Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-02

AUTHORS

Hatef Darabi, Kamila Czene, Wanting Zhao, Jianjun Liu, Per Hall, Keith Humphreys

ABSTRACT

INTRODUCTION: Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes. METHODS: We investigate the performance of an up-to-date 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured. RESULTS: The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (P = 4.69 × 10-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 × 10-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice. CONCLUSIONS: Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer. More... »

PAGES

r25

References to SciGraph publications

  • 2009-05. Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2 in NATURE GENETICS
  • 2005-11. Mammographic Breast Density and the Gail Model for Breast Cancer Risk Prediction in a Screening Population in BREAST CANCER RESEARCH AND TREATMENT
  • 2002-05. Polygenic susceptibility to breast cancer and implications for prevention in NATURE GENETICS
  • 2009-05. A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1) in NATURE GENETICS
  • 2009-03. Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1 in NATURE GENETICS
  • 2011-11. Breast cancer risk assessment in women aged 70 and older in BREAST CANCER RESEARCH AND TREATMENT
  • 2008-04. The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions in BRITISH JOURNAL OF CANCER
  • 2008-06. Common variants on chromosome 5p12 confer susceptibility to estrogen receptor–positive breast cancer in NATURE GENETICS
  • 2011-12. Mammographic density and breast cancer risk: current understanding and future prospects in BREAST CANCER RESEARCH
  • 2010-06. Genome-wide association study identifies five new breast cancer susceptibility loci in NATURE GENETICS
  • 2007-07. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor–positive breast cancer in NATURE GENETICS
  • 2007-12. Mammographic density, breast cancer risk and risk prediction in BREAST CANCER RESEARCH
  • 2007-07. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer in NATURE GENETICS
  • 2011-05. Polygenic susceptibility to prostate and breast cancer: implications for personalised screening in BRITISH JOURNAL OF CANCER
  • 2007-06-28. Genome-wide association study identifies novel breast cancer susceptibility loci in NATURE
  • 2007-03. A common coding variant in CASP8 is associated with breast cancer risk in NATURE GENETICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/bcr3110

    DOI

    http://dx.doi.org/10.1186/bcr3110

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1032911992

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/22314178


    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/2008/1117", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Public Health and Health Services", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Medical and Health 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": "Area Under Curve", 
            "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": "Case-Control Studies", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Early Detection of Cancer", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Female", 
            "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": "Mammary Glands, Human", 
            "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": "Models, Biological", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Polymorphism, Single Nucleotide", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Postmenopause", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Precision Medicine", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "ROC Curve", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Risk Factors", 
            "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, P.O. Box 281, 177 71, Stockholm, Sweden"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Darabi", 
            "givenName": "Hatef", 
            "id": "sg:person.0714133215.07", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0714133215.07"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Karolinska Institute", 
              "id": "https://www.grid.ac/institutes/grid.4714.6", 
              "name": [
                "Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, 177 71, 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"
          }, 
          {
            "affiliation": {
              "alternateName": "Genome Institute of Singapore", 
              "id": "https://www.grid.ac/institutes/grid.418377.e", 
              "name": [
                "Human Genetics, Genome Institute of Singapore, 60 Biopolis St, 138672, Singapore, Singapore"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhao", 
            "givenName": "Wanting", 
            "id": "sg:person.01372072214.78", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01372072214.78"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Genome Institute of Singapore", 
              "id": "https://www.grid.ac/institutes/grid.418377.e", 
              "name": [
                "Human Genetics, Genome Institute of Singapore, 60 Biopolis St, 138672, Singapore, Singapore"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Liu", 
            "givenName": "Jianjun", 
            "id": "sg:person.011251153047.07", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011251153047.07"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Karolinska Institute", 
              "id": "https://www.grid.ac/institutes/grid.4714.6", 
              "name": [
                "Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, 177 71, 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, P.O. Box 281, 177 71, 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"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s10549-011-1576-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000151477", 
              "https://doi.org/10.1007/s10549-011-1576-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.353", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000555917", 
              "https://doi.org/10.1038/ng.353"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.353", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000555917", 
              "https://doi.org/10.1038/ng.353"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pgen.1001012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004829920"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.354", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004838062", 
              "https://doi.org/10.1038/ng.354"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.586", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006256853", 
              "https://doi.org/10.1038/ng.586"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.586", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006256853", 
              "https://doi.org/10.1038/ng.586"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/bcr2942", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008028099", 
              "https://doi.org/10.1186/bcr2942"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng1981", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008452967", 
              "https://doi.org/10.1038/ng1981"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1055-9965.epi-08-0631", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011838485"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10549-005-5152-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012338974", 
              "https://doi.org/10.1007/s10549-005-5152-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10549-005-5152-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012338974", 
              "https://doi.org/10.1007/s10549-005-5152-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/(sici)1097-0215(19970529)71:5<800::aid-ijc18>3.0.co;2-b", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014587486"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/sim.1668", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015215277"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jnci/93.5.358", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018900530"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/bcr1829", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020053537", 
              "https://doi.org/10.1186/bcr1829"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/bcr1829", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020053537", 
              "https://doi.org/10.1186/bcr1829"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1097/00008469-199610000-00003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023164993"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1097/00008469-199610000-00003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023164993"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1158/1055-9965.epi-06-0345", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024579986"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jnci/djq526", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026160041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/bjc.2011.118", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026293422", 
              "https://doi.org/10.1038/bjc.2011.118"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/sj.bjc.6604305", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027765597", 
              "https://doi.org/10.1038/sj.bjc.6604305"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature05887", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027991306", 
              "https://doi.org/10.1038/nature05887"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jnci/djj332", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029066633"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/ijc.24786", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029194629"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/ijc.24786", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029194629"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.318", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029457545", 
              "https://doi.org/10.1038/ng.318"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0800441105", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030781622"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jnci/djq388", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033158924"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.131", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038518938", 
              "https://doi.org/10.1038/ng.131"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/hmg/ddn287", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038688844"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng853", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041614038", 
              "https://doi.org/10.1038/ng853"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng853", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041614038", 
              "https://doi.org/10.1038/ng853"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1056/nejmsa0708739", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044787515"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jnci/djn180", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044813602"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/sim.2929", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044960408"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng2064", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045095990", 
              "https://doi.org/10.1038/ng2064"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jnci/djp130", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046333320"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pgen.1001230", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046583364"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1056/nejmoa0907727", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048643469"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng2075", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049470927", 
              "https://doi.org/10.1038/ng2075"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/aje/kwm305", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050656601"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1136/jmg.40.11.807", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052170953"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jnci/81.24.1879", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1059815546"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2307/2531595", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069977037"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/oxfordjournals.aje.a114174", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1080101683"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2012-02", 
        "datePublishedReg": "2012-02-01", 
        "description": "INTRODUCTION: Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes.\nMETHODS: We investigate the performance of an up-to-date 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured.\nRESULTS: The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (P = 4.69 \u00d7 10-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 \u00d7 10-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice.\nCONCLUSIONS: Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1186/bcr3110", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.2645134", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2659203", 
            "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": "14"
          }
        ], 
        "name": "Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement", 
        "pagination": "r25", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "9cc9f3f92dd004276ee98014e23f12a4c9b624c3e3da09eea7bf7e150c2ebe19"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "22314178"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "100927353"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/bcr3110"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1032911992"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/bcr3110", 
          "https://app.dimensions.ai/details/publication/pub.1032911992"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T09:40", 
        "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/v90/0000000346_0000000346/records_99839_00000002.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1186%2Fbcr3110"
      }
    ]
     

    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/bcr3110'

    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/bcr3110'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/bcr3110'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/bcr3110'


     

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

    323 TRIPLES      21 PREDICATES      88 URIs      40 LITERALS      28 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/bcr3110 schema:about N02800f97644a4d29b8c1afa8f8e07b98
    2 N07cd2f878f3b44e899c9cfd5f757ae92
    3 N1fd82cec2f2946b18f3c59435578bb08
    4 N3f372d642dee4f2c8468f04585320630
    5 N4324860231b94c1b93c4007d88955731
    6 N45fbb1a593544d349b67feccfa463c6b
    7 N4603f74e3f90400ebae34a109f87ddf7
    8 N47a1627ab76e4c9e954fc53079ae78c1
    9 N74ba0933f893478da943ef5191314df6
    10 N74c1c6c8ae2d4a87ba0d868bcf5432f7
    11 N8105a818cf334f18964a9dfb916dba1a
    12 N864def4a9abd43dca21ef8d6b4b5811d
    13 Na3abd7a58e934147b82c7532ccd02cbd
    14 Nb0289601e0a641eab50a595ea509e38f
    15 Nc94b77269b774afaac330162e72c3639
    16 Nd5b9af17c5e546498b7ef3d3afdb69e2
    17 Nf020de15bda245bfb1b8329209c54840
    18 Nf4a343ed9f194404bb5ce1e747dc5a15
    19 Nf7129fa650cf4de183b2d45d073adf37
    20 anzsrc-for:11
    21 anzsrc-for:1117
    22 schema:author Nc06503d9cfa845b094ecc2f2cc465d28
    23 schema:citation sg:pub.10.1007/s10549-005-5152-4
    24 sg:pub.10.1007/s10549-011-1576-1
    25 sg:pub.10.1038/bjc.2011.118
    26 sg:pub.10.1038/nature05887
    27 sg:pub.10.1038/ng.131
    28 sg:pub.10.1038/ng.318
    29 sg:pub.10.1038/ng.353
    30 sg:pub.10.1038/ng.354
    31 sg:pub.10.1038/ng.586
    32 sg:pub.10.1038/ng1981
    33 sg:pub.10.1038/ng2064
    34 sg:pub.10.1038/ng2075
    35 sg:pub.10.1038/ng853
    36 sg:pub.10.1038/sj.bjc.6604305
    37 sg:pub.10.1186/bcr1829
    38 sg:pub.10.1186/bcr2942
    39 https://doi.org/10.1002/(sici)1097-0215(19970529)71:5<800::aid-ijc18>3.0.co;2-b
    40 https://doi.org/10.1002/ijc.24786
    41 https://doi.org/10.1002/sim.1668
    42 https://doi.org/10.1002/sim.2929
    43 https://doi.org/10.1056/nejmoa0907727
    44 https://doi.org/10.1056/nejmsa0708739
    45 https://doi.org/10.1073/pnas.0800441105
    46 https://doi.org/10.1093/aje/kwm305
    47 https://doi.org/10.1093/hmg/ddn287
    48 https://doi.org/10.1093/jnci/81.24.1879
    49 https://doi.org/10.1093/jnci/93.5.358
    50 https://doi.org/10.1093/jnci/djj332
    51 https://doi.org/10.1093/jnci/djn180
    52 https://doi.org/10.1093/jnci/djp130
    53 https://doi.org/10.1093/jnci/djq388
    54 https://doi.org/10.1093/jnci/djq526
    55 https://doi.org/10.1093/oxfordjournals.aje.a114174
    56 https://doi.org/10.1097/00008469-199610000-00003
    57 https://doi.org/10.1136/jmg.40.11.807
    58 https://doi.org/10.1158/1055-9965.epi-06-0345
    59 https://doi.org/10.1158/1055-9965.epi-08-0631
    60 https://doi.org/10.1371/journal.pgen.1001012
    61 https://doi.org/10.1371/journal.pgen.1001230
    62 https://doi.org/10.2307/2531595
    63 schema:datePublished 2012-02
    64 schema:datePublishedReg 2012-02-01
    65 schema:description INTRODUCTION: Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes. METHODS: We investigate the performance of an up-to-date 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured. RESULTS: The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (P = 4.69 × 10-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 × 10-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice. CONCLUSIONS: Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.
    66 schema:genre research_article
    67 schema:inLanguage en
    68 schema:isAccessibleForFree true
    69 schema:isPartOf N6f91d8cfdee64ec288dc2a6d0b0b77b4
    70 Nbfc2e59c0f934fb6958955e28c0e7f76
    71 sg:journal.1022375
    72 schema:name Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement
    73 schema:pagination r25
    74 schema:productId N146e3fd00ac142b09648eb4a43d9e23a
    75 N2aee9e82d66645bebc59544bc52362dc
    76 N7e46156ccd9e4405b03b85fcaf2a836e
    77 Nbb6fd82a44154c2fa32d49b4340600f4
    78 Nf22cf887a1c5432e840e999c81e77bc8
    79 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032911992
    80 https://doi.org/10.1186/bcr3110
    81 schema:sdDatePublished 2019-04-11T09:40
    82 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    83 schema:sdPublisher Ne0bc3f8ae8734042bf19b51b320764cf
    84 schema:url http://link.springer.com/10.1186%2Fbcr3110
    85 sgo:license sg:explorer/license/
    86 sgo:sdDataset articles
    87 rdf:type schema:ScholarlyArticle
    88 N02800f97644a4d29b8c1afa8f8e07b98 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    89 schema:name Precision Medicine
    90 rdf:type schema:DefinedTerm
    91 N07cd2f878f3b44e899c9cfd5f757ae92 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    92 schema:name Risk Factors
    93 rdf:type schema:DefinedTerm
    94 N0904673a1199497094f564b2cc86e07c rdf:first sg:person.01372072214.78
    95 rdf:rest Neb31a59f17e548f3944a47cadb50c0bb
    96 N146e3fd00ac142b09648eb4a43d9e23a schema:name pubmed_id
    97 schema:value 22314178
    98 rdf:type schema:PropertyValue
    99 N1fd82cec2f2946b18f3c59435578bb08 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    100 schema:name Models, Biological
    101 rdf:type schema:DefinedTerm
    102 N2aee9e82d66645bebc59544bc52362dc schema:name nlm_unique_id
    103 schema:value 100927353
    104 rdf:type schema:PropertyValue
    105 N3f372d642dee4f2c8468f04585320630 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    106 schema:name Middle Aged
    107 rdf:type schema:DefinedTerm
    108 N4324860231b94c1b93c4007d88955731 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    109 schema:name Postmenopause
    110 rdf:type schema:DefinedTerm
    111 N45fbb1a593544d349b67feccfa463c6b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    112 schema:name Case-Control Studies
    113 rdf:type schema:DefinedTerm
    114 N4603f74e3f90400ebae34a109f87ddf7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    115 schema:name Mammary Glands, Human
    116 rdf:type schema:DefinedTerm
    117 N47a1627ab76e4c9e954fc53079ae78c1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    118 schema:name Humans
    119 rdf:type schema:DefinedTerm
    120 N557ae5e0a6fb4f8aa8dac47bf148da04 rdf:first sg:person.01010701573.25
    121 rdf:rest Nce3b0fd8ed184d2c8f0ce1ec75c5ab11
    122 N6f91d8cfdee64ec288dc2a6d0b0b77b4 schema:issueNumber 1
    123 rdf:type schema:PublicationIssue
    124 N74ba0933f893478da943ef5191314df6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    125 schema:name Aged
    126 rdf:type schema:DefinedTerm
    127 N74c1c6c8ae2d4a87ba0d868bcf5432f7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    128 schema:name Polymorphism, Single Nucleotide
    129 rdf:type schema:DefinedTerm
    130 N7c6e3162a6a042d8aad60fec21cba8b8 rdf:first sg:person.013117404317.63
    131 rdf:rest N0904673a1199497094f564b2cc86e07c
    132 N7e46156ccd9e4405b03b85fcaf2a836e schema:name doi
    133 schema:value 10.1186/bcr3110
    134 rdf:type schema:PropertyValue
    135 N8105a818cf334f18964a9dfb916dba1a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    136 schema:name Mammography
    137 rdf:type schema:DefinedTerm
    138 N864def4a9abd43dca21ef8d6b4b5811d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    139 schema:name Breast Neoplasms
    140 rdf:type schema:DefinedTerm
    141 Na3abd7a58e934147b82c7532ccd02cbd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    142 schema:name ROC Curve
    143 rdf:type schema:DefinedTerm
    144 Nb0289601e0a641eab50a595ea509e38f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    145 schema:name Adult
    146 rdf:type schema:DefinedTerm
    147 Nbb6fd82a44154c2fa32d49b4340600f4 schema:name readcube_id
    148 schema:value 9cc9f3f92dd004276ee98014e23f12a4c9b624c3e3da09eea7bf7e150c2ebe19
    149 rdf:type schema:PropertyValue
    150 Nbfc2e59c0f934fb6958955e28c0e7f76 schema:volumeNumber 14
    151 rdf:type schema:PublicationVolume
    152 Nc06503d9cfa845b094ecc2f2cc465d28 rdf:first sg:person.0714133215.07
    153 rdf:rest N7c6e3162a6a042d8aad60fec21cba8b8
    154 Nc94b77269b774afaac330162e72c3639 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    155 schema:name Female
    156 rdf:type schema:DefinedTerm
    157 Nce3b0fd8ed184d2c8f0ce1ec75c5ab11 rdf:first sg:person.0624052041.17
    158 rdf:rest rdf:nil
    159 Nd5b9af17c5e546498b7ef3d3afdb69e2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    160 schema:name Genetic Predisposition to Disease
    161 rdf:type schema:DefinedTerm
    162 Ne0bc3f8ae8734042bf19b51b320764cf schema:name Springer Nature - SN SciGraph project
    163 rdf:type schema:Organization
    164 Neb31a59f17e548f3944a47cadb50c0bb rdf:first sg:person.011251153047.07
    165 rdf:rest N557ae5e0a6fb4f8aa8dac47bf148da04
    166 Nf020de15bda245bfb1b8329209c54840 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    167 schema:name Area Under Curve
    168 rdf:type schema:DefinedTerm
    169 Nf22cf887a1c5432e840e999c81e77bc8 schema:name dimensions_id
    170 schema:value pub.1032911992
    171 rdf:type schema:PropertyValue
    172 Nf4a343ed9f194404bb5ce1e747dc5a15 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    173 schema:name Breast Density
    174 rdf:type schema:DefinedTerm
    175 Nf7129fa650cf4de183b2d45d073adf37 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    176 schema:name Early Detection of Cancer
    177 rdf:type schema:DefinedTerm
    178 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    179 schema:name Medical and Health Sciences
    180 rdf:type schema:DefinedTerm
    181 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
    182 schema:name Public Health and Health Services
    183 rdf:type schema:DefinedTerm
    184 sg:grant.2645134 http://pending.schema.org/fundedItem sg:pub.10.1186/bcr3110
    185 rdf:type schema:MonetaryGrant
    186 sg:grant.2659203 http://pending.schema.org/fundedItem sg:pub.10.1186/bcr3110
    187 rdf:type schema:MonetaryGrant
    188 sg:journal.1022375 schema:issn 1465-5411
    189 1465-542X
    190 schema:name Breast Cancer Research
    191 rdf:type schema:Periodical
    192 sg:person.01010701573.25 schema:affiliation https://www.grid.ac/institutes/grid.4714.6
    193 schema:familyName Hall
    194 schema:givenName Per
    195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01010701573.25
    196 rdf:type schema:Person
    197 sg:person.011251153047.07 schema:affiliation https://www.grid.ac/institutes/grid.418377.e
    198 schema:familyName Liu
    199 schema:givenName Jianjun
    200 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011251153047.07
    201 rdf:type schema:Person
    202 sg:person.013117404317.63 schema:affiliation https://www.grid.ac/institutes/grid.4714.6
    203 schema:familyName Czene
    204 schema:givenName Kamila
    205 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013117404317.63
    206 rdf:type schema:Person
    207 sg:person.01372072214.78 schema:affiliation https://www.grid.ac/institutes/grid.418377.e
    208 schema:familyName Zhao
    209 schema:givenName Wanting
    210 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01372072214.78
    211 rdf:type schema:Person
    212 sg:person.0624052041.17 schema:affiliation https://www.grid.ac/institutes/grid.4714.6
    213 schema:familyName Humphreys
    214 schema:givenName Keith
    215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0624052041.17
    216 rdf:type schema:Person
    217 sg:person.0714133215.07 schema:affiliation https://www.grid.ac/institutes/grid.4714.6
    218 schema:familyName Darabi
    219 schema:givenName Hatef
    220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0714133215.07
    221 rdf:type schema:Person
    222 sg:pub.10.1007/s10549-005-5152-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012338974
    223 https://doi.org/10.1007/s10549-005-5152-4
    224 rdf:type schema:CreativeWork
    225 sg:pub.10.1007/s10549-011-1576-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000151477
    226 https://doi.org/10.1007/s10549-011-1576-1
    227 rdf:type schema:CreativeWork
    228 sg:pub.10.1038/bjc.2011.118 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026293422
    229 https://doi.org/10.1038/bjc.2011.118
    230 rdf:type schema:CreativeWork
    231 sg:pub.10.1038/nature05887 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027991306
    232 https://doi.org/10.1038/nature05887
    233 rdf:type schema:CreativeWork
    234 sg:pub.10.1038/ng.131 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038518938
    235 https://doi.org/10.1038/ng.131
    236 rdf:type schema:CreativeWork
    237 sg:pub.10.1038/ng.318 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029457545
    238 https://doi.org/10.1038/ng.318
    239 rdf:type schema:CreativeWork
    240 sg:pub.10.1038/ng.353 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000555917
    241 https://doi.org/10.1038/ng.353
    242 rdf:type schema:CreativeWork
    243 sg:pub.10.1038/ng.354 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004838062
    244 https://doi.org/10.1038/ng.354
    245 rdf:type schema:CreativeWork
    246 sg:pub.10.1038/ng.586 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006256853
    247 https://doi.org/10.1038/ng.586
    248 rdf:type schema:CreativeWork
    249 sg:pub.10.1038/ng1981 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008452967
    250 https://doi.org/10.1038/ng1981
    251 rdf:type schema:CreativeWork
    252 sg:pub.10.1038/ng2064 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045095990
    253 https://doi.org/10.1038/ng2064
    254 rdf:type schema:CreativeWork
    255 sg:pub.10.1038/ng2075 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049470927
    256 https://doi.org/10.1038/ng2075
    257 rdf:type schema:CreativeWork
    258 sg:pub.10.1038/ng853 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041614038
    259 https://doi.org/10.1038/ng853
    260 rdf:type schema:CreativeWork
    261 sg:pub.10.1038/sj.bjc.6604305 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027765597
    262 https://doi.org/10.1038/sj.bjc.6604305
    263 rdf:type schema:CreativeWork
    264 sg:pub.10.1186/bcr1829 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020053537
    265 https://doi.org/10.1186/bcr1829
    266 rdf:type schema:CreativeWork
    267 sg:pub.10.1186/bcr2942 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008028099
    268 https://doi.org/10.1186/bcr2942
    269 rdf:type schema:CreativeWork
    270 https://doi.org/10.1002/(sici)1097-0215(19970529)71:5<800::aid-ijc18>3.0.co;2-b schema:sameAs https://app.dimensions.ai/details/publication/pub.1014587486
    271 rdf:type schema:CreativeWork
    272 https://doi.org/10.1002/ijc.24786 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029194629
    273 rdf:type schema:CreativeWork
    274 https://doi.org/10.1002/sim.1668 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015215277
    275 rdf:type schema:CreativeWork
    276 https://doi.org/10.1002/sim.2929 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044960408
    277 rdf:type schema:CreativeWork
    278 https://doi.org/10.1056/nejmoa0907727 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048643469
    279 rdf:type schema:CreativeWork
    280 https://doi.org/10.1056/nejmsa0708739 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044787515
    281 rdf:type schema:CreativeWork
    282 https://doi.org/10.1073/pnas.0800441105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030781622
    283 rdf:type schema:CreativeWork
    284 https://doi.org/10.1093/aje/kwm305 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050656601
    285 rdf:type schema:CreativeWork
    286 https://doi.org/10.1093/hmg/ddn287 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038688844
    287 rdf:type schema:CreativeWork
    288 https://doi.org/10.1093/jnci/81.24.1879 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059815546
    289 rdf:type schema:CreativeWork
    290 https://doi.org/10.1093/jnci/93.5.358 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018900530
    291 rdf:type schema:CreativeWork
    292 https://doi.org/10.1093/jnci/djj332 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029066633
    293 rdf:type schema:CreativeWork
    294 https://doi.org/10.1093/jnci/djn180 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044813602
    295 rdf:type schema:CreativeWork
    296 https://doi.org/10.1093/jnci/djp130 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046333320
    297 rdf:type schema:CreativeWork
    298 https://doi.org/10.1093/jnci/djq388 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033158924
    299 rdf:type schema:CreativeWork
    300 https://doi.org/10.1093/jnci/djq526 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026160041
    301 rdf:type schema:CreativeWork
    302 https://doi.org/10.1093/oxfordjournals.aje.a114174 schema:sameAs https://app.dimensions.ai/details/publication/pub.1080101683
    303 rdf:type schema:CreativeWork
    304 https://doi.org/10.1097/00008469-199610000-00003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023164993
    305 rdf:type schema:CreativeWork
    306 https://doi.org/10.1136/jmg.40.11.807 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052170953
    307 rdf:type schema:CreativeWork
    308 https://doi.org/10.1158/1055-9965.epi-06-0345 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024579986
    309 rdf:type schema:CreativeWork
    310 https://doi.org/10.1158/1055-9965.epi-08-0631 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011838485
    311 rdf:type schema:CreativeWork
    312 https://doi.org/10.1371/journal.pgen.1001012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004829920
    313 rdf:type schema:CreativeWork
    314 https://doi.org/10.1371/journal.pgen.1001230 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046583364
    315 rdf:type schema:CreativeWork
    316 https://doi.org/10.2307/2531595 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069977037
    317 rdf:type schema:CreativeWork
    318 https://www.grid.ac/institutes/grid.418377.e schema:alternateName Genome Institute of Singapore
    319 schema:name Human Genetics, Genome Institute of Singapore, 60 Biopolis St, 138672, Singapore, Singapore
    320 rdf:type schema:Organization
    321 https://www.grid.ac/institutes/grid.4714.6 schema:alternateName Karolinska Institute
    322 schema:name Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, 177 71, Stockholm, Sweden
    323 rdf:type schema:Organization
     




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


    ...