Can cross-country genomic predictions be a reasonable strategy to support germplasm exchange? A case study with hydrogen cyanide in cassava.
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1149230 https://doi.org/10.3389/fpls.2021.742638 |
Resumo: | Genomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava?s products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm?s of Embrapa Mandioca e Fruticultura (Embrapa, Brazil) and The International Institute of Tropical Agriculture (IITA, Nigeria) for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, ~14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs' estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute/country and might need to use the markers? effects that were trained with data from other research institutes/country's germplasm to estimate their clones? GEBV. Fixation index (FST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8-0.85) compared to the prediction accuracy of either germplasm?s sources individually (0.51-0.67). CC GP proved to have potential use under the present study's scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa's germplasm, whereas for IITA?s it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field. |
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Can cross-country genomic predictions be a reasonable strategy to support germplasm exchange? A case study with hydrogen cyanide in cassava.GenomaGermoplasmaCianetoMandiocaPopulation structureBreedingGenomicsHydrogen cyanideCassavaGenomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava?s products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm?s of Embrapa Mandioca e Fruticultura (Embrapa, Brazil) and The International Institute of Tropical Agriculture (IITA, Nigeria) for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, ~14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs' estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute/country and might need to use the markers? effects that were trained with data from other research institutes/country's germplasm to estimate their clones? GEBV. Fixation index (FST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8-0.85) compared to the prediction accuracy of either germplasm?s sources individually (0.51-0.67). CC GP proved to have potential use under the present study's scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa's germplasm, whereas for IITA?s it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field.LÍVIA GOMES TORRES, UFV; EDER JORGE DE OLIVEIRA, CNPMF; ALEX C. OGBONNA, CORNELL UNIVERSITY; GUILLAUME J. BAUCHET, BOYCE THOMPSON INSTITUTE; LUKAS A. MUELLER, CORNELL UNIVERSITY; CAMILA FERREIRA AZEVEDO, UFV; FABYANO FONSECA E SILVA, UFV; GUILHERME FERREIRA SIMIQUELI, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa.TORRES, L. G.OLIVEIRA, E. J. deOGBONNA, A. C.BAUCHET, G. J.MUELLER, L. A.AZEVEDO, C. F.SILVA, F. F.SIMIQUELI, G. F.RESENDE, M. D. V. de2022-12-05T19:01:18Z2022-12-05T19:01:18Z2022-12-052021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleFrontiers in Plant Science, v. 12, 742638, 2021.1664-462Xhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1149230https://doi.org/10.3389/fpls.2021.742638enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2022-12-05T19:01:18Zoai:www.alice.cnptia.embrapa.br:doc/1149230Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542022-12-05T19:01:18falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542022-12-05T19:01:18Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Can cross-country genomic predictions be a reasonable strategy to support germplasm exchange? A case study with hydrogen cyanide in cassava. |
title |
Can cross-country genomic predictions be a reasonable strategy to support germplasm exchange? A case study with hydrogen cyanide in cassava. |
spellingShingle |
Can cross-country genomic predictions be a reasonable strategy to support germplasm exchange? A case study with hydrogen cyanide in cassava. TORRES, L. G. Genoma Germoplasma Cianeto Mandioca Population structure Breeding Genomics Hydrogen cyanide Cassava |
title_short |
Can cross-country genomic predictions be a reasonable strategy to support germplasm exchange? A case study with hydrogen cyanide in cassava. |
title_full |
Can cross-country genomic predictions be a reasonable strategy to support germplasm exchange? A case study with hydrogen cyanide in cassava. |
title_fullStr |
Can cross-country genomic predictions be a reasonable strategy to support germplasm exchange? A case study with hydrogen cyanide in cassava. |
title_full_unstemmed |
Can cross-country genomic predictions be a reasonable strategy to support germplasm exchange? A case study with hydrogen cyanide in cassava. |
title_sort |
Can cross-country genomic predictions be a reasonable strategy to support germplasm exchange? A case study with hydrogen cyanide in cassava. |
author |
TORRES, L. G. |
author_facet |
TORRES, L. G. OLIVEIRA, E. J. de OGBONNA, A. C. BAUCHET, G. J. MUELLER, L. A. AZEVEDO, C. F. SILVA, F. F. SIMIQUELI, G. F. RESENDE, M. D. V. de |
author_role |
author |
author2 |
OLIVEIRA, E. J. de OGBONNA, A. C. BAUCHET, G. J. MUELLER, L. A. AZEVEDO, C. F. SILVA, F. F. SIMIQUELI, G. F. RESENDE, M. D. V. de |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
LÍVIA GOMES TORRES, UFV; EDER JORGE DE OLIVEIRA, CNPMF; ALEX C. OGBONNA, CORNELL UNIVERSITY; GUILLAUME J. BAUCHET, BOYCE THOMPSON INSTITUTE; LUKAS A. MUELLER, CORNELL UNIVERSITY; CAMILA FERREIRA AZEVEDO, UFV; FABYANO FONSECA E SILVA, UFV; GUILHERME FERREIRA SIMIQUELI, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa. |
dc.contributor.author.fl_str_mv |
TORRES, L. G. OLIVEIRA, E. J. de OGBONNA, A. C. BAUCHET, G. J. MUELLER, L. A. AZEVEDO, C. F. SILVA, F. F. SIMIQUELI, G. F. RESENDE, M. D. V. de |
dc.subject.por.fl_str_mv |
Genoma Germoplasma Cianeto Mandioca Population structure Breeding Genomics Hydrogen cyanide Cassava |
topic |
Genoma Germoplasma Cianeto Mandioca Population structure Breeding Genomics Hydrogen cyanide Cassava |
description |
Genomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava?s products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm?s of Embrapa Mandioca e Fruticultura (Embrapa, Brazil) and The International Institute of Tropical Agriculture (IITA, Nigeria) for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, ~14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs' estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute/country and might need to use the markers? effects that were trained with data from other research institutes/country's germplasm to estimate their clones? GEBV. Fixation index (FST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8-0.85) compared to the prediction accuracy of either germplasm?s sources individually (0.51-0.67). CC GP proved to have potential use under the present study's scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa's germplasm, whereas for IITA?s it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2022-12-05T19:01:18Z 2022-12-05T19:01:18Z 2022-12-05 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Frontiers in Plant Science, v. 12, 742638, 2021. 1664-462X http://www.alice.cnptia.embrapa.br/alice/handle/doc/1149230 https://doi.org/10.3389/fpls.2021.742638 |
identifier_str_mv |
Frontiers in Plant Science, v. 12, 742638, 2021. 1664-462X |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1149230 https://doi.org/10.3389/fpls.2021.742638 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
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EMBRAPA |
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EMBRAPA |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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