Mixed inheritance model for resistance to agromyzid beanfly (Melanagromyza sojae Zehntner) in soybean View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2001-10

AUTHORS

Jiankang Wang, Junyi Gai

ABSTRACT

A quantitative trait could be controlled by a few major genes and many polygenes. Distinguishing the effects of major genes from polygenes and/or environments is important for understanding the expression of a major gene in relation to its genetic background, and for predicting the segregation of a cross in breeding. Our objective was to re-analyze the resistance of soybean to agromyzid beanfly by a mixed inheritance model. Number of insects in stem (NIS) was used as an indicator of resistance. The previous result from the segregation ratio of resistance and susceptibility was that resistance was controlled by one dominant gene. The major results from the mixed inheritance model were (1) the inheritance of resistance was controlled by one major gene along with minor genes; (2) Additive and dominance effects of minor genes were generally less than those of the major gene and varied among crosses, indicating different minor gene systems; (3) Heritability was higher for the major gene than for the minor genes; (4) The F2 plants and F2:3 lines were classified into appropriate genotypes according to their posterior probabilities and the critical value to distinguish resistant and susceptible plants was given for NIS based on the classification. These results indicated that mixed major gene and polygene genetic analysis was superior to the frequently used classical Mendelian method. More... »

PAGES

9-18

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1012649506212

DOI

http://dx.doi.org/10.1023/a:1012649506212

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031598902


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": "Henan Academy of Agricultural Sciences", 
          "id": "https://www.grid.ac/institutes/grid.495707.8", 
          "name": [
            "Laboratory Center, Henan Academy of Agricultural Sciences, Zhengzhou, 450002, Henan, PR China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Jiankang", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Nanjing Agricultural University", 
          "id": "https://www.grid.ac/institutes/grid.27871.3b", 
          "name": [
            "Soybean Research Institute, Nanjing Agricultural University, 210095, Nanjing, PR China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gai", 
        "givenName": "Junyi", 
        "id": "sg:person.01174466073.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01174466073.42"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf00223932", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011199623", 
          "https://doi.org/10.1007/bf00223932"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s001220100628", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015106868", 
          "https://doi.org/10.1007/s001220100628"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s001220100628", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015106868", 
          "https://doi.org/10.1007/s001220100628"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00288836", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032322612", 
          "https://doi.org/10.1007/bf00288836"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s001220051005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038784876", 
          "https://doi.org/10.1007/s001220051005"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s001220051005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038784876", 
          "https://doi.org/10.1007/s001220051005"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/hdy.1992.44", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039855164", 
          "https://doi.org/10.1038/hdy.1992.44"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/hdy.1992.44", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039855164", 
          "https://doi.org/10.1038/hdy.1992.44"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/ee/12.1.260", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059496217"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2533203", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069978608"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2527/jas1986.63168x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070901644"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1076298140", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1080543807", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1080630303", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1081780132", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1082668595", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2001-10", 
    "datePublishedReg": "2001-10-01", 
    "description": "A quantitative trait could be controlled by a few major genes and many polygenes. Distinguishing the effects of major genes from polygenes and/or environments is important for understanding the expression of a major gene in relation to its genetic background, and for predicting the segregation of a cross in breeding. Our objective was to re-analyze the resistance of soybean to agromyzid beanfly by a mixed inheritance model. Number of insects in stem (NIS) was used as an indicator of resistance. The previous result from the segregation ratio of resistance and susceptibility was that resistance was controlled by one dominant gene. The major results from the mixed inheritance model were (1) the inheritance of resistance was controlled by one major gene along with minor genes; (2) Additive and dominance effects of minor genes were generally less than those of the major gene and varied among crosses, indicating different minor gene systems; (3) Heritability was higher for the major gene than for the minor genes; (4) The F2 plants and F2:3 lines were classified into appropriate genotypes according to their posterior probabilities and the critical value to distinguish resistant and susceptible plants was given for NIS based on the classification. These results indicated that mixed major gene and polygene genetic analysis was superior to the frequently used classical Mendelian method.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1023/a:1012649506212", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1028679", 
        "issn": [
          "0014-2336", 
          "1573-5060"
        ], 
        "name": "Euphytica", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "122"
      }
    ], 
    "name": "Mixed inheritance model for resistance to agromyzid beanfly (Melanagromyza sojae Zehntner) in soybean", 
    "pagination": "9-18", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "9687d04d036c9f7f07b9ff331fa47228acb93039429a24462869956cf88bae9d"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1023/a:1012649506212"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1031598902"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1023/a:1012649506212", 
      "https://app.dimensions.ai/details/publication/pub.1031598902"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T21:31", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8687_00000489.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1023/A:1012649506212"
  }
]
 

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.10.1023/a:1012649506212'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1023/a:1012649506212'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1023/a:1012649506212'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1023/a:1012649506212'


 

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

109 TRIPLES      21 PREDICATES      40 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1023/a:1012649506212 schema:about anzsrc-for:06
2 anzsrc-for:0604
3 schema:author N96a812017e3b411eaa0b1f969d6298c0
4 schema:citation sg:pub.10.1007/bf00223932
5 sg:pub.10.1007/bf00288836
6 sg:pub.10.1007/s001220051005
7 sg:pub.10.1007/s001220100628
8 sg:pub.10.1038/hdy.1992.44
9 https://app.dimensions.ai/details/publication/pub.1076298140
10 https://app.dimensions.ai/details/publication/pub.1080543807
11 https://app.dimensions.ai/details/publication/pub.1080630303
12 https://app.dimensions.ai/details/publication/pub.1081780132
13 https://app.dimensions.ai/details/publication/pub.1082668595
14 https://doi.org/10.1093/ee/12.1.260
15 https://doi.org/10.2307/2533203
16 https://doi.org/10.2527/jas1986.63168x
17 schema:datePublished 2001-10
18 schema:datePublishedReg 2001-10-01
19 schema:description A quantitative trait could be controlled by a few major genes and many polygenes. Distinguishing the effects of major genes from polygenes and/or environments is important for understanding the expression of a major gene in relation to its genetic background, and for predicting the segregation of a cross in breeding. Our objective was to re-analyze the resistance of soybean to agromyzid beanfly by a mixed inheritance model. Number of insects in stem (NIS) was used as an indicator of resistance. The previous result from the segregation ratio of resistance and susceptibility was that resistance was controlled by one dominant gene. The major results from the mixed inheritance model were (1) the inheritance of resistance was controlled by one major gene along with minor genes; (2) Additive and dominance effects of minor genes were generally less than those of the major gene and varied among crosses, indicating different minor gene systems; (3) Heritability was higher for the major gene than for the minor genes; (4) The F2 plants and F2:3 lines were classified into appropriate genotypes according to their posterior probabilities and the critical value to distinguish resistant and susceptible plants was given for NIS based on the classification. These results indicated that mixed major gene and polygene genetic analysis was superior to the frequently used classical Mendelian method.
20 schema:genre research_article
21 schema:inLanguage en
22 schema:isAccessibleForFree false
23 schema:isPartOf N650edbedb0a64c299a6cb828986be017
24 N9243a6031d904310ad84ae1e1040c518
25 sg:journal.1028679
26 schema:name Mixed inheritance model for resistance to agromyzid beanfly (Melanagromyza sojae Zehntner) in soybean
27 schema:pagination 9-18
28 schema:productId N133270389e8749e7b9944e24fac88764
29 N691c10f38edd40199410d81d93b29a40
30 Nb4c09efec8a749a3ac72354969db009d
31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031598902
32 https://doi.org/10.1023/a:1012649506212
33 schema:sdDatePublished 2019-04-10T21:31
34 schema:sdLicense https://scigraph.springernature.com/explorer/license/
35 schema:sdPublisher N309a1a20d543488394e0aa25c0acec4b
36 schema:url http://link.springer.com/10.1023/A:1012649506212
37 sgo:license sg:explorer/license/
38 sgo:sdDataset articles
39 rdf:type schema:ScholarlyArticle
40 N133270389e8749e7b9944e24fac88764 schema:name dimensions_id
41 schema:value pub.1031598902
42 rdf:type schema:PropertyValue
43 N2f179a0ca82a4ea88cf7c7a19edd207a schema:affiliation https://www.grid.ac/institutes/grid.495707.8
44 schema:familyName Wang
45 schema:givenName Jiankang
46 rdf:type schema:Person
47 N309a1a20d543488394e0aa25c0acec4b schema:name Springer Nature - SN SciGraph project
48 rdf:type schema:Organization
49 N358cc8535a1f4a36b328210869c85fa5 rdf:first sg:person.01174466073.42
50 rdf:rest rdf:nil
51 N650edbedb0a64c299a6cb828986be017 schema:volumeNumber 122
52 rdf:type schema:PublicationVolume
53 N691c10f38edd40199410d81d93b29a40 schema:name doi
54 schema:value 10.1023/a:1012649506212
55 rdf:type schema:PropertyValue
56 N9243a6031d904310ad84ae1e1040c518 schema:issueNumber 1
57 rdf:type schema:PublicationIssue
58 N96a812017e3b411eaa0b1f969d6298c0 rdf:first N2f179a0ca82a4ea88cf7c7a19edd207a
59 rdf:rest N358cc8535a1f4a36b328210869c85fa5
60 Nb4c09efec8a749a3ac72354969db009d schema:name readcube_id
61 schema:value 9687d04d036c9f7f07b9ff331fa47228acb93039429a24462869956cf88bae9d
62 rdf:type schema:PropertyValue
63 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
64 schema:name Biological Sciences
65 rdf:type schema:DefinedTerm
66 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
67 schema:name Genetics
68 rdf:type schema:DefinedTerm
69 sg:journal.1028679 schema:issn 0014-2336
70 1573-5060
71 schema:name Euphytica
72 rdf:type schema:Periodical
73 sg:person.01174466073.42 schema:affiliation https://www.grid.ac/institutes/grid.27871.3b
74 schema:familyName Gai
75 schema:givenName Junyi
76 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01174466073.42
77 rdf:type schema:Person
78 sg:pub.10.1007/bf00223932 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011199623
79 https://doi.org/10.1007/bf00223932
80 rdf:type schema:CreativeWork
81 sg:pub.10.1007/bf00288836 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032322612
82 https://doi.org/10.1007/bf00288836
83 rdf:type schema:CreativeWork
84 sg:pub.10.1007/s001220051005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038784876
85 https://doi.org/10.1007/s001220051005
86 rdf:type schema:CreativeWork
87 sg:pub.10.1007/s001220100628 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015106868
88 https://doi.org/10.1007/s001220100628
89 rdf:type schema:CreativeWork
90 sg:pub.10.1038/hdy.1992.44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039855164
91 https://doi.org/10.1038/hdy.1992.44
92 rdf:type schema:CreativeWork
93 https://app.dimensions.ai/details/publication/pub.1076298140 schema:CreativeWork
94 https://app.dimensions.ai/details/publication/pub.1080543807 schema:CreativeWork
95 https://app.dimensions.ai/details/publication/pub.1080630303 schema:CreativeWork
96 https://app.dimensions.ai/details/publication/pub.1081780132 schema:CreativeWork
97 https://app.dimensions.ai/details/publication/pub.1082668595 schema:CreativeWork
98 https://doi.org/10.1093/ee/12.1.260 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059496217
99 rdf:type schema:CreativeWork
100 https://doi.org/10.2307/2533203 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069978608
101 rdf:type schema:CreativeWork
102 https://doi.org/10.2527/jas1986.63168x schema:sameAs https://app.dimensions.ai/details/publication/pub.1070901644
103 rdf:type schema:CreativeWork
104 https://www.grid.ac/institutes/grid.27871.3b schema:alternateName Nanjing Agricultural University
105 schema:name Soybean Research Institute, Nanjing Agricultural University, 210095, Nanjing, PR China
106 rdf:type schema:Organization
107 https://www.grid.ac/institutes/grid.495707.8 schema:alternateName Henan Academy of Agricultural Sciences
108 schema:name Laboratory Center, Henan Academy of Agricultural Sciences, Zhengzhou, 450002, Henan, PR China
109 rdf:type schema:Organization
 




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


...