The use of MapPop1.0 for choosing a QTL mapping sample from an advanced backcross population View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-04

AUTHORS

C. Birolleau-Touchard, E. Hanocq, A. Bouchez, C. Bauland, I. Dourlen, J. -P. Seret, D. Rabier, S. Hervet, J. -F. Allienne, Ph. Lucas, O. Jaminon, R. Etienne, G. Baudhuin, C. Giauffret

ABSTRACT

QTL detection is a good way to assess the genetic basis of quantitative traits such as the plant response to its environment, but requires large mapping populations. Experimental constraints, however, may require a restriction of the population size, risking a decrease in the quality level of QTL mapping. The purpose of this paper was to test if an advanced backcross population sample chosen by MapPop 1.0 could limit the effect of size restriction and improve the QTL detection when compared to random samples. We used the genotypic and phenotypic data obtained for 280 genotypes, considered as the reference population. The "MapPop sample" of 100 genotypes was first compared to the reference population, and genetic maps, genotypic and phenotypic data and QTL results were analysed. Despite the increase in donor allele frequency in the MapPop sample, this did not lead to an increase of the genetic map length or a biased phenotypic distribution. Three QTL among the 10 QTL found in the reference population were also detected in the MapPop sample. Next, the MapPop sample results were compared to those from 500 random samples of the same size. The main conclusion was that the MapPop software avoided the selection of biased samples and the detection of false QTL and appears particularly interesting to select a sample from an unbalanced population. More... »

PAGES

1019-1028

References to SciGraph publications

  • 2003-08. Searching for quantitative trait loci controlling root traits in maize: a critical appraisal in PLANT AND SOIL
  • 2001-01. Quantitative trait loci for yield and yield components in an Oryza sativa×Oryza rufipogon BC2F2 population evaluated in an upland environment in THEORETICAL AND APPLIED GENETICS
  • 2002-01. Multifactorial genetics: Mapping and analysis of quantitative trait loci in experimental populations in NATURE REVIEWS GENETICS
  • 2003-09. Incorporation of tropical maize germplasm into inbred lines derived from temperate × temperate-adapted tropical line crosses: agronomic and molecular assessment in THEORETICAL AND APPLIED GENETICS
  • 2004-05. Comparative AB-QTL analysis in barley using a single exotic donor of Hordeum vulgare ssp. spontaneum. in THEORETICAL AND APPLIED GENETICS
  • 1998-08. Advanced backcross QTL analysis in tomato. I. Identification of QTLs for traits of agronomic importance from Lycopersicon hirsutum in THEORETICAL AND APPLIED GENETICS
  • 1996-02. Advanced backcross QTL analysis: a method for the simultaneous discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines in THEORETICAL AND APPLIED GENETICS
  • 1976-01. On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines in THEORETICAL AND APPLIED GENETICS
  • 2003-02. QTL analysis of genotype × environment interactions affecting cotton fiber quality in THEORETICAL AND APPLIED GENETICS
  • 2005-11. Effect of population size on the estimation of QTL: a test using resistance to barley stripe rust in THEORETICAL AND APPLIED GENETICS
  • 1996-02. Advanced backcross QTL analysis in a cross between an elite processing line of tomato and its wild relative L. pimpinellifolium in THEORETICAL AND APPLIED GENETICS
  • 2003-07. Advanced backcross QTL analysis in barley (Hordeum vulgare L.) in THEORETICAL AND APPLIED GENETICS
  • 2000-04. Optimal marker density for interval mapping in a backcross population in HEREDITY
  • 1993. Detection, number and effects of QTLs for a complex character in AGRONOMY FOR SUSTAINABLE DEVELOPMENT
  • 1992-10. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers in HEREDITY
  • 2004-07. What proportion of declared QTL in plants are false? in THEORETICAL AND APPLIED GENETICS
  • 2004-09. Genetic analysis of cold-tolerance of photosynthesis in maize in PLANT MOLECULAR BIOLOGY
  • 1998-07. Advanced backcross QTL analysis of tomato. II. Evaluation of near-isogenic lines carrying single-donor introgressions for desirable wild QTL-alleles derived from Lycopersicon hirsutum and L. pimpinellifolium in THEORETICAL AND APPLIED GENETICS
  • 1996-12. Estimation of the contribution of quantitative trait loci (QTL) to the variance of a quantitative trait by means of genetic markers in THEORETICAL AND APPLIED GENETICS
  • 1995-09. QTL analysis: unreliability and bias in estimation procedures in MOLECULAR BREEDING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00122-006-0495-8

    DOI

    http://dx.doi.org/10.1007/s00122-006-0495-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1010513857

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/17394032


    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": "Alleles", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Chromosome Mapping", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Chromosomes, Plant", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Crosses, Genetic", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Flowers", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gene Frequency", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genetic Markers", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genetics, Population", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genotype", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Homozygote", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Lod Score", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Quantitative Trait Loci", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Software", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Zea mays", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Birolleau-Touchard", 
            "givenName": "C.", 
            "id": "sg:person.0620513374.60", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0620513374.60"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hanocq", 
            "givenName": "E.", 
            "id": "sg:person.0632555004.34", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0632555004.34"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France", 
                "INRA, UMR CARRTEL, 75 av. de Corzent, BP 511, 74203, Thonon Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bouchez", 
            "givenName": "A.", 
            "id": "sg:person.01013372621.72", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01013372621.72"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "G\u00e9n\u00e9tique Quantitative et \u00c9volution Le Moulon", 
              "id": "https://www.grid.ac/institutes/grid.462625.1", 
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France", 
                "INRA-UPS-CNRS-INA.PG, UMR de G\u00e9n\u00e9tique V\u00e9g\u00e9tale, 91190, Gif-sur-Yvette, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bauland", 
            "givenName": "C.", 
            "id": "sg:person.0654227061.40", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0654227061.40"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Dourlen", 
            "givenName": "I.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Seret", 
            "givenName": "J. -P.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Rabier", 
            "givenName": "D.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hervet", 
            "givenName": "S.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Allienne", 
            "givenName": "J. -F.", 
            "id": "sg:person.01017273171.46", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01017273171.46"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lucas", 
            "givenName": "Ph.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Jaminon", 
            "givenName": "O.", 
            "id": "sg:person.01103510160.54", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01103510160.54"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Etienne", 
            "givenName": "R.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Baudhuin", 
            "givenName": "G.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "INRA-USTL, UMR Stress abiotiques et diff\u00e9renciation des v\u00e9g\u00e9taux cultiv\u00e9s, Estr\u00e9es-Mons, BP 50136, 80203, P\u00e9ronne Cedex, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Giauffret", 
            "givenName": "C.", 
            "id": "sg:person.0606113661.73", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0606113661.73"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s001220051616", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001758556", 
              "https://doi.org/10.1007/s001220051616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220051616", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001758556", 
              "https://doi.org/10.1007/s001220051616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/hdy.1992.131", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003515428", 
              "https://doi.org/10.1038/hdy.1992.131"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/hdy.1992.131", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003515428", 
              "https://doi.org/10.1038/hdy.1992.131"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-005-0043-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004659746", 
              "https://doi.org/10.1007/s00122-005-0043-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-005-0043-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004659746", 
              "https://doi.org/10.1007/s00122-005-0043-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1046/j.1439-0523.2002.730285.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005493270"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1026146615248", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007206473", 
              "https://doi.org/10.1023/a:1026146615248"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11103-004-3353-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014049353", 
              "https://doi.org/10.1007/s11103-004-3353-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-004-1639-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014751442", 
              "https://doi.org/10.1007/s00122-004-1639-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-004-1639-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014751442", 
              "https://doi.org/10.1007/s00122-004-1639-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0888-7543(87)90010-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015284325"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1051/agro:19930805", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016219177", 
              "https://doi.org/10.1051/agro:19930805"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00223450", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016443085", 
              "https://doi.org/10.1007/bf00223450"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00223378", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017104256", 
              "https://doi.org/10.1007/bf00223378"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1139/g02-091", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019292245"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1534/genetics.104.033746", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022634658"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1534/genetics.104.033746", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022634658"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-004-1586-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028769456", 
              "https://doi.org/10.1007/s00122-004-1586-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-004-1586-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028769456", 
              "https://doi.org/10.1007/s00122-004-1586-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-003-1341-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030104927", 
              "https://doi.org/10.1007/s00122-003-1341-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-003-1341-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030104927", 
              "https://doi.org/10.1007/s00122-003-1341-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1534/genetics.104.032375", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031153348"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1534/genetics.104.032375", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031153348"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02277427", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032996180", 
              "https://doi.org/10.1007/bf02277427"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02277427", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032996180", 
              "https://doi.org/10.1007/bf02277427"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jhered/93.3.227", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034906815"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-002-1025-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034953694", 
              "https://doi.org/10.1007/s00122-002-1025-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jhered/93.1.77", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035613632"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00223376", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039666151", 
              "https://doi.org/10.1007/bf00223376"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg703", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039905920", 
              "https://doi.org/10.1038/nrg703"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg703", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039905920", 
              "https://doi.org/10.1038/nrg703"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00277402", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041089424", 
              "https://doi.org/10.1007/bf00277402"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00277402", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041089424", 
              "https://doi.org/10.1007/bf00277402"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220050882", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042082245", 
              "https://doi.org/10.1007/s001220050882"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220050882", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042082245", 
              "https://doi.org/10.1007/s001220050882"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-003-1253-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042523068", 
              "https://doi.org/10.1007/s00122-003-1253-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-003-1253-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042523068", 
              "https://doi.org/10.1007/s00122-003-1253-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1534/genetics.104.027524", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042930610"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1534/genetics.104.027524", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042930610"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220050908", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044254921", 
              "https://doi.org/10.1007/s001220050908"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220050908", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044254921", 
              "https://doi.org/10.1007/s001220050908"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1046/j.1365-2540.2000.00678.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046203834", 
              "https://doi.org/10.1046/j.1365-2540.2000.00678.x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1051/agro:2000129", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1056944012"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2135/cropsci2004.0278", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069028438"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1074632804", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1074658253", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1079037915", 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2007-04", 
        "datePublishedReg": "2007-04-01", 
        "description": "QTL detection is a good way to assess the genetic basis of quantitative traits such as the plant response to its environment, but requires large mapping populations. Experimental constraints, however, may require a restriction of the population size, risking a decrease in the quality level of QTL mapping. The purpose of this paper was to test if an advanced backcross population sample chosen by MapPop 1.0 could limit the effect of size restriction and improve the QTL detection when compared to random samples. We used the genotypic and phenotypic data obtained for 280 genotypes, considered as the reference population. The \"MapPop sample\" of 100 genotypes was first compared to the reference population, and genetic maps, genotypic and phenotypic data and QTL results were analysed. Despite the increase in donor allele frequency in the MapPop sample, this did not lead to an increase of the genetic map length or a biased phenotypic distribution. Three QTL among the 10 QTL found in the reference population were also detected in the MapPop sample. Next, the MapPop sample results were compared to those from 500 random samples of the same size. The main conclusion was that the MapPop software avoided the selection of biased samples and the detection of false QTL and appears particularly interesting to select a sample from an unbalanced population.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00122-006-0495-8", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1135804", 
            "issn": [
              "0040-5752", 
              "1432-2242"
            ], 
            "name": "Theoretical and Applied Genetics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "6", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "114"
          }
        ], 
        "name": "The use of MapPop1.0 for choosing a QTL mapping sample from an advanced backcross population", 
        "pagination": "1019-1028", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "3216af3be6cff12db34adf809b2be9f9b9e43fb35e996ecadff728c305d75151"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "17394032"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "0145600"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00122-006-0495-8"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1010513857"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00122-006-0495-8", 
          "https://app.dimensions.ai/details/publication/pub.1010513857"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T14:30", 
        "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/v90/0000000373_0000000373/records_13090_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007/s00122-006-0495-8"
      }
    ]
     

    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/s00122-006-0495-8'

    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/s00122-006-0495-8'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00122-006-0495-8'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00122-006-0495-8'


     

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

    353 TRIPLES      21 PREDICATES      76 URIs      35 LITERALS      23 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00122-006-0495-8 schema:about N0790241b30a64bbfa19ae361b75447a4
    2 N0e587324fbe449df9697187f8161ae56
    3 N168fea7b5e64418685a632d578646832
    4 N23ca822e73124c83a1a5771d42ee480b
    5 N34f0a8fd3c7b48c2b8a65fe9de44e51a
    6 N378a61a9f9274426953deec167cd8578
    7 N5b664026a9f4419c8944f9caac16dd96
    8 N6852c2a09b434b8e8a2fb24a571f9baa
    9 N9383272ecd144e2f83ca4b8d7c8a7e8d
    10 N96852cd204d94d4ca59deb5069986d4d
    11 Naaf5ab6a8cf846058d64f42e9b4d5da3
    12 Nb6d1b96afb144eec9893a03f157f7054
    13 Nbd0a029994d84a768548ab8ac1cdd2b9
    14 Ncff814bb3a094660b0d25472d3649786
    15 anzsrc-for:06
    16 anzsrc-for:0604
    17 schema:author N3f9d714fa4764e5fb83d78f183040e32
    18 schema:citation sg:pub.10.1007/bf00223376
    19 sg:pub.10.1007/bf00223378
    20 sg:pub.10.1007/bf00223450
    21 sg:pub.10.1007/bf00277402
    22 sg:pub.10.1007/bf02277427
    23 sg:pub.10.1007/s00122-002-1025-y
    24 sg:pub.10.1007/s00122-003-1253-9
    25 sg:pub.10.1007/s00122-003-1341-x
    26 sg:pub.10.1007/s00122-004-1586-z
    27 sg:pub.10.1007/s00122-004-1639-3
    28 sg:pub.10.1007/s00122-005-0043-y
    29 sg:pub.10.1007/s001220050882
    30 sg:pub.10.1007/s001220050908
    31 sg:pub.10.1007/s001220051616
    32 sg:pub.10.1007/s11103-004-3353-6
    33 sg:pub.10.1023/a:1026146615248
    34 sg:pub.10.1038/hdy.1992.131
    35 sg:pub.10.1038/nrg703
    36 sg:pub.10.1046/j.1365-2540.2000.00678.x
    37 sg:pub.10.1051/agro:19930805
    38 https://app.dimensions.ai/details/publication/pub.1074632804
    39 https://app.dimensions.ai/details/publication/pub.1074658253
    40 https://app.dimensions.ai/details/publication/pub.1079037915
    41 https://doi.org/10.1016/0888-7543(87)90010-3
    42 https://doi.org/10.1046/j.1439-0523.2002.730285.x
    43 https://doi.org/10.1051/agro:2000129
    44 https://doi.org/10.1093/jhered/93.1.77
    45 https://doi.org/10.1093/jhered/93.3.227
    46 https://doi.org/10.1139/g02-091
    47 https://doi.org/10.1534/genetics.104.027524
    48 https://doi.org/10.1534/genetics.104.032375
    49 https://doi.org/10.1534/genetics.104.033746
    50 https://doi.org/10.2135/cropsci2004.0278
    51 schema:datePublished 2007-04
    52 schema:datePublishedReg 2007-04-01
    53 schema:description QTL detection is a good way to assess the genetic basis of quantitative traits such as the plant response to its environment, but requires large mapping populations. Experimental constraints, however, may require a restriction of the population size, risking a decrease in the quality level of QTL mapping. The purpose of this paper was to test if an advanced backcross population sample chosen by MapPop 1.0 could limit the effect of size restriction and improve the QTL detection when compared to random samples. We used the genotypic and phenotypic data obtained for 280 genotypes, considered as the reference population. The "MapPop sample" of 100 genotypes was first compared to the reference population, and genetic maps, genotypic and phenotypic data and QTL results were analysed. Despite the increase in donor allele frequency in the MapPop sample, this did not lead to an increase of the genetic map length or a biased phenotypic distribution. Three QTL among the 10 QTL found in the reference population were also detected in the MapPop sample. Next, the MapPop sample results were compared to those from 500 random samples of the same size. The main conclusion was that the MapPop software avoided the selection of biased samples and the detection of false QTL and appears particularly interesting to select a sample from an unbalanced population.
    54 schema:genre research_article
    55 schema:inLanguage en
    56 schema:isAccessibleForFree false
    57 schema:isPartOf N9540538b8b33440f90442a5ed9ae1e26
    58 Ne09e2490a11f4e38bb9bb50a1be6262b
    59 sg:journal.1135804
    60 schema:name The use of MapPop1.0 for choosing a QTL mapping sample from an advanced backcross population
    61 schema:pagination 1019-1028
    62 schema:productId N68b6783d653f484b8c852e6eaf411250
    63 Naf9310930251460ba34cb501d6a140e8
    64 Nb21a01390e464f60984da18b04f4945b
    65 Ndef948e6f3c3467ca62807cf2b3b17f8
    66 Ne185354f66e040099a609fd8b4582ce9
    67 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010513857
    68 https://doi.org/10.1007/s00122-006-0495-8
    69 schema:sdDatePublished 2019-04-11T14:30
    70 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    71 schema:sdPublisher Nbbb91264c57b4ede99d6cffe752c73f1
    72 schema:url http://link.springer.com/10.1007/s00122-006-0495-8
    73 sgo:license sg:explorer/license/
    74 sgo:sdDataset articles
    75 rdf:type schema:ScholarlyArticle
    76 N0790241b30a64bbfa19ae361b75447a4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    77 schema:name Alleles
    78 rdf:type schema:DefinedTerm
    79 N0e587324fbe449df9697187f8161ae56 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    80 schema:name Genetic Markers
    81 rdf:type schema:DefinedTerm
    82 N10cb374264474ea680a2d76352c46e05 schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    83 rdf:type schema:Organization
    84 N168fea7b5e64418685a632d578646832 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    85 schema:name Homozygote
    86 rdf:type schema:DefinedTerm
    87 N23ca822e73124c83a1a5771d42ee480b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    88 schema:name Genetics, Population
    89 rdf:type schema:DefinedTerm
    90 N2a694ff589b54ef9add0276da7092e9b schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    91 rdf:type schema:Organization
    92 N34f0a8fd3c7b48c2b8a65fe9de44e51a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    93 schema:name Software
    94 rdf:type schema:DefinedTerm
    95 N378a61a9f9274426953deec167cd8578 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    96 schema:name Lod Score
    97 rdf:type schema:DefinedTerm
    98 N3f9d714fa4764e5fb83d78f183040e32 rdf:first sg:person.0620513374.60
    99 rdf:rest Ne314c6fe61954d3aa18d9098c30486c5
    100 N46d2a72b3c5b40fc993bd2c2acd8c9c5 rdf:first Nd06f0a40e3b945ab88cb14b4e53c699a
    101 rdf:rest N5534af7bf14742b68bbaffe60f42cb56
    102 N49850574d9114982b43c36cea83ee2ea schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    103 rdf:type schema:Organization
    104 N51fa1ccd56a746498cea9a0617e3bb00 schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    105 rdf:type schema:Organization
    106 N5534af7bf14742b68bbaffe60f42cb56 rdf:first N8f79f81352b042a2a7d43ba11bdce8e1
    107 rdf:rest Nd053c33392a9463889354b2dee9be6ec
    108 N5b664026a9f4419c8944f9caac16dd96 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    109 schema:name Chromosomes, Plant
    110 rdf:type schema:DefinedTerm
    111 N5ca1460af1ba4ff8a36a4d8e9f1da90c schema:affiliation Nd0d14c7c191e411fb32f2266507bb339
    112 schema:familyName Rabier
    113 schema:givenName D.
    114 rdf:type schema:Person
    115 N6852c2a09b434b8e8a2fb24a571f9baa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    116 schema:name Chromosome Mapping
    117 rdf:type schema:DefinedTerm
    118 N68b6783d653f484b8c852e6eaf411250 schema:name nlm_unique_id
    119 schema:value 0145600
    120 rdf:type schema:PropertyValue
    121 N6f8cae20cc55403bb6a4ab0955c7e147 schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    122 rdf:type schema:Organization
    123 N719dcf4dc1c646209465fb3e664d8848 rdf:first N911d0b0745764de7866570ff9260b3e2
    124 rdf:rest Nd852819a82894fdeb1f3482be25d0b44
    125 N77a5a89d531e4289858496787de0946f rdf:first N946b46bb9a354427922de05fdf041f80
    126 rdf:rest N8bbcc10fcbce48e0b46554a1b9bdd157
    127 N86d6bda6a7f542069422daf3a237ae81 schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    128 rdf:type schema:Organization
    129 N8bbcc10fcbce48e0b46554a1b9bdd157 rdf:first sg:person.01017273171.46
    130 rdf:rest Nc419fa61bb2744d4850c56af69566c6e
    131 N8e7cb090c0a943ef9d9f3726d6abd801 schema:name INRA, UMR CARRTEL, 75 av. de Corzent, BP 511, 74203, Thonon Cedex, France
    132 INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    133 rdf:type schema:Organization
    134 N8f79f81352b042a2a7d43ba11bdce8e1 schema:affiliation Naa9279b850e04ac6b8a889a3fd209061
    135 schema:familyName Seret
    136 schema:givenName J. -P.
    137 rdf:type schema:Person
    138 N911d0b0745764de7866570ff9260b3e2 schema:affiliation N2a694ff589b54ef9add0276da7092e9b
    139 schema:familyName Baudhuin
    140 schema:givenName G.
    141 rdf:type schema:Person
    142 N931ff097d5ad448fa70089b30e874185 schema:affiliation Nf15cba21ffc24e17a7a243df7ea8a566
    143 schema:familyName Lucas
    144 schema:givenName Ph.
    145 rdf:type schema:Person
    146 N9383272ecd144e2f83ca4b8d7c8a7e8d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    147 schema:name Zea mays
    148 rdf:type schema:DefinedTerm
    149 N946b46bb9a354427922de05fdf041f80 schema:affiliation N86d6bda6a7f542069422daf3a237ae81
    150 schema:familyName Hervet
    151 schema:givenName S.
    152 rdf:type schema:Person
    153 N9540538b8b33440f90442a5ed9ae1e26 schema:volumeNumber 114
    154 rdf:type schema:PublicationVolume
    155 N96852cd204d94d4ca59deb5069986d4d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    156 schema:name Gene Frequency
    157 rdf:type schema:DefinedTerm
    158 Na3731a7ed3b34af4a9e84ba21c159ca8 rdf:first sg:person.01103510160.54
    159 rdf:rest Nb202c70acbc741999b6adff7d8d6f4f5
    160 Naa9279b850e04ac6b8a889a3fd209061 schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    161 rdf:type schema:Organization
    162 Naaf5ab6a8cf846058d64f42e9b4d5da3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    163 schema:name Crosses, Genetic
    164 rdf:type schema:DefinedTerm
    165 Naf9310930251460ba34cb501d6a140e8 schema:name readcube_id
    166 schema:value 3216af3be6cff12db34adf809b2be9f9b9e43fb35e996ecadff728c305d75151
    167 rdf:type schema:PropertyValue
    168 Nb202c70acbc741999b6adff7d8d6f4f5 rdf:first Nbdf3e82c27a546618773324e4a8f1972
    169 rdf:rest N719dcf4dc1c646209465fb3e664d8848
    170 Nb21a01390e464f60984da18b04f4945b schema:name pubmed_id
    171 schema:value 17394032
    172 rdf:type schema:PropertyValue
    173 Nb38d077f298b445295367843ccbcd794 rdf:first sg:person.0654227061.40
    174 rdf:rest N46d2a72b3c5b40fc993bd2c2acd8c9c5
    175 Nb6d1b96afb144eec9893a03f157f7054 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    176 schema:name Genotype
    177 rdf:type schema:DefinedTerm
    178 Nbbb91264c57b4ede99d6cffe752c73f1 schema:name Springer Nature - SN SciGraph project
    179 rdf:type schema:Organization
    180 Nbd0a029994d84a768548ab8ac1cdd2b9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    181 schema:name Quantitative Trait Loci
    182 rdf:type schema:DefinedTerm
    183 Nbdf3e82c27a546618773324e4a8f1972 schema:affiliation N49850574d9114982b43c36cea83ee2ea
    184 schema:familyName Etienne
    185 schema:givenName R.
    186 rdf:type schema:Person
    187 Nc419fa61bb2744d4850c56af69566c6e rdf:first N931ff097d5ad448fa70089b30e874185
    188 rdf:rest Na3731a7ed3b34af4a9e84ba21c159ca8
    189 Ncdc8592d65724e9daabeee8fce8bfeb1 schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    190 rdf:type schema:Organization
    191 Ncff814bb3a094660b0d25472d3649786 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    192 schema:name Flowers
    193 rdf:type schema:DefinedTerm
    194 Nd053c33392a9463889354b2dee9be6ec rdf:first N5ca1460af1ba4ff8a36a4d8e9f1da90c
    195 rdf:rest N77a5a89d531e4289858496787de0946f
    196 Nd06f0a40e3b945ab88cb14b4e53c699a schema:affiliation N6f8cae20cc55403bb6a4ab0955c7e147
    197 schema:familyName Dourlen
    198 schema:givenName I.
    199 rdf:type schema:Person
    200 Nd0d14c7c191e411fb32f2266507bb339 schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    201 rdf:type schema:Organization
    202 Nd852819a82894fdeb1f3482be25d0b44 rdf:first sg:person.0606113661.73
    203 rdf:rest rdf:nil
    204 Nd860c1ae15164ecaa7a782dd762e0cb3 schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    205 rdf:type schema:Organization
    206 Ndef948e6f3c3467ca62807cf2b3b17f8 schema:name doi
    207 schema:value 10.1007/s00122-006-0495-8
    208 rdf:type schema:PropertyValue
    209 Ne01b5395354342eab3da2f92d865e5ef rdf:first sg:person.01013372621.72
    210 rdf:rest Nb38d077f298b445295367843ccbcd794
    211 Ne09e2490a11f4e38bb9bb50a1be6262b schema:issueNumber 6
    212 rdf:type schema:PublicationIssue
    213 Ne185354f66e040099a609fd8b4582ce9 schema:name dimensions_id
    214 schema:value pub.1010513857
    215 rdf:type schema:PropertyValue
    216 Ne314c6fe61954d3aa18d9098c30486c5 rdf:first sg:person.0632555004.34
    217 rdf:rest Ne01b5395354342eab3da2f92d865e5ef
    218 Nf15cba21ffc24e17a7a243df7ea8a566 schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    219 rdf:type schema:Organization
    220 Nfb05b20b0a4e4f78a36f7964d2da3a61 schema:name INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    221 rdf:type schema:Organization
    222 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    223 schema:name Biological Sciences
    224 rdf:type schema:DefinedTerm
    225 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    226 schema:name Genetics
    227 rdf:type schema:DefinedTerm
    228 sg:journal.1135804 schema:issn 0040-5752
    229 1432-2242
    230 schema:name Theoretical and Applied Genetics
    231 rdf:type schema:Periodical
    232 sg:person.01013372621.72 schema:affiliation N8e7cb090c0a943ef9d9f3726d6abd801
    233 schema:familyName Bouchez
    234 schema:givenName A.
    235 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01013372621.72
    236 rdf:type schema:Person
    237 sg:person.01017273171.46 schema:affiliation Nd860c1ae15164ecaa7a782dd762e0cb3
    238 schema:familyName Allienne
    239 schema:givenName J. -F.
    240 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01017273171.46
    241 rdf:type schema:Person
    242 sg:person.01103510160.54 schema:affiliation N10cb374264474ea680a2d76352c46e05
    243 schema:familyName Jaminon
    244 schema:givenName O.
    245 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01103510160.54
    246 rdf:type schema:Person
    247 sg:person.0606113661.73 schema:affiliation Ncdc8592d65724e9daabeee8fce8bfeb1
    248 schema:familyName Giauffret
    249 schema:givenName C.
    250 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0606113661.73
    251 rdf:type schema:Person
    252 sg:person.0620513374.60 schema:affiliation N51fa1ccd56a746498cea9a0617e3bb00
    253 schema:familyName Birolleau-Touchard
    254 schema:givenName C.
    255 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0620513374.60
    256 rdf:type schema:Person
    257 sg:person.0632555004.34 schema:affiliation Nfb05b20b0a4e4f78a36f7964d2da3a61
    258 schema:familyName Hanocq
    259 schema:givenName E.
    260 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0632555004.34
    261 rdf:type schema:Person
    262 sg:person.0654227061.40 schema:affiliation https://www.grid.ac/institutes/grid.462625.1
    263 schema:familyName Bauland
    264 schema:givenName C.
    265 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0654227061.40
    266 rdf:type schema:Person
    267 sg:pub.10.1007/bf00223376 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039666151
    268 https://doi.org/10.1007/bf00223376
    269 rdf:type schema:CreativeWork
    270 sg:pub.10.1007/bf00223378 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017104256
    271 https://doi.org/10.1007/bf00223378
    272 rdf:type schema:CreativeWork
    273 sg:pub.10.1007/bf00223450 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016443085
    274 https://doi.org/10.1007/bf00223450
    275 rdf:type schema:CreativeWork
    276 sg:pub.10.1007/bf00277402 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041089424
    277 https://doi.org/10.1007/bf00277402
    278 rdf:type schema:CreativeWork
    279 sg:pub.10.1007/bf02277427 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032996180
    280 https://doi.org/10.1007/bf02277427
    281 rdf:type schema:CreativeWork
    282 sg:pub.10.1007/s00122-002-1025-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1034953694
    283 https://doi.org/10.1007/s00122-002-1025-y
    284 rdf:type schema:CreativeWork
    285 sg:pub.10.1007/s00122-003-1253-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042523068
    286 https://doi.org/10.1007/s00122-003-1253-9
    287 rdf:type schema:CreativeWork
    288 sg:pub.10.1007/s00122-003-1341-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1030104927
    289 https://doi.org/10.1007/s00122-003-1341-x
    290 rdf:type schema:CreativeWork
    291 sg:pub.10.1007/s00122-004-1586-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1028769456
    292 https://doi.org/10.1007/s00122-004-1586-z
    293 rdf:type schema:CreativeWork
    294 sg:pub.10.1007/s00122-004-1639-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014751442
    295 https://doi.org/10.1007/s00122-004-1639-3
    296 rdf:type schema:CreativeWork
    297 sg:pub.10.1007/s00122-005-0043-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1004659746
    298 https://doi.org/10.1007/s00122-005-0043-y
    299 rdf:type schema:CreativeWork
    300 sg:pub.10.1007/s001220050882 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042082245
    301 https://doi.org/10.1007/s001220050882
    302 rdf:type schema:CreativeWork
    303 sg:pub.10.1007/s001220050908 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044254921
    304 https://doi.org/10.1007/s001220050908
    305 rdf:type schema:CreativeWork
    306 sg:pub.10.1007/s001220051616 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001758556
    307 https://doi.org/10.1007/s001220051616
    308 rdf:type schema:CreativeWork
    309 sg:pub.10.1007/s11103-004-3353-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014049353
    310 https://doi.org/10.1007/s11103-004-3353-6
    311 rdf:type schema:CreativeWork
    312 sg:pub.10.1023/a:1026146615248 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007206473
    313 https://doi.org/10.1023/a:1026146615248
    314 rdf:type schema:CreativeWork
    315 sg:pub.10.1038/hdy.1992.131 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003515428
    316 https://doi.org/10.1038/hdy.1992.131
    317 rdf:type schema:CreativeWork
    318 sg:pub.10.1038/nrg703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039905920
    319 https://doi.org/10.1038/nrg703
    320 rdf:type schema:CreativeWork
    321 sg:pub.10.1046/j.1365-2540.2000.00678.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1046203834
    322 https://doi.org/10.1046/j.1365-2540.2000.00678.x
    323 rdf:type schema:CreativeWork
    324 sg:pub.10.1051/agro:19930805 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016219177
    325 https://doi.org/10.1051/agro:19930805
    326 rdf:type schema:CreativeWork
    327 https://app.dimensions.ai/details/publication/pub.1074632804 schema:CreativeWork
    328 https://app.dimensions.ai/details/publication/pub.1074658253 schema:CreativeWork
    329 https://app.dimensions.ai/details/publication/pub.1079037915 schema:CreativeWork
    330 https://doi.org/10.1016/0888-7543(87)90010-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015284325
    331 rdf:type schema:CreativeWork
    332 https://doi.org/10.1046/j.1439-0523.2002.730285.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1005493270
    333 rdf:type schema:CreativeWork
    334 https://doi.org/10.1051/agro:2000129 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056944012
    335 rdf:type schema:CreativeWork
    336 https://doi.org/10.1093/jhered/93.1.77 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035613632
    337 rdf:type schema:CreativeWork
    338 https://doi.org/10.1093/jhered/93.3.227 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034906815
    339 rdf:type schema:CreativeWork
    340 https://doi.org/10.1139/g02-091 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019292245
    341 rdf:type schema:CreativeWork
    342 https://doi.org/10.1534/genetics.104.027524 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042930610
    343 rdf:type schema:CreativeWork
    344 https://doi.org/10.1534/genetics.104.032375 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031153348
    345 rdf:type schema:CreativeWork
    346 https://doi.org/10.1534/genetics.104.033746 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022634658
    347 rdf:type schema:CreativeWork
    348 https://doi.org/10.2135/cropsci2004.0278 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069028438
    349 rdf:type schema:CreativeWork
    350 https://www.grid.ac/institutes/grid.462625.1 schema:alternateName Génétique Quantitative et Évolution Le Moulon
    351 schema:name INRA-UPS-CNRS-INA.PG, UMR de Génétique Végétale, 91190, Gif-sur-Yvette, France
    352 INRA-USTL, UMR Stress abiotiques et différenciation des végétaux cultivés, Estrées-Mons, BP 50136, 80203, Péronne Cedex, France
    353 rdf:type schema:Organization
     




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


    ...