Genetic dissection of seed weight by QTL analysis and detection of allelic variation in Indian and east European gene pool ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-02

AUTHORS

Namrata Dhaka, Kadambini Rout, Satish K. Yadava, Yaspal Singh Sodhi, Vibha Gupta, Deepak Pental, Akshay K. Pradhan

ABSTRACT

KEY MESSAGE: Seed weight QTL identified in different populations were synthesized into consensus QTL which were shown to harbor candidate genes by in silico mapping. Allelic variation inferred would be useful in breeding B. juncea lines with high seed weight. Seed weight is an important yield influencing trait in oilseed Brassicas and is a multigenic trait. Among the oilseed Brassicas, Brassica juncea harbors the maximum phenotypic variation wherein thousand seed weight varies from around 2.0 g to more than 7.0 g. In this study, we have undertaken quantitative trait locus/quantitative trait loci (QTL) analysis of seed weight in B. juncea using four bi-parental doubled-haploid populations. These four populations were derived from six lines (three Indian and three east European lines) with parental phenotypic values for thousand seed weight ranging from 2.0 to 7.6 g in different environments. Multi-environment QTL analysis of the four populations identified a total of 65 QTL ranging from 10 to 25 in each population. Meta-analysis of these component QTL of the four populations identified six 'consensus' QTL (C-QTL) in A3, A7, A10 and B3 by merging 33 of the 65 component Tsw QTL from different bi-parental populations. Allelic diversity analysis of these six C-QTL showed that Indian lines, Pusajaikisan and Varuna, hold the most positive allele in all the six C-QTL. In silico mapping of candidate genes with the consensus QTL localized 11 genes known to influence seed weight in Arabidopsis thaliana and also showed conserved crucifer blocks harboring seed weight QTL between the A subgenomes of B. juncea and B. rapa. These findings pave the way for a better understanding of the genetics of seed weight in the oilseed crop B. juncea and reveal the scope available for improvement of seed weight through marker-assisted breeding. More... »

PAGES

293-307

References to SciGraph publications

  • 2010-11-03. Genetics of Brassica juncea in GENETICS AND GENOMICS OF THE BRASSICACEAE
  • 2012-11. QTL mapping of yield-associated traits in Brassica juncea: meta-analysis and epistatic interactions using two different crosses between east European and Indian gene pool lines in THEORETICAL AND APPLIED GENETICS
  • 2006-12. A new cytoplasmic male sterility system for hybrid seed production in Indian oilseed mustard Brassica juncea in THEORETICAL AND APPLIED GENETICS
  • 2011-12. Rapid analysis of seed size in Arabidopsis for mutant and QTL discovery in PLANT METHODS
  • 1993-01. Heterosis breeding in Indian mustard (Brassica juncea L. Czern & Coss): Analysis of component characters contributing to heterosis for yield in EUPHYTICA
  • 2014-12. A combined linkage and regional association mapping validation and fine mapping of two major pleiotropic QTLs for seed weight and silique length in rapeseed (Brassica napus L.) in BMC PLANT BIOLOGY
  • 2015-11. Comparative quantitative trait loci for silique length and seed weight in Brassica napus in SCIENTIFIC REPORTS
  • 2010-11. Mapping of quantitative trait loci and development of allele-specific markers for seed weight in Brassica napus in THEORETICAL AND APPLIED GENETICS
  • 2011-10. The genome of the mesopolyploid crop species Brassica rapa in NATURE GENETICS
  • 2001-02. AFLP-based genetic diversity assessment amongst agronomically important natural and some newly synthesized lines of Brassica juncea in THEORETICAL AND APPLIED GENETICS
  • 2014-01. Epistasis and quantitative traits: using model organisms to study gene–gene interactions in NATURE REVIEWS GENETICS
  • 2007-10. Mapping of yield influencing QTL in Brassica juncea: implications for breeding of a major oilseed crop of dryland areas in THEORETICAL AND APPLIED GENETICS
  • 2012-10. Multi-environment mapping and meta-analysis of 100-seed weight in soybean in MOLECULAR BIOLOGY REPORTS
  • 2014-03. Use of Mutants to Dissect the Role of Ethylene Signalling in Organ Senescence and the Regulation of Yield in Arabidopsis thaliana in JOURNAL OF PLANT GROWTH REGULATION
  • 2012-07. Identification of a major QTL for silique length and seed weight in oilseed rape (Brassicanapus L.) in THEORETICAL AND APPLIED GENETICS
  • 2015-04. Deciphering allelic variations for seed glucosinolate traits in oilseed mustard (Brassica juncea) using two bi-parental mapping populations in THEORETICAL AND APPLIED GENETICS
  • 2000-06. The genetic basis of seed-weight variation: tomato as a model system in THEORETICAL AND APPLIED GENETICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00122-016-2811-2

    DOI

    http://dx.doi.org/10.1007/s00122-016-2811-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1036327379

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27744489


    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": "Consensus Sequence", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Epistasis, Genetic", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gene Pool", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Genetics, Population", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Haploidy", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Mustard Plant", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Phenotype", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Quantitative Trait Loci", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Seeds", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of Delhi", 
              "id": "https://www.grid.ac/institutes/grid.8195.5", 
              "name": [
                "Department of Genetics, University of Delhi South Campus, Benito Juarez Road, 110021, New Delhi, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Dhaka", 
            "givenName": "Namrata", 
            "id": "sg:person.012703270251.10", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012703270251.10"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Delhi", 
              "id": "https://www.grid.ac/institutes/grid.8195.5", 
              "name": [
                "Centre for Genetic Manipulation of Crop Plants, University of Delhi South Campus, Benito Juarez Road, 110021, New Delhi, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Rout", 
            "givenName": "Kadambini", 
            "id": "sg:person.01327771536.32", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01327771536.32"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Delhi", 
              "id": "https://www.grid.ac/institutes/grid.8195.5", 
              "name": [
                "Centre for Genetic Manipulation of Crop Plants, University of Delhi South Campus, Benito Juarez Road, 110021, New Delhi, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Yadava", 
            "givenName": "Satish K.", 
            "id": "sg:person.01017473354.36", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01017473354.36"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Delhi", 
              "id": "https://www.grid.ac/institutes/grid.8195.5", 
              "name": [
                "Centre for Genetic Manipulation of Crop Plants, University of Delhi South Campus, Benito Juarez Road, 110021, New Delhi, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sodhi", 
            "givenName": "Yaspal Singh", 
            "id": "sg:person.01074147016.08", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01074147016.08"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Delhi", 
              "id": "https://www.grid.ac/institutes/grid.8195.5", 
              "name": [
                "Centre for Genetic Manipulation of Crop Plants, University of Delhi South Campus, Benito Juarez Road, 110021, New Delhi, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Gupta", 
            "givenName": "Vibha", 
            "id": "sg:person.0707106054.43", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707106054.43"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Delhi", 
              "id": "https://www.grid.ac/institutes/grid.8195.5", 
              "name": [
                "Centre for Genetic Manipulation of Crop Plants, University of Delhi South Campus, Benito Juarez Road, 110021, New Delhi, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Pental", 
            "givenName": "Deepak", 
            "id": "sg:person.01230565713.24", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01230565713.24"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Delhi", 
              "id": "https://www.grid.ac/institutes/grid.8195.5", 
              "name": [
                "Department of Genetics, University of Delhi South Campus, Benito Juarez Road, 110021, New Delhi, India", 
                "Centre for Genetic Manipulation of Crop Plants, University of Delhi South Campus, Benito Juarez Road, 110021, New Delhi, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Pradhan", 
            "givenName": "Akshay K.", 
            "id": "sg:person.01137563054.00", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01137563054.00"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s00122-007-0610-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000587413", 
              "https://doi.org/10.1007/s00122-007-0610-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-007-0610-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000587413", 
              "https://doi.org/10.1007/s00122-007-0610-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-006-0413-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003326201", 
              "https://doi.org/10.1007/s00122-006-0413-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-006-0413-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003326201", 
              "https://doi.org/10.1007/s00122-006-0413-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1104/pp.113.217703", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010924199"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11033-012-1808-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013211578", 
              "https://doi.org/10.1007/s11033-012-1808-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jxb/eru125", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014446486"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0508418102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016104028"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0509021102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016161259"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00022368", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016943654", 
              "https://doi.org/10.1007/bf00022368"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00022368", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016943654", 
              "https://doi.org/10.1007/bf00022368"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4419-7118-0_11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017081400", 
              "https://doi.org/10.1007/978-1-4419-7118-0_11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4419-7118-0_11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017081400", 
              "https://doi.org/10.1007/978-1-4419-7118-0_11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/dnares/dss029", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017271925"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/pbr.12131", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019540723"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00344-013-9382-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020551385", 
              "https://doi.org/10.1007/s00344-013-9382-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tplants.2006.09.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021466974"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-012-1934-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022239797", 
              "https://doi.org/10.1007/s00122-012-1934-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0147580", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023193813"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0147580", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023193813"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0147580", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023193813"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1105/tpc.108.064972", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025108076"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrg3627", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025596426", 
              "https://doi.org/10.1038/nrg3627"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220051635", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026795799", 
              "https://doi.org/10.1007/s001220051635"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220051635", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026795799", 
              "https://doi.org/10.1007/s001220051635"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.96.8.4710", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027009396"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2229-14-114", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028609878", 
              "https://doi.org/10.1186/1471-2229-14-114"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btm143", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029867719"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/jxb/eru549", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030430787"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1365-294x.2012.05522.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033113663"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-010-1388-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034292101", 
              "https://doi.org/10.1007/s00122-010-1388-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-010-1388-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034292101", 
              "https://doi.org/10.1007/s00122-010-1388-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.919", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035519342", 
              "https://doi.org/10.1038/ng.919"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220051433", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036705836", 
              "https://doi.org/10.1007/s001220051433"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s001220051433", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036705836", 
              "https://doi.org/10.1007/s001220051433"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tplants.2014.01.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038320544"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1071/ea05228", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038570145"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-015-2461-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043016375", 
              "https://doi.org/10.1007/s00122-015-2461-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00122-012-1833-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046864506", 
              "https://doi.org/10.1007/s00122-012-1833-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/bth230", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047038250"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1105/tpc.104.027136", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050256820"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1746-4811-7-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052813181", 
              "https://doi.org/10.1186/1746-4811-7-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep14407", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052843588", 
              "https://doi.org/10.1038/srep14407"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3389/fpls.2015.01032", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053565078"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1042/bst20140040", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1056717628"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1042/bst20140040", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1056717628"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2135/cropsci2007.04.0191", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069030309"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.4161/psb.25928", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1072308128"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1074632808", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1082535482", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1082948111", 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-02", 
        "datePublishedReg": "2017-02-01", 
        "description": "KEY MESSAGE: Seed weight QTL identified in different populations were synthesized into consensus QTL which were shown to harbor candidate genes by in silico mapping. Allelic variation inferred would be useful in breeding B. juncea lines with high seed weight. Seed weight is an important yield influencing trait in oilseed Brassicas and is a multigenic trait. Among the oilseed Brassicas, Brassica juncea harbors the maximum phenotypic variation wherein thousand seed weight varies from around 2.0\u00a0g to more than 7.0\u00a0g. In this study, we have undertaken quantitative trait locus/quantitative trait loci (QTL) analysis of seed weight in B. juncea using four bi-parental doubled-haploid populations. These four populations were derived from six lines (three Indian and three east European lines) with parental phenotypic values for thousand seed weight ranging from 2.0 to 7.6\u00a0g in different environments. Multi-environment QTL analysis of the four populations identified a total of 65 QTL ranging from 10 to 25 in each population. Meta-analysis of these component QTL of the four populations identified six 'consensus' QTL (C-QTL) in A3, A7, A10 and B3 by merging 33 of the 65 component Tsw QTL from different bi-parental populations. Allelic diversity analysis of these six C-QTL showed that Indian lines, Pusajaikisan and Varuna, hold the most positive allele in all the six C-QTL. In silico mapping of candidate genes with the consensus QTL localized 11 genes known to influence seed weight in Arabidopsis thaliana and also showed conserved crucifer blocks harboring seed weight QTL between the A subgenomes of B. juncea and B. rapa. These findings pave the way for a better understanding of the genetics of seed weight in the oilseed crop B. juncea and reveal the scope available for improvement of seed weight through marker-assisted breeding.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s00122-016-2811-2", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1135804", 
            "issn": [
              "0040-5752", 
              "1432-2242"
            ], 
            "name": "Theoretical and Applied Genetics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "130"
          }
        ], 
        "name": "Genetic dissection of seed weight by QTL analysis and detection of allelic variation in Indian and east European gene pool lines of Brassica juncea", 
        "pagination": "293-307", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "ad0c8f46fd93ae1505b7488fe8915120033455afa08c9998b1af1d992f85c04c"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "27744489"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "0145600"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00122-016-2811-2"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1036327379"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00122-016-2811-2", 
          "https://app.dimensions.ai/details/publication/pub.1036327379"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T12:39", 
        "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/v90/0000000363_0000000363/records_70046_00000001.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs00122-016-2811-2"
      }
    ]
     

    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/s00122-016-2811-2'

    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/s00122-016-2811-2'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00122-016-2811-2'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00122-016-2811-2'


     

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

    293 TRIPLES      21 PREDICATES      81 URIs      32 LITERALS      20 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00122-016-2811-2 schema:about N071e1a4abff2404dad3c084854170c57
    2 N1241f0b7b62d49b4ae81d47f20fa7e07
    3 N3ad7d90b1c4142809007f2fc2d3fdacf
    4 N7556a994248f497491406eb5f01b23b3
    5 N775427f5475f49b0af0275c63af1a917
    6 N8d1c4c1cf3e94a9e87ae604a667eab8c
    7 N9fb81775ab4a4027b0c0158415354ce3
    8 Nab2ea59949924debad54bf1f1c4b43b8
    9 Nbb3634e466df43268d438849f45ff3ed
    10 Ne2d2afc9a51449c69a9b66dffeee05db
    11 Nf2783a55690d4d1f86ea57fe45fc6c20
    12 anzsrc-for:06
    13 anzsrc-for:0604
    14 schema:author N9a7a356b68d546458609fa002adc9a2c
    15 schema:citation sg:pub.10.1007/978-1-4419-7118-0_11
    16 sg:pub.10.1007/bf00022368
    17 sg:pub.10.1007/s00122-006-0413-0
    18 sg:pub.10.1007/s00122-007-0610-5
    19 sg:pub.10.1007/s00122-010-1388-4
    20 sg:pub.10.1007/s00122-012-1833-7
    21 sg:pub.10.1007/s00122-012-1934-3
    22 sg:pub.10.1007/s00122-015-2461-9
    23 sg:pub.10.1007/s001220051433
    24 sg:pub.10.1007/s001220051635
    25 sg:pub.10.1007/s00344-013-9382-0
    26 sg:pub.10.1007/s11033-012-1808-4
    27 sg:pub.10.1038/ng.919
    28 sg:pub.10.1038/nrg3627
    29 sg:pub.10.1038/srep14407
    30 sg:pub.10.1186/1471-2229-14-114
    31 sg:pub.10.1186/1746-4811-7-3
    32 https://app.dimensions.ai/details/publication/pub.1074632808
    33 https://app.dimensions.ai/details/publication/pub.1082535482
    34 https://app.dimensions.ai/details/publication/pub.1082948111
    35 https://doi.org/10.1016/j.tplants.2006.09.002
    36 https://doi.org/10.1016/j.tplants.2014.01.001
    37 https://doi.org/10.1042/bst20140040
    38 https://doi.org/10.1071/ea05228
    39 https://doi.org/10.1073/pnas.0508418102
    40 https://doi.org/10.1073/pnas.0509021102
    41 https://doi.org/10.1073/pnas.96.8.4710
    42 https://doi.org/10.1093/bioinformatics/bth230
    43 https://doi.org/10.1093/bioinformatics/btm143
    44 https://doi.org/10.1093/dnares/dss029
    45 https://doi.org/10.1093/jxb/eru125
    46 https://doi.org/10.1093/jxb/eru549
    47 https://doi.org/10.1104/pp.113.217703
    48 https://doi.org/10.1105/tpc.104.027136
    49 https://doi.org/10.1105/tpc.108.064972
    50 https://doi.org/10.1111/j.1365-294x.2012.05522.x
    51 https://doi.org/10.1111/pbr.12131
    52 https://doi.org/10.1371/journal.pone.0147580
    53 https://doi.org/10.2135/cropsci2007.04.0191
    54 https://doi.org/10.3389/fpls.2015.01032
    55 https://doi.org/10.4161/psb.25928
    56 schema:datePublished 2017-02
    57 schema:datePublishedReg 2017-02-01
    58 schema:description KEY MESSAGE: Seed weight QTL identified in different populations were synthesized into consensus QTL which were shown to harbor candidate genes by in silico mapping. Allelic variation inferred would be useful in breeding B. juncea lines with high seed weight. Seed weight is an important yield influencing trait in oilseed Brassicas and is a multigenic trait. Among the oilseed Brassicas, Brassica juncea harbors the maximum phenotypic variation wherein thousand seed weight varies from around 2.0 g to more than 7.0 g. In this study, we have undertaken quantitative trait locus/quantitative trait loci (QTL) analysis of seed weight in B. juncea using four bi-parental doubled-haploid populations. These four populations were derived from six lines (three Indian and three east European lines) with parental phenotypic values for thousand seed weight ranging from 2.0 to 7.6 g in different environments. Multi-environment QTL analysis of the four populations identified a total of 65 QTL ranging from 10 to 25 in each population. Meta-analysis of these component QTL of the four populations identified six 'consensus' QTL (C-QTL) in A3, A7, A10 and B3 by merging 33 of the 65 component Tsw QTL from different bi-parental populations. Allelic diversity analysis of these six C-QTL showed that Indian lines, Pusajaikisan and Varuna, hold the most positive allele in all the six C-QTL. In silico mapping of candidate genes with the consensus QTL localized 11 genes known to influence seed weight in Arabidopsis thaliana and also showed conserved crucifer blocks harboring seed weight QTL between the A subgenomes of B. juncea and B. rapa. These findings pave the way for a better understanding of the genetics of seed weight in the oilseed crop B. juncea and reveal the scope available for improvement of seed weight through marker-assisted breeding.
    59 schema:genre research_article
    60 schema:inLanguage en
    61 schema:isAccessibleForFree false
    62 schema:isPartOf N064f500c82be4962abd17e0d3365e7d9
    63 N9776a83939194f05a3a5d96e8d15cfe5
    64 sg:journal.1135804
    65 schema:name Genetic dissection of seed weight by QTL analysis and detection of allelic variation in Indian and east European gene pool lines of Brassica juncea
    66 schema:pagination 293-307
    67 schema:productId N0fd74cc14df34f18bd29264676de2058
    68 N1fb6491fd240411ab6e0814a0a5c19de
    69 Nd745a5e359b34ee5bd354fbb46bbdaf2
    70 Ne134eeb610694227af39dc3e27217d18
    71 Ne867ec2960f94c2480fde73539d0bf61
    72 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036327379
    73 https://doi.org/10.1007/s00122-016-2811-2
    74 schema:sdDatePublished 2019-04-11T12:39
    75 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    76 schema:sdPublisher Nebca6a9c4c6646d5922731a9fdf04305
    77 schema:url https://link.springer.com/10.1007%2Fs00122-016-2811-2
    78 sgo:license sg:explorer/license/
    79 sgo:sdDataset articles
    80 rdf:type schema:ScholarlyArticle
    81 N064f500c82be4962abd17e0d3365e7d9 schema:volumeNumber 130
    82 rdf:type schema:PublicationVolume
    83 N071e1a4abff2404dad3c084854170c57 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    84 schema:name Epistasis, Genetic
    85 rdf:type schema:DefinedTerm
    86 N0b303cab376b483ca0d435ae17f25839 rdf:first sg:person.01017473354.36
    87 rdf:rest N20b232fa6d4e47d784714926a2634613
    88 N0fd74cc14df34f18bd29264676de2058 schema:name readcube_id
    89 schema:value ad0c8f46fd93ae1505b7488fe8915120033455afa08c9998b1af1d992f85c04c
    90 rdf:type schema:PropertyValue
    91 N1241f0b7b62d49b4ae81d47f20fa7e07 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    92 schema:name Mustard Plant
    93 rdf:type schema:DefinedTerm
    94 N1fb6491fd240411ab6e0814a0a5c19de schema:name dimensions_id
    95 schema:value pub.1036327379
    96 rdf:type schema:PropertyValue
    97 N20b232fa6d4e47d784714926a2634613 rdf:first sg:person.01074147016.08
    98 rdf:rest Na5cf40859deb445b9eed08a808e72a0b
    99 N3ad7d90b1c4142809007f2fc2d3fdacf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    100 schema:name Alleles
    101 rdf:type schema:DefinedTerm
    102 N51295e7ed1694a5db9b26054aeadc8eb rdf:first sg:person.01327771536.32
    103 rdf:rest N0b303cab376b483ca0d435ae17f25839
    104 N7556a994248f497491406eb5f01b23b3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    105 schema:name Phenotype
    106 rdf:type schema:DefinedTerm
    107 N775427f5475f49b0af0275c63af1a917 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    108 schema:name Consensus Sequence
    109 rdf:type schema:DefinedTerm
    110 N8d1c4c1cf3e94a9e87ae604a667eab8c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    111 schema:name Quantitative Trait Loci
    112 rdf:type schema:DefinedTerm
    113 N8d6a7bb5b8a7436c963f6be8e6a8f65c rdf:first sg:person.01230565713.24
    114 rdf:rest Nc2ffe09f18eb46f98e23b81a48bbec17
    115 N9776a83939194f05a3a5d96e8d15cfe5 schema:issueNumber 2
    116 rdf:type schema:PublicationIssue
    117 N9a7a356b68d546458609fa002adc9a2c rdf:first sg:person.012703270251.10
    118 rdf:rest N51295e7ed1694a5db9b26054aeadc8eb
    119 N9fb81775ab4a4027b0c0158415354ce3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    120 schema:name Genetics, Population
    121 rdf:type schema:DefinedTerm
    122 Na5cf40859deb445b9eed08a808e72a0b rdf:first sg:person.0707106054.43
    123 rdf:rest N8d6a7bb5b8a7436c963f6be8e6a8f65c
    124 Nab2ea59949924debad54bf1f1c4b43b8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    125 schema:name Haploidy
    126 rdf:type schema:DefinedTerm
    127 Nbb3634e466df43268d438849f45ff3ed schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    128 schema:name Gene Pool
    129 rdf:type schema:DefinedTerm
    130 Nc2ffe09f18eb46f98e23b81a48bbec17 rdf:first sg:person.01137563054.00
    131 rdf:rest rdf:nil
    132 Nd745a5e359b34ee5bd354fbb46bbdaf2 schema:name pubmed_id
    133 schema:value 27744489
    134 rdf:type schema:PropertyValue
    135 Ne134eeb610694227af39dc3e27217d18 schema:name doi
    136 schema:value 10.1007/s00122-016-2811-2
    137 rdf:type schema:PropertyValue
    138 Ne2d2afc9a51449c69a9b66dffeee05db schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    139 schema:name Seeds
    140 rdf:type schema:DefinedTerm
    141 Ne867ec2960f94c2480fde73539d0bf61 schema:name nlm_unique_id
    142 schema:value 0145600
    143 rdf:type schema:PropertyValue
    144 Nebca6a9c4c6646d5922731a9fdf04305 schema:name Springer Nature - SN SciGraph project
    145 rdf:type schema:Organization
    146 Nf2783a55690d4d1f86ea57fe45fc6c20 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    147 schema:name Chromosome Mapping
    148 rdf:type schema:DefinedTerm
    149 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    150 schema:name Biological Sciences
    151 rdf:type schema:DefinedTerm
    152 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    153 schema:name Genetics
    154 rdf:type schema:DefinedTerm
    155 sg:journal.1135804 schema:issn 0040-5752
    156 1432-2242
    157 schema:name Theoretical and Applied Genetics
    158 rdf:type schema:Periodical
    159 sg:person.01017473354.36 schema:affiliation https://www.grid.ac/institutes/grid.8195.5
    160 schema:familyName Yadava
    161 schema:givenName Satish K.
    162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01017473354.36
    163 rdf:type schema:Person
    164 sg:person.01074147016.08 schema:affiliation https://www.grid.ac/institutes/grid.8195.5
    165 schema:familyName Sodhi
    166 schema:givenName Yaspal Singh
    167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01074147016.08
    168 rdf:type schema:Person
    169 sg:person.01137563054.00 schema:affiliation https://www.grid.ac/institutes/grid.8195.5
    170 schema:familyName Pradhan
    171 schema:givenName Akshay K.
    172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01137563054.00
    173 rdf:type schema:Person
    174 sg:person.01230565713.24 schema:affiliation https://www.grid.ac/institutes/grid.8195.5
    175 schema:familyName Pental
    176 schema:givenName Deepak
    177 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01230565713.24
    178 rdf:type schema:Person
    179 sg:person.012703270251.10 schema:affiliation https://www.grid.ac/institutes/grid.8195.5
    180 schema:familyName Dhaka
    181 schema:givenName Namrata
    182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012703270251.10
    183 rdf:type schema:Person
    184 sg:person.01327771536.32 schema:affiliation https://www.grid.ac/institutes/grid.8195.5
    185 schema:familyName Rout
    186 schema:givenName Kadambini
    187 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01327771536.32
    188 rdf:type schema:Person
    189 sg:person.0707106054.43 schema:affiliation https://www.grid.ac/institutes/grid.8195.5
    190 schema:familyName Gupta
    191 schema:givenName Vibha
    192 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707106054.43
    193 rdf:type schema:Person
    194 sg:pub.10.1007/978-1-4419-7118-0_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017081400
    195 https://doi.org/10.1007/978-1-4419-7118-0_11
    196 rdf:type schema:CreativeWork
    197 sg:pub.10.1007/bf00022368 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016943654
    198 https://doi.org/10.1007/bf00022368
    199 rdf:type schema:CreativeWork
    200 sg:pub.10.1007/s00122-006-0413-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003326201
    201 https://doi.org/10.1007/s00122-006-0413-0
    202 rdf:type schema:CreativeWork
    203 sg:pub.10.1007/s00122-007-0610-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000587413
    204 https://doi.org/10.1007/s00122-007-0610-5
    205 rdf:type schema:CreativeWork
    206 sg:pub.10.1007/s00122-010-1388-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034292101
    207 https://doi.org/10.1007/s00122-010-1388-4
    208 rdf:type schema:CreativeWork
    209 sg:pub.10.1007/s00122-012-1833-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046864506
    210 https://doi.org/10.1007/s00122-012-1833-7
    211 rdf:type schema:CreativeWork
    212 sg:pub.10.1007/s00122-012-1934-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022239797
    213 https://doi.org/10.1007/s00122-012-1934-3
    214 rdf:type schema:CreativeWork
    215 sg:pub.10.1007/s00122-015-2461-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043016375
    216 https://doi.org/10.1007/s00122-015-2461-9
    217 rdf:type schema:CreativeWork
    218 sg:pub.10.1007/s001220051433 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036705836
    219 https://doi.org/10.1007/s001220051433
    220 rdf:type schema:CreativeWork
    221 sg:pub.10.1007/s001220051635 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026795799
    222 https://doi.org/10.1007/s001220051635
    223 rdf:type schema:CreativeWork
    224 sg:pub.10.1007/s00344-013-9382-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020551385
    225 https://doi.org/10.1007/s00344-013-9382-0
    226 rdf:type schema:CreativeWork
    227 sg:pub.10.1007/s11033-012-1808-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013211578
    228 https://doi.org/10.1007/s11033-012-1808-4
    229 rdf:type schema:CreativeWork
    230 sg:pub.10.1038/ng.919 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035519342
    231 https://doi.org/10.1038/ng.919
    232 rdf:type schema:CreativeWork
    233 sg:pub.10.1038/nrg3627 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025596426
    234 https://doi.org/10.1038/nrg3627
    235 rdf:type schema:CreativeWork
    236 sg:pub.10.1038/srep14407 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052843588
    237 https://doi.org/10.1038/srep14407
    238 rdf:type schema:CreativeWork
    239 sg:pub.10.1186/1471-2229-14-114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028609878
    240 https://doi.org/10.1186/1471-2229-14-114
    241 rdf:type schema:CreativeWork
    242 sg:pub.10.1186/1746-4811-7-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052813181
    243 https://doi.org/10.1186/1746-4811-7-3
    244 rdf:type schema:CreativeWork
    245 https://app.dimensions.ai/details/publication/pub.1074632808 schema:CreativeWork
    246 https://app.dimensions.ai/details/publication/pub.1082535482 schema:CreativeWork
    247 https://app.dimensions.ai/details/publication/pub.1082948111 schema:CreativeWork
    248 https://doi.org/10.1016/j.tplants.2006.09.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021466974
    249 rdf:type schema:CreativeWork
    250 https://doi.org/10.1016/j.tplants.2014.01.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038320544
    251 rdf:type schema:CreativeWork
    252 https://doi.org/10.1042/bst20140040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056717628
    253 rdf:type schema:CreativeWork
    254 https://doi.org/10.1071/ea05228 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038570145
    255 rdf:type schema:CreativeWork
    256 https://doi.org/10.1073/pnas.0508418102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016104028
    257 rdf:type schema:CreativeWork
    258 https://doi.org/10.1073/pnas.0509021102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016161259
    259 rdf:type schema:CreativeWork
    260 https://doi.org/10.1073/pnas.96.8.4710 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027009396
    261 rdf:type schema:CreativeWork
    262 https://doi.org/10.1093/bioinformatics/bth230 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047038250
    263 rdf:type schema:CreativeWork
    264 https://doi.org/10.1093/bioinformatics/btm143 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029867719
    265 rdf:type schema:CreativeWork
    266 https://doi.org/10.1093/dnares/dss029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017271925
    267 rdf:type schema:CreativeWork
    268 https://doi.org/10.1093/jxb/eru125 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014446486
    269 rdf:type schema:CreativeWork
    270 https://doi.org/10.1093/jxb/eru549 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030430787
    271 rdf:type schema:CreativeWork
    272 https://doi.org/10.1104/pp.113.217703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010924199
    273 rdf:type schema:CreativeWork
    274 https://doi.org/10.1105/tpc.104.027136 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050256820
    275 rdf:type schema:CreativeWork
    276 https://doi.org/10.1105/tpc.108.064972 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025108076
    277 rdf:type schema:CreativeWork
    278 https://doi.org/10.1111/j.1365-294x.2012.05522.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1033113663
    279 rdf:type schema:CreativeWork
    280 https://doi.org/10.1111/pbr.12131 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019540723
    281 rdf:type schema:CreativeWork
    282 https://doi.org/10.1371/journal.pone.0147580 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023193813
    283 rdf:type schema:CreativeWork
    284 https://doi.org/10.2135/cropsci2007.04.0191 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069030309
    285 rdf:type schema:CreativeWork
    286 https://doi.org/10.3389/fpls.2015.01032 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053565078
    287 rdf:type schema:CreativeWork
    288 https://doi.org/10.4161/psb.25928 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072308128
    289 rdf:type schema:CreativeWork
    290 https://www.grid.ac/institutes/grid.8195.5 schema:alternateName University of Delhi
    291 schema:name Centre for Genetic Manipulation of Crop Plants, University of Delhi South Campus, Benito Juarez Road, 110021, New Delhi, India
    292 Department of Genetics, University of Delhi South Campus, Benito Juarez Road, 110021, New Delhi, India
    293 rdf:type schema:Organization
     




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


    ...