Robust QTL effect estimation using the Minimum Distance method View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1999-09

AUTHORS

M Pérez-Enciso, M A Toro

ABSTRACT

Robustness has received little attention in QTL studies. We compare Maximum Likelihood (ML) and the Minimum Distance (MD) methods when there exists data contamination caused by outliers. A backcross population of size (N) 200 and 500 and 0, 5 or 25 outliers was simulated. The mean and standard deviation of the first QTL genotype were set to 1. Four cases were considered: (i) micro2=1, sigma2=1; (ii)micro2=1, sigma2=1.25; (iii) micro2=1.252, sigma2=1; (iv) micro2=1.282, sigma2=1.25, where micro2 and sigma2 are the mean and standard deviation of the second genotype. Either full or selective genotyping was considered. A Monte Carlo MD method is proposed to deal with missing genotypes. MD estimates were much more robust than ML estimates, especially with respect to scale parameter estimates, and with selective genotyping. More... »

PAGES

347

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/sj.hdy.6885800

DOI

http://dx.doi.org/10.1038/sj.hdy.6885800

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018317162

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/10504433


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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genotype", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Likelihood Functions", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Genetic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Monte Carlo Method", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Quantitative Trait, Heritable", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Lleida", 
          "id": "https://www.grid.ac/institutes/grid.15043.33", 
          "name": [
            "Area de Producci\u00f3 Animal, Centre UdL-IRTA, 25198 Lleida, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "P\u00e9rez-Enciso", 
        "givenName": "M", 
        "id": "sg:person.01041433710.67", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041433710.67"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Instituto Nacional de Investigaci\u00f3n y Tecnolog\u00eda Agraria y Alimentaria", 
          "id": "https://www.grid.ac/institutes/grid.419190.4", 
          "name": [
            "Area de Biotecnolog\u00eda y Mejora Gen\u00e9tica Animal, CIT-INIA, Carretera Coru\u00f1a km 7, 28040 Madrid, Spain"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Toro", 
        "givenName": "M A", 
        "id": "sg:person.01352141722.38", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01352141722.38"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/hdy.1992.131", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003515428", 
          "https://doi.org/10.1038/hdy.1992.131"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/hdy.1992.131", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003515428", 
          "https://doi.org/10.1038/hdy.1992.131"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/hdy.1992.5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004498447", 
          "https://doi.org/10.1038/hdy.1992.5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/hdy.1992.5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004498447", 
          "https://doi.org/10.1038/hdy.1992.5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s001220050773", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012423079", 
          "https://doi.org/10.1007/s001220050773"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-9473(94)00065-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015270444"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4684-0192-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017252284", 
          "https://doi.org/10.1007/978-1-4684-0192-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4684-0192-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017252284", 
          "https://doi.org/10.1007/978-1-4684-0192-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/hdy.1992.121", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025014176", 
          "https://doi.org/10.1038/hdy.1992.121"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/hdy.1992.121", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025014176", 
          "https://doi.org/10.1038/hdy.1992.121"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aoms/1177729694", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026070931"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/hdy.1993.36", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038215098", 
          "https://doi.org/10.1038/hdy.1993.36"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/hdy.1993.36", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038215098", 
          "https://doi.org/10.1038/hdy.1993.36"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00225729", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045009929", 
          "https://doi.org/10.1007/bf00225729"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aoms/1177707038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046495817"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1980.10477522", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058302345"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1984.10478085", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058303011"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1990.10474930", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058303870"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2411042", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069919751"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2533860", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069979087"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1079037915", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1082499359", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1082535482", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1082609466", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1083285718", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1558-5646.1997.tb03960.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085739361"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1106884602", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/9781118165485", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1106884602"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1999-09", 
    "datePublishedReg": "1999-09-01", 
    "description": "Robustness has received little attention in QTL studies. We compare Maximum Likelihood (ML) and the Minimum Distance (MD) methods when there exists data contamination caused by outliers. A backcross population of size (N) 200 and 500 and 0, 5 or 25 outliers was simulated. The mean and standard deviation of the first QTL genotype were set to 1. Four cases were considered: (i) micro2=1, sigma2=1; (ii)micro2=1, sigma2=1.25; (iii) micro2=1.252, sigma2=1; (iv) micro2=1.282, sigma2=1.25, where micro2 and sigma2 are the mean and standard deviation of the second genotype. Either full or selective genotyping was considered. A Monte Carlo MD method is proposed to deal with missing genotypes. MD estimates were much more robust than ML estimates, especially with respect to scale parameter estimates, and with selective genotyping.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/sj.hdy.6885800", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1017442", 
        "issn": [
          "0018-067X", 
          "1365-2540"
        ], 
        "name": "Heredity", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "83"
      }
    ], 
    "name": "Robust QTL effect estimation using the Minimum Distance method", 
    "pagination": "347", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "574619728a21cc528a6111c4393992601da825889d859840bb09d503283590e1"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "10504433"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "0373007"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/sj.hdy.6885800"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1018317162"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/sj.hdy.6885800", 
      "https://app.dimensions.ai/details/publication/pub.1018317162"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:04", 
    "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/v90/0000000360_0000000360/records_118318_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/6885800"
  }
]
 

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.10.1038/sj.hdy.6885800'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/sj.hdy.6885800'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/sj.hdy.6885800'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/sj.hdy.6885800'


 

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

169 TRIPLES      21 PREDICATES      57 URIs      26 LITERALS      14 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/sj.hdy.6885800 schema:about N30653d9d6b2d4eb1bc31baa9d73cafaa
2 N35259e38aaad460787b1e709ee65841f
3 Na5bb2907b09a4785b3df03a0a96ee2ea
4 Nc7cf84f74931415888fa21de4f52bcec
5 Nea39425cd03243ea95bd63b084012aae
6 anzsrc-for:01
7 anzsrc-for:0104
8 schema:author N46430a1086614310934e484324bac493
9 schema:citation sg:pub.10.1007/978-1-4684-0192-9
10 sg:pub.10.1007/bf00225729
11 sg:pub.10.1007/s001220050773
12 sg:pub.10.1038/hdy.1992.121
13 sg:pub.10.1038/hdy.1992.131
14 sg:pub.10.1038/hdy.1992.5
15 sg:pub.10.1038/hdy.1993.36
16 https://app.dimensions.ai/details/publication/pub.1079037915
17 https://app.dimensions.ai/details/publication/pub.1082499359
18 https://app.dimensions.ai/details/publication/pub.1082535482
19 https://app.dimensions.ai/details/publication/pub.1082609466
20 https://app.dimensions.ai/details/publication/pub.1083285718
21 https://app.dimensions.ai/details/publication/pub.1106884602
22 https://doi.org/10.1002/9781118165485
23 https://doi.org/10.1016/0167-9473(94)00065-4
24 https://doi.org/10.1080/01621459.1980.10477522
25 https://doi.org/10.1080/01621459.1984.10478085
26 https://doi.org/10.1080/01621459.1990.10474930
27 https://doi.org/10.1111/j.1558-5646.1997.tb03960.x
28 https://doi.org/10.1214/aoms/1177707038
29 https://doi.org/10.1214/aoms/1177729694
30 https://doi.org/10.2307/2411042
31 https://doi.org/10.2307/2533860
32 schema:datePublished 1999-09
33 schema:datePublishedReg 1999-09-01
34 schema:description Robustness has received little attention in QTL studies. We compare Maximum Likelihood (ML) and the Minimum Distance (MD) methods when there exists data contamination caused by outliers. A backcross population of size (N) 200 and 500 and 0, 5 or 25 outliers was simulated. The mean and standard deviation of the first QTL genotype were set to 1. Four cases were considered: (i) micro2=1, sigma2=1; (ii)micro2=1, sigma2=1.25; (iii) micro2=1.252, sigma2=1; (iv) micro2=1.282, sigma2=1.25, where micro2 and sigma2 are the mean and standard deviation of the second genotype. Either full or selective genotyping was considered. A Monte Carlo MD method is proposed to deal with missing genotypes. MD estimates were much more robust than ML estimates, especially with respect to scale parameter estimates, and with selective genotyping.
35 schema:genre research_article
36 schema:inLanguage en
37 schema:isAccessibleForFree true
38 schema:isPartOf N2b6439a1ef194f24b40704aeb2c84e53
39 N3a18113d9d4e41f5be1bf21572304b34
40 sg:journal.1017442
41 schema:name Robust QTL effect estimation using the Minimum Distance method
42 schema:pagination 347
43 schema:productId N09b55b66e78c472e9e5af5baefdabb66
44 N516074a2744b44d6b398fb3ca95a1a44
45 N5bf588f0c9b045ec892196f87ebba83b
46 Nbed1cf57588843ed894e4fea4164ca6b
47 Ndf6595d3728648f6b587af36f5a3128c
48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018317162
49 https://doi.org/10.1038/sj.hdy.6885800
50 schema:sdDatePublished 2019-04-11T12:04
51 schema:sdLicense https://scigraph.springernature.com/explorer/license/
52 schema:sdPublisher Nd555594ad26947e28c421681bb79266c
53 schema:url https://www.nature.com/articles/6885800
54 sgo:license sg:explorer/license/
55 sgo:sdDataset articles
56 rdf:type schema:ScholarlyArticle
57 N09b55b66e78c472e9e5af5baefdabb66 schema:name pubmed_id
58 schema:value 10504433
59 rdf:type schema:PropertyValue
60 N2b6439a1ef194f24b40704aeb2c84e53 schema:issueNumber 3
61 rdf:type schema:PublicationIssue
62 N30653d9d6b2d4eb1bc31baa9d73cafaa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
63 schema:name Likelihood Functions
64 rdf:type schema:DefinedTerm
65 N35259e38aaad460787b1e709ee65841f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
66 schema:name Monte Carlo Method
67 rdf:type schema:DefinedTerm
68 N3a18113d9d4e41f5be1bf21572304b34 schema:volumeNumber 83
69 rdf:type schema:PublicationVolume
70 N46430a1086614310934e484324bac493 rdf:first sg:person.01041433710.67
71 rdf:rest Nf92698be8f4547eabccd443764ae63dc
72 N516074a2744b44d6b398fb3ca95a1a44 schema:name doi
73 schema:value 10.1038/sj.hdy.6885800
74 rdf:type schema:PropertyValue
75 N5bf588f0c9b045ec892196f87ebba83b schema:name dimensions_id
76 schema:value pub.1018317162
77 rdf:type schema:PropertyValue
78 Na5bb2907b09a4785b3df03a0a96ee2ea schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
79 schema:name Genotype
80 rdf:type schema:DefinedTerm
81 Nbed1cf57588843ed894e4fea4164ca6b schema:name nlm_unique_id
82 schema:value 0373007
83 rdf:type schema:PropertyValue
84 Nc7cf84f74931415888fa21de4f52bcec schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
85 schema:name Quantitative Trait, Heritable
86 rdf:type schema:DefinedTerm
87 Nd555594ad26947e28c421681bb79266c schema:name Springer Nature - SN SciGraph project
88 rdf:type schema:Organization
89 Ndf6595d3728648f6b587af36f5a3128c schema:name readcube_id
90 schema:value 574619728a21cc528a6111c4393992601da825889d859840bb09d503283590e1
91 rdf:type schema:PropertyValue
92 Nea39425cd03243ea95bd63b084012aae schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
93 schema:name Models, Genetic
94 rdf:type schema:DefinedTerm
95 Nf92698be8f4547eabccd443764ae63dc rdf:first sg:person.01352141722.38
96 rdf:rest rdf:nil
97 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
98 schema:name Mathematical Sciences
99 rdf:type schema:DefinedTerm
100 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
101 schema:name Statistics
102 rdf:type schema:DefinedTerm
103 sg:journal.1017442 schema:issn 0018-067X
104 1365-2540
105 schema:name Heredity
106 rdf:type schema:Periodical
107 sg:person.01041433710.67 schema:affiliation https://www.grid.ac/institutes/grid.15043.33
108 schema:familyName Pérez-Enciso
109 schema:givenName M
110 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041433710.67
111 rdf:type schema:Person
112 sg:person.01352141722.38 schema:affiliation https://www.grid.ac/institutes/grid.419190.4
113 schema:familyName Toro
114 schema:givenName M A
115 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01352141722.38
116 rdf:type schema:Person
117 sg:pub.10.1007/978-1-4684-0192-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017252284
118 https://doi.org/10.1007/978-1-4684-0192-9
119 rdf:type schema:CreativeWork
120 sg:pub.10.1007/bf00225729 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045009929
121 https://doi.org/10.1007/bf00225729
122 rdf:type schema:CreativeWork
123 sg:pub.10.1007/s001220050773 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012423079
124 https://doi.org/10.1007/s001220050773
125 rdf:type schema:CreativeWork
126 sg:pub.10.1038/hdy.1992.121 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025014176
127 https://doi.org/10.1038/hdy.1992.121
128 rdf:type schema:CreativeWork
129 sg:pub.10.1038/hdy.1992.131 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003515428
130 https://doi.org/10.1038/hdy.1992.131
131 rdf:type schema:CreativeWork
132 sg:pub.10.1038/hdy.1992.5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004498447
133 https://doi.org/10.1038/hdy.1992.5
134 rdf:type schema:CreativeWork
135 sg:pub.10.1038/hdy.1993.36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038215098
136 https://doi.org/10.1038/hdy.1993.36
137 rdf:type schema:CreativeWork
138 https://app.dimensions.ai/details/publication/pub.1079037915 schema:CreativeWork
139 https://app.dimensions.ai/details/publication/pub.1082499359 schema:CreativeWork
140 https://app.dimensions.ai/details/publication/pub.1082535482 schema:CreativeWork
141 https://app.dimensions.ai/details/publication/pub.1082609466 schema:CreativeWork
142 https://app.dimensions.ai/details/publication/pub.1083285718 schema:CreativeWork
143 https://app.dimensions.ai/details/publication/pub.1106884602 schema:CreativeWork
144 https://doi.org/10.1002/9781118165485 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106884602
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/0167-9473(94)00065-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015270444
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1080/01621459.1980.10477522 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058302345
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1080/01621459.1984.10478085 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058303011
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1080/01621459.1990.10474930 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058303870
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1111/j.1558-5646.1997.tb03960.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1085739361
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1214/aoms/1177707038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046495817
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1214/aoms/1177729694 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026070931
159 rdf:type schema:CreativeWork
160 https://doi.org/10.2307/2411042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069919751
161 rdf:type schema:CreativeWork
162 https://doi.org/10.2307/2533860 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069979087
163 rdf:type schema:CreativeWork
164 https://www.grid.ac/institutes/grid.15043.33 schema:alternateName University of Lleida
165 schema:name Area de Producció Animal, Centre UdL-IRTA, 25198 Lleida, Spain
166 rdf:type schema:Organization
167 https://www.grid.ac/institutes/grid.419190.4 schema:alternateName Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria
168 schema:name Area de Biotecnología y Mejora Genética Animal, CIT-INIA, Carretera Coruña km 7, 28040 Madrid, Spain
169 rdf:type schema:Organization
 




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


...