Improving genomic predictions by correction of genotypes from genotyping by sequencing in livestock populations View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Xiao Wang, Mogens Sandø Lund, Peipei Ma, Luc Janss, Haja N. Kadarmideen, Guosheng Su

ABSTRACT

Background: Genotyping by sequencing (GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes, dependent on the genotyping depths. In this study, a method correcting this type of genotyping error was demonstrated. The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations. Results: Chip array (Chip) and four depths of GBS data was simulated. After quality control (call rate ≥ 0.8 and MAF ≥ 0.01), the remaining number of Chip and GBS SNPs were both approximately 7,000, averaged over 10 replicates. GBS genotypes were corrected with the proposed method. The reliability of genomic prediction was calculated using GBS, corrected GBS (GBSc), true genotypes for the GBS loci (GBSr) and Chip data. The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS. For genomic prediction, using Chip data resulted in the highest reliability. As the depth increased to 10, the prediction reliabilities using GBS and GBSc data approached those using true GBS data. The reliabilities of genomic prediction using GBSc data were 0.604, 0.672, 0.684 and 0.704 after genomic correction, with the improved values of 0.013, 0.009, 0.006 and 0.001 at depth = 2, 4, 5 and 10, respectively. Conclusions: The current study showed that a correction method for GBS data increased the genotype accuracies and, consequently, improved genomic predictions. These results suggest that a correction of GBS genotype is necessary, especially for the GBS data with low depths. More... »

PAGES

8

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40104-019-0315-z

DOI

http://dx.doi.org/10.1186/s40104-019-0315-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111756276

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30719286


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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Technical University of Denmark", 
          "id": "https://www.grid.ac/institutes/grid.5170.3", 
          "name": [
            "Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark", 
            "Department of Bio and Health Informatics and Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Xiao", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Aarhus University", 
          "id": "https://www.grid.ac/institutes/grid.7048.b", 
          "name": [
            "Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lund", 
        "givenName": "Mogens Sand\u00f8", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shanghai Jiao Tong University", 
          "id": "https://www.grid.ac/institutes/grid.16821.3c", 
          "name": [
            "Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark", 
            "School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ma", 
        "givenName": "Peipei", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Aarhus University", 
          "id": "https://www.grid.ac/institutes/grid.7048.b", 
          "name": [
            "Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Janss", 
        "givenName": "Luc", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Technical University of Denmark", 
          "id": "https://www.grid.ac/institutes/grid.5170.3", 
          "name": [
            "Department of Bio and Health Informatics and Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kadarmideen", 
        "givenName": "Haja N.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Aarhus University", 
          "id": "https://www.grid.ac/institutes/grid.7048.b", 
          "name": [
            "Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Su", 
        "givenName": "Guosheng", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/1471-2156-8-74", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000111034", 
          "https://doi.org/10.1186/1471-2156-8-74"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0062137", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000309952"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg777", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000451502", 
          "https://doi.org/10.1038/nrg777"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg777", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000451502", 
          "https://doi.org/10.1038/nrg777"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0019379", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005754650"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fgene.2015.00337", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011523174"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fpls.2014.00484", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015293823"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng.2283", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020345108", 
          "https://doi.org/10.1038/ng.2283"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/mec.12105", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021966307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btr509", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022904685"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1439-0388.2012.01015.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024366489"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pgen.1005631", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024990670"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12864-015-2252-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026298432", 
          "https://doi.org/10.1186/s12864-015-2252-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btp045", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029494370"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg3012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034382223", 
          "https://doi.org/10.1038/nrg3012"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pgen.1006091", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035066068"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/genetics.112.147710", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039966655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/genetics.112.147710", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039966655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0032253", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041572486"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12859-014-0356-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042585636", 
          "https://doi.org/10.1186/s12859-014-0356-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12859-014-0356-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042585636", 
          "https://doi.org/10.1186/s12859-014-0356-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12859-014-0356-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042585636", 
          "https://doi.org/10.1186/s12859-014-0356-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12711-015-0102-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046946886", 
          "https://doi.org/10.1186/s12711-015-0102-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12711-015-0102-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046946886", 
          "https://doi.org/10.1186/s12711-015-0102-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg2575", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051461847", 
          "https://doi.org/10.1038/nrg2575"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg2575", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051461847", 
          "https://doi.org/10.1038/nrg2575"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.117259.110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053247679"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2135/cropsci2007.02.0085tpg", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069030260"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2529430", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069975084"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3835/plantgenome2012.05.0005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071447816"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.7150/ijbs.3.166", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1073618655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1479-7364-3-4-371", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1077949089", 
          "https://doi.org/10.1186/1479-7364-3-4-371"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/g3.117.039008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079397729"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1534/g3.117.039008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079397729"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/raq.12193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083548222"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fpls.2018.00369", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101630956"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/380899", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105919040"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/380899", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105919040"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/380899", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105919040"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "Background: Genotyping by sequencing (GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes, dependent on the genotyping depths. In this study, a method correcting this type of genotyping error was demonstrated. The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations.\nResults: Chip array (Chip) and four depths of GBS data was simulated. After quality control (call rate\u2009\u2265\u20090.8 and MAF\u2009\u2265\u20090.01), the remaining number of Chip and GBS SNPs were both approximately 7,000, averaged over 10 replicates. GBS genotypes were corrected with the proposed method. The reliability of genomic prediction was calculated using GBS, corrected GBS (GBSc), true genotypes for the GBS loci (GBSr) and Chip data. The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS. For genomic prediction, using Chip data resulted in the highest reliability. As the depth increased to 10, the prediction reliabilities using GBS and GBSc data approached those using true GBS data. The reliabilities of genomic prediction using GBSc data were 0.604, 0.672, 0.684 and 0.704 after genomic correction, with the improved values of 0.013, 0.009, 0.006 and 0.001 at depth\u2009=\u20092, 4, 5 and 10, respectively.\nConclusions: The current study showed that a correction method for GBS data increased the genotype accuracies and, consequently, improved genomic predictions. These results suggest that a correction of GBS genotype is necessary, especially for the GBS data with low depths.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s40104-019-0315-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1046697", 
        "issn": [
          "1674-9782", 
          "2049-1891"
        ], 
        "name": "Journal of Animal Science and Biotechnology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "10"
      }
    ], 
    "name": "Improving genomic predictions by correction of genotypes from genotyping by sequencing in livestock populations", 
    "pagination": "8", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "b73639f345cbe87af46425d192fe458c6efd41dbba1c44dcba05715681918a01"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30719286"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101581293"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s40104-019-0315-z"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1111756276"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s40104-019-0315-z", 
      "https://app.dimensions.ai/details/publication/pub.1111756276"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:01", 
    "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/v90/0000000329_0000000329/records_74697_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs40104-019-0315-z"
  }
]
 

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.10.1186/s40104-019-0315-z'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s40104-019-0315-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s40104-019-0315-z'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s40104-019-0315-z'


 

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

205 TRIPLES      21 PREDICATES      59 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s40104-019-0315-z schema:about anzsrc-for:06
2 anzsrc-for:0604
3 schema:author N5de9a206104144c893db82f69e9ed2bd
4 schema:citation sg:pub.10.1038/ng.2283
5 sg:pub.10.1038/nrg2575
6 sg:pub.10.1038/nrg3012
7 sg:pub.10.1038/nrg777
8 sg:pub.10.1186/1471-2156-8-74
9 sg:pub.10.1186/1479-7364-3-4-371
10 sg:pub.10.1186/s12711-015-0102-z
11 sg:pub.10.1186/s12859-014-0356-4
12 sg:pub.10.1186/s12864-015-2252-3
13 https://doi.org/10.1093/bioinformatics/btp045
14 https://doi.org/10.1093/bioinformatics/btr509
15 https://doi.org/10.1101/380899
16 https://doi.org/10.1101/gr.117259.110
17 https://doi.org/10.1111/j.1439-0388.2012.01015.x
18 https://doi.org/10.1111/mec.12105
19 https://doi.org/10.1111/raq.12193
20 https://doi.org/10.1371/journal.pgen.1005631
21 https://doi.org/10.1371/journal.pgen.1006091
22 https://doi.org/10.1371/journal.pone.0019379
23 https://doi.org/10.1371/journal.pone.0032253
24 https://doi.org/10.1371/journal.pone.0062137
25 https://doi.org/10.1534/g3.117.039008
26 https://doi.org/10.1534/genetics.112.147710
27 https://doi.org/10.2135/cropsci2007.02.0085tpg
28 https://doi.org/10.2307/2529430
29 https://doi.org/10.3389/fgene.2015.00337
30 https://doi.org/10.3389/fpls.2014.00484
31 https://doi.org/10.3389/fpls.2018.00369
32 https://doi.org/10.3835/plantgenome2012.05.0005
33 https://doi.org/10.7150/ijbs.3.166
34 schema:datePublished 2019-12
35 schema:datePublishedReg 2019-12-01
36 schema:description Background: Genotyping by sequencing (GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes, dependent on the genotyping depths. In this study, a method correcting this type of genotyping error was demonstrated. The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations. Results: Chip array (Chip) and four depths of GBS data was simulated. After quality control (call rate ≥ 0.8 and MAF ≥ 0.01), the remaining number of Chip and GBS SNPs were both approximately 7,000, averaged over 10 replicates. GBS genotypes were corrected with the proposed method. The reliability of genomic prediction was calculated using GBS, corrected GBS (GBSc), true genotypes for the GBS loci (GBSr) and Chip data. The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS. For genomic prediction, using Chip data resulted in the highest reliability. As the depth increased to 10, the prediction reliabilities using GBS and GBSc data approached those using true GBS data. The reliabilities of genomic prediction using GBSc data were 0.604, 0.672, 0.684 and 0.704 after genomic correction, with the improved values of 0.013, 0.009, 0.006 and 0.001 at depth = 2, 4, 5 and 10, respectively. Conclusions: The current study showed that a correction method for GBS data increased the genotype accuracies and, consequently, improved genomic predictions. These results suggest that a correction of GBS genotype is necessary, especially for the GBS data with low depths.
37 schema:genre research_article
38 schema:inLanguage en
39 schema:isAccessibleForFree true
40 schema:isPartOf N8a8179f68d9a4378ad766da8399dc834
41 Ncfca36ba85004067a687936890ad266a
42 sg:journal.1046697
43 schema:name Improving genomic predictions by correction of genotypes from genotyping by sequencing in livestock populations
44 schema:pagination 8
45 schema:productId N10d0a1c0fe584a05be338f9f08cab03e
46 N5f77fcd2fcba43cdb19f8de792bdab92
47 N817eb351ad0f42f696002bb49bc4be97
48 Na487d68bc1f14f3581c5f75039748243
49 Nbf651042213c465c9387799ee5a012be
50 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111756276
51 https://doi.org/10.1186/s40104-019-0315-z
52 schema:sdDatePublished 2019-04-11T09:01
53 schema:sdLicense https://scigraph.springernature.com/explorer/license/
54 schema:sdPublisher Nd06a2d8907904ea4a4b2a4000b1771ba
55 schema:url https://link.springer.com/10.1186%2Fs40104-019-0315-z
56 sgo:license sg:explorer/license/
57 sgo:sdDataset articles
58 rdf:type schema:ScholarlyArticle
59 N10d0a1c0fe584a05be338f9f08cab03e schema:name readcube_id
60 schema:value b73639f345cbe87af46425d192fe458c6efd41dbba1c44dcba05715681918a01
61 rdf:type schema:PropertyValue
62 N2a78fb84b206410f87b2023559df4bef rdf:first N69faf285f3704f8599e74796522931f5
63 rdf:rest Na1e8e43ea4d3480896ecb4972ffb0c50
64 N30896f425b564ae7925d21dc25902b2e rdf:first Nb8dcdf209eca499aa0100639b8d4e060
65 rdf:rest Nd7ef6b696d8a40d08242cff75e8798fc
66 N49a7b8225e4149e9b7243cca504cb556 rdf:first N4de3cd6b78094f7ab8318edb9b9dc7fb
67 rdf:rest N2a78fb84b206410f87b2023559df4bef
68 N4de3cd6b78094f7ab8318edb9b9dc7fb schema:affiliation https://www.grid.ac/institutes/grid.7048.b
69 schema:familyName Janss
70 schema:givenName Luc
71 rdf:type schema:Person
72 N5de9a206104144c893db82f69e9ed2bd rdf:first Naa879d01485447f2bb9301d519de6070
73 rdf:rest N30896f425b564ae7925d21dc25902b2e
74 N5f77fcd2fcba43cdb19f8de792bdab92 schema:name dimensions_id
75 schema:value pub.1111756276
76 rdf:type schema:PropertyValue
77 N69faf285f3704f8599e74796522931f5 schema:affiliation https://www.grid.ac/institutes/grid.5170.3
78 schema:familyName Kadarmideen
79 schema:givenName Haja N.
80 rdf:type schema:Person
81 N817eb351ad0f42f696002bb49bc4be97 schema:name doi
82 schema:value 10.1186/s40104-019-0315-z
83 rdf:type schema:PropertyValue
84 N8a8179f68d9a4378ad766da8399dc834 schema:volumeNumber 10
85 rdf:type schema:PublicationVolume
86 Na1e8e43ea4d3480896ecb4972ffb0c50 rdf:first Na58d2eaf25f24b52ac7c7f0b550f978f
87 rdf:rest rdf:nil
88 Na487d68bc1f14f3581c5f75039748243 schema:name nlm_unique_id
89 schema:value 101581293
90 rdf:type schema:PropertyValue
91 Na58d2eaf25f24b52ac7c7f0b550f978f schema:affiliation https://www.grid.ac/institutes/grid.7048.b
92 schema:familyName Su
93 schema:givenName Guosheng
94 rdf:type schema:Person
95 Naa879d01485447f2bb9301d519de6070 schema:affiliation https://www.grid.ac/institutes/grid.5170.3
96 schema:familyName Wang
97 schema:givenName Xiao
98 rdf:type schema:Person
99 Nb8dcdf209eca499aa0100639b8d4e060 schema:affiliation https://www.grid.ac/institutes/grid.7048.b
100 schema:familyName Lund
101 schema:givenName Mogens Sandø
102 rdf:type schema:Person
103 Nbf651042213c465c9387799ee5a012be schema:name pubmed_id
104 schema:value 30719286
105 rdf:type schema:PropertyValue
106 Ncfca36ba85004067a687936890ad266a schema:issueNumber 1
107 rdf:type schema:PublicationIssue
108 Nd06a2d8907904ea4a4b2a4000b1771ba schema:name Springer Nature - SN SciGraph project
109 rdf:type schema:Organization
110 Nd7ef6b696d8a40d08242cff75e8798fc rdf:first Nfc1b4583abd9446489bf5800ddb2828d
111 rdf:rest N49a7b8225e4149e9b7243cca504cb556
112 Nfc1b4583abd9446489bf5800ddb2828d schema:affiliation https://www.grid.ac/institutes/grid.16821.3c
113 schema:familyName Ma
114 schema:givenName Peipei
115 rdf:type schema:Person
116 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
117 schema:name Biological Sciences
118 rdf:type schema:DefinedTerm
119 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
120 schema:name Genetics
121 rdf:type schema:DefinedTerm
122 sg:journal.1046697 schema:issn 1674-9782
123 2049-1891
124 schema:name Journal of Animal Science and Biotechnology
125 rdf:type schema:Periodical
126 sg:pub.10.1038/ng.2283 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020345108
127 https://doi.org/10.1038/ng.2283
128 rdf:type schema:CreativeWork
129 sg:pub.10.1038/nrg2575 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051461847
130 https://doi.org/10.1038/nrg2575
131 rdf:type schema:CreativeWork
132 sg:pub.10.1038/nrg3012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034382223
133 https://doi.org/10.1038/nrg3012
134 rdf:type schema:CreativeWork
135 sg:pub.10.1038/nrg777 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000451502
136 https://doi.org/10.1038/nrg777
137 rdf:type schema:CreativeWork
138 sg:pub.10.1186/1471-2156-8-74 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000111034
139 https://doi.org/10.1186/1471-2156-8-74
140 rdf:type schema:CreativeWork
141 sg:pub.10.1186/1479-7364-3-4-371 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077949089
142 https://doi.org/10.1186/1479-7364-3-4-371
143 rdf:type schema:CreativeWork
144 sg:pub.10.1186/s12711-015-0102-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1046946886
145 https://doi.org/10.1186/s12711-015-0102-z
146 rdf:type schema:CreativeWork
147 sg:pub.10.1186/s12859-014-0356-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042585636
148 https://doi.org/10.1186/s12859-014-0356-4
149 rdf:type schema:CreativeWork
150 sg:pub.10.1186/s12864-015-2252-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026298432
151 https://doi.org/10.1186/s12864-015-2252-3
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1093/bioinformatics/btp045 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029494370
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1093/bioinformatics/btr509 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022904685
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1101/380899 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105919040
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1101/gr.117259.110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053247679
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1111/j.1439-0388.2012.01015.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1024366489
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1111/mec.12105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021966307
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1111/raq.12193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083548222
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1371/journal.pgen.1005631 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024990670
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1371/journal.pgen.1006091 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035066068
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1371/journal.pone.0019379 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005754650
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1371/journal.pone.0032253 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041572486
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1371/journal.pone.0062137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000309952
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1534/g3.117.039008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079397729
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1534/genetics.112.147710 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039966655
180 rdf:type schema:CreativeWork
181 https://doi.org/10.2135/cropsci2007.02.0085tpg schema:sameAs https://app.dimensions.ai/details/publication/pub.1069030260
182 rdf:type schema:CreativeWork
183 https://doi.org/10.2307/2529430 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069975084
184 rdf:type schema:CreativeWork
185 https://doi.org/10.3389/fgene.2015.00337 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011523174
186 rdf:type schema:CreativeWork
187 https://doi.org/10.3389/fpls.2014.00484 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015293823
188 rdf:type schema:CreativeWork
189 https://doi.org/10.3389/fpls.2018.00369 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101630956
190 rdf:type schema:CreativeWork
191 https://doi.org/10.3835/plantgenome2012.05.0005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071447816
192 rdf:type schema:CreativeWork
193 https://doi.org/10.7150/ijbs.3.166 schema:sameAs https://app.dimensions.ai/details/publication/pub.1073618655
194 rdf:type schema:CreativeWork
195 https://www.grid.ac/institutes/grid.16821.3c schema:alternateName Shanghai Jiao Tong University
196 schema:name Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
197 School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, China
198 rdf:type schema:Organization
199 https://www.grid.ac/institutes/grid.5170.3 schema:alternateName Technical University of Denmark
200 schema:name Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
201 Department of Bio and Health Informatics and Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
202 rdf:type schema:Organization
203 https://www.grid.ac/institutes/grid.7048.b schema:alternateName Aarhus University
204 schema:name Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
205 rdf:type schema:Organization
 




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


...