Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix
With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome,...
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| Veröffentlicht in: | PloS one 2010-09, Vol.5 (9), p.e12648 |
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| creator | Zhang, Zhe Liu, Jianfeng Ding, Xiangdong Bijma, Piter de Koning, Dirk-Jan Zhang, Qin |
| description | With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework of mixed model equations, by using a matrix of relationships among individuals that is derived from the markers. Here we extend that approach by deriving a marker-based relationship matrix specifically for the trait of interest.
In the framework of mixed model equations, a new best linear unbiased prediction (BLUP) method including a trait-specific relationship matrix (TA) was presented and termed TABLUP. The TA matrix was constructed on the basis of marker genotypes and their weights in relation to the trait of interest. A simulation study with 1,000 individuals as the training population and five successive generations as candidate population was carried out to validate the proposed method. The proposed TABLUP method outperformed the ridge regression BLUP (RRBLUP) and BLUP with realized relationship matrix (GBLUP). It performed slightly worse than BayesB with an accuracy of 0.79 in the standard scenario.
The proposed TABLUP method is an improvement of the RRBLUP and GBLUP method. It might be equivalent to the BayesB method but it has additional benefits like the calculation of accuracies for individual breeding values. The results also showed that the TA-matrix performs better in predicting ability than the classical numerator relationship matrix and the realized relationship matrix which are derived solely from pedigree or markers without regard to the trait. This is because the TA-matrix not only accounts for the Mendelian sampling term, but also puts the greater emphasis on those markers that explain more of the genetic variance in the trait. |
| doi_str_mv | 10.1371/journal.pone.0012648 |
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In the framework of mixed model equations, a new best linear unbiased prediction (BLUP) method including a trait-specific relationship matrix (TA) was presented and termed TABLUP. The TA matrix was constructed on the basis of marker genotypes and their weights in relation to the trait of interest. A simulation study with 1,000 individuals as the training population and five successive generations as candidate population was carried out to validate the proposed method. The proposed TABLUP method outperformed the ridge regression BLUP (RRBLUP) and BLUP with realized relationship matrix (GBLUP). It performed slightly worse than BayesB with an accuracy of 0.79 in the standard scenario.
The proposed TABLUP method is an improvement of the RRBLUP and GBLUP method. It might be equivalent to the BayesB method but it has additional benefits like the calculation of accuracies for individual breeding values. The results also showed that the TA-matrix performs better in predicting ability than the classical numerator relationship matrix and the realized relationship matrix which are derived solely from pedigree or markers without regard to the trait. This is because the TA-matrix not only accounts for the Mendelian sampling term, but also puts the greater emphasis on those markers that explain more of the genetic variance in the trait.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0012648</identifier><identifier>PMID: 20844593</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Agriculture ; Algorithms ; Animal sciences ; Animals ; Aquaculture ; Aquaculture industry ; Bayesian analysis ; Breeding ; Cattle ; Computational Biology/Population Genetics ; Computer Simulation ; dairy-cattle ; Economic importance ; Female ; Gene polymorphism ; Genetic diversity ; Genetic Markers ; Genetic variance ; genetic-relationship information ; Genetics and Genomics/Animal Genetics ; Genetics and Genomics/Complex Traits ; Genetics, Population ; Genomes ; Genomics ; Genotype ; Genotypes ; Humans ; impact ; Laboratories ; Linear Models ; Male ; Markers ; Mathematical analysis ; Mathematical models ; Matrix methods ; Methods ; Models, Genetic ; Pedigree ; Performance prediction ; Plant breeding ; Polymorphism ; Polymorphism, Single Nucleotide ; Population ; populations ; programs ; Quantitative genetics ; Quantitative Trait, Heritable ; Simulation ; Single nucleotide polymorphisms ; Single-nucleotide polymorphism ; snp ; Studies ; wide selection ; Zoology</subject><ispartof>PloS one, 2010-09, Vol.5 (9), p.e12648</ispartof><rights>COPYRIGHT 2010 Public Library of Science</rights><rights>2010 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Zhang et al. 2010</rights><rights>Wageningen University & Research</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c840t-4832e5fe9a7c9d8a95125cab3f1621a25803d03fcead19e1fbe171322e94b3013</citedby><cites>FETCH-LOGICAL-c840t-4832e5fe9a7c9d8a95125cab3f1621a25803d03fcead19e1fbe171322e94b3013</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/PMC2936569/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2936569/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20844593$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Mailund, Thomas</contributor><creatorcontrib>Zhang, Zhe</creatorcontrib><creatorcontrib>Liu, Jianfeng</creatorcontrib><creatorcontrib>Ding, Xiangdong</creatorcontrib><creatorcontrib>Bijma, Piter</creatorcontrib><creatorcontrib>de Koning, Dirk-Jan</creatorcontrib><creatorcontrib>Zhang, Qin</creatorcontrib><title>Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework of mixed model equations, by using a matrix of relationships among individuals that is derived from the markers. Here we extend that approach by deriving a marker-based relationship matrix specifically for the trait of interest.
In the framework of mixed model equations, a new best linear unbiased prediction (BLUP) method including a trait-specific relationship matrix (TA) was presented and termed TABLUP. The TA matrix was constructed on the basis of marker genotypes and their weights in relation to the trait of interest. A simulation study with 1,000 individuals as the training population and five successive generations as candidate population was carried out to validate the proposed method. The proposed TABLUP method outperformed the ridge regression BLUP (RRBLUP) and BLUP with realized relationship matrix (GBLUP). It performed slightly worse than BayesB with an accuracy of 0.79 in the standard scenario.
The proposed TABLUP method is an improvement of the RRBLUP and GBLUP method. It might be equivalent to the BayesB method but it has additional benefits like the calculation of accuracies for individual breeding values. The results also showed that the TA-matrix performs better in predicting ability than the classical numerator relationship matrix and the realized relationship matrix which are derived solely from pedigree or markers without regard to the trait. This is because the TA-matrix not only accounts for the Mendelian sampling term, but also puts the greater emphasis on those markers that explain more of the genetic variance in the trait.</description><subject>Accuracy</subject><subject>Agriculture</subject><subject>Algorithms</subject><subject>Animal sciences</subject><subject>Animals</subject><subject>Aquaculture</subject><subject>Aquaculture industry</subject><subject>Bayesian analysis</subject><subject>Breeding</subject><subject>Cattle</subject><subject>Computational Biology/Population Genetics</subject><subject>Computer Simulation</subject><subject>dairy-cattle</subject><subject>Economic importance</subject><subject>Female</subject><subject>Gene polymorphism</subject><subject>Genetic diversity</subject><subject>Genetic Markers</subject><subject>Genetic variance</subject><subject>genetic-relationship information</subject><subject>Genetics and Genomics/Animal Genetics</subject><subject>Genetics and Genomics/Complex Traits</subject><subject>Genetics, Population</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Genotype</subject><subject>Genotypes</subject><subject>Humans</subject><subject>impact</subject><subject>Laboratories</subject><subject>Linear Models</subject><subject>Male</subject><subject>Markers</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Matrix methods</subject><subject>Methods</subject><subject>Models, Genetic</subject><subject>Pedigree</subject><subject>Performance prediction</subject><subject>Plant breeding</subject><subject>Polymorphism</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Population</subject><subject>populations</subject><subject>programs</subject><subject>Quantitative genetics</subject><subject>Quantitative Trait, Heritable</subject><subject>Simulation</subject><subject>Single nucleotide polymorphisms</subject><subject>Single-nucleotide polymorphism</subject><subject>snp</subject><subject>Studies</subject><subject>wide 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linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix</title><author>Zhang, Zhe ; Liu, Jianfeng ; Ding, Xiangdong ; Bijma, Piter ; de Koning, Dirk-Jan ; Zhang, Qin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c840t-4832e5fe9a7c9d8a95125cab3f1621a25803d03fcead19e1fbe171322e94b3013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Accuracy</topic><topic>Agriculture</topic><topic>Algorithms</topic><topic>Animal sciences</topic><topic>Animals</topic><topic>Aquaculture</topic><topic>Aquaculture industry</topic><topic>Bayesian analysis</topic><topic>Breeding</topic><topic>Cattle</topic><topic>Computational Biology/Population Genetics</topic><topic>Computer Simulation</topic><topic>dairy-cattle</topic><topic>Economic importance</topic><topic>Female</topic><topic>Gene polymorphism</topic><topic>Genetic 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one</jtitle><addtitle>PLoS One</addtitle><date>2010-09-09</date><risdate>2010</risdate><volume>5</volume><issue>9</issue><spage>e12648</spage><pages>e12648-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework of mixed model equations, by using a matrix of relationships among individuals that is derived from the markers. Here we extend that approach by deriving a marker-based relationship matrix specifically for the trait of interest.
In the framework of mixed model equations, a new best linear unbiased prediction (BLUP) method including a trait-specific relationship matrix (TA) was presented and termed TABLUP. The TA matrix was constructed on the basis of marker genotypes and their weights in relation to the trait of interest. A simulation study with 1,000 individuals as the training population and five successive generations as candidate population was carried out to validate the proposed method. The proposed TABLUP method outperformed the ridge regression BLUP (RRBLUP) and BLUP with realized relationship matrix (GBLUP). It performed slightly worse than BayesB with an accuracy of 0.79 in the standard scenario.
The proposed TABLUP method is an improvement of the RRBLUP and GBLUP method. It might be equivalent to the BayesB method but it has additional benefits like the calculation of accuracies for individual breeding values. The results also showed that the TA-matrix performs better in predicting ability than the classical numerator relationship matrix and the realized relationship matrix which are derived solely from pedigree or markers without regard to the trait. This is because the TA-matrix not only accounts for the Mendelian sampling term, but also puts the greater emphasis on those markers that explain more of the genetic variance in the trait.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>20844593</pmid><doi>10.1371/journal.pone.0012648</doi><tpages>e12648</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | PloS one, 2010-09, Vol.5 (9), p.e12648 |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Agriculture Algorithms Animal sciences Animals Aquaculture Aquaculture industry Bayesian analysis Breeding Cattle Computational Biology/Population Genetics Computer Simulation dairy-cattle Economic importance Female Gene polymorphism Genetic diversity Genetic Markers Genetic variance genetic-relationship information Genetics and Genomics/Animal Genetics Genetics and Genomics/Complex Traits Genetics, Population Genomes Genomics Genotype Genotypes Humans impact Laboratories Linear Models Male Markers Mathematical analysis Mathematical models Matrix methods Methods Models, Genetic Pedigree Performance prediction Plant breeding Polymorphism Polymorphism, Single Nucleotide Population populations programs Quantitative genetics Quantitative Trait, Heritable Simulation Single nucleotide polymorphisms Single-nucleotide polymorphism snp Studies wide selection Zoology |
| title | Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T18%3A47%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Best%20linear%20unbiased%20prediction%20of%20genomic%20breeding%20values%20using%20a%20trait-specific%20marker-derived%20relationship%20matrix&rft.jtitle=PloS%20one&rft.au=Zhang,%20Zhe&rft.date=2010-09-09&rft.volume=5&rft.issue=9&rft.spage=e12648&rft.pages=e12648-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0012648&rft_dat=%3Cgale_plos_%3EA473860831%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1292581828&rft_id=info:pmid/20844593&rft_galeid=A473860831&rft_doaj_id=oai_doaj_org_article_403f4541c141480388b7a07ff804868f&rfr_iscdi=true |