Methodisch-statistische Herausforderungen an die genombasierte Vorhersage von Erkrankungen View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-02

AUTHORS

Ronja Foraita, M. Jäger, I. Pigeot

ABSTRACT

The rapidly developing genotyping technology has led to the detection of many genetic factors that contribute to the pathogenesis of complex diseases. From this, the aim arose to use these results to offer tailored preventive measures or therapies based on an individual genetic profile. For this purpose, genetic tests are being developed that should allow us to identify individuals who belong to a high risk group with respect to a certain disease due to their genetic predisposition. Such tests are often based on known genetic risk factors that have been identified in genome-wide association studies. Typically, the effect estimates obtained from these studies are further used to construct a genetic risk measure to predict a certain phenotype. This paper describes several statistical and methodological challenges that must be coped with when establishing a genetic prediction model: Starting with the goal to obtain unbiased effect estimates to identify appropriate genetic risk predictors, genetic risk measures must be developed, and the predictive value of a new genetic test must be established. These key requirements of a statistical risk prediction in genetics will be discussed in three sections and finally discussed from a public health perspective. More... »

PAGES

131-138

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00103-014-2091-4

DOI

http://dx.doi.org/10.1007/s00103-014-2091-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034746121

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25432454


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

JSON-LD is the canonical representation for SciGraph data.

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

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Genetics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Chromosome Mapping", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Data Interpretation, Statistical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genetic Predisposition to Disease", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genetic Testing", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genome, Human", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genome-Wide Association Study", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Germany", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Precision Medicine", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk Assessment", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Leibniz Institute for Prevention Research and Epidemiology - BIPS", 
          "id": "https://www.grid.ac/institutes/grid.418465.a", 
          "name": [
            "Leibniz-Institut f\u00fcr Pr\u00e4ventionsforschung und Epidemiologie \u2013 BIPS, Achterstr. 30, 28359, Bremen, Deutschland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Foraita", 
        "givenName": "Ronja", 
        "id": "sg:person.01320706365.23", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01320706365.23"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Leibniz Institute for Prevention Research and Epidemiology - BIPS", 
          "id": "https://www.grid.ac/institutes/grid.418465.a", 
          "name": [
            "Leibniz-Institut f\u00fcr Pr\u00e4ventionsforschung und Epidemiologie \u2013 BIPS, Achterstr. 30, 28359, Bremen, Deutschland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "J\u00e4ger", 
        "givenName": "M.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Leibniz Institute for Prevention Research and Epidemiology - BIPS", 
          "id": "https://www.grid.ac/institutes/grid.418465.a", 
          "name": [
            "Leibniz-Institut f\u00fcr Pr\u00e4ventionsforschung und Epidemiologie \u2013 BIPS, Achterstr. 30, 28359, Bremen, Deutschland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pigeot", 
        "givenName": "I.", 
        "id": "sg:person.015370023320.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015370023320.82"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1515/1544-6115.1796", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001399434"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sim.4006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001673184"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sim.4006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001673184"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/bimj.200710398", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002099562"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmp0810107", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004252172"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature08494", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005147626", 
          "https://doi.org/10.1038/nature08494"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature08494", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005147626", 
          "https://doi.org/10.1038/nature08494"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-11-724", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005310527", 
          "https://doi.org/10.1186/1471-2164-11-724"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmra0905980", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006094606"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pgen.1000231", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007295306"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0140-6736(07)60111-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008308895"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1742-4658.2012.08810.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010427309"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg3523", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010613501", 
          "https://doi.org/10.1038/nrg3523"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btr295", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011507087"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1014237288", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1014237288", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1097/01.gim.0000229689.18263.f4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016523464", 
          "https://doi.org/10.1097/01.gim.0000229689.18263.f4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1097/01.gim.0000229689.18263.f4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016523464", 
          "https://doi.org/10.1097/01.gim.0000229689.18263.f4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/jme.2004.010272", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017204533"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/jme.2004.010272", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017204533"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa075819", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019183847"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1159/000083330", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019417145"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0903103106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020299353"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11825-011-0295-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020453049", 
          "https://doi.org/10.1007/s11825-011-0295-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0037979", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021222745"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/0471142905.hg0119s68", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023229342"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.2010.119", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023385197"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pgen.1000337", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023458577"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng.610", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025253932", 
          "https://doi.org/10.1038/ng.610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng.610", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025253932", 
          "https://doi.org/10.1038/ng.610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bib/bbq074", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026452996"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/hmg/ddn250", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026942174"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1373/clinchem.2006.074591", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027001096"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg2516", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027055889", 
          "https://doi.org/10.1038/nrg2516"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/ard.2009.120170", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028179140"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3109/15622975.2012.662282", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028495330"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/b978-0-12-373932-2.00144-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032011478"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1474-4422(09)70275-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032910836"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/db08-0425", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034664316"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/bmj.b4838", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036459127"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/gepi.20509", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039610415"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/gepi.20509", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039610415"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1097/01.gim.0000206278.37405.25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040720062", 
          "https://doi.org/10.1097/01.gim.0000206278.37405.25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1097/01.gim.0000206278.37405.25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040720062", 
          "https://doi.org/10.1097/01.gim.0000206278.37405.25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1172/jci34772", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046196569"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00439-012-1194-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046424518", 
          "https://doi.org/10.1007/s00439-012-1194-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00439-012-1194-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046424518", 
          "https://doi.org/10.1007/s00439-012-1194-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00439-012-1194-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046424518", 
          "https://doi.org/10.1007/s00439-012-1194-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg2322", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046913669", 
          "https://doi.org/10.1038/nrg2322"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gm123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047808714", 
          "https://doi.org/10.1186/gm123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1001/jama.291.13.1642", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049238214"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/gepi.20516", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049458939"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/gepi.20516", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049458939"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.gde.2008.07.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050395869"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/hmg/ddr378", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052661441"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015-02", 
    "datePublishedReg": "2015-02-01", 
    "description": "The rapidly developing genotyping technology has led to the detection of many genetic factors that contribute to the pathogenesis of complex diseases. From this, the aim arose to use these results to offer tailored preventive measures or therapies based on an individual genetic profile. For this purpose, genetic tests are being developed that should allow us to identify individuals who belong to a high risk group with respect to a certain disease due to their genetic predisposition. Such tests are often based on known genetic risk factors that have been identified in genome-wide association studies. Typically, the effect estimates obtained from these studies are further used to construct a genetic risk measure to predict a certain phenotype. This paper describes several statistical and methodological challenges that must be coped with when establishing a genetic prediction model: Starting with the goal to obtain unbiased effect estimates to identify appropriate genetic risk predictors, genetic risk measures must be developed, and the predictive value of a new genetic test must be established. These key requirements of a statistical risk prediction in genetics will be discussed in three sections and finally discussed from a public health perspective. ", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00103-014-2091-4", 
    "inLanguage": [
      "de"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1032465", 
        "issn": [
          "1436-9990", 
          "1437-1588"
        ], 
        "name": "Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "58"
      }
    ], 
    "name": "Methodisch-statistische Herausforderungen an die genombasierte Vorhersage von Erkrankungen", 
    "pagination": "131-138", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "a5841ce5e8f4460d80078597d6992d0bf6baed2dee81ee10bab5271d9f680581"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "25432454"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101181368"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00103-014-2091-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1034746121"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00103-014-2091-4", 
      "https://app.dimensions.ai/details/publication/pub.1034746121"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T17:30", 
    "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/v90/0000000001_0000000264/records_8672_00000506.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs00103-014-2091-4"
  }
]
 

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.10.1007/s00103-014-2091-4'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00103-014-2091-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00103-014-2091-4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00103-014-2091-4'


 

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

264 TRIPLES      21 PREDICATES      83 URIs      31 LITERALS      19 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00103-014-2091-4 schema:about N0b5e42a0c1624efc8260cab994a0fea3
2 N2bd199360b7448e8a5163631004ccbd6
3 N313387e58cf3418d828f863344361a21
4 N7575ce06450f4a2899bd9751aecf40a6
5 N8e7932af16144758bbcab44d166c0bb8
6 N9c83dc53a36141798079077c34a6fc7e
7 Nb7c2798752bd46468ae7756cfcbc5d17
8 Nd45120db28174d87a4415ac1cc4aabb1
9 Ne684adcc42fb458eaedcf4385d3f4115
10 Ne6b839bd201f4ad487c5d3f728e13ed2
11 anzsrc-for:06
12 anzsrc-for:0604
13 schema:author Naf3e5a500c5c492dac4837738807cb41
14 schema:citation sg:pub.10.1007/s00439-012-1194-y
15 sg:pub.10.1007/s11825-011-0295-7
16 sg:pub.10.1038/nature08494
17 sg:pub.10.1038/ng.610
18 sg:pub.10.1038/nrg2322
19 sg:pub.10.1038/nrg2516
20 sg:pub.10.1038/nrg3523
21 sg:pub.10.1097/01.gim.0000206278.37405.25
22 sg:pub.10.1097/01.gim.0000229689.18263.f4
23 sg:pub.10.1186/1471-2164-11-724
24 sg:pub.10.1186/gm123
25 https://app.dimensions.ai/details/publication/pub.1014237288
26 https://doi.org/10.1001/jama.2010.119
27 https://doi.org/10.1001/jama.291.13.1642
28 https://doi.org/10.1002/0471142905.hg0119s68
29 https://doi.org/10.1002/bimj.200710398
30 https://doi.org/10.1002/gepi.20509
31 https://doi.org/10.1002/gepi.20516
32 https://doi.org/10.1002/sim.4006
33 https://doi.org/10.1016/b978-0-12-373932-2.00144-7
34 https://doi.org/10.1016/j.gde.2008.07.006
35 https://doi.org/10.1016/s0140-6736(07)60111-1
36 https://doi.org/10.1016/s1474-4422(09)70275-3
37 https://doi.org/10.1056/nejmoa075819
38 https://doi.org/10.1056/nejmp0810107
39 https://doi.org/10.1056/nejmra0905980
40 https://doi.org/10.1073/pnas.0903103106
41 https://doi.org/10.1093/bib/bbq074
42 https://doi.org/10.1093/bioinformatics/btr295
43 https://doi.org/10.1093/hmg/ddn250
44 https://doi.org/10.1093/hmg/ddr378
45 https://doi.org/10.1111/j.1742-4658.2012.08810.x
46 https://doi.org/10.1136/ard.2009.120170
47 https://doi.org/10.1136/bmj.b4838
48 https://doi.org/10.1136/jme.2004.010272
49 https://doi.org/10.1159/000083330
50 https://doi.org/10.1172/jci34772
51 https://doi.org/10.1371/journal.pgen.1000231
52 https://doi.org/10.1371/journal.pgen.1000337
53 https://doi.org/10.1371/journal.pone.0037979
54 https://doi.org/10.1373/clinchem.2006.074591
55 https://doi.org/10.1515/1544-6115.1796
56 https://doi.org/10.2337/db08-0425
57 https://doi.org/10.3109/15622975.2012.662282
58 schema:datePublished 2015-02
59 schema:datePublishedReg 2015-02-01
60 schema:description The rapidly developing genotyping technology has led to the detection of many genetic factors that contribute to the pathogenesis of complex diseases. From this, the aim arose to use these results to offer tailored preventive measures or therapies based on an individual genetic profile. For this purpose, genetic tests are being developed that should allow us to identify individuals who belong to a high risk group with respect to a certain disease due to their genetic predisposition. Such tests are often based on known genetic risk factors that have been identified in genome-wide association studies. Typically, the effect estimates obtained from these studies are further used to construct a genetic risk measure to predict a certain phenotype. This paper describes several statistical and methodological challenges that must be coped with when establishing a genetic prediction model: Starting with the goal to obtain unbiased effect estimates to identify appropriate genetic risk predictors, genetic risk measures must be developed, and the predictive value of a new genetic test must be established. These key requirements of a statistical risk prediction in genetics will be discussed in three sections and finally discussed from a public health perspective.
61 schema:genre research_article
62 schema:inLanguage de
63 schema:isAccessibleForFree false
64 schema:isPartOf N156667ceb849401489351cc916c805fc
65 Nea7f44ac170f4ab698ae460f142e19f3
66 sg:journal.1032465
67 schema:name Methodisch-statistische Herausforderungen an die genombasierte Vorhersage von Erkrankungen
68 schema:pagination 131-138
69 schema:productId N27ec81fc6539413db5a3f0ec2c1581d1
70 N43ae47d2e5fc46e1a05025ec499a382c
71 N63806677874949a98486eb66cf384c33
72 N93f4deed0a7f4788be7ac9a281c30ba4
73 Ned3cc31aae86415faff9d37226075401
74 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034746121
75 https://doi.org/10.1007/s00103-014-2091-4
76 schema:sdDatePublished 2019-04-10T17:30
77 schema:sdLicense https://scigraph.springernature.com/explorer/license/
78 schema:sdPublisher N368511c19fc44611a77f8d42afb08272
79 schema:url http://link.springer.com/10.1007%2Fs00103-014-2091-4
80 sgo:license sg:explorer/license/
81 sgo:sdDataset articles
82 rdf:type schema:ScholarlyArticle
83 N0b5e42a0c1624efc8260cab994a0fea3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Chromosome Mapping
85 rdf:type schema:DefinedTerm
86 N156667ceb849401489351cc916c805fc schema:issueNumber 2
87 rdf:type schema:PublicationIssue
88 N2262db3dc0b242829892b9215fda96f8 schema:affiliation https://www.grid.ac/institutes/grid.418465.a
89 schema:familyName Jäger
90 schema:givenName M.
91 rdf:type schema:Person
92 N27ec81fc6539413db5a3f0ec2c1581d1 schema:name dimensions_id
93 schema:value pub.1034746121
94 rdf:type schema:PropertyValue
95 N2bd199360b7448e8a5163631004ccbd6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
96 schema:name Precision Medicine
97 rdf:type schema:DefinedTerm
98 N313387e58cf3418d828f863344361a21 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
99 schema:name Genome-Wide Association Study
100 rdf:type schema:DefinedTerm
101 N368511c19fc44611a77f8d42afb08272 schema:name Springer Nature - SN SciGraph project
102 rdf:type schema:Organization
103 N43ae47d2e5fc46e1a05025ec499a382c schema:name readcube_id
104 schema:value a5841ce5e8f4460d80078597d6992d0bf6baed2dee81ee10bab5271d9f680581
105 rdf:type schema:PropertyValue
106 N63806677874949a98486eb66cf384c33 schema:name nlm_unique_id
107 schema:value 101181368
108 rdf:type schema:PropertyValue
109 N7575ce06450f4a2899bd9751aecf40a6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
110 schema:name Data Interpretation, Statistical
111 rdf:type schema:DefinedTerm
112 N8e7932af16144758bbcab44d166c0bb8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
113 schema:name Germany
114 rdf:type schema:DefinedTerm
115 N93f4deed0a7f4788be7ac9a281c30ba4 schema:name pubmed_id
116 schema:value 25432454
117 rdf:type schema:PropertyValue
118 N9c83dc53a36141798079077c34a6fc7e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
119 schema:name Humans
120 rdf:type schema:DefinedTerm
121 Naf3e5a500c5c492dac4837738807cb41 rdf:first sg:person.01320706365.23
122 rdf:rest Ncc8c55c9778945b8b3cd7c60396a4bf7
123 Nb7c2798752bd46468ae7756cfcbc5d17 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
124 schema:name Genetic Predisposition to Disease
125 rdf:type schema:DefinedTerm
126 Ncc8c55c9778945b8b3cd7c60396a4bf7 rdf:first N2262db3dc0b242829892b9215fda96f8
127 rdf:rest Ne4c2eb920ec645268fc70a4db6abe43f
128 Nd45120db28174d87a4415ac1cc4aabb1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
129 schema:name Risk Assessment
130 rdf:type schema:DefinedTerm
131 Ne4c2eb920ec645268fc70a4db6abe43f rdf:first sg:person.015370023320.82
132 rdf:rest rdf:nil
133 Ne684adcc42fb458eaedcf4385d3f4115 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Genetic Testing
135 rdf:type schema:DefinedTerm
136 Ne6b839bd201f4ad487c5d3f728e13ed2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
137 schema:name Genome, Human
138 rdf:type schema:DefinedTerm
139 Nea7f44ac170f4ab698ae460f142e19f3 schema:volumeNumber 58
140 rdf:type schema:PublicationVolume
141 Ned3cc31aae86415faff9d37226075401 schema:name doi
142 schema:value 10.1007/s00103-014-2091-4
143 rdf:type schema:PropertyValue
144 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
145 schema:name Biological Sciences
146 rdf:type schema:DefinedTerm
147 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
148 schema:name Genetics
149 rdf:type schema:DefinedTerm
150 sg:journal.1032465 schema:issn 1436-9990
151 1437-1588
152 schema:name Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz
153 rdf:type schema:Periodical
154 sg:person.01320706365.23 schema:affiliation https://www.grid.ac/institutes/grid.418465.a
155 schema:familyName Foraita
156 schema:givenName Ronja
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01320706365.23
158 rdf:type schema:Person
159 sg:person.015370023320.82 schema:affiliation https://www.grid.ac/institutes/grid.418465.a
160 schema:familyName Pigeot
161 schema:givenName I.
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015370023320.82
163 rdf:type schema:Person
164 sg:pub.10.1007/s00439-012-1194-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1046424518
165 https://doi.org/10.1007/s00439-012-1194-y
166 rdf:type schema:CreativeWork
167 sg:pub.10.1007/s11825-011-0295-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020453049
168 https://doi.org/10.1007/s11825-011-0295-7
169 rdf:type schema:CreativeWork
170 sg:pub.10.1038/nature08494 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005147626
171 https://doi.org/10.1038/nature08494
172 rdf:type schema:CreativeWork
173 sg:pub.10.1038/ng.610 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025253932
174 https://doi.org/10.1038/ng.610
175 rdf:type schema:CreativeWork
176 sg:pub.10.1038/nrg2322 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046913669
177 https://doi.org/10.1038/nrg2322
178 rdf:type schema:CreativeWork
179 sg:pub.10.1038/nrg2516 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027055889
180 https://doi.org/10.1038/nrg2516
181 rdf:type schema:CreativeWork
182 sg:pub.10.1038/nrg3523 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010613501
183 https://doi.org/10.1038/nrg3523
184 rdf:type schema:CreativeWork
185 sg:pub.10.1097/01.gim.0000206278.37405.25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040720062
186 https://doi.org/10.1097/01.gim.0000206278.37405.25
187 rdf:type schema:CreativeWork
188 sg:pub.10.1097/01.gim.0000229689.18263.f4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016523464
189 https://doi.org/10.1097/01.gim.0000229689.18263.f4
190 rdf:type schema:CreativeWork
191 sg:pub.10.1186/1471-2164-11-724 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005310527
192 https://doi.org/10.1186/1471-2164-11-724
193 rdf:type schema:CreativeWork
194 sg:pub.10.1186/gm123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047808714
195 https://doi.org/10.1186/gm123
196 rdf:type schema:CreativeWork
197 https://app.dimensions.ai/details/publication/pub.1014237288 schema:CreativeWork
198 https://doi.org/10.1001/jama.2010.119 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023385197
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1001/jama.291.13.1642 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049238214
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1002/0471142905.hg0119s68 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023229342
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1002/bimj.200710398 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002099562
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1002/gepi.20509 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039610415
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1002/gepi.20516 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049458939
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1002/sim.4006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001673184
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1016/b978-0-12-373932-2.00144-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032011478
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1016/j.gde.2008.07.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050395869
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1016/s0140-6736(07)60111-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008308895
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1016/s1474-4422(09)70275-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032910836
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1056/nejmoa075819 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019183847
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1056/nejmp0810107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004252172
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1056/nejmra0905980 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006094606
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1073/pnas.0903103106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020299353
227 rdf:type schema:CreativeWork
228 https://doi.org/10.1093/bib/bbq074 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026452996
229 rdf:type schema:CreativeWork
230 https://doi.org/10.1093/bioinformatics/btr295 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011507087
231 rdf:type schema:CreativeWork
232 https://doi.org/10.1093/hmg/ddn250 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026942174
233 rdf:type schema:CreativeWork
234 https://doi.org/10.1093/hmg/ddr378 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052661441
235 rdf:type schema:CreativeWork
236 https://doi.org/10.1111/j.1742-4658.2012.08810.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1010427309
237 rdf:type schema:CreativeWork
238 https://doi.org/10.1136/ard.2009.120170 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028179140
239 rdf:type schema:CreativeWork
240 https://doi.org/10.1136/bmj.b4838 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036459127
241 rdf:type schema:CreativeWork
242 https://doi.org/10.1136/jme.2004.010272 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017204533
243 rdf:type schema:CreativeWork
244 https://doi.org/10.1159/000083330 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019417145
245 rdf:type schema:CreativeWork
246 https://doi.org/10.1172/jci34772 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046196569
247 rdf:type schema:CreativeWork
248 https://doi.org/10.1371/journal.pgen.1000231 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007295306
249 rdf:type schema:CreativeWork
250 https://doi.org/10.1371/journal.pgen.1000337 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023458577
251 rdf:type schema:CreativeWork
252 https://doi.org/10.1371/journal.pone.0037979 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021222745
253 rdf:type schema:CreativeWork
254 https://doi.org/10.1373/clinchem.2006.074591 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027001096
255 rdf:type schema:CreativeWork
256 https://doi.org/10.1515/1544-6115.1796 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001399434
257 rdf:type schema:CreativeWork
258 https://doi.org/10.2337/db08-0425 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034664316
259 rdf:type schema:CreativeWork
260 https://doi.org/10.3109/15622975.2012.662282 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028495330
261 rdf:type schema:CreativeWork
262 https://www.grid.ac/institutes/grid.418465.a schema:alternateName Leibniz Institute for Prevention Research and Epidemiology - BIPS
263 schema:name Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS, Achterstr. 30, 28359, Bremen, Deutschland
264 rdf:type schema:Organization
 




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


...