Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat
Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a c...
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| Veröffentlicht in: | Heredity 2014-06, Vol.112 (6), p.638-645 |
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| creator | Zhao, Y Mette, M F Gowda, M Longin, C F H Reif, J C |
| description | Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCπ, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCπ. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection. |
| doi_str_mv | 10.1038/hdy.2014.1 |
| format | Article |
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For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCπ, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCπ. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection.</description><identifier>ISSN: 0018-067X</identifier><identifier>EISSN: 1365-2540</identifier><identifier>DOI: 10.1038/hdy.2014.1</identifier><identifier>PMID: 24518889</identifier><identifier>CODEN: HDTYAT</identifier><language>eng</language><publisher>England: Springer Nature B.V</publisher><subject>Alleles ; Breeding ; Datasets as Topic ; Genetic Markers ; Genetic Variation ; Genome, Plant ; Genomics ; Genotype ; Height ; Hybridization ; Hybridization, Genetic ; Hybrids ; Linkage Disequilibrium ; Models, Genetic ; Original ; Phenotype ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci ; Quantitative Trait, Heritable ; Reproducibility of Results ; Selection, Genetic ; Triticum - genetics ; Triticum aestivum ; Wheat ; Winter wheat</subject><ispartof>Heredity, 2014-06, Vol.112 (6), p.638-645</ispartof><rights>Copyright Nature Publishing Group Jun 2014</rights><rights>Copyright © 2014 The Genetics Society 2014 The Genetics Society</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c505t-3eab076f31d92c55b143dc8f253734ec8f5388f9046ea13b12b4f0a6011848cb3</citedby><cites>FETCH-LOGICAL-c505t-3eab076f31d92c55b143dc8f253734ec8f5388f9046ea13b12b4f0a6011848cb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4023446/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4023446/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24518889$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhao, Y</creatorcontrib><creatorcontrib>Mette, M F</creatorcontrib><creatorcontrib>Gowda, M</creatorcontrib><creatorcontrib>Longin, C F H</creatorcontrib><creatorcontrib>Reif, J C</creatorcontrib><title>Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat</title><title>Heredity</title><addtitle>Heredity (Edinb)</addtitle><description>Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. 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A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection.</description><subject>Alleles</subject><subject>Breeding</subject><subject>Datasets as Topic</subject><subject>Genetic Markers</subject><subject>Genetic Variation</subject><subject>Genome, Plant</subject><subject>Genomics</subject><subject>Genotype</subject><subject>Height</subject><subject>Hybridization</subject><subject>Hybridization, Genetic</subject><subject>Hybrids</subject><subject>Linkage Disequilibrium</subject><subject>Models, Genetic</subject><subject>Original</subject><subject>Phenotype</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Quantitative Trait Loci</subject><subject>Quantitative Trait, Heritable</subject><subject>Reproducibility of Results</subject><subject>Selection, Genetic</subject><subject>Triticum - 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genetics</topic><topic>Triticum aestivum</topic><topic>Wheat</topic><topic>Winter wheat</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Y</creatorcontrib><creatorcontrib>Mette, M F</creatorcontrib><creatorcontrib>Gowda, M</creatorcontrib><creatorcontrib>Longin, C F H</creatorcontrib><creatorcontrib>Reif, J C</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection (ProQuest)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Science Database (ProQuest)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Heredity</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Y</au><au>Mette, M F</au><au>Gowda, M</au><au>Longin, C F H</au><au>Reif, J C</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat</atitle><jtitle>Heredity</jtitle><addtitle>Heredity (Edinb)</addtitle><date>2014-06-01</date><risdate>2014</risdate><volume>112</volume><issue>6</issue><spage>638</spage><epage>645</epage><pages>638-645</pages><issn>0018-067X</issn><eissn>1365-2540</eissn><coden>HDTYAT</coden><abstract>Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCπ, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCπ. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection.</abstract><cop>England</cop><pub>Springer Nature B.V</pub><pmid>24518889</pmid><doi>10.1038/hdy.2014.1</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Alleles Breeding Datasets as Topic Genetic Markers Genetic Variation Genome, Plant Genomics Genotype Height Hybridization Hybridization, Genetic Hybrids Linkage Disequilibrium Models, Genetic Original Phenotype Polymorphism, Single Nucleotide Quantitative Trait Loci Quantitative Trait, Heritable Reproducibility of Results Selection, Genetic Triticum - genetics Triticum aestivum Wheat Winter wheat |
| title | Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat |
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