Common genetic variation and novel loci associated with volumetric mammographic density View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Judith S. Brand, Keith Humphreys, Jingmei Li, Robert Karlsson, Per Hall, Kamila Czene

ABSTRACT

BACKGROUND: Mammographic density (MD) is a strong and heritable intermediate phenotype of breast cancer, but much of its genetic variation remains unexplained. METHODS: We conducted a genetic association study of volumetric MD in a Swedish mammography screening cohort (n = 9498) to identify novel MD loci. Associations with volumetric MD phenotypes (percent dense volume, absolute dense volume, and absolute nondense volume) were estimated using linear regression adjusting for age, body mass index, menopausal status, and six principal components. We also estimated the proportion of MD variance explained by additive contributions from single-nucleotide polymorphisms (SNP-based heritability [h2SNP]) in 4948 participants of the cohort. RESULTS: In total, three novel MD loci were identified (at P < 5 × 10- 8): one for percent dense volume (HABP2) and two for the absolute dense volume (INHBB, LINC01483). INHBB is an established locus for ER-negative breast cancer, and HABP2 and LINC01483 represent putative new breast cancer susceptibility loci, because both loci were associated with breast cancer in available meta-analysis data including 122,977 breast cancer cases and 105,974 control subjects (P < 0.05). h2SNP (SE) estimates for percent dense, absolute dense, and nondense volume were 0.29 (0.07), 0.31 (0.07), and 0.25 (0.07), respectively. Corresponding ratios of h2SNP to previously observed narrow-sense h2 estimates in the same cohort were 0.46, 0.72, and 0.41, respectively. CONCLUSIONS: These findings provide new insights into the genetic basis of MD and biological mechanisms linking MD to breast cancer risk. Apart from identifying three novel loci, we demonstrate that at least 25% of the MD variance is explained by common genetic variation with h2SNP/h2 ratios varying between dense and nondense MD components. More... »

PAGES

30

References to SciGraph publications

  • 2017-11. Association analysis identifies 65 new breast cancer risk loci in NATURE
  • 2012-08. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing in NATURE GENETICS
  • 2012-11. An integrated map of genetic variation from 1,092 human genomes in NATURE
  • 2011-10. Nondense mammographic area and risk of breast cancer in BREAST CANCER RESEARCH
  • 2015-12. Second-generation PLINK: rising to the challenge of larger and richer datasets in GIGASCIENCE
  • 2010-11. The G12 family proteins upregulate matrix metalloproteinase-2 via p53 leading to human breast cell invasion in BREAST CANCER RESEARCH AND TREATMENT
  • 2015-10. The reciprocal association between mammographic breast density, hyaluronan synthesis and patient outcome in BREAST CANCER RESEARCH AND TREATMENT
  • 2014-10. Digital mammographic density and breast cancer risk: a case–control study of six alternative density assessment methods in BREAST CANCER RESEARCH
  • 2015-12. Identification of two novel mammographic density loci at 6Q25.1 in BREAST CANCER RESEARCH
  • 2012-09. An integrated encyclopedia of DNA elements in the human genome in NATURE
  • 2010. Robust Breast Composition Measurement - VolparaTM in DIGITAL MAMMOGRAPHY
  • 2014-12. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk in NATURE COMMUNICATIONS
  • 2013. Genome-Wide Complex Trait Analysis (GCTA): Methods, Data Analyses, and Interpretations in GENOME-WIDE ASSOCIATION STUDIES AND GENOMIC PREDICTION
  • 2011-03. Common variants in ZNF365 are associated with both mammographic density and breast cancer risk in NATURE GENETICS
  • 2017-12. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer in NATURE GENETICS
  • 2018-12. A comparison of five methods of measuring mammographic density: a case-control study in BREAST CANCER RESEARCH
  • 2012-12. Genetic variants associated with breast size also influence breast cancer risk in BMC MEDICAL GENETICS
  • 2007-07. A new multipoint method for genome-wide association studies by imputation of genotypes in NATURE GENETICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13058-018-0954-6

    DOI

    http://dx.doi.org/10.1186/s13058-018-0954-6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1103409362

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29665850


    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/2008/0604", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Genetics", 
            "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"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Adult", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Aged", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Breast", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Breast Density", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Breast Neoplasms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "DNA Mutational Analysis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Estrogen Receptor alpha", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Female", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genetic Association Studies", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genetic Predisposition to Disease", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Inhibin-beta Subunits", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Mammography", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Middle Aged", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Polymorphism, Single Nucleotide", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Serine Endopeptidases", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Karolinska Institute", 
              "id": "https://www.grid.ac/institutes/grid.4714.6", 
              "name": [
                "Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels V\u00e4g 12A, 171 77, Stockholm, Sweden"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Brand", 
            "givenName": "Judith S.", 
            "id": "sg:person.01203377330.66", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01203377330.66"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Karolinska Institute", 
              "id": "https://www.grid.ac/institutes/grid.4714.6", 
              "name": [
                "Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels V\u00e4g 12A, 171 77, Stockholm, Sweden"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Humphreys", 
            "givenName": "Keith", 
            "id": "sg:person.0624052041.17", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0624052041.17"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Genome Institute of Singapore", 
              "id": "https://www.grid.ac/institutes/grid.418377.e", 
              "name": [
                "Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels V\u00e4g 12A, 171 77, Stockholm, Sweden", 
                "Human Genetics, Genome Institute of Singapore, Singapore, Singapore"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Li", 
            "givenName": "Jingmei", 
            "id": "sg:person.013760043547.76", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013760043547.76"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Karolinska Institute", 
              "id": "https://www.grid.ac/institutes/grid.4714.6", 
              "name": [
                "Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels V\u00e4g 12A, 171 77, Stockholm, Sweden"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Karlsson", 
            "givenName": "Robert", 
            "id": "sg:person.0744020556.20", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0744020556.20"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Karolinska Institute", 
              "id": "https://www.grid.ac/institutes/grid.4714.6", 
              "name": [
                "Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels V\u00e4g 12A, 171 77, Stockholm, Sweden"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hall", 
            "givenName": "Per", 
            "id": "sg:person.01010701573.25", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01010701573.25"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Karolinska Institute", 
              "id": "https://www.grid.ac/institutes/grid.4714.6", 
              "name": [
                "Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels V\u00e4g 12A, 171 77, Stockholm, Sweden"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Czene", 
            "givenName": "Kamila", 
            "id": "sg:person.013117404317.63", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013117404317.63"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/nature11632", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000661742", 
              "https://doi.org/10.1038/nature11632"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13058-014-0439-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003025389", 
              "https://doi.org/10.1186/s13058-014-0439-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13058-014-0439-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003025389", 
              "https://doi.org/10.1186/s13058-014-0439-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13058-014-0439-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003025389", 
              "https://doi.org/10.1186/s13058-014-0439-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2350-13-53", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003305792", 
              "https://doi.org/10.1186/1471-2350-13-53"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jnci/dju334", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008103180"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jnci/dju334", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008103180"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ajhg.2010.11.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009497006"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0085952", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012440900"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms6303", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015726036", 
              "https://doi.org/10.1038/ncomms6303"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ncomms6303", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015726036", 
              "https://doi.org/10.1038/ncomms6303"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jnci/dju078", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018039237"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/ijc.2910460309", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024301813"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10549-009-0697-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025205313", 
              "https://doi.org/10.1007/s10549-009-0697-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10549-009-0697-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025205313", 
              "https://doi.org/10.1007/s10549-009-0697-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.bbrc.2006.04.030", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025856840"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10549-015-3567-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025861492", 
              "https://doi.org/10.1007/s10549-015-3567-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10549-015-3567-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025861492", 
              "https://doi.org/10.1007/s10549-015-3567-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-62703-447-0_9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026993766", 
              "https://doi.org/10.1007/978-1-62703-447-0_9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/bcr3041", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028231393", 
              "https://doi.org/10.1186/bcr3041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.760", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028836856", 
              "https://doi.org/10.1038/ng.760"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13058-015-0591-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028921540", 
              "https://doi.org/10.1186/s13058-015-0591-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13058-015-0591-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028921540", 
              "https://doi.org/10.1186/s13058-015-0591-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13058-015-0591-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028921540", 
              "https://doi.org/10.1186/s13058-015-0591-2"
            ], 
            "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.10.1038/ng.2354", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029723318", 
              "https://doi.org/10.1038/ng.2354"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1055-9965.epi-08-0480", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030901623"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1055-9965.epi-13-1219", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031019946"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1055-9965.epi-13-1219", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031019946"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.137323.112", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034124863"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/hmg/dds158", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035890712"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btq419", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036490983"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1136/jmedgenet-2013-101708", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037052698"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13742-015-0047-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037894462", 
              "https://doi.org/10.1186/s13742-015-0047-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/ijc.29975", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042643793"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/0008-5472.can-14-2012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043172665"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/0008-5472.can-14-2012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043172665"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-13666-5_46", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043583356", 
              "https://doi.org/10.1007/978-3-642-13666-5_46"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-13666-5_46", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043583356", 
              "https://doi.org/10.1007/978-3-642-13666-5_46"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/ijc.29299", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044570216"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/ijc.29299", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044570216"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng2088", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046979341", 
              "https://doi.org/10.1038/ng2088"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btq340", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047276303"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0110690", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047410241"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1055-9965.epi-10-0703", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048852802"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1055-9965.epi-06-0034", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049006559"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0002-9440(10)64757-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050042470"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/0008-5472.can-06-1461", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050661777"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s1470-2045(05)70390-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052464959"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkr917", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052960118"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1088/0031-9155/39/10/008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059022537"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1088/0031-9155/57/16/5155", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059029337"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/ije/dyw357", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059676914"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmi.2005.862741", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061694821"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1055-9965.epi-16-0106", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063224354"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1083118359", 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature24284", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092349555", 
              "https://doi.org/10.1038/nature24284"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature24284", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092349555", 
              "https://doi.org/10.1038/nature24284"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.3785", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092350084", 
              "https://doi.org/10.1038/ng.3785"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.3785", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092350084", 
              "https://doi.org/10.1038/ng.3785"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13058-018-0932-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100823773", 
              "https://doi.org/10.1186/s13058-018-0932-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13058-018-0932-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100823773", 
              "https://doi.org/10.1186/s13058-018-0932-z"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2018-12", 
        "datePublishedReg": "2018-12-01", 
        "description": "BACKGROUND: Mammographic density (MD) is a strong and heritable intermediate phenotype of breast cancer, but much of its genetic variation remains unexplained.\nMETHODS: We conducted a genetic association study of volumetric MD in a Swedish mammography screening cohort (n = 9498) to identify novel MD loci. Associations with volumetric MD phenotypes (percent dense volume, absolute dense volume, and absolute nondense volume) were estimated using linear regression adjusting for age, body mass index, menopausal status, and six principal components. We also estimated the proportion of MD variance explained by additive contributions from single-nucleotide polymorphisms (SNP-based heritability [h2SNP]) in 4948 participants of the cohort.\nRESULTS: In total, three novel MD loci were identified (at P < 5 \u00d7 10-\u20098): one for percent dense volume (HABP2) and two for the absolute dense volume (INHBB, LINC01483). INHBB is an established locus for ER-negative breast cancer, and HABP2 and LINC01483 represent putative new breast cancer susceptibility loci, because both loci were associated with breast cancer in\u00a0available meta-analysis\u00a0data including 122,977\u00a0breast cancer cases and 105,974 control\u00a0subjects\u00a0(P < 0.05). h2SNP (SE) estimates for percent dense, absolute dense, and nondense volume were 0.29 (0.07), 0.31 (0.07), and 0.25 (0.07), respectively. Corresponding ratios of h2SNP to previously observed narrow-sense h2 estimates in the same cohort were 0.46, 0.72, and 0.41, respectively.\nCONCLUSIONS: These findings provide new insights into the genetic basis of MD and biological mechanisms linking MD to breast cancer risk. Apart from identifying three novel loci, we demonstrate that at least 25% of the MD variance is explained by common genetic\u00a0variation with h2SNP/h2 ratios varying between dense and nondense MD components.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1186/s13058-018-0954-6", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.2695966", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.3938745", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.5135789", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.5143107", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.5140627", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.3772173", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.3938779", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1022375", 
            "issn": [
              "1465-5411", 
              "1465-542X"
            ], 
            "name": "Breast Cancer Research", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "20"
          }
        ], 
        "name": "Common genetic variation and novel loci associated with volumetric mammographic density", 
        "pagination": "30", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "7b1c7d6a4408b904ac8c5626bd96aa9187a2555ce961c51463921b37005cc3d3"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "29665850"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "100927353"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s13058-018-0954-6"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1103409362"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s13058-018-0954-6", 
          "https://app.dimensions.ai/details/publication/pub.1103409362"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T12:53", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000364_0000000364/records_72850_00000001.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1186%2Fs13058-018-0954-6"
      }
    ]
     

    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/s13058-018-0954-6'

    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/s13058-018-0954-6'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13058-018-0954-6'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13058-018-0954-6'


     

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

    344 TRIPLES      21 PREDICATES      92 URIs      37 LITERALS      25 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s13058-018-0954-6 schema:about N0765c87c95674743a9f5575f77d73f0b
    2 N0abcdd44b0494cee852e684fadad5d1b
    3 N183200102c324309acf947e253f61315
    4 N35587b93c49e4834a5bd1450c13dca47
    5 N3a517506dc1e4588a7f7295296861977
    6 N5c404e1f14a74ff4b771c7fa4d76e7ec
    7 N5ff25fec5e0f464faf0cd55103c101bc
    8 N633ac38731c64694b54516d72268947a
    9 N67c2b92a5f114a55a7cae4f7b273f24a
    10 N92c2f73f891f4fe9802fecb36eb0aa46
    11 N9ca12686f86e405e8c52494b2915da3b
    12 Na7e5a44013af409f9feb0c313c732899
    13 Nae40f318bad248b0a371d7c2fa334f33
    14 Nccd1f0f83acd4aa980552ea0db55a55b
    15 Ne3cfce7d05e04e74a99db4b2e1ca0d1e
    16 Ne681f9725e5644b1bcf43b815e144cab
    17 anzsrc-for:06
    18 anzsrc-for:0604
    19 schema:author N35a6facb8c44499887099ac8aaf73f46
    20 schema:citation sg:pub.10.1007/978-1-62703-447-0_9
    21 sg:pub.10.1007/978-3-642-13666-5_46
    22 sg:pub.10.1007/s10549-009-0697-2
    23 sg:pub.10.1007/s10549-015-3567-0
    24 sg:pub.10.1038/nature11247
    25 sg:pub.10.1038/nature11632
    26 sg:pub.10.1038/nature24284
    27 sg:pub.10.1038/ncomms6303
    28 sg:pub.10.1038/ng.2354
    29 sg:pub.10.1038/ng.3785
    30 sg:pub.10.1038/ng.760
    31 sg:pub.10.1038/ng2088
    32 sg:pub.10.1186/1471-2350-13-53
    33 sg:pub.10.1186/bcr3041
    34 sg:pub.10.1186/s13058-014-0439-1
    35 sg:pub.10.1186/s13058-015-0591-2
    36 sg:pub.10.1186/s13058-018-0932-z
    37 sg:pub.10.1186/s13742-015-0047-8
    38 https://app.dimensions.ai/details/publication/pub.1083118359
    39 https://doi.org/10.1002/ijc.2910460309
    40 https://doi.org/10.1002/ijc.29299
    41 https://doi.org/10.1002/ijc.29975
    42 https://doi.org/10.1016/j.ajhg.2010.11.011
    43 https://doi.org/10.1016/j.bbrc.2006.04.030
    44 https://doi.org/10.1016/s0002-9440(10)64757-8
    45 https://doi.org/10.1016/s1470-2045(05)70390-9
    46 https://doi.org/10.1088/0031-9155/39/10/008
    47 https://doi.org/10.1088/0031-9155/57/16/5155
    48 https://doi.org/10.1093/bioinformatics/btq340
    49 https://doi.org/10.1093/bioinformatics/btq419
    50 https://doi.org/10.1093/hmg/dds158
    51 https://doi.org/10.1093/ije/dyw357
    52 https://doi.org/10.1093/jnci/dju078
    53 https://doi.org/10.1093/jnci/dju334
    54 https://doi.org/10.1093/nar/gkr917
    55 https://doi.org/10.1101/gr.137323.112
    56 https://doi.org/10.1109/tmi.2005.862741
    57 https://doi.org/10.1136/jmedgenet-2013-101708
    58 https://doi.org/10.1158/0008-5472.can-06-1461
    59 https://doi.org/10.1158/0008-5472.can-14-2012
    60 https://doi.org/10.1158/1055-9965.epi-06-0034
    61 https://doi.org/10.1158/1055-9965.epi-08-0480
    62 https://doi.org/10.1158/1055-9965.epi-10-0703
    63 https://doi.org/10.1158/1055-9965.epi-13-1219
    64 https://doi.org/10.1158/1055-9965.epi-16-0106
    65 https://doi.org/10.1371/journal.pone.0085952
    66 https://doi.org/10.1371/journal.pone.0110690
    67 schema:datePublished 2018-12
    68 schema:datePublishedReg 2018-12-01
    69 schema:description BACKGROUND: Mammographic density (MD) is a strong and heritable intermediate phenotype of breast cancer, but much of its genetic variation remains unexplained. METHODS: We conducted a genetic association study of volumetric MD in a Swedish mammography screening cohort (n = 9498) to identify novel MD loci. Associations with volumetric MD phenotypes (percent dense volume, absolute dense volume, and absolute nondense volume) were estimated using linear regression adjusting for age, body mass index, menopausal status, and six principal components. We also estimated the proportion of MD variance explained by additive contributions from single-nucleotide polymorphisms (SNP-based heritability [h2SNP]) in 4948 participants of the cohort. RESULTS: In total, three novel MD loci were identified (at P < 5 × 10- 8): one for percent dense volume (HABP2) and two for the absolute dense volume (INHBB, LINC01483). INHBB is an established locus for ER-negative breast cancer, and HABP2 and LINC01483 represent putative new breast cancer susceptibility loci, because both loci were associated with breast cancer in available meta-analysis data including 122,977 breast cancer cases and 105,974 control subjects (P < 0.05). h2SNP (SE) estimates for percent dense, absolute dense, and nondense volume were 0.29 (0.07), 0.31 (0.07), and 0.25 (0.07), respectively. Corresponding ratios of h2SNP to previously observed narrow-sense h2 estimates in the same cohort were 0.46, 0.72, and 0.41, respectively. CONCLUSIONS: These findings provide new insights into the genetic basis of MD and biological mechanisms linking MD to breast cancer risk. Apart from identifying three novel loci, we demonstrate that at least 25% of the MD variance is explained by common genetic variation with h2SNP/h2 ratios varying between dense and nondense MD components.
    70 schema:genre research_article
    71 schema:inLanguage en
    72 schema:isAccessibleForFree true
    73 schema:isPartOf N3cef5124fb0f491c964a7e5c0b8a656c
    74 N707d7447f36f4588bd9e868439ccb0f9
    75 sg:journal.1022375
    76 schema:name Common genetic variation and novel loci associated with volumetric mammographic density
    77 schema:pagination 30
    78 schema:productId N015151f31f0340e68fa1ae56ef04715f
    79 N109566eea0b4473fb89164c143074bc6
    80 N79d950e04bd54d948433f0962c8e5d4a
    81 N9c525a59ac164a189adb4a8050bf4029
    82 Nb28dad5df76b4cd8b7c0bac9ceaeee72
    83 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103409362
    84 https://doi.org/10.1186/s13058-018-0954-6
    85 schema:sdDatePublished 2019-04-11T12:53
    86 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    87 schema:sdPublisher N32370f03345946f0b9e056cee202a7f0
    88 schema:url https://link.springer.com/10.1186%2Fs13058-018-0954-6
    89 sgo:license sg:explorer/license/
    90 sgo:sdDataset articles
    91 rdf:type schema:ScholarlyArticle
    92 N015151f31f0340e68fa1ae56ef04715f schema:name dimensions_id
    93 schema:value pub.1103409362
    94 rdf:type schema:PropertyValue
    95 N0765c87c95674743a9f5575f77d73f0b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    96 schema:name Serine Endopeptidases
    97 rdf:type schema:DefinedTerm
    98 N0abcdd44b0494cee852e684fadad5d1b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    99 schema:name Mammography
    100 rdf:type schema:DefinedTerm
    101 N109566eea0b4473fb89164c143074bc6 schema:name readcube_id
    102 schema:value 7b1c7d6a4408b904ac8c5626bd96aa9187a2555ce961c51463921b37005cc3d3
    103 rdf:type schema:PropertyValue
    104 N183200102c324309acf947e253f61315 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    105 schema:name Adult
    106 rdf:type schema:DefinedTerm
    107 N32370f03345946f0b9e056cee202a7f0 schema:name Springer Nature - SN SciGraph project
    108 rdf:type schema:Organization
    109 N35587b93c49e4834a5bd1450c13dca47 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    110 schema:name Aged
    111 rdf:type schema:DefinedTerm
    112 N35a6facb8c44499887099ac8aaf73f46 rdf:first sg:person.01203377330.66
    113 rdf:rest N91b883657cee49be8425678bdafa1757
    114 N3a517506dc1e4588a7f7295296861977 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    115 schema:name Estrogen Receptor alpha
    116 rdf:type schema:DefinedTerm
    117 N3cef5124fb0f491c964a7e5c0b8a656c schema:volumeNumber 20
    118 rdf:type schema:PublicationVolume
    119 N5c404e1f14a74ff4b771c7fa4d76e7ec schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    120 schema:name Inhibin-beta Subunits
    121 rdf:type schema:DefinedTerm
    122 N5ff25fec5e0f464faf0cd55103c101bc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    123 schema:name Breast Neoplasms
    124 rdf:type schema:DefinedTerm
    125 N633ac38731c64694b54516d72268947a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    126 schema:name Polymorphism, Single Nucleotide
    127 rdf:type schema:DefinedTerm
    128 N67c2b92a5f114a55a7cae4f7b273f24a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    129 schema:name Genetic Association Studies
    130 rdf:type schema:DefinedTerm
    131 N707d7447f36f4588bd9e868439ccb0f9 schema:issueNumber 1
    132 rdf:type schema:PublicationIssue
    133 N73cfda9b29d446a59b2b4bb9db2a7627 rdf:first sg:person.01010701573.25
    134 rdf:rest Na11ec9d6c97f430292e7b0de3c7c9ea3
    135 N79d950e04bd54d948433f0962c8e5d4a schema:name pubmed_id
    136 schema:value 29665850
    137 rdf:type schema:PropertyValue
    138 N91b883657cee49be8425678bdafa1757 rdf:first sg:person.0624052041.17
    139 rdf:rest Nba84c70bddaa48f095823b7ba3d9bb74
    140 N92c2f73f891f4fe9802fecb36eb0aa46 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    141 schema:name Humans
    142 rdf:type schema:DefinedTerm
    143 N9c525a59ac164a189adb4a8050bf4029 schema:name doi
    144 schema:value 10.1186/s13058-018-0954-6
    145 rdf:type schema:PropertyValue
    146 N9ca12686f86e405e8c52494b2915da3b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    147 schema:name Genetic Predisposition to Disease
    148 rdf:type schema:DefinedTerm
    149 Na11ec9d6c97f430292e7b0de3c7c9ea3 rdf:first sg:person.013117404317.63
    150 rdf:rest rdf:nil
    151 Na7e5a44013af409f9feb0c313c732899 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    152 schema:name Middle Aged
    153 rdf:type schema:DefinedTerm
    154 Nae40f318bad248b0a371d7c2fa334f33 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    155 schema:name DNA Mutational Analysis
    156 rdf:type schema:DefinedTerm
    157 Nb28dad5df76b4cd8b7c0bac9ceaeee72 schema:name nlm_unique_id
    158 schema:value 100927353
    159 rdf:type schema:PropertyValue
    160 Nba84c70bddaa48f095823b7ba3d9bb74 rdf:first sg:person.013760043547.76
    161 rdf:rest Nf41b35f6c9d648f98e5b825b0d4bbfc0
    162 Nccd1f0f83acd4aa980552ea0db55a55b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    163 schema:name Breast
    164 rdf:type schema:DefinedTerm
    165 Ne3cfce7d05e04e74a99db4b2e1ca0d1e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    166 schema:name Female
    167 rdf:type schema:DefinedTerm
    168 Ne681f9725e5644b1bcf43b815e144cab schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    169 schema:name Breast Density
    170 rdf:type schema:DefinedTerm
    171 Nf41b35f6c9d648f98e5b825b0d4bbfc0 rdf:first sg:person.0744020556.20
    172 rdf:rest N73cfda9b29d446a59b2b4bb9db2a7627
    173 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    174 schema:name Biological Sciences
    175 rdf:type schema:DefinedTerm
    176 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    177 schema:name Genetics
    178 rdf:type schema:DefinedTerm
    179 sg:grant.2695966 http://pending.schema.org/fundedItem sg:pub.10.1186/s13058-018-0954-6
    180 rdf:type schema:MonetaryGrant
    181 sg:grant.3772173 http://pending.schema.org/fundedItem sg:pub.10.1186/s13058-018-0954-6
    182 rdf:type schema:MonetaryGrant
    183 sg:grant.3938745 http://pending.schema.org/fundedItem sg:pub.10.1186/s13058-018-0954-6
    184 rdf:type schema:MonetaryGrant
    185 sg:grant.3938779 http://pending.schema.org/fundedItem sg:pub.10.1186/s13058-018-0954-6
    186 rdf:type schema:MonetaryGrant
    187 sg:grant.5135789 http://pending.schema.org/fundedItem sg:pub.10.1186/s13058-018-0954-6
    188 rdf:type schema:MonetaryGrant
    189 sg:grant.5140627 http://pending.schema.org/fundedItem sg:pub.10.1186/s13058-018-0954-6
    190 rdf:type schema:MonetaryGrant
    191 sg:grant.5143107 http://pending.schema.org/fundedItem sg:pub.10.1186/s13058-018-0954-6
    192 rdf:type schema:MonetaryGrant
    193 sg:journal.1022375 schema:issn 1465-5411
    194 1465-542X
    195 schema:name Breast Cancer Research
    196 rdf:type schema:Periodical
    197 sg:person.01010701573.25 schema:affiliation https://www.grid.ac/institutes/grid.4714.6
    198 schema:familyName Hall
    199 schema:givenName Per
    200 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01010701573.25
    201 rdf:type schema:Person
    202 sg:person.01203377330.66 schema:affiliation https://www.grid.ac/institutes/grid.4714.6
    203 schema:familyName Brand
    204 schema:givenName Judith S.
    205 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01203377330.66
    206 rdf:type schema:Person
    207 sg:person.013117404317.63 schema:affiliation https://www.grid.ac/institutes/grid.4714.6
    208 schema:familyName Czene
    209 schema:givenName Kamila
    210 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013117404317.63
    211 rdf:type schema:Person
    212 sg:person.013760043547.76 schema:affiliation https://www.grid.ac/institutes/grid.418377.e
    213 schema:familyName Li
    214 schema:givenName Jingmei
    215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013760043547.76
    216 rdf:type schema:Person
    217 sg:person.0624052041.17 schema:affiliation https://www.grid.ac/institutes/grid.4714.6
    218 schema:familyName Humphreys
    219 schema:givenName Keith
    220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0624052041.17
    221 rdf:type schema:Person
    222 sg:person.0744020556.20 schema:affiliation https://www.grid.ac/institutes/grid.4714.6
    223 schema:familyName Karlsson
    224 schema:givenName Robert
    225 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0744020556.20
    226 rdf:type schema:Person
    227 sg:pub.10.1007/978-1-62703-447-0_9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026993766
    228 https://doi.org/10.1007/978-1-62703-447-0_9
    229 rdf:type schema:CreativeWork
    230 sg:pub.10.1007/978-3-642-13666-5_46 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043583356
    231 https://doi.org/10.1007/978-3-642-13666-5_46
    232 rdf:type schema:CreativeWork
    233 sg:pub.10.1007/s10549-009-0697-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025205313
    234 https://doi.org/10.1007/s10549-009-0697-2
    235 rdf:type schema:CreativeWork
    236 sg:pub.10.1007/s10549-015-3567-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025861492
    237 https://doi.org/10.1007/s10549-015-3567-0
    238 rdf:type schema:CreativeWork
    239 sg:pub.10.1038/nature11247 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029065430
    240 https://doi.org/10.1038/nature11247
    241 rdf:type schema:CreativeWork
    242 sg:pub.10.1038/nature11632 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000661742
    243 https://doi.org/10.1038/nature11632
    244 rdf:type schema:CreativeWork
    245 sg:pub.10.1038/nature24284 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092349555
    246 https://doi.org/10.1038/nature24284
    247 rdf:type schema:CreativeWork
    248 sg:pub.10.1038/ncomms6303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015726036
    249 https://doi.org/10.1038/ncomms6303
    250 rdf:type schema:CreativeWork
    251 sg:pub.10.1038/ng.2354 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029723318
    252 https://doi.org/10.1038/ng.2354
    253 rdf:type schema:CreativeWork
    254 sg:pub.10.1038/ng.3785 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092350084
    255 https://doi.org/10.1038/ng.3785
    256 rdf:type schema:CreativeWork
    257 sg:pub.10.1038/ng.760 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028836856
    258 https://doi.org/10.1038/ng.760
    259 rdf:type schema:CreativeWork
    260 sg:pub.10.1038/ng2088 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046979341
    261 https://doi.org/10.1038/ng2088
    262 rdf:type schema:CreativeWork
    263 sg:pub.10.1186/1471-2350-13-53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003305792
    264 https://doi.org/10.1186/1471-2350-13-53
    265 rdf:type schema:CreativeWork
    266 sg:pub.10.1186/bcr3041 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028231393
    267 https://doi.org/10.1186/bcr3041
    268 rdf:type schema:CreativeWork
    269 sg:pub.10.1186/s13058-014-0439-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003025389
    270 https://doi.org/10.1186/s13058-014-0439-1
    271 rdf:type schema:CreativeWork
    272 sg:pub.10.1186/s13058-015-0591-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028921540
    273 https://doi.org/10.1186/s13058-015-0591-2
    274 rdf:type schema:CreativeWork
    275 sg:pub.10.1186/s13058-018-0932-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1100823773
    276 https://doi.org/10.1186/s13058-018-0932-z
    277 rdf:type schema:CreativeWork
    278 sg:pub.10.1186/s13742-015-0047-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037894462
    279 https://doi.org/10.1186/s13742-015-0047-8
    280 rdf:type schema:CreativeWork
    281 https://app.dimensions.ai/details/publication/pub.1083118359 schema:CreativeWork
    282 https://doi.org/10.1002/ijc.2910460309 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024301813
    283 rdf:type schema:CreativeWork
    284 https://doi.org/10.1002/ijc.29299 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044570216
    285 rdf:type schema:CreativeWork
    286 https://doi.org/10.1002/ijc.29975 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042643793
    287 rdf:type schema:CreativeWork
    288 https://doi.org/10.1016/j.ajhg.2010.11.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009497006
    289 rdf:type schema:CreativeWork
    290 https://doi.org/10.1016/j.bbrc.2006.04.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025856840
    291 rdf:type schema:CreativeWork
    292 https://doi.org/10.1016/s0002-9440(10)64757-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050042470
    293 rdf:type schema:CreativeWork
    294 https://doi.org/10.1016/s1470-2045(05)70390-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052464959
    295 rdf:type schema:CreativeWork
    296 https://doi.org/10.1088/0031-9155/39/10/008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059022537
    297 rdf:type schema:CreativeWork
    298 https://doi.org/10.1088/0031-9155/57/16/5155 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059029337
    299 rdf:type schema:CreativeWork
    300 https://doi.org/10.1093/bioinformatics/btq340 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047276303
    301 rdf:type schema:CreativeWork
    302 https://doi.org/10.1093/bioinformatics/btq419 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036490983
    303 rdf:type schema:CreativeWork
    304 https://doi.org/10.1093/hmg/dds158 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035890712
    305 rdf:type schema:CreativeWork
    306 https://doi.org/10.1093/ije/dyw357 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059676914
    307 rdf:type schema:CreativeWork
    308 https://doi.org/10.1093/jnci/dju078 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018039237
    309 rdf:type schema:CreativeWork
    310 https://doi.org/10.1093/jnci/dju334 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008103180
    311 rdf:type schema:CreativeWork
    312 https://doi.org/10.1093/nar/gkr917 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052960118
    313 rdf:type schema:CreativeWork
    314 https://doi.org/10.1101/gr.137323.112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034124863
    315 rdf:type schema:CreativeWork
    316 https://doi.org/10.1109/tmi.2005.862741 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061694821
    317 rdf:type schema:CreativeWork
    318 https://doi.org/10.1136/jmedgenet-2013-101708 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037052698
    319 rdf:type schema:CreativeWork
    320 https://doi.org/10.1158/0008-5472.can-06-1461 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050661777
    321 rdf:type schema:CreativeWork
    322 https://doi.org/10.1158/0008-5472.can-14-2012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043172665
    323 rdf:type schema:CreativeWork
    324 https://doi.org/10.1158/1055-9965.epi-06-0034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049006559
    325 rdf:type schema:CreativeWork
    326 https://doi.org/10.1158/1055-9965.epi-08-0480 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030901623
    327 rdf:type schema:CreativeWork
    328 https://doi.org/10.1158/1055-9965.epi-10-0703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048852802
    329 rdf:type schema:CreativeWork
    330 https://doi.org/10.1158/1055-9965.epi-13-1219 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031019946
    331 rdf:type schema:CreativeWork
    332 https://doi.org/10.1158/1055-9965.epi-16-0106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063224354
    333 rdf:type schema:CreativeWork
    334 https://doi.org/10.1371/journal.pone.0085952 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012440900
    335 rdf:type schema:CreativeWork
    336 https://doi.org/10.1371/journal.pone.0110690 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047410241
    337 rdf:type schema:CreativeWork
    338 https://www.grid.ac/institutes/grid.418377.e schema:alternateName Genome Institute of Singapore
    339 schema:name Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
    340 Human Genetics, Genome Institute of Singapore, Singapore, Singapore
    341 rdf:type schema:Organization
    342 https://www.grid.ac/institutes/grid.4714.6 schema:alternateName Karolinska Institute
    343 schema:name Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
    344 rdf:type schema:Organization
     




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


    ...