Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato.
Identifieur interne : 000688 ( Main/Exploration ); précédent : 000687; suivant : 000689Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato.
Auteurs : Benjamin Stich [Allemagne] ; Delphine Van Inghelandt [Allemagne]Source :
- Frontiers in plant science [ 1664-462X ] ; 2018.
Abstract
Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i) examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii) investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii) assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP), BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a Phytophthora infestans infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY), and tuber yield (TY) of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs.
DOI: 10.3389/fpls.2018.00159
PubMed: 29563919
PubMed Central: PMC5845909
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato.</title><author><name sortKey="Stich, Benjamin" sort="Stich, Benjamin" uniqKey="Stich B" first="Benjamin" last="Stich">Benjamin Stich</name><affiliation wicri:level="3"><nlm:affiliation>Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf</wicri:regionArea><placeName><region type="land" nuts="1">Rhénanie-du-Nord-Westphalie</region><region type="district" nuts="2">District de Düsseldorf</region><settlement type="city">Düsseldorf</settlement></placeName></affiliation><affiliation wicri:level="3"><nlm:affiliation>Cluster of Excellence on Plant Sciences, From Complex Traits towards Synthetic Modules, Düsseldorf, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Cluster of Excellence on Plant Sciences, From Complex Traits towards Synthetic Modules, Düsseldorf</wicri:regionArea><placeName><region type="land" nuts="1">Rhénanie-du-Nord-Westphalie</region><region type="district" nuts="2">District de Düsseldorf</region><settlement type="city">Düsseldorf</settlement></placeName></affiliation></author><author><name sortKey="Van Inghelandt, Delphine" sort="Van Inghelandt, Delphine" uniqKey="Van Inghelandt D" first="Delphine" last="Van Inghelandt">Delphine Van Inghelandt</name><affiliation wicri:level="3"><nlm:affiliation>Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf</wicri:regionArea><placeName><region type="land" nuts="1">Rhénanie-du-Nord-Westphalie</region><region type="district" nuts="2">District de Düsseldorf</region><settlement type="city">Düsseldorf</settlement></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2018">2018</date><idno type="RBID">pubmed:29563919</idno><idno type="pmid">29563919</idno><idno type="doi">10.3389/fpls.2018.00159</idno><idno type="pmc">PMC5845909</idno><idno type="wicri:Area/Main/Corpus">000794</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000794</idno><idno type="wicri:Area/Main/Curation">000794</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000794</idno><idno type="wicri:Area/Main/Exploration">000794</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato.</title><author><name sortKey="Stich, Benjamin" sort="Stich, Benjamin" uniqKey="Stich B" first="Benjamin" last="Stich">Benjamin Stich</name><affiliation wicri:level="3"><nlm:affiliation>Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf</wicri:regionArea><placeName><region type="land" nuts="1">Rhénanie-du-Nord-Westphalie</region><region type="district" nuts="2">District de Düsseldorf</region><settlement type="city">Düsseldorf</settlement></placeName></affiliation><affiliation wicri:level="3"><nlm:affiliation>Cluster of Excellence on Plant Sciences, From Complex Traits towards Synthetic Modules, Düsseldorf, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Cluster of Excellence on Plant Sciences, From Complex Traits towards Synthetic Modules, Düsseldorf</wicri:regionArea><placeName><region type="land" nuts="1">Rhénanie-du-Nord-Westphalie</region><region type="district" nuts="2">District de Düsseldorf</region><settlement type="city">Düsseldorf</settlement></placeName></affiliation></author><author><name sortKey="Van Inghelandt, Delphine" sort="Van Inghelandt, Delphine" uniqKey="Van Inghelandt D" first="Delphine" last="Van Inghelandt">Delphine Van Inghelandt</name><affiliation wicri:level="3"><nlm:affiliation>Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany.</nlm:affiliation><country xml:lang="fr">Allemagne</country><wicri:regionArea>Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf</wicri:regionArea><placeName><region type="land" nuts="1">Rhénanie-du-Nord-Westphalie</region><region type="district" nuts="2">District de Düsseldorf</region><settlement type="city">Düsseldorf</settlement></placeName></affiliation></author></analytic><series><title level="j">Frontiers in plant science</title><idno type="ISSN">1664-462X</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i) examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii) investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii) assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP), BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a <i>Phytophthora infestans</i> infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY), and tuber yield (TY) of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">29563919</PMID><DateRevised><Year>2020</Year><Month>10</Month><Day>01</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">1664-462X</ISSN><JournalIssue CitedMedium="Print"><Volume>9</Volume><PubDate><Year>2018</Year></PubDate></JournalIssue><Title>Frontiers in plant science</Title><ISOAbbreviation>Front Plant Sci</ISOAbbreviation></Journal><ArticleTitle>Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato.</ArticleTitle><Pagination><MedlinePgn>159</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3389/fpls.2018.00159</ELocationID><Abstract><AbstractText>Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i) examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii) investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii) assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP), BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a <i>Phytophthora infestans</i> infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY), and tuber yield (TY) of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Stich</LastName><ForeName>Benjamin</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Cluster of Excellence on Plant Sciences, From Complex Traits towards Synthetic Modules, Düsseldorf, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Van Inghelandt</LastName><ForeName>Delphine</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Institute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>03</Month><Day>07</Day></ArticleDate></Article><MedlineJournalInfo><Country>Switzerland</Country><MedlineTA>Front Plant Sci</MedlineTA><NlmUniqueID>101568200</NlmUniqueID><ISSNLinking>1664-462X</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Phytophthora infestans</Keyword><Keyword MajorTopicYN="N">genomic prediction</Keyword><Keyword MajorTopicYN="N">maturity</Keyword><Keyword MajorTopicYN="N">tetraploid potato</Keyword><Keyword MajorTopicYN="N">tuber starch content</Keyword><Keyword MajorTopicYN="N">tuber yield</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2017</Year><Month>11</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2018</Year><Month>01</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2018</Year><Month>3</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2018</Year><Month>3</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2018</Year><Month>3</Month><Day>23</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">29563919</ArticleId><ArticleId IdType="doi">10.3389/fpls.2018.00159</ArticleId><ArticleId IdType="pmc">PMC5845909</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Methods Mol Biol. 2013;1019:299-320</Citation><ArticleIdList><ArticleId IdType="pubmed">23756896</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 2014 Dec;198(4):1759-68</Citation><ArticleIdList><ArticleId IdType="pubmed">25324160</ArticleId></ArticleIdList></Reference><Reference><Citation>Theor Appl Genet. 2010 Oct;121(6):1151-70</Citation><ArticleIdList><ArticleId IdType="pubmed">20563789</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2015 Mar 20;10(3):e0121153</Citation><ArticleIdList><ArticleId IdType="pubmed">25793512</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 2009 Mar;181(3):1115-27</Citation><ArticleIdList><ArticleId IdType="pubmed">19139145</ArticleId></ArticleIdList></Reference><Reference><Citation>Trends Genet. 2013 Apr;29(4):248-56</Citation><ArticleIdList><ArticleId IdType="pubmed">23261028</ArticleId></ArticleIdList></Reference><Reference><Citation>G3 (Bethesda). 2012 Nov;2(11):1427-36</Citation><ArticleIdList><ArticleId IdType="pubmed">23173094</ArticleId></ArticleIdList></Reference><Reference><Citation>J Dairy Sci. 2008 Nov;91(11):4414-23</Citation><ArticleIdList><ArticleId IdType="pubmed">18946147</ArticleId></ArticleIdList></Reference><Reference><Citation>Evolution. 2004 Jul;58(7):1434-45</Citation><ArticleIdList><ArticleId IdType="pubmed">15341147</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 1993 May;134(1):369-75</Citation><ArticleIdList><ArticleId IdType="pubmed">8514144</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 2008 Jan;178(1):553-61</Citation><ArticleIdList><ArticleId IdType="pubmed">18202394</ArticleId></ArticleIdList></Reference><Reference><Citation>Plant Genome. 2016 Jul;9(2):null</Citation><ArticleIdList><ArticleId IdType="pubmed">27898814</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 2013 Jun;194(2):493-503</Citation><ArticleIdList><ArticleId IdType="pubmed">23535384</ArticleId></ArticleIdList></Reference><Reference><Citation>Theor Appl Genet. 2017 Apr;130(4):635-647</Citation><ArticleIdList><ArticleId IdType="pubmed">27995275</ArticleId></ArticleIdList></Reference><Reference><Citation>Plant Genome. 2016 Nov;9(3):null</Citation><ArticleIdList><ArticleId IdType="pubmed">27902807</ArticleId></ArticleIdList></Reference><Reference><Citation>Theor Appl Genet. 2013 Oct;126(10):2575-86</Citation><ArticleIdList><ArticleId IdType="pubmed">23907359</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2014 Jan 08;9(1):e85792</Citation><ArticleIdList><ArticleId IdType="pubmed">24416447</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 2010 Jul;185(3):1021-31</Citation><ArticleIdList><ArticleId IdType="pubmed">20407128</ArticleId></ArticleIdList></Reference><Reference><Citation>Heredity (Edinb). 2011 Dec;107(6):537-47</Citation><ArticleIdList><ArticleId IdType="pubmed">21673745</ArticleId></ArticleIdList></Reference><Reference><Citation>Mol Ecol. 2010 Sep;19(18):3845-52</Citation><ArticleIdList><ArticleId IdType="pubmed">20735737</ArticleId></ArticleIdList></Reference><Reference><Citation>Front Genet. 2015 Sep 23;6:294</Citation><ArticleIdList><ArticleId IdType="pubmed">26442110</ArticleId></ArticleIdList></Reference><Reference><Citation>Genet Sel Evol. 2010 Feb 19;42:5</Citation><ArticleIdList><ArticleId IdType="pubmed">20170500</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 2004 May;167(1):485-98</Citation><ArticleIdList><ArticleId IdType="pubmed">15166171</ArticleId></ArticleIdList></Reference><Reference><Citation>Nat Rev Genet. 2014 Jan;15(1):22-33</Citation><ArticleIdList><ArticleId IdType="pubmed">24296533</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 2014 Oct;198(2):483-95</Citation><ArticleIdList><ArticleId IdType="pubmed">25009151</ArticleId></ArticleIdList></Reference><Reference><Citation>Theor Appl Genet. 2014 Apr;127(4):809-20</Citation><ArticleIdList><ArticleId IdType="pubmed">24374468</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 2013 Feb;193(2):327-45</Citation><ArticleIdList><ArticleId IdType="pubmed">22745228</ArticleId></ArticleIdList></Reference><Reference><Citation>Theor Appl Genet. 2017 Oct;130(10 ):2091-2108</Citation><ArticleIdList><ArticleId IdType="pubmed">28707250</ArticleId></ArticleIdList></Reference><Reference><Citation>Trends Plant Sci. 2014 Sep;19(9):592-601</Citation><ArticleIdList><ArticleId IdType="pubmed">24970707</ArticleId></ArticleIdList></Reference><Reference><Citation>BMC Genomics. 2011 Jun 09;12:302</Citation><ArticleIdList><ArticleId IdType="pubmed">21658273</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2016 Jun 06;11(6):e0156744</Citation><ArticleIdList><ArticleId IdType="pubmed">27271781</ArticleId></ArticleIdList></Reference><Reference><Citation>Theor Appl Genet. 1968 Jun;38(6):226-31</Citation><ArticleIdList><ArticleId IdType="pubmed">24442307</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 2001 Apr;157(4):1819-29</Citation><ArticleIdList><ArticleId IdType="pubmed">11290733</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 2003 Aug;164(4):1627-33</Citation><ArticleIdList><ArticleId IdType="pubmed">12930766</ArticleId></ArticleIdList></Reference><Reference><Citation>Plant Cell Environ. 2010 Dec;33(12):2149-61</Citation><ArticleIdList><ArticleId IdType="pubmed">20716067</ArticleId></ArticleIdList></Reference><Reference><Citation>PLoS One. 2016 Jun 09;11(6):e0156254</Citation><ArticleIdList><ArticleId IdType="pubmed">27281327</ArticleId></ArticleIdList></Reference><Reference><Citation>Theor Appl Genet. 2009 Dec;120(1):151-61</Citation><ArticleIdList><ArticleId IdType="pubmed">19841887</ArticleId></ArticleIdList></Reference><Reference><Citation>Genet Sel Evol. 2009 Nov 24;41:51</Citation><ArticleIdList><ArticleId IdType="pubmed">19930712</ArticleId></ArticleIdList></Reference><Reference><Citation>BMC Bioinformatics. 2011 May 23;12:186</Citation><ArticleIdList><ArticleId IdType="pubmed">21605355</ArticleId></ArticleIdList></Reference><Reference><Citation>Genetics. 1993 Oct;135(2):367-74</Citation><ArticleIdList><ArticleId IdType="pubmed">8244001</ArticleId></ArticleIdList></Reference><Reference><Citation>Heredity (Edinb). 2016 Jul;117(1):33-41</Citation><ArticleIdList><ArticleId IdType="pubmed">27118156</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Allemagne</li></country><region><li>District de Düsseldorf</li><li>Rhénanie-du-Nord-Westphalie</li></region><settlement><li>Düsseldorf</li></settlement></list><tree><country name="Allemagne"><region name="Rhénanie-du-Nord-Westphalie"><name sortKey="Stich, Benjamin" sort="Stich, Benjamin" uniqKey="Stich B" first="Benjamin" last="Stich">Benjamin Stich</name></region><name sortKey="Stich, Benjamin" sort="Stich, Benjamin" uniqKey="Stich B" first="Benjamin" last="Stich">Benjamin Stich</name><name sortKey="Van Inghelandt, Delphine" sort="Van Inghelandt, Delphine" uniqKey="Van Inghelandt D" first="Delphine" last="Van Inghelandt">Delphine Van Inghelandt</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Bois/explor/PhytophthoraV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000688 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000688 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Bois
|area= PhytophthoraV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= pubmed:29563919
|texte= Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:29563919" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \
| NlmPubMed2Wicri -a PhytophthoraV1
| This area was generated with Dilib version V0.6.38. |  |