Efficient Bayesian mixed-model analysis increases association power in large cohorts View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-02-02

AUTHORS

Po-Ru Loh, George Tucker, Brendan K Bulik-Sullivan, Bjarni J Vilhjálmsson, Hilary K Finucane, Rany M Salem, Daniel I Chasman, Paul M Ridker, Benjamin M Neale, Bonnie Berger, Nick Patterson, Alkes L Price

ABSTRACT

Alkes Price, Po-Ru Loh and colleagues report the BOLT-LMM method for mixed-model association. They apply their method to 9 quantitative traits in 23,294 samples and demonstrate that it provides improvements in computational efficiency as well as gains in power that increase with the size of the cohort, making it useful for the analysis of large cohorts. More... »

PAGES

284-290

References to SciGraph publications

  • 2011-05-08. Genome partitioning of genetic variation for complex traits using common SNPs in NATURE GENETICS
  • 2005-07-24. Demonstrating stratification in a European American population in NATURE GENETICS
  • 2013-06-18. Pitfalls of predicting complex traits from SNPs in NATURE REVIEWS GENETICS
  • 2015-02-02. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies in NATURE GENETICS
  • 2013-05-09. The benefits of selecting phenotype-specific variants for applications of mixed models in genomics in SCIENTIFIC REPORTS
  • 2012-06-17. Genome-wide efficient mixed-model analysis for association studies in NATURE GENETICS
  • 2011-09-04. FaST linear mixed models for genome-wide association studies in NATURE METHODS
  • 2012-09-16. Rapid variance components–based method for whole-genome association analysis in NATURE GENETICS
  • 2009-10. Bayesian statistical methods for genetic association studies in NATURE REVIEWS GENETICS
  • 2009-01-05. A fast algorithm for BayesB type of prediction of genome-wide estimates of genetic value in GENETICS SELECTION EVOLUTION
  • 2006-07-23. Principal components analysis corrects for stratification in genome-wide association studies in NATURE GENETICS
  • 2010-01-27. A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis in BMC BIOINFORMATICS
  • 2014-01-29. Advantages and pitfalls in the application of mixed-model association methods in NATURE GENETICS
  • 2013-04-26. FaST-LMM-Select for addressing confounding from spatial structure and rare variants in NATURE GENETICS
  • 2010-03-07. Mixed linear model approach adapted for genome-wide association studies in NATURE GENETICS
  • 2010-03-07. Variance component model to account for sample structure in genome-wide association studies in NATURE GENETICS
  • 2011-08-10. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis in NATURE
  • 2010-06-20. Common SNPs explain a large proportion of the heritability for human height in NATURE GENETICS
  • 2013-05-05. Nonsense mutation in the LGR4 gene is associated with several human diseases and other traits in NATURE
  • 2012-06-17. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations in NATURE GENETICS
  • 2011-03-16. Genomic inflation factors under polygenic inheritance in EUROPEAN JOURNAL OF HUMAN GENETICS
  • 2012-05-30. Improved linear mixed models for genome-wide association studies in NATURE METHODS
  • 2005-12-25. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness in NATURE GENETICS
  • 2012-08-19. A mixed-model approach for genome-wide association studies of correlated traits in structured populations in NATURE GENETICS
  • 2012-03-25. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis in NATURE GENETICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ng.3190

    DOI

    http://dx.doi.org/10.1038/ng.3190

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1041665159

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/25642633


    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/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
            "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": "Algorithms", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Bayes Theorem", 
            "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": "Genome, Human", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genotyping Techniques", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Linear Models", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Polymorphism, Single Nucleotide", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Quantitative Trait Loci", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.66859.34", 
              "name": [
                "Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA", 
                "Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Loh", 
            "givenName": "Po-Ru", 
            "id": "sg:person.014211634457.16", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014211634457.16"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.116068.8", 
              "name": [
                "Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA", 
                "Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA", 
                "Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Tucker", 
            "givenName": "George", 
            "id": "sg:person.01151246250.00", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01151246250.00"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.32224.35", 
              "name": [
                "Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA", 
                "Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bulik-Sullivan", 
            "givenName": "Brendan K", 
            "id": "sg:person.01201444625.22", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01201444625.22"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.66859.34", 
              "name": [
                "Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA", 
                "Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Vilhj\u00e1lmsson", 
            "givenName": "Bjarni J", 
            "id": "sg:person.0603337465.80", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0603337465.80"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.116068.8", 
              "name": [
                "Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Finucane", 
            "givenName": "Hilary K", 
            "id": "sg:person.01217411134.93", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01217411134.93"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Endocrinology, Children's Hospital Boston, Boston, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.2515.3", 
              "name": [
                "Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA", 
                "Department of Endocrinology, Children's Hospital Boston, Boston, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Salem", 
            "givenName": "Rany M", 
            "id": "sg:person.01076204230.50", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01076204230.50"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.62560.37", 
              "name": [
                "Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Chasman", 
            "givenName": "Daniel I", 
            "id": "sg:person.0760704165.88", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0760704165.88"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.62560.37", 
              "name": [
                "Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ridker", 
            "givenName": "Paul M", 
            "id": "sg:person.013142315002.26", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013142315002.26"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.32224.35", 
              "name": [
                "Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA", 
                "Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Neale", 
            "givenName": "Benjamin M", 
            "id": "sg:person.014377465057.81", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014377465057.81"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.116068.8", 
              "name": [
                "Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA", 
                "Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Berger", 
            "givenName": "Bonnie", 
            "id": "sg:person.01213307265.33", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213307265.33"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.66859.34", 
              "name": [
                "Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Patterson", 
            "givenName": "Nick", 
            "id": "sg:person.0735501366.78", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0735501366.78"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA", 
              "id": "http://www.grid.ac/institutes/grid.38142.3c", 
              "name": [
                "Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA", 
                "Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA", 
                "Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Price", 
            "givenName": "Alkes L", 
            "id": "sg:person.01342616137.05", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01342616137.05"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1186/1297-9686-41-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038813560", 
              "https://doi.org/10.1186/1297-9686-41-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-11-58", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004614446", 
              "https://doi.org/10.1186/1471-2105-11-58"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature12124", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032844135", 
              "https://doi.org/10.1038/nature12124"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.2620", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045134688", 
              "https://doi.org/10.1038/ng.2620"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.548", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016055940", 
              "https://doi.org/10.1038/ng.548"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep01815", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051842723", 
              "https://doi.org/10.1038/srep01815"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng1607", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018430963", 
              "https://doi.org/10.1038/ng1607"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.608", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015057090", 
              "https://doi.org/10.1038/ng.608"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.2232", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017682249", 
              "https://doi.org/10.1038/ng.2232"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.2876", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016276818", 
              "https://doi.org/10.1038/ng.2876"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg2615", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021006418", 
              "https://doi.org/10.1038/nrg2615"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.2410", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036140888", 
              "https://doi.org/10.1038/ng.2410"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.2376", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041264575", 
              "https://doi.org/10.1038/ng.2376"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.546", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017784241", 
              "https://doi.org/10.1038/ng.546"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature10251", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038635012", 
              "https://doi.org/10.1038/nature10251"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.2314", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007536029", 
              "https://doi.org/10.1038/ng.2314"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.823", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007907762", 
              "https://doi.org/10.1038/ng.823"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.2037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045203738", 
              "https://doi.org/10.1038/nmeth.2037"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ejhg.2011.39", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051554988", 
              "https://doi.org/10.1038/ejhg.2011.39"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nmeth.1681", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006195414", 
              "https://doi.org/10.1038/nmeth.1681"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng1847", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031429813", 
              "https://doi.org/10.1038/ng1847"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.3211", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044444076", 
              "https://doi.org/10.1038/ng.3211"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.2310", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046029840", 
              "https://doi.org/10.1038/ng.2310"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng1702", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035781360", 
              "https://doi.org/10.1038/ng1702"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg3457", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052559381", 
              "https://doi.org/10.1038/nrg3457"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2015-02-02", 
        "datePublishedReg": "2015-02-02", 
        "description": "Alkes Price, Po-Ru Loh and colleagues report the BOLT-LMM method for mixed-model association. They apply their method to 9 quantitative traits in 23,294 samples and demonstrate that it provides improvements in computational efficiency as well as gains in power that increase with the size of the cohort, making it useful for the analysis of large cohorts.", 
        "genre": "article", 
        "id": "sg:pub.10.1038/ng.3190", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.3806025", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2540450", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2469915", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.3625430", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2529444", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.3805242", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2533515", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.3624272", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1103138", 
            "issn": [
              "1061-4036", 
              "1546-1718"
            ], 
            "name": "Nature Genetics", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "47"
          }
        ], 
        "keywords": [
          "mixed model association", 
          "computational efficiency", 
          "association power", 
          "power", 
          "quantitative traits", 
          "analysis", 
          "efficiency", 
          "gain", 
          "prices", 
          "size", 
          "mixed model analysis", 
          "improvement", 
          "samples", 
          "increase", 
          "colleagues", 
          "traits", 
          "LOH", 
          "association", 
          "large cohort", 
          "cohort", 
          "method"
        ], 
        "name": "Efficient Bayesian mixed-model analysis increases association power in large cohorts", 
        "pagination": "284-290", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1041665159"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/ng.3190"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "25642633"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/ng.3190", 
          "https://app.dimensions.ai/details/publication/pub.1041665159"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-11-24T20:59", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_674.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1038/ng.3190"
      }
    ]
     

    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.10.1038/ng.3190'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/ng.3190'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/ng.3190'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/ng.3190'


     

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

    338 TRIPLES      21 PREDICATES      81 URIs      48 LITERALS      17 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/ng.3190 schema:about N03f7423d47a04f7db5c1b27093e5b793
    2 N0e1e94203f594ffb897f4ada337e8212
    3 N329e0fa7a9e147c983cf63175f802569
    4 N3df6c5f054fe4e628a621f1f11d27269
    5 N4c256168b7c546fd8aa72de723606754
    6 N6f04229159dc4932bf8af473235aaebb
    7 N785a531757bb4cdab991e15fcb52435e
    8 Nd051c4382c5d4ee28178c4a78bb1004d
    9 Ne382dacbd8534621ad0738919f20311a
    10 Ne6893d40a3734a50adc7d7c2bdc8470d
    11 anzsrc-for:06
    12 anzsrc-for:11
    13 schema:author Ndf7814c72fcb40dd87804290f3262c6b
    14 schema:citation sg:pub.10.1038/ejhg.2011.39
    15 sg:pub.10.1038/nature10251
    16 sg:pub.10.1038/nature12124
    17 sg:pub.10.1038/ng.2232
    18 sg:pub.10.1038/ng.2310
    19 sg:pub.10.1038/ng.2314
    20 sg:pub.10.1038/ng.2376
    21 sg:pub.10.1038/ng.2410
    22 sg:pub.10.1038/ng.2620
    23 sg:pub.10.1038/ng.2876
    24 sg:pub.10.1038/ng.3211
    25 sg:pub.10.1038/ng.546
    26 sg:pub.10.1038/ng.548
    27 sg:pub.10.1038/ng.608
    28 sg:pub.10.1038/ng.823
    29 sg:pub.10.1038/ng1607
    30 sg:pub.10.1038/ng1702
    31 sg:pub.10.1038/ng1847
    32 sg:pub.10.1038/nmeth.1681
    33 sg:pub.10.1038/nmeth.2037
    34 sg:pub.10.1038/nrg2615
    35 sg:pub.10.1038/nrg3457
    36 sg:pub.10.1038/srep01815
    37 sg:pub.10.1186/1297-9686-41-2
    38 sg:pub.10.1186/1471-2105-11-58
    39 schema:datePublished 2015-02-02
    40 schema:datePublishedReg 2015-02-02
    41 schema:description Alkes Price, Po-Ru Loh and colleagues report the BOLT-LMM method for mixed-model association. They apply their method to 9 quantitative traits in 23,294 samples and demonstrate that it provides improvements in computational efficiency as well as gains in power that increase with the size of the cohort, making it useful for the analysis of large cohorts.
    42 schema:genre article
    43 schema:isAccessibleForFree true
    44 schema:isPartOf N17d7513d36694ec186f00e1246ccd577
    45 Nc0901d668084430ab7c91405bd34f798
    46 sg:journal.1103138
    47 schema:keywords LOH
    48 analysis
    49 association
    50 association power
    51 cohort
    52 colleagues
    53 computational efficiency
    54 efficiency
    55 gain
    56 improvement
    57 increase
    58 large cohort
    59 method
    60 mixed model analysis
    61 mixed model association
    62 power
    63 prices
    64 quantitative traits
    65 samples
    66 size
    67 traits
    68 schema:name Efficient Bayesian mixed-model analysis increases association power in large cohorts
    69 schema:pagination 284-290
    70 schema:productId N1bfda356b38c40ba91893ce6ea4747f1
    71 N72bc5adfcddf472cb8813f713ec04791
    72 N8a0ae8bf5dff4750983ba0b3351a2a8e
    73 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041665159
    74 https://doi.org/10.1038/ng.3190
    75 schema:sdDatePublished 2022-11-24T20:59
    76 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    77 schema:sdPublisher N4933a8b0c63140dba985bdf9deca754b
    78 schema:url https://doi.org/10.1038/ng.3190
    79 sgo:license sg:explorer/license/
    80 sgo:sdDataset articles
    81 rdf:type schema:ScholarlyArticle
    82 N03f7423d47a04f7db5c1b27093e5b793 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    83 schema:name Bayes Theorem
    84 rdf:type schema:DefinedTerm
    85 N051a20247aab406ea929abd8a8bb1cb4 rdf:first sg:person.01342616137.05
    86 rdf:rest rdf:nil
    87 N0a4afe26bd854e6ba0391f671f2dd467 rdf:first sg:person.01076204230.50
    88 rdf:rest N1407adb1d4ff44ce85afc4b535ba086b
    89 N0e1e94203f594ffb897f4ada337e8212 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    90 schema:name Linear Models
    91 rdf:type schema:DefinedTerm
    92 N1407adb1d4ff44ce85afc4b535ba086b rdf:first sg:person.0760704165.88
    93 rdf:rest Nca8d40adf4d942448ef9d0ad0e744ebd
    94 N17d7513d36694ec186f00e1246ccd577 schema:volumeNumber 47
    95 rdf:type schema:PublicationVolume
    96 N1bfda356b38c40ba91893ce6ea4747f1 schema:name dimensions_id
    97 schema:value pub.1041665159
    98 rdf:type schema:PropertyValue
    99 N1d5409bea4144ae0944014966b105fa8 rdf:first sg:person.0735501366.78
    100 rdf:rest N051a20247aab406ea929abd8a8bb1cb4
    101 N329e0fa7a9e147c983cf63175f802569 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    102 schema:name Genetic Association Studies
    103 rdf:type schema:DefinedTerm
    104 N38327d03940c4e8eb3f505b6d20b6afa rdf:first sg:person.01217411134.93
    105 rdf:rest N0a4afe26bd854e6ba0391f671f2dd467
    106 N3df6c5f054fe4e628a621f1f11d27269 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    107 schema:name Humans
    108 rdf:type schema:DefinedTerm
    109 N4933a8b0c63140dba985bdf9deca754b schema:name Springer Nature - SN SciGraph project
    110 rdf:type schema:Organization
    111 N4c256168b7c546fd8aa72de723606754 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    112 schema:name Female
    113 rdf:type schema:DefinedTerm
    114 N58bbf0d3ab7a4925b130eb8db4d470f0 rdf:first sg:person.01213307265.33
    115 rdf:rest N1d5409bea4144ae0944014966b105fa8
    116 N6f04229159dc4932bf8af473235aaebb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    117 schema:name Genome, Human
    118 rdf:type schema:DefinedTerm
    119 N72bc5adfcddf472cb8813f713ec04791 schema:name pubmed_id
    120 schema:value 25642633
    121 rdf:type schema:PropertyValue
    122 N785a531757bb4cdab991e15fcb52435e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    123 schema:name Quantitative Trait Loci
    124 rdf:type schema:DefinedTerm
    125 N8a0ae8bf5dff4750983ba0b3351a2a8e schema:name doi
    126 schema:value 10.1038/ng.3190
    127 rdf:type schema:PropertyValue
    128 N9e19ebc54def4a289f5fcffdeaa54df8 rdf:first sg:person.01201444625.22
    129 rdf:rest Ncab7351780e546a7a98318120860a6f2
    130 Nc0901d668084430ab7c91405bd34f798 schema:issueNumber 3
    131 rdf:type schema:PublicationIssue
    132 Nca8d40adf4d942448ef9d0ad0e744ebd rdf:first sg:person.013142315002.26
    133 rdf:rest Nd26b7978d8a1454e9f275cac4c9c7f30
    134 Ncab7351780e546a7a98318120860a6f2 rdf:first sg:person.0603337465.80
    135 rdf:rest N38327d03940c4e8eb3f505b6d20b6afa
    136 Nd051c4382c5d4ee28178c4a78bb1004d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    137 schema:name Algorithms
    138 rdf:type schema:DefinedTerm
    139 Nd26b7978d8a1454e9f275cac4c9c7f30 rdf:first sg:person.014377465057.81
    140 rdf:rest N58bbf0d3ab7a4925b130eb8db4d470f0
    141 Nda41e72be279409ea5335cf0d6e6767b rdf:first sg:person.01151246250.00
    142 rdf:rest N9e19ebc54def4a289f5fcffdeaa54df8
    143 Ndf7814c72fcb40dd87804290f3262c6b rdf:first sg:person.014211634457.16
    144 rdf:rest Nda41e72be279409ea5335cf0d6e6767b
    145 Ne382dacbd8534621ad0738919f20311a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    146 schema:name Polymorphism, Single Nucleotide
    147 rdf:type schema:DefinedTerm
    148 Ne6893d40a3734a50adc7d7c2bdc8470d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    149 schema:name Genotyping Techniques
    150 rdf:type schema:DefinedTerm
    151 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    152 schema:name Biological Sciences
    153 rdf:type schema:DefinedTerm
    154 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
    155 schema:name Medical and Health Sciences
    156 rdf:type schema:DefinedTerm
    157 sg:grant.2469915 http://pending.schema.org/fundedItem sg:pub.10.1038/ng.3190
    158 rdf:type schema:MonetaryGrant
    159 sg:grant.2529444 http://pending.schema.org/fundedItem sg:pub.10.1038/ng.3190
    160 rdf:type schema:MonetaryGrant
    161 sg:grant.2533515 http://pending.schema.org/fundedItem sg:pub.10.1038/ng.3190
    162 rdf:type schema:MonetaryGrant
    163 sg:grant.2540450 http://pending.schema.org/fundedItem sg:pub.10.1038/ng.3190
    164 rdf:type schema:MonetaryGrant
    165 sg:grant.3624272 http://pending.schema.org/fundedItem sg:pub.10.1038/ng.3190
    166 rdf:type schema:MonetaryGrant
    167 sg:grant.3625430 http://pending.schema.org/fundedItem sg:pub.10.1038/ng.3190
    168 rdf:type schema:MonetaryGrant
    169 sg:grant.3805242 http://pending.schema.org/fundedItem sg:pub.10.1038/ng.3190
    170 rdf:type schema:MonetaryGrant
    171 sg:grant.3806025 http://pending.schema.org/fundedItem sg:pub.10.1038/ng.3190
    172 rdf:type schema:MonetaryGrant
    173 sg:journal.1103138 schema:issn 1061-4036
    174 1546-1718
    175 schema:name Nature Genetics
    176 schema:publisher Springer Nature
    177 rdf:type schema:Periodical
    178 sg:person.01076204230.50 schema:affiliation grid-institutes:grid.2515.3
    179 schema:familyName Salem
    180 schema:givenName Rany M
    181 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01076204230.50
    182 rdf:type schema:Person
    183 sg:person.01151246250.00 schema:affiliation grid-institutes:grid.116068.8
    184 schema:familyName Tucker
    185 schema:givenName George
    186 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01151246250.00
    187 rdf:type schema:Person
    188 sg:person.01201444625.22 schema:affiliation grid-institutes:grid.32224.35
    189 schema:familyName Bulik-Sullivan
    190 schema:givenName Brendan K
    191 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01201444625.22
    192 rdf:type schema:Person
    193 sg:person.01213307265.33 schema:affiliation grid-institutes:grid.116068.8
    194 schema:familyName Berger
    195 schema:givenName Bonnie
    196 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213307265.33
    197 rdf:type schema:Person
    198 sg:person.01217411134.93 schema:affiliation grid-institutes:grid.116068.8
    199 schema:familyName Finucane
    200 schema:givenName Hilary K
    201 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01217411134.93
    202 rdf:type schema:Person
    203 sg:person.013142315002.26 schema:affiliation grid-institutes:grid.62560.37
    204 schema:familyName Ridker
    205 schema:givenName Paul M
    206 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013142315002.26
    207 rdf:type schema:Person
    208 sg:person.01342616137.05 schema:affiliation grid-institutes:grid.38142.3c
    209 schema:familyName Price
    210 schema:givenName Alkes L
    211 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01342616137.05
    212 rdf:type schema:Person
    213 sg:person.014211634457.16 schema:affiliation grid-institutes:grid.66859.34
    214 schema:familyName Loh
    215 schema:givenName Po-Ru
    216 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014211634457.16
    217 rdf:type schema:Person
    218 sg:person.014377465057.81 schema:affiliation grid-institutes:grid.32224.35
    219 schema:familyName Neale
    220 schema:givenName Benjamin M
    221 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014377465057.81
    222 rdf:type schema:Person
    223 sg:person.0603337465.80 schema:affiliation grid-institutes:grid.66859.34
    224 schema:familyName Vilhjálmsson
    225 schema:givenName Bjarni J
    226 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0603337465.80
    227 rdf:type schema:Person
    228 sg:person.0735501366.78 schema:affiliation grid-institutes:grid.66859.34
    229 schema:familyName Patterson
    230 schema:givenName Nick
    231 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0735501366.78
    232 rdf:type schema:Person
    233 sg:person.0760704165.88 schema:affiliation grid-institutes:grid.62560.37
    234 schema:familyName Chasman
    235 schema:givenName Daniel I
    236 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0760704165.88
    237 rdf:type schema:Person
    238 sg:pub.10.1038/ejhg.2011.39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051554988
    239 https://doi.org/10.1038/ejhg.2011.39
    240 rdf:type schema:CreativeWork
    241 sg:pub.10.1038/nature10251 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038635012
    242 https://doi.org/10.1038/nature10251
    243 rdf:type schema:CreativeWork
    244 sg:pub.10.1038/nature12124 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032844135
    245 https://doi.org/10.1038/nature12124
    246 rdf:type schema:CreativeWork
    247 sg:pub.10.1038/ng.2232 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017682249
    248 https://doi.org/10.1038/ng.2232
    249 rdf:type schema:CreativeWork
    250 sg:pub.10.1038/ng.2310 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046029840
    251 https://doi.org/10.1038/ng.2310
    252 rdf:type schema:CreativeWork
    253 sg:pub.10.1038/ng.2314 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007536029
    254 https://doi.org/10.1038/ng.2314
    255 rdf:type schema:CreativeWork
    256 sg:pub.10.1038/ng.2376 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041264575
    257 https://doi.org/10.1038/ng.2376
    258 rdf:type schema:CreativeWork
    259 sg:pub.10.1038/ng.2410 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036140888
    260 https://doi.org/10.1038/ng.2410
    261 rdf:type schema:CreativeWork
    262 sg:pub.10.1038/ng.2620 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045134688
    263 https://doi.org/10.1038/ng.2620
    264 rdf:type schema:CreativeWork
    265 sg:pub.10.1038/ng.2876 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016276818
    266 https://doi.org/10.1038/ng.2876
    267 rdf:type schema:CreativeWork
    268 sg:pub.10.1038/ng.3211 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044444076
    269 https://doi.org/10.1038/ng.3211
    270 rdf:type schema:CreativeWork
    271 sg:pub.10.1038/ng.546 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017784241
    272 https://doi.org/10.1038/ng.546
    273 rdf:type schema:CreativeWork
    274 sg:pub.10.1038/ng.548 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016055940
    275 https://doi.org/10.1038/ng.548
    276 rdf:type schema:CreativeWork
    277 sg:pub.10.1038/ng.608 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015057090
    278 https://doi.org/10.1038/ng.608
    279 rdf:type schema:CreativeWork
    280 sg:pub.10.1038/ng.823 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007907762
    281 https://doi.org/10.1038/ng.823
    282 rdf:type schema:CreativeWork
    283 sg:pub.10.1038/ng1607 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018430963
    284 https://doi.org/10.1038/ng1607
    285 rdf:type schema:CreativeWork
    286 sg:pub.10.1038/ng1702 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035781360
    287 https://doi.org/10.1038/ng1702
    288 rdf:type schema:CreativeWork
    289 sg:pub.10.1038/ng1847 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031429813
    290 https://doi.org/10.1038/ng1847
    291 rdf:type schema:CreativeWork
    292 sg:pub.10.1038/nmeth.1681 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006195414
    293 https://doi.org/10.1038/nmeth.1681
    294 rdf:type schema:CreativeWork
    295 sg:pub.10.1038/nmeth.2037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045203738
    296 https://doi.org/10.1038/nmeth.2037
    297 rdf:type schema:CreativeWork
    298 sg:pub.10.1038/nrg2615 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021006418
    299 https://doi.org/10.1038/nrg2615
    300 rdf:type schema:CreativeWork
    301 sg:pub.10.1038/nrg3457 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052559381
    302 https://doi.org/10.1038/nrg3457
    303 rdf:type schema:CreativeWork
    304 sg:pub.10.1038/srep01815 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051842723
    305 https://doi.org/10.1038/srep01815
    306 rdf:type schema:CreativeWork
    307 sg:pub.10.1186/1297-9686-41-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038813560
    308 https://doi.org/10.1186/1297-9686-41-2
    309 rdf:type schema:CreativeWork
    310 sg:pub.10.1186/1471-2105-11-58 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004614446
    311 https://doi.org/10.1186/1471-2105-11-58
    312 rdf:type schema:CreativeWork
    313 grid-institutes:grid.116068.8 schema:alternateName Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA
    314 Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
    315 schema:name Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA
    316 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
    317 Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
    318 rdf:type schema:Organization
    319 grid-institutes:grid.2515.3 schema:alternateName Department of Endocrinology, Children's Hospital Boston, Boston, Massachusetts, USA
    320 schema:name Department of Endocrinology, Children's Hospital Boston, Boston, Massachusetts, USA
    321 Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
    322 rdf:type schema:Organization
    323 grid-institutes:grid.32224.35 schema:alternateName Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
    324 schema:name Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
    325 Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
    326 rdf:type schema:Organization
    327 grid-institutes:grid.38142.3c schema:alternateName Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
    328 schema:name Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
    329 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
    330 Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
    331 rdf:type schema:Organization
    332 grid-institutes:grid.62560.37 schema:alternateName Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
    333 schema:name Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
    334 rdf:type schema:Organization
    335 grid-institutes:grid.66859.34 schema:alternateName Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
    336 schema:name Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
    337 Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
    338 rdf:type schema:Organization
     




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


    ...