Cassava yield traits predicted by genomic selection methods
Genomic selection (GS) has been used to optimize genetic gains when phenotypic selection is considered costly and difficult to measure. The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different metho...
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| description | Genomic selection (GS) has been used to optimize genetic gains when phenotypic selection is considered costly and difficult to measure. The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different methods, taking into account the effect of population structure. BLUPs and deregressed BLUPs were obtained for 888 cassava accessions and evaluated for fresh root yield, dry root yield and dry matter content in roots in 21 trials conducted from 2011 to 2016. The deregressed BLUPs obtained for the accessions from a 48K single nucleotide polymorphism dataset were used for genomic predictions based on the BayesB, BLASSO, RR-BLUP, G-BLUP and RKHS methods. The accessions' BLUPs were used in the validation step using four cross-validation strategies, taking into account population structure and different GS methods. Similar estimates of predictive ability and bias were identified for the different genomic selection methods in the first cross-validation strategy. Lower predictive ability was observed for fresh root yield (0.4569 -RR-BLUP to 0.4756-RKHS) and dry root yield (0.4689 -G-BLUP to 0.4818-RKHS) in comparison with dry matter content (0.5655 -BLASSO to 0.5670 -RKHS). However, the RKHS method exhibited higher efficiency and consistency in most of the validation scenarios in terms of prediction ability for fresh root yield and dry root yield. The correlations of the genomic estimated breeding values between the genomic selection methods were quite high (0.99-1.00), resulting in high coincidence of clone selection regardless of the genomic selection method. The deviance analyses within and between the validation clusters formed by the discriminant analysis of principal components were significant for all traits. Therefore, this study indicated that i) the prediction of dry matter content was more accurate compared to that of yield traits, possibly as a result of the smaller influence of non-additive genetic effects; ii) the RKHS method resulted in high and stable prediction ability in most of the validation scenarios; and iii) some kinship between the validation and training populations is desirable in order for genomic selection to succeed due to the significant effect of population structure on genomic selection predictions. |
| doi_str_mv | 10.1371/journal.pone.0224920 |
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The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different methods, taking into account the effect of population structure. BLUPs and deregressed BLUPs were obtained for 888 cassava accessions and evaluated for fresh root yield, dry root yield and dry matter content in roots in 21 trials conducted from 2011 to 2016. The deregressed BLUPs obtained for the accessions from a 48K single nucleotide polymorphism dataset were used for genomic predictions based on the BayesB, BLASSO, RR-BLUP, G-BLUP and RKHS methods. The accessions' BLUPs were used in the validation step using four cross-validation strategies, taking into account population structure and different GS methods. Similar estimates of predictive ability and bias were identified for the different genomic selection methods in the first cross-validation strategy. Lower predictive ability was observed for fresh root yield (0.4569 -RR-BLUP to 0.4756-RKHS) and dry root yield (0.4689 -G-BLUP to 0.4818-RKHS) in comparison with dry matter content (0.5655 -BLASSO to 0.5670 -RKHS). However, the RKHS method exhibited higher efficiency and consistency in most of the validation scenarios in terms of prediction ability for fresh root yield and dry root yield. The correlations of the genomic estimated breeding values between the genomic selection methods were quite high (0.99-1.00), resulting in high coincidence of clone selection regardless of the genomic selection method. The deviance analyses within and between the validation clusters formed by the discriminant analysis of principal components were significant for all traits. Therefore, this study indicated that i) the prediction of dry matter content was more accurate compared to that of yield traits, possibly as a result of the smaller influence of non-additive genetic effects; ii) the RKHS method resulted in high and stable prediction ability in most of the validation scenarios; and iii) some kinship between the validation and training populations is desirable in order for genomic selection to succeed due to the significant effect of population structure on genomic selection predictions.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0224920</identifier><identifier>PMID: 31725759</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Agricultural production ; Analysis ; Biology and Life Sciences ; Breeding ; Cassava ; Cluster Analysis ; Consistency ; Crop diseases ; Crop yield ; Crops ; Discriminant analysis ; Dry matter ; Efficiency ; Engineering and Technology ; Evaluation ; Genetic aspects ; Genetic effects ; Genetic improvement ; Genetic polymorphisms ; Genomes ; Genomics ; Genomics - methods ; Identification methods ; Manihot - genetics ; Manihot - growth & development ; Manihot esculenta ; Methods ; Models, Genetic ; Nucleotides ; Physical Sciences ; Plant Breeding ; Plant Roots - genetics ; Plant Roots - growth & development ; Polymorphism ; Population ; Population structure ; Predictions ; Quantitative genetics ; Quantitative Trait, Heritable ; Reproducibility of Results ; Research and Analysis Methods ; Single nucleotide polymorphisms ; Single-nucleotide polymorphism</subject><ispartof>PloS one, 2019-11, Vol.14 (11), p.e0224920-e0224920</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Andrade et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://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>2019 Andrade et al 2019 Andrade et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-bb0d21ad723205fe40ab1850b358563bd00032d6631964809587ea6bbc9a375b3</citedby><cites>FETCH-LOGICAL-c692t-bb0d21ad723205fe40ab1850b358563bd00032d6631964809587ea6bbc9a375b3</cites><orcidid>0000-0003-4752-1164 ; 0000-0001-8992-7459 ; 0000-0003-0438-5123</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6855463/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6855463/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31725759$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Andrade, Luciano Rogério Braatz de</creatorcontrib><creatorcontrib>Sousa, Massaine Bandeira E</creatorcontrib><creatorcontrib>Oliveira, Eder Jorge</creatorcontrib><creatorcontrib>Resende, Marcos Deon Vilela de</creatorcontrib><creatorcontrib>Azevedo, Camila Ferreira</creatorcontrib><title>Cassava yield traits predicted by genomic selection methods</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Genomic selection (GS) has been used to optimize genetic gains when phenotypic selection is considered costly and difficult to measure. The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different methods, taking into account the effect of population structure. BLUPs and deregressed BLUPs were obtained for 888 cassava accessions and evaluated for fresh root yield, dry root yield and dry matter content in roots in 21 trials conducted from 2011 to 2016. The deregressed BLUPs obtained for the accessions from a 48K single nucleotide polymorphism dataset were used for genomic predictions based on the BayesB, BLASSO, RR-BLUP, G-BLUP and RKHS methods. The accessions' BLUPs were used in the validation step using four cross-validation strategies, taking into account population structure and different GS methods. Similar estimates of predictive ability and bias were identified for the different genomic selection methods in the first cross-validation strategy. Lower predictive ability was observed for fresh root yield (0.4569 -RR-BLUP to 0.4756-RKHS) and dry root yield (0.4689 -G-BLUP to 0.4818-RKHS) in comparison with dry matter content (0.5655 -BLASSO to 0.5670 -RKHS). However, the RKHS method exhibited higher efficiency and consistency in most of the validation scenarios in terms of prediction ability for fresh root yield and dry root yield. The correlations of the genomic estimated breeding values between the genomic selection methods were quite high (0.99-1.00), resulting in high coincidence of clone selection regardless of the genomic selection method. The deviance analyses within and between the validation clusters formed by the discriminant analysis of principal components were significant for all traits. Therefore, this study indicated that i) the prediction of dry matter content was more accurate compared to that of yield traits, possibly as a result of the smaller influence of non-additive genetic effects; ii) the RKHS method resulted in high and stable prediction ability in most of the validation scenarios; and iii) some kinship between the validation and training populations is desirable in order for genomic selection to succeed due to the significant effect of population structure on genomic selection predictions.</description><subject>Accuracy</subject><subject>Agricultural production</subject><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Breeding</subject><subject>Cassava</subject><subject>Cluster Analysis</subject><subject>Consistency</subject><subject>Crop diseases</subject><subject>Crop yield</subject><subject>Crops</subject><subject>Discriminant analysis</subject><subject>Dry matter</subject><subject>Efficiency</subject><subject>Engineering and Technology</subject><subject>Evaluation</subject><subject>Genetic aspects</subject><subject>Genetic effects</subject><subject>Genetic improvement</subject><subject>Genetic polymorphisms</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Genomics - methods</subject><subject>Identification methods</subject><subject>Manihot - genetics</subject><subject>Manihot - growth & development</subject><subject>Manihot esculenta</subject><subject>Methods</subject><subject>Models, Genetic</subject><subject>Nucleotides</subject><subject>Physical Sciences</subject><subject>Plant Breeding</subject><subject>Plant Roots - genetics</subject><subject>Plant Roots - growth & development</subject><subject>Polymorphism</subject><subject>Population</subject><subject>Population structure</subject><subject>Predictions</subject><subject>Quantitative genetics</subject><subject>Quantitative Trait, Heritable</subject><subject>Reproducibility of Results</subject><subject>Research and Analysis 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yield traits predicted by genomic selection methods</title><author>Andrade, Luciano Rogério Braatz de ; Sousa, Massaine Bandeira E ; Oliveira, Eder Jorge ; Resende, Marcos Deon Vilela de ; Azevedo, Camila Ferreira</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-bb0d21ad723205fe40ab1850b358563bd00032d6631964809587ea6bbc9a375b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Accuracy</topic><topic>Agricultural production</topic><topic>Analysis</topic><topic>Biology and Life Sciences</topic><topic>Breeding</topic><topic>Cassava</topic><topic>Cluster Analysis</topic><topic>Consistency</topic><topic>Crop diseases</topic><topic>Crop yield</topic><topic>Crops</topic><topic>Discriminant analysis</topic><topic>Dry matter</topic><topic>Efficiency</topic><topic>Engineering and Technology</topic><topic>Evaluation</topic><topic>Genetic aspects</topic><topic>Genetic 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Andrade, Luciano Rogério Braatz de</au><au>Sousa, Massaine Bandeira E</au><au>Oliveira, Eder Jorge</au><au>Resende, Marcos Deon Vilela de</au><au>Azevedo, Camila Ferreira</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cassava yield traits predicted by genomic selection methods</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-11-14</date><risdate>2019</risdate><volume>14</volume><issue>11</issue><spage>e0224920</spage><epage>e0224920</epage><pages>e0224920-e0224920</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Genomic selection (GS) has been used to optimize genetic gains when phenotypic selection is considered costly and difficult to measure. The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different methods, taking into account the effect of population structure. BLUPs and deregressed BLUPs were obtained for 888 cassava accessions and evaluated for fresh root yield, dry root yield and dry matter content in roots in 21 trials conducted from 2011 to 2016. The deregressed BLUPs obtained for the accessions from a 48K single nucleotide polymorphism dataset were used for genomic predictions based on the BayesB, BLASSO, RR-BLUP, G-BLUP and RKHS methods. The accessions' BLUPs were used in the validation step using four cross-validation strategies, taking into account population structure and different GS methods. Similar estimates of predictive ability and bias were identified for the different genomic selection methods in the first cross-validation strategy. Lower predictive ability was observed for fresh root yield (0.4569 -RR-BLUP to 0.4756-RKHS) and dry root yield (0.4689 -G-BLUP to 0.4818-RKHS) in comparison with dry matter content (0.5655 -BLASSO to 0.5670 -RKHS). However, the RKHS method exhibited higher efficiency and consistency in most of the validation scenarios in terms of prediction ability for fresh root yield and dry root yield. The correlations of the genomic estimated breeding values between the genomic selection methods were quite high (0.99-1.00), resulting in high coincidence of clone selection regardless of the genomic selection method. The deviance analyses within and between the validation clusters formed by the discriminant analysis of principal components were significant for all traits. Therefore, this study indicated that i) the prediction of dry matter content was more accurate compared to that of yield traits, possibly as a result of the smaller influence of non-additive genetic effects; ii) the RKHS method resulted in high and stable prediction ability in most of the validation scenarios; and iii) some kinship between the validation and training populations is desirable in order for genomic selection to succeed due to the significant effect of population structure on genomic selection predictions.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31725759</pmid><doi>10.1371/journal.pone.0224920</doi><tpages>e0224920</tpages><orcidid>https://orcid.org/0000-0003-4752-1164</orcidid><orcidid>https://orcid.org/0000-0001-8992-7459</orcidid><orcidid>https://orcid.org/0000-0003-0438-5123</orcidid><oa>free_for_read</oa></addata></record> |
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| recordid | cdi_plos_journals_2314541297 |
| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Agricultural production Analysis Biology and Life Sciences Breeding Cassava Cluster Analysis Consistency Crop diseases Crop yield Crops Discriminant analysis Dry matter Efficiency Engineering and Technology Evaluation Genetic aspects Genetic effects Genetic improvement Genetic polymorphisms Genomes Genomics Genomics - methods Identification methods Manihot - genetics Manihot - growth & development Manihot esculenta Methods Models, Genetic Nucleotides Physical Sciences Plant Breeding Plant Roots - genetics Plant Roots - growth & development Polymorphism Population Population structure Predictions Quantitative genetics Quantitative Trait, Heritable Reproducibility of Results Research and Analysis Methods Single nucleotide polymorphisms Single-nucleotide polymorphism |
| title | Cassava yield traits predicted by genomic selection methods |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-12T10%3A28%3A36IST&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=Cassava%20yield%20traits%20predicted%20by%20genomic%20selection%20methods&rft.jtitle=PloS%20one&rft.au=Andrade,%20Luciano%20Rog%C3%A9rio%20Braatz%20de&rft.date=2019-11-14&rft.volume=14&rft.issue=11&rft.spage=e0224920&rft.epage=e0224920&rft.pages=e0224920-e0224920&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0224920&rft_dat=%3Cgale_plos_%3EA605799538%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=2314541297&rft_id=info:pmid/31725759&rft_galeid=A605799538&rft_doaj_id=oai_doaj_org_article_84e8f5ce4b69401195ec9705e26894d5&rfr_iscdi=true |