Detecting mislabeling and identifying unique progeny in Acacia mapping population using SNP markers View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-05-05

AUTHORS

Asif Javed Muhammad, Mohd Zaki Abdullah, Norwati Muhammad, Wickneswari Ratnam

ABSTRACT

Acacia hybrids offer a great potential for paper industry in Southeast Asia due to their fast growth and ability to grow on abandoned or marginal lands. Breeding Acacia hybrids with desirable traits can be achieved through marker assisted selection (MAS) breeding. To develop a MAS program requires development of linkage maps and QTL analysis. Two mapping populations were developed through interspecific hybridization for linkage mapping and QTL analysis. All seeds per pod were cultured initially to improve hybrid yield as quality and density of linkage mapping is affected by the size of the mapping population. Progenies from two mapping populations were field planted for phenotypic and genotypic evaluation at three locations in Malaysia, (1) Forest Research Institute Malaysia field station at Segamat, Johor, (2) Borneo Tree Seeds and Seedlings Supplies Sdn, Bhd. (BTS) field trial site at Bintulu, Sarawak, and (3) Asiaprima RCF field trial site at Lancang, Pahang. During field planting, mislabeling was reported at Segamat, Johor, and a similar problem was suspected for Bintulu, Sarawak. Early screening with two isozymes effectively selected hybrid progenies, and these hybrids were subsequently further confirmed by using species-specific SNPs. During field planting, clonal mislabeling was reported and later confirmed by using a small set of STMS markers. A large set of SNPs were also used to screen all ramets in both populations. A total of 65.36% mislabeled ramets were encountered in the wood density population and 60.34% in the fibre length mapping population. No interpopulation pollen contamination was detected because all ramets found their match within the same population in question. However, mislabeling was detected among ramets of the same population. Mislabeled individuals were identified and grouped as they originated from 93 pods for wood density and 53 pods for fibre length mapping populations. On average 2 meiotically unique seeds per pod (179 seeds/93 pods) for wood density and 3 meiotically unique seeds per pod (174 seeds/53 pods) for fibre length mapping population were found. A single step statistical method was used to evaluate the most informative set of SNPs that could subsequently be used for routine checks for mislabeling in multi-location field trials and for labelling superior clones to protect breeder’s rights. A preliminary set of SNPs with a high degree of informativeness was selected for the mislabeling analysis in conjunction with an assignment test. Two subsets were successfully identified, i.e., 51 SNPs for wood density and 64 SNPs for fibre length mapping populations to identify all mislabeled ramets which had been previously identified. Mislabeling seems to be a common problem due to the complexity involved in the production of mapping populations. Therefore, checking for mislabeling is imperative for breeding activities and for analyses such as linkage mapping in which a correlation between genotypic and phenotypic data is determined. More... »

PAGES

1119-1127

References to SciGraph publications

  • 2015-10-19. Cross-specific amplification of microsatellite DNA markers in Shorea platyclados in JOURNAL OF FORESTRY RESEARCH
  • 1998-08-01. RFLP diversity in the nuclear genome of Acacia mangium in HEREDITY
  • 2009-03-06. Mating system and seed variation of Acacia hybrid (A. mangium × A. auriculiformis) in JOURNAL OF GENETICS
  • 1992-03. The use of electrophoretic markers in seed orchard research in NEW FORESTS
  • 2007-08-22. Complex mutational patterns and size homoplasy at maize microsatellite loci in THEORETICAL AND APPLIED GENETICS
  • 2010-01-15. High-throughput SNP discovery through deep resequencing of a reduced representation library to anchor and orient scaffolds in the soybean whole genome sequence in BMC GENOMICS
  • 1997-03. Use of RAPD patterns for clone verification and in studying provenance relationships in Norway spruce (Picea abies) in THEORETICAL AND APPLIED GENETICS
  • 2005-11-15. Gene flow and mating system in five Cryptomeria japonica D. Don seed orchards as revealed by analysis of microsatellite markers in TREE GENETICS & GENOMES
  • 2009-12-01. Effect of the lateral growth rate on wood properties in fast-growing hardwood species in JOURNAL OF WOOD SCIENCE
  • 2011-05-13. Evaluation of approaches for identifying population informative markers from high density SNP Chips in BMC GENOMIC DATA
  • 2011-11-08. A 48 SNP set for grapevine cultivar identification in BMC PLANT BIOLOGY
  • 2010-11-05. A practical method for apple cultivar identification and parent-offspring analysis using simple sequence repeat markers in EUPHYTICA
  • 2010-01-06. A comparison of SNP and STR loci for delineating population structure and performing individual genetic assignment in BMC GENOMIC DATA
  • 2008-10. Prepare for the deluge in NATURE BIOTECHNOLOGY
  • 2000-12. Development, inheritance and cross-species amplification of microsatellite markers from Acacia mangium in THEORETICAL AND APPLIED GENETICS
  • 2012-12-24. Development of high-throughput SNP-based genotyping in Acacia auriculiformis x A. mangium hybrids using short-read transcriptome data in BMC GENOMICS
  • 2006-05. Use of Genetic Markers in the Management of Micropropagated Eucalyptus Germplasm in NEW FORESTS
  • 1997-10. Reproducibility testing of RAPD, AFLP and SSR markers in plants by a network of European laboratories in MOLECULAR BREEDING
  • 2003-11-04. A new approach to extending the wheat marker pool by anchored PCR amplification of compound SSRs in THEORETICAL AND APPLIED GENETICS
  • 2007-10-11. High outcrossing and random pollen dispersal in a planted stand of Acacia saligna subsp. saligna revealed by paternity analysis using microsatellites in TREE GENETICS & GENOMES
  • 2009-11-18. Clonal fingerprinting in the genus Populus L. by nuclear microsatellite loci regarding differences between sections, species and hybrids in TREE GENETICS & GENOMES
  • 1994-03. High resolution of human evolutionary trees with polymorphic microsatellites in NATURE
  • 2007-03-07. Pollen flow in Eucalyptus grandis determined by paternity analysis using microsatellite markers in TREE GENETICS & GENOMES
  • 2000-02. Genetic linkage mapping in Acacia mangium 1. Evaluation of restriction endonucleases, inheritance of RFLP loci and their conservation across species in THEORETICAL AND APPLIED GENETICS
  • 2000-09. Genetic linkage mapping in Acacia mangium. 2. Development of an integrated map from two outbred pedigrees using RFLP and microsatellite loci in THEORETICAL AND APPLIED GENETICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11676-017-0405-8

    DOI

    http://dx.doi.org/10.1007/s11676-017-0405-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1085179165


    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/2008/07", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Agricultural and Veterinary Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0705", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Forestry Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Forestry and Range Management, University of Agriculture Faisalabad, Faisalabad, Pakistan", 
              "id": "http://www.grid.ac/institutes/grid.413016.1", 
              "name": [
                "Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia", 
                "Department of Forestry and Range Management, University of Agriculture Faisalabad, Faisalabad, Pakistan"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Javed Muhammad", 
            "givenName": "Asif", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Forestry Biotechnology Division, Forest Research Institute Malaysia, 52110, Kepong, Selangor Darul Ehsan, Malaysia", 
              "id": "http://www.grid.ac/institutes/grid.434305.5", 
              "name": [
                "Forestry Biotechnology Division, Forest Research Institute Malaysia, 52110, Kepong, Selangor Darul Ehsan, Malaysia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Abdullah", 
            "givenName": "Mohd Zaki", 
            "id": "sg:person.015701744541.63", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015701744541.63"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Forestry Biotechnology Division, Forest Research Institute Malaysia, 52110, Kepong, Selangor Darul Ehsan, Malaysia", 
              "id": "http://www.grid.ac/institutes/grid.434305.5", 
              "name": [
                "Forestry Biotechnology Division, Forest Research Institute Malaysia, 52110, Kepong, Selangor Darul Ehsan, Malaysia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Muhammad", 
            "givenName": "Norwati", 
            "id": "sg:person.01055170164.87", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01055170164.87"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia", 
              "id": "http://www.grid.ac/institutes/grid.412113.4", 
              "name": [
                "Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ratnam", 
            "givenName": "Wickneswari", 
            "id": "sg:person.07774253741.09", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07774253741.09"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1186/1471-2156-11-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036078550", 
              "https://doi.org/10.1186/1471-2156-11-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11295-005-0023-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020727284", 
              "https://doi.org/10.1007/s11295-005-0023-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10681-010-0295-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050195939", 
              "https://doi.org/10.1007/s10681-010-0295-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10086-009-1057-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031902241", 
              "https://doi.org/10.1007/s10086-009-1057-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-13-726", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020892934", 
              "https://doi.org/10.1186/1471-2164-13-726"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220051521", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050220602", 
              "https://doi.org/10.1007/s001220051521"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220051608", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032939723", 
              "https://doi.org/10.1007/s001220051608"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-003-1480-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034346723", 
              "https://doi.org/10.1007/s00122-003-1480-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2156-12-45", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042352726", 
              "https://doi.org/10.1186/1471-2156-12-45"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11056-005-8677-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036820553", 
              "https://doi.org/10.1007/s11056-005-8677-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11295-007-0086-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021008507", 
              "https://doi.org/10.1007/s11295-007-0086-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-11-38", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044425441", 
              "https://doi.org/10.1186/1471-2164-11-38"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-007-0625-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030269799", 
              "https://doi.org/10.1007/s00122-007-0625-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2229-11-153", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031420518", 
              "https://doi.org/10.1186/1471-2229-11-153"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1046/j.1365-2540.1998.00392.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037560297", 
              "https://doi.org/10.1046/j.1365-2540.1998.00392.x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220050076", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019426644", 
              "https://doi.org/10.1007/s001220050076"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11676-015-0134-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022792368", 
              "https://doi.org/10.1007/s11676-015-0134-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/368455a0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046878300", 
              "https://doi.org/10.1038/368455a0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00120650", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050340558", 
              "https://doi.org/10.1007/bf00120650"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220050440", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052358833", 
              "https://doi.org/10.1007/s001220050440"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11295-009-0246-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008482321", 
              "https://doi.org/10.1007/s11295-009-0246-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12041-009-0004-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045215442", 
              "https://doi.org/10.1007/s12041-009-0004-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt1008-1099", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026300654", 
              "https://doi.org/10.1038/nbt1008-1099"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11295-007-0115-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028516092", 
              "https://doi.org/10.1007/s11295-007-0115-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1009612517139", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029316007", 
              "https://doi.org/10.1023/a:1009612517139"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-05-05", 
        "datePublishedReg": "2017-05-05", 
        "description": "Acacia hybrids offer a great potential for paper industry in Southeast Asia due to their fast growth and ability to grow on abandoned or marginal lands. Breeding Acacia hybrids with desirable traits can be achieved through marker assisted selection (MAS) breeding. To develop a MAS program requires development of linkage maps and QTL analysis. Two mapping populations were developed through interspecific hybridization for linkage mapping and QTL analysis. All seeds per pod were cultured initially to improve hybrid yield as quality and density of linkage mapping is affected by the size of the mapping population. Progenies from two mapping populations were field planted for phenotypic and genotypic evaluation at three locations in Malaysia, (1) Forest Research Institute Malaysia field station at Segamat, Johor, (2) Borneo Tree Seeds and Seedlings Supplies Sdn, Bhd. (BTS) field trial site at Bintulu, Sarawak, and (3) Asiaprima RCF field trial site at Lancang, Pahang. During field planting, mislabeling was reported at Segamat, Johor, and a similar problem was suspected for Bintulu, Sarawak. Early screening with two isozymes effectively selected hybrid progenies, and these hybrids were subsequently further confirmed by using species-specific SNPs. During field planting, clonal mislabeling was reported and later confirmed by using a small set of STMS markers. A large set of SNPs were also used to screen all ramets in both populations. A total of 65.36% mislabeled ramets were encountered in the wood density population and 60.34% in the fibre length mapping population. No interpopulation pollen contamination was detected because all ramets found their match within the same population in question. However, mislabeling was detected among ramets of the same population. Mislabeled individuals were identified and grouped as they originated from 93 pods for wood density and 53 pods for fibre length mapping populations. On average 2 meiotically unique seeds per pod (179 seeds/93 pods) for wood density and 3 meiotically unique seeds per pod (174 seeds/53 pods) for fibre length mapping population were found. A single step statistical method was used to evaluate the most informative set of SNPs that could subsequently be used for routine checks for mislabeling in multi-location field trials and for labelling superior clones to protect breeder\u2019s rights. A preliminary set of SNPs with a high degree of informativeness was selected for the mislabeling analysis in conjunction with an assignment test. Two subsets were successfully identified, i.e., 51 SNPs for wood density and 64 SNPs for fibre length mapping populations to identify all mislabeled ramets which had been previously identified. Mislabeling seems to be a common problem due to the complexity involved in the production of mapping populations. Therefore, checking for mislabeling is imperative for breeding activities and for analyses such as linkage mapping in which a correlation between genotypic and phenotypic data is determined.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s11676-017-0405-8", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1136234", 
            "issn": [
              "1007-662X", 
              "1993-0607"
            ], 
            "name": "Journal of Forestry Research", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "6", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "28"
          }
        ], 
        "keywords": [
          "mapping population", 
          "field trial sites", 
          "wood density", 
          "linkage mapping", 
          "field planting", 
          "QTL analysis", 
          "unique seed", 
          "multi-location field trials", 
          "trial sites", 
          "marginal lands", 
          "selection breeding", 
          "hybrid yield", 
          "breeders\u2019 rights", 
          "desirable traits", 
          "superior clones", 
          "Acacia hybrid", 
          "MAS program", 
          "pollen contamination", 
          "SNP markers", 
          "field trials", 
          "breeding activity", 
          "linkage map", 
          "genotypic evaluation", 
          "hybrid progeny", 
          "STMS markers", 
          "tree seeds", 
          "interspecific hybridization", 
          "pods", 
          "phenotypic data", 
          "seeds", 
          "ramets", 
          "field station", 
          "planting", 
          "density populations", 
          "progeny", 
          "fast growth", 
          "SNPs", 
          "same population", 
          "mislabeling", 
          "assignment tests", 
          "Southeast Asia", 
          "hybrids", 
          "species-specific SNPs", 
          "Bintulu", 
          "Segamat", 
          "breeding", 
          "Acacia", 
          "yield", 
          "Sarawak", 
          "traits", 
          "population", 
          "land", 
          "paper industry", 
          "genotypic", 
          "great potential", 
          "production", 
          "density", 
          "phenotypic", 
          "markers", 
          "informative set", 
          "mapping", 
          "growth", 
          "Lancang", 
          "contamination", 
          "Asia", 
          "check", 
          "clones", 
          "Johor", 
          "industry", 
          "sites", 
          "Pahang", 
          "quality", 
          "high degree", 
          "potential", 
          "stations", 
          "program", 
          "hybridization", 
          "Malaysia", 
          "location", 
          "isozymes", 
          "field", 
          "trials", 
          "statistical methods", 
          "maps", 
          "development", 
          "total", 
          "routine check", 
          "analysis", 
          "large set", 
          "size", 
          "correlation", 
          "ability", 
          "small set", 
          "common problem", 
          "set", 
          "evaluation", 
          "BHD", 
          "activity", 
          "data", 
          "conjunction", 
          "problem", 
          "subset", 
          "degree", 
          "similar problems", 
          "individuals", 
          "preliminary set", 
          "test", 
          "method", 
          "rights", 
          "informativeness", 
          "complexity", 
          "questions", 
          "match", 
          "SDN"
        ], 
        "name": "Detecting mislabeling and identifying unique progeny in Acacia mapping population using SNP markers", 
        "pagination": "1119-1127", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1085179165"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11676-017-0405-8"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11676-017-0405-8", 
          "https://app.dimensions.ai/details/publication/pub.1085179165"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-08-04T17:06", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_737.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s11676-017-0405-8"
      }
    ]
     

    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/s11676-017-0405-8'

    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/s11676-017-0405-8'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11676-017-0405-8'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11676-017-0405-8'


     

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

    298 TRIPLES      21 PREDICATES      163 URIs      130 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11676-017-0405-8 schema:about anzsrc-for:07
    2 anzsrc-for:0705
    3 schema:author N7c2404bbbe084e0da48b7f2b4e33a7c6
    4 schema:citation sg:pub.10.1007/bf00120650
    5 sg:pub.10.1007/s00122-003-1480-0
    6 sg:pub.10.1007/s00122-007-0625-y
    7 sg:pub.10.1007/s001220050076
    8 sg:pub.10.1007/s001220050440
    9 sg:pub.10.1007/s001220051521
    10 sg:pub.10.1007/s001220051608
    11 sg:pub.10.1007/s10086-009-1057-x
    12 sg:pub.10.1007/s10681-010-0295-8
    13 sg:pub.10.1007/s11056-005-8677-9
    14 sg:pub.10.1007/s11295-005-0023-z
    15 sg:pub.10.1007/s11295-007-0086-0
    16 sg:pub.10.1007/s11295-007-0115-z
    17 sg:pub.10.1007/s11295-009-0246-5
    18 sg:pub.10.1007/s11676-015-0134-9
    19 sg:pub.10.1007/s12041-009-0004-3
    20 sg:pub.10.1023/a:1009612517139
    21 sg:pub.10.1038/368455a0
    22 sg:pub.10.1038/nbt1008-1099
    23 sg:pub.10.1046/j.1365-2540.1998.00392.x
    24 sg:pub.10.1186/1471-2156-11-2
    25 sg:pub.10.1186/1471-2156-12-45
    26 sg:pub.10.1186/1471-2164-11-38
    27 sg:pub.10.1186/1471-2164-13-726
    28 sg:pub.10.1186/1471-2229-11-153
    29 schema:datePublished 2017-05-05
    30 schema:datePublishedReg 2017-05-05
    31 schema:description Acacia hybrids offer a great potential for paper industry in Southeast Asia due to their fast growth and ability to grow on abandoned or marginal lands. Breeding Acacia hybrids with desirable traits can be achieved through marker assisted selection (MAS) breeding. To develop a MAS program requires development of linkage maps and QTL analysis. Two mapping populations were developed through interspecific hybridization for linkage mapping and QTL analysis. All seeds per pod were cultured initially to improve hybrid yield as quality and density of linkage mapping is affected by the size of the mapping population. Progenies from two mapping populations were field planted for phenotypic and genotypic evaluation at three locations in Malaysia, (1) Forest Research Institute Malaysia field station at Segamat, Johor, (2) Borneo Tree Seeds and Seedlings Supplies Sdn, Bhd. (BTS) field trial site at Bintulu, Sarawak, and (3) Asiaprima RCF field trial site at Lancang, Pahang. During field planting, mislabeling was reported at Segamat, Johor, and a similar problem was suspected for Bintulu, Sarawak. Early screening with two isozymes effectively selected hybrid progenies, and these hybrids were subsequently further confirmed by using species-specific SNPs. During field planting, clonal mislabeling was reported and later confirmed by using a small set of STMS markers. A large set of SNPs were also used to screen all ramets in both populations. A total of 65.36% mislabeled ramets were encountered in the wood density population and 60.34% in the fibre length mapping population. No interpopulation pollen contamination was detected because all ramets found their match within the same population in question. However, mislabeling was detected among ramets of the same population. Mislabeled individuals were identified and grouped as they originated from 93 pods for wood density and 53 pods for fibre length mapping populations. On average 2 meiotically unique seeds per pod (179 seeds/93 pods) for wood density and 3 meiotically unique seeds per pod (174 seeds/53 pods) for fibre length mapping population were found. A single step statistical method was used to evaluate the most informative set of SNPs that could subsequently be used for routine checks for mislabeling in multi-location field trials and for labelling superior clones to protect breeder’s rights. A preliminary set of SNPs with a high degree of informativeness was selected for the mislabeling analysis in conjunction with an assignment test. Two subsets were successfully identified, i.e., 51 SNPs for wood density and 64 SNPs for fibre length mapping populations to identify all mislabeled ramets which had been previously identified. Mislabeling seems to be a common problem due to the complexity involved in the production of mapping populations. Therefore, checking for mislabeling is imperative for breeding activities and for analyses such as linkage mapping in which a correlation between genotypic and phenotypic data is determined.
    32 schema:genre article
    33 schema:isAccessibleForFree false
    34 schema:isPartOf N5231bc4a317444fca3e17935c805a2d9
    35 Ndaae239c094e47629c241e47dbfdaf3c
    36 sg:journal.1136234
    37 schema:keywords Acacia
    38 Acacia hybrid
    39 Asia
    40 BHD
    41 Bintulu
    42 Johor
    43 Lancang
    44 MAS program
    45 Malaysia
    46 Pahang
    47 QTL analysis
    48 SDN
    49 SNP markers
    50 SNPs
    51 STMS markers
    52 Sarawak
    53 Segamat
    54 Southeast Asia
    55 ability
    56 activity
    57 analysis
    58 assignment tests
    59 breeders’ rights
    60 breeding
    61 breeding activity
    62 check
    63 clones
    64 common problem
    65 complexity
    66 conjunction
    67 contamination
    68 correlation
    69 data
    70 degree
    71 density
    72 density populations
    73 desirable traits
    74 development
    75 evaluation
    76 fast growth
    77 field
    78 field planting
    79 field station
    80 field trial sites
    81 field trials
    82 genotypic
    83 genotypic evaluation
    84 great potential
    85 growth
    86 high degree
    87 hybrid progeny
    88 hybrid yield
    89 hybridization
    90 hybrids
    91 individuals
    92 industry
    93 informative set
    94 informativeness
    95 interspecific hybridization
    96 isozymes
    97 land
    98 large set
    99 linkage map
    100 linkage mapping
    101 location
    102 mapping
    103 mapping population
    104 maps
    105 marginal lands
    106 markers
    107 match
    108 method
    109 mislabeling
    110 multi-location field trials
    111 paper industry
    112 phenotypic
    113 phenotypic data
    114 planting
    115 pods
    116 pollen contamination
    117 population
    118 potential
    119 preliminary set
    120 problem
    121 production
    122 progeny
    123 program
    124 quality
    125 questions
    126 ramets
    127 rights
    128 routine check
    129 same population
    130 seeds
    131 selection breeding
    132 set
    133 similar problems
    134 sites
    135 size
    136 small set
    137 species-specific SNPs
    138 stations
    139 statistical methods
    140 subset
    141 superior clones
    142 test
    143 total
    144 traits
    145 tree seeds
    146 trial sites
    147 trials
    148 unique seed
    149 wood density
    150 yield
    151 schema:name Detecting mislabeling and identifying unique progeny in Acacia mapping population using SNP markers
    152 schema:pagination 1119-1127
    153 schema:productId N45ec3f2bbb484d72bd979ecfde0d9cd2
    154 Nc72b461e16b648e0b7640752c024f901
    155 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085179165
    156 https://doi.org/10.1007/s11676-017-0405-8
    157 schema:sdDatePublished 2022-08-04T17:06
    158 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    159 schema:sdPublisher N5200fefe3361411bb47bc9ad989b7a60
    160 schema:url https://doi.org/10.1007/s11676-017-0405-8
    161 sgo:license sg:explorer/license/
    162 sgo:sdDataset articles
    163 rdf:type schema:ScholarlyArticle
    164 N24e4c86cbcf44f6aa9d964064eaced9b rdf:first sg:person.01055170164.87
    165 rdf:rest Nf66b4c2ed81a434e979b063cd172ad5d
    166 N45ec3f2bbb484d72bd979ecfde0d9cd2 schema:name doi
    167 schema:value 10.1007/s11676-017-0405-8
    168 rdf:type schema:PropertyValue
    169 N5200fefe3361411bb47bc9ad989b7a60 schema:name Springer Nature - SN SciGraph project
    170 rdf:type schema:Organization
    171 N5231bc4a317444fca3e17935c805a2d9 schema:volumeNumber 28
    172 rdf:type schema:PublicationVolume
    173 N7c2404bbbe084e0da48b7f2b4e33a7c6 rdf:first Nf0ccf955d3e04fd287b0b31566dc6e07
    174 rdf:rest Ne4e7dc055d0d4faa873922ca1f71e590
    175 Nc72b461e16b648e0b7640752c024f901 schema:name dimensions_id
    176 schema:value pub.1085179165
    177 rdf:type schema:PropertyValue
    178 Ndaae239c094e47629c241e47dbfdaf3c schema:issueNumber 6
    179 rdf:type schema:PublicationIssue
    180 Ne4e7dc055d0d4faa873922ca1f71e590 rdf:first sg:person.015701744541.63
    181 rdf:rest N24e4c86cbcf44f6aa9d964064eaced9b
    182 Nf0ccf955d3e04fd287b0b31566dc6e07 schema:affiliation grid-institutes:grid.413016.1
    183 schema:familyName Javed Muhammad
    184 schema:givenName Asif
    185 rdf:type schema:Person
    186 Nf66b4c2ed81a434e979b063cd172ad5d rdf:first sg:person.07774253741.09
    187 rdf:rest rdf:nil
    188 anzsrc-for:07 schema:inDefinedTermSet anzsrc-for:
    189 schema:name Agricultural and Veterinary Sciences
    190 rdf:type schema:DefinedTerm
    191 anzsrc-for:0705 schema:inDefinedTermSet anzsrc-for:
    192 schema:name Forestry Sciences
    193 rdf:type schema:DefinedTerm
    194 sg:journal.1136234 schema:issn 1007-662X
    195 1993-0607
    196 schema:name Journal of Forestry Research
    197 schema:publisher Springer Nature
    198 rdf:type schema:Periodical
    199 sg:person.01055170164.87 schema:affiliation grid-institutes:grid.434305.5
    200 schema:familyName Muhammad
    201 schema:givenName Norwati
    202 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01055170164.87
    203 rdf:type schema:Person
    204 sg:person.015701744541.63 schema:affiliation grid-institutes:grid.434305.5
    205 schema:familyName Abdullah
    206 schema:givenName Mohd Zaki
    207 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015701744541.63
    208 rdf:type schema:Person
    209 sg:person.07774253741.09 schema:affiliation grid-institutes:grid.412113.4
    210 schema:familyName Ratnam
    211 schema:givenName Wickneswari
    212 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07774253741.09
    213 rdf:type schema:Person
    214 sg:pub.10.1007/bf00120650 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050340558
    215 https://doi.org/10.1007/bf00120650
    216 rdf:type schema:CreativeWork
    217 sg:pub.10.1007/s00122-003-1480-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034346723
    218 https://doi.org/10.1007/s00122-003-1480-0
    219 rdf:type schema:CreativeWork
    220 sg:pub.10.1007/s00122-007-0625-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1030269799
    221 https://doi.org/10.1007/s00122-007-0625-y
    222 rdf:type schema:CreativeWork
    223 sg:pub.10.1007/s001220050076 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019426644
    224 https://doi.org/10.1007/s001220050076
    225 rdf:type schema:CreativeWork
    226 sg:pub.10.1007/s001220050440 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052358833
    227 https://doi.org/10.1007/s001220050440
    228 rdf:type schema:CreativeWork
    229 sg:pub.10.1007/s001220051521 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050220602
    230 https://doi.org/10.1007/s001220051521
    231 rdf:type schema:CreativeWork
    232 sg:pub.10.1007/s001220051608 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032939723
    233 https://doi.org/10.1007/s001220051608
    234 rdf:type schema:CreativeWork
    235 sg:pub.10.1007/s10086-009-1057-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1031902241
    236 https://doi.org/10.1007/s10086-009-1057-x
    237 rdf:type schema:CreativeWork
    238 sg:pub.10.1007/s10681-010-0295-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050195939
    239 https://doi.org/10.1007/s10681-010-0295-8
    240 rdf:type schema:CreativeWork
    241 sg:pub.10.1007/s11056-005-8677-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036820553
    242 https://doi.org/10.1007/s11056-005-8677-9
    243 rdf:type schema:CreativeWork
    244 sg:pub.10.1007/s11295-005-0023-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1020727284
    245 https://doi.org/10.1007/s11295-005-0023-z
    246 rdf:type schema:CreativeWork
    247 sg:pub.10.1007/s11295-007-0086-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021008507
    248 https://doi.org/10.1007/s11295-007-0086-0
    249 rdf:type schema:CreativeWork
    250 sg:pub.10.1007/s11295-007-0115-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1028516092
    251 https://doi.org/10.1007/s11295-007-0115-z
    252 rdf:type schema:CreativeWork
    253 sg:pub.10.1007/s11295-009-0246-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008482321
    254 https://doi.org/10.1007/s11295-009-0246-5
    255 rdf:type schema:CreativeWork
    256 sg:pub.10.1007/s11676-015-0134-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022792368
    257 https://doi.org/10.1007/s11676-015-0134-9
    258 rdf:type schema:CreativeWork
    259 sg:pub.10.1007/s12041-009-0004-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045215442
    260 https://doi.org/10.1007/s12041-009-0004-3
    261 rdf:type schema:CreativeWork
    262 sg:pub.10.1023/a:1009612517139 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029316007
    263 https://doi.org/10.1023/a:1009612517139
    264 rdf:type schema:CreativeWork
    265 sg:pub.10.1038/368455a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046878300
    266 https://doi.org/10.1038/368455a0
    267 rdf:type schema:CreativeWork
    268 sg:pub.10.1038/nbt1008-1099 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026300654
    269 https://doi.org/10.1038/nbt1008-1099
    270 rdf:type schema:CreativeWork
    271 sg:pub.10.1046/j.1365-2540.1998.00392.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1037560297
    272 https://doi.org/10.1046/j.1365-2540.1998.00392.x
    273 rdf:type schema:CreativeWork
    274 sg:pub.10.1186/1471-2156-11-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036078550
    275 https://doi.org/10.1186/1471-2156-11-2
    276 rdf:type schema:CreativeWork
    277 sg:pub.10.1186/1471-2156-12-45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042352726
    278 https://doi.org/10.1186/1471-2156-12-45
    279 rdf:type schema:CreativeWork
    280 sg:pub.10.1186/1471-2164-11-38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044425441
    281 https://doi.org/10.1186/1471-2164-11-38
    282 rdf:type schema:CreativeWork
    283 sg:pub.10.1186/1471-2164-13-726 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020892934
    284 https://doi.org/10.1186/1471-2164-13-726
    285 rdf:type schema:CreativeWork
    286 sg:pub.10.1186/1471-2229-11-153 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031420518
    287 https://doi.org/10.1186/1471-2229-11-153
    288 rdf:type schema:CreativeWork
    289 grid-institutes:grid.412113.4 schema:alternateName Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
    290 schema:name Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
    291 rdf:type schema:Organization
    292 grid-institutes:grid.413016.1 schema:alternateName Department of Forestry and Range Management, University of Agriculture Faisalabad, Faisalabad, Pakistan
    293 schema:name Department of Forestry and Range Management, University of Agriculture Faisalabad, Faisalabad, Pakistan
    294 Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
    295 rdf:type schema:Organization
    296 grid-institutes:grid.434305.5 schema:alternateName Forestry Biotechnology Division, Forest Research Institute Malaysia, 52110, Kepong, Selangor Darul Ehsan, Malaysia
    297 schema:name Forestry Biotechnology Division, Forest Research Institute Malaysia, 52110, Kepong, Selangor Darul Ehsan, Malaysia
    298 rdf:type schema:Organization
     




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


    ...