Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP
Genomic Best Linear Unbiased Prediction (GBLUP) in tree breeding typically only uses information from genotyped trees. However, information from phenotyped but non-genotyped trees can also be highly valuable. The single-step GBLUP approach(ssGBLUP) allows genomic prediction to takeinto account both...
| Main Authors: | , , , , , |
|---|---|
| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
| Published: |
Elsevier
2019
|
| Subjects: | |
| Online Access: | https://www.sciencedirect.com/science/article/pii/S0168945218314134 http://hdl.handle.net/20.500.12123/6227 https://doi.org/10.1016/j.plantsci.2019.03.017 |
| _version_ | 1855035637881634816 |
|---|---|
| author | Cappa, Eduardo Pablo de Lima, Bruno Marco Silva-Junior, Orzenil B. da García, Carla C. Mansfield, Shawn D. Grattapaglia, Dario |
| author_browse | Cappa, Eduardo Pablo García, Carla C. Grattapaglia, Dario Mansfield, Shawn D. Silva-Junior, Orzenil B. da de Lima, Bruno Marco |
| author_facet | Cappa, Eduardo Pablo de Lima, Bruno Marco Silva-Junior, Orzenil B. da García, Carla C. Mansfield, Shawn D. Grattapaglia, Dario |
| author_sort | Cappa, Eduardo Pablo |
| collection | INTA Digital |
| description | Genomic Best Linear Unbiased Prediction (GBLUP) in tree breeding typically only uses information from genotyped trees. However, information from phenotyped but non-genotyped trees can also be highly valuable. The single-step GBLUP approach(ssGBLUP) allows genomic prediction to takeinto account both genotyped and nongenotyped trees simultaneously in a single evaluation. In this study, we investigated the advantage, in terms of breeding value accuracy and bias, of including phenotypic observation from non-genotyped trees in a standard tree GBLUP evaluation. We compared the efficiency of the conventional pedigree-based (ABLUP), GBLUP and ssGBLUP approaches to evaluate eight growth and wood quality traits in a Eucalyptus hybrid population, genotyped with 33,398 single nucleotide polymorphisms (SNPs) using the EucHIP60k. Theoretical accuracies, predictive ability and bias were calculated by ten-fold cross validation on all traits. The use of additional phenotypic information from non-genotyped trees by means of ssGBLUP provided higher predictive ability (from 37% to 75%) and lower prediction bias (from 21% to 73%) for the genetic component of non-phenotyped but genotyped trees when compared to GBLUP. The increase (decrease) in the prediction accuracy (bias) became stronger as trait heritability decreased. We concluded that ssGBLUP is a promising breeding tool to improve accuracies and bias over classical GBLUP for genomic evaluation in Eucalyptus breeding practice. |
| format | info:ar-repo/semantics/artículo |
| id | INTA6227 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA62272019-10-29T14:13:41Z Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP Cappa, Eduardo Pablo de Lima, Bruno Marco Silva-Junior, Orzenil B. da García, Carla C. Mansfield, Shawn D. Grattapaglia, Dario Eucalyptus Evaluación Información Fenotípico Genomic Features Evaluation Phenotypic Information Genetics Genética Accuracy Bias Sesgo de Precisión Características Genómicas Genomic Best Linear Unbiased Prediction (GBLUP) in tree breeding typically only uses information from genotyped trees. However, information from phenotyped but non-genotyped trees can also be highly valuable. The single-step GBLUP approach(ssGBLUP) allows genomic prediction to takeinto account both genotyped and nongenotyped trees simultaneously in a single evaluation. In this study, we investigated the advantage, in terms of breeding value accuracy and bias, of including phenotypic observation from non-genotyped trees in a standard tree GBLUP evaluation. We compared the efficiency of the conventional pedigree-based (ABLUP), GBLUP and ssGBLUP approaches to evaluate eight growth and wood quality traits in a Eucalyptus hybrid population, genotyped with 33,398 single nucleotide polymorphisms (SNPs) using the EucHIP60k. Theoretical accuracies, predictive ability and bias were calculated by ten-fold cross validation on all traits. The use of additional phenotypic information from non-genotyped trees by means of ssGBLUP provided higher predictive ability (from 37% to 75%) and lower prediction bias (from 21% to 73%) for the genetic component of non-phenotyped but genotyped trees when compared to GBLUP. The increase (decrease) in the prediction accuracy (bias) became stronger as trait heritability decreased. We concluded that ssGBLUP is a promising breeding tool to improve accuracies and bias over classical GBLUP for genomic evaluation in Eucalyptus breeding practice. Fil: Cappa, Eduardo Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Recursos Biológicos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: de Lima, Bruno Marco. FIBRIA S.A. Technology Center; Brasil Fil: Silva-Junior, Orzenil B. da. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; Brasil Fil: García, Carla C. International Paper of Brazil; Brasil Fil: Mansfield, Shawn D. University of British Columbia. Faculty of Forestry. Department of Wood Science; Canadá Fil: Grattapaglia, Dario. EMBRAPA Recursos Genéticos e Biotecnologia; Brasil. Universidade Católica de Brasilia. Programa de Ciencias Genéticas y Biotecnología; Brasil 2019-10-29T13:54:32Z 2019-10-29T13:54:32Z 2019-03-28 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.sciencedirect.com/science/article/pii/S0168945218314134 http://hdl.handle.net/20.500.12123/6227 0168-9452 https://doi.org/10.1016/j.plantsci.2019.03.017 eng info:eu-repo/semantics/restrictedAccess application/pdf Elsevier Plant Science 284 : 9-15 (July 2019) |
| spellingShingle | Eucalyptus Evaluación Información Fenotípico Genomic Features Evaluation Phenotypic Information Genetics Genética Accuracy Bias Sesgo de Precisión Características Genómicas Cappa, Eduardo Pablo de Lima, Bruno Marco Silva-Junior, Orzenil B. da García, Carla C. Mansfield, Shawn D. Grattapaglia, Dario Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP |
| title | Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP |
| title_full | Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP |
| title_fullStr | Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP |
| title_full_unstemmed | Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP |
| title_short | Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP |
| title_sort | improving genomic prediction of growth and wood traits in eucalyptus using phenotypes from non genotyped trees by single step gblup |
| topic | Eucalyptus Evaluación Información Fenotípico Genomic Features Evaluation Phenotypic Information Genetics Genética Accuracy Bias Sesgo de Precisión Características Genómicas |
| url | https://www.sciencedirect.com/science/article/pii/S0168945218314134 http://hdl.handle.net/20.500.12123/6227 https://doi.org/10.1016/j.plantsci.2019.03.017 |
| work_keys_str_mv | AT cappaeduardopablo improvinggenomicpredictionofgrowthandwoodtraitsineucalyptususingphenotypesfromnongenotypedtreesbysinglestepgblup AT delimabrunomarco improvinggenomicpredictionofgrowthandwoodtraitsineucalyptususingphenotypesfromnongenotypedtreesbysinglestepgblup AT silvajuniororzenilbda improvinggenomicpredictionofgrowthandwoodtraitsineucalyptususingphenotypesfromnongenotypedtreesbysinglestepgblup AT garciacarlac improvinggenomicpredictionofgrowthandwoodtraitsineucalyptususingphenotypesfromnongenotypedtreesbysinglestepgblup AT mansfieldshawnd improvinggenomicpredictionofgrowthandwoodtraitsineucalyptususingphenotypesfromnongenotypedtreesbysinglestepgblup AT grattapagliadario improvinggenomicpredictionofgrowthandwoodtraitsineucalyptususingphenotypesfromnongenotypedtreesbysinglestepgblup |