f58be5d4f3f94f36048954a8da3
    159 N81a591ec3c4c4f139db55b167f2c00e7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    160 schema:name CpG Islands
    161 rdf:type schema:DefinedTerm
    162 N8ee72663d716481bb5597a7c56835c2b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    163 schema:name Lupus Erythematosus, Systemic
    164 rdf:type schema:DefinedTerm
    165 N9250d12f7f8542cc85e6c3e03fe205eb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    166 schema:name Epigenomics
    167 rdf:type schema:DefinedTerm
    168 N942e4226c8714d049747159cea74fe24 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    169 schema:name DNA Methylation
    170 rdf:type schema:DefinedTerm
    171 N9849694398374c7ba5118e33626695a7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    172 schema:name Smoking
    173 rdf:type schema:DefinedTerm
    174 Na02d417daa0e413bbe4675027ae4deeb schema:affiliation grid-institutes:grid.412277.5
    175 schema:familyName An
    176 schema:givenName Zhiyin
    177 rdf:type schema:Person
    178 Ncfbe82d4c4c242d3afc7719044e592ea rdf:first sg:person.013166044574.14
    179 rdf:rest Ndb14b22e3d984036924830b4abcf9f53
    180 Ndb14b22e3d984036924830b4abcf9f53 rdf:first sg:person.01023412627.52
    181 rdf:rest rdf:nil
    182 Ne49f9994f7114b3fbff20d7001078011 schema:name dimensions_id
    183 schema:value pub.1126182170
    184 rdf:type schema:PropertyValue
    185 Ne67837a65aa5477b927387f10ade95ac schema:affiliation grid-institutes:grid.412277.5
    186 schema:familyName Ruan
    187 schema:givenName Shulin
    188 rdf:type schema:Person
    189 Ne8b6fc8af6a64f4f9401067b4f517fe8 rdf:first Ne67837a65aa5477b927387f10ade95ac
    190 rdf:rest N21d36793970b4f81ba8bb23ed711cd73
    191 Nea5b8bdfd77e47a285ad966e8bab6ba0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    192 schema:name Humans
    193 rdf:type schema:DefinedTerm
    194 Nef671f58be5d4f3f94f36048954a8da3 rdf:first sg:person.014161707567.81
    195 rdf:rest N391692b96a324fe19a8075dab32fa6f2
    196 anzsrc-for:05 schema:inDefinedTermSet anzsrc-for:
    197 schema:name Environmental Sciences
    198 rdf:type schema:DefinedTerm
    199 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    200 schema:name Biological Sciences
    201 rdf:type schema:DefinedTerm
    202 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    203 schema:name Information and Computing Sciences
    204 rdf:type schema:DefinedTerm
    205 sg:grant.7705535 http://pending.schema.org/fundedItem sg:pub.10.1186/s13059-020-02001-7
    206 rdf:type schema:MonetaryGrant
    207 sg:grant.8348956 http://pending.schema.org/fundedItem sg:pub.10.1186/s13059-020-02001-7
    208 rdf:type schema:MonetaryGrant
    209 sg:grant.8368115 http://pending.schema.org/fundedItem sg:pub.10.1186/s13059-020-02001-7
    210 rdf:type schema:MonetaryGrant
    211 sg:journal.1023439 schema:issn 1465-6906
    212 1474-760X
    213 schema:name Genome Biology
    214 schema:publisher Springer Nature
    215 rdf:type schema:Periodical
    216 sg:person.01023412627.52 schema:affiliation grid-institutes:grid.66875.3a
    217 schema:familyName Chen
    218 schema:givenName Jun
    219 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01023412627.52
    220 rdf:type schema:Person
    221 sg:person.011017774303.50 schema:affiliation grid-institutes:grid.412277.5
    222 schema:familyName Bai
    223 schema:givenName Ling
    224 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011017774303.50
    225 rdf:type schema:Person
    226 sg:person.011565206334.42 schema:affiliation grid-institutes:grid.66875.3a
    227 schema:familyName Yu
    228 schema:givenName Yue
    229 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011565206334.42
    230 rdf:type schema:Person
    231 sg:person.013166044574.14 schema:affiliation grid-institutes:grid.264756.4
    232 schema:familyName Zhang
    233 schema:givenName Xianyang
    234 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013166044574.14
    235 rdf:type schema:Person
    236 sg:person.01363744764.18 schema:affiliation grid-institutes:grid.412277.5
    237 schema:familyName Huang
    238 schema:givenName Jinyan
    239 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01363744764.18
    240 rdf:type schema:Person
    241 sg:person.014161707567.81 schema:affiliation grid-institutes:grid.412277.5
    242 schema:familyName Wu
    243 schema:givenName Liang
    244 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014161707567.81
    245 rdf:type schema:Person
    246 sg:person.07425033303.37 schema:affiliation grid-institutes:grid.412277.5
    247 schema:familyName Cui
    248 schema:givenName Bowen
    249 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07425033303.37
    250 rdf:type schema:Person
    251 sg:pub.10.1007/978-3-319-24277-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028525626
    252 https://doi.org/10.1007/978-3-319-24277-4
    253 rdf:type schema:CreativeWork
    254 sg:pub.10.1007/s00204-014-1409-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041015293
    255 https://doi.org/10.1007/s00204-014-1409-1
    256 rdf:type schema:CreativeWork
    257 sg:pub.10.1038/leu.2013.173 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047612708
    258 https://doi.org/10.1038/leu.2013.173
    259 rdf:type schema:CreativeWork
    260 sg:pub.10.1038/leu.2017.186 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085998914
    261 https://doi.org/10.1038/leu.2017.186
    262 rdf:type schema:CreativeWork
    263 sg:pub.10.1038/nature11247 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029065430
    264 https://doi.org/10.1038/nature11247
    265 rdf:type schema:CreativeWork
    266 sg:pub.10.1038/nmeth.3885 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026367943
    267 https://doi.org/10.1038/nmeth.3885
    268 rdf:type schema:CreativeWork
    269 sg:pub.10.1038/nn.3782 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015134701
    270 https://doi.org/10.1038/nn.3782
    271 rdf:type schema:CreativeWork
    272 sg:pub.10.1038/nn.3786 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037762791
    273 https://doi.org/10.1038/nn.3786
    274 rdf:type schema:CreativeWork
    275 sg:pub.10.1038/nrg2825 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037809833
    276 https://doi.org/10.1038/nrg2825
    277 rdf:type schema:CreativeWork
    278 sg:pub.10.1038/nrg2987 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038478288
    279 https://doi.org/10.1038/nrg2987
    280 rdf:type schema:CreativeWork
    281 sg:pub.10.1038/nrg3354 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016511715
    282 https://doi.org/10.1038/nrg3354
    283 rdf:type schema:CreativeWork
    284 sg:pub.10.1038/srep13107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045248336
    285 https://doi.org/10.1038/srep13107
    286 rdf:type schema:CreativeWork
    287 sg:pub.10.1038/srep34460 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048488091
    288 https://doi.org/10.1038/srep34460
    289 rdf:type schema:CreativeWork
    290 sg:pub.10.1186/1471-2105-10-11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022886012
    291 https://doi.org/10.1186/1471-2105-10-11
    292 rdf:type schema:CreativeWork
    293 sg:pub.10.1186/1471-2105-11-587 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025448148
    294 https://doi.org/10.1186/1471-2105-11-587
    295 rdf:type schema:CreativeWork
    296 sg:pub.10.1186/1471-2105-15-199 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021310489
    297 https://doi.org/10.1186/1471-2105-15-199
    298 rdf:type schema:CreativeWork
    299 sg:pub.10.1186/1471-2105-15-312 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017248116
    300 https://doi.org/10.1186/1471-2105-15-312
    301 rdf:type schema:CreativeWork
    302 sg:pub.10.1186/gb-2013-14-9-r105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031523910
    303 https://doi.org/10.1186/gb-2013-14-9-r105
    304 rdf:type schema:CreativeWork
    305 sg:pub.10.1186/s12864-017-3808-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085598099
    306 https://doi.org/10.1186/s12864-017-3808-1
    307 rdf:type schema:CreativeWork
    308 sg:pub.10.1186/s12967-016-1113-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012167400
    309 https://doi.org/10.1186/s12967-016-1113-4
    310 rdf:type schema:CreativeWork
    311 sg:pub.10.1186/s13059-016-0935-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1014176746
    312 https://doi.org/10.1186/s13059-016-0935-y
    313 rdf:type schema:CreativeWork
    314 sg:pub.10.1186/s13059-019-1716-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1116652806
    315 https://doi.org/10.1186/s13059-019-1716-1
    316 rdf:type schema:CreativeWork
    317 sg:pub.10.1186/s13072-016-0055-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020685274
    318 https://doi.org/10.1186/s13072-016-0055-7
    319 rdf:type schema:CreativeWork
    320 sg:pub.10.1186/s13072-016-0074-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050956810
    321 https://doi.org/10.1186/s13072-016-0074-4
    322 rdf:type schema:CreativeWork
    323 sg:pub.10.1186/s13148-015-0148-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045280957
    324 https://doi.org/10.1186/s13148-015-0148-3
    325 rdf:type schema:CreativeWork
    326 sg:pub.10.1186/s13148-017-0336-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084612603
    327 https://doi.org/10.1186/s13148-017-0336-4
    328 rdf:type schema:CreativeWork
    329 grid-institutes:grid.264756.4 schema:alternateName Department of Statistics, Texas A&M University, Blocker 449D, 77843, College Station, TX, USA
    330 schema:name Department of Statistics, Texas A&M University, Blocker 449D, 77843, College Station, TX, USA
    331 rdf:type schema:Organization
    332 grid-institutes:grid.412277.5 schema:alternateName Department of General Surgery, Rui-Jin Hospital, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China
    333 State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China
    334 schema:name Department of General Surgery, Rui-Jin Hospital, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China
    335 State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, 200025, Shanghai, China
    336 rdf:type schema:Organization
    337 grid-institutes:grid.66875.3a schema:alternateName Division of Biomedical Statistics and Informatics, Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA
    338 Division of Digital Health Sciences, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA
    339 schema:name Division of Biomedical Statistics and Informatics, Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA
    340 Division of Digital Health Sciences, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA
    341 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...