Alfalfa genomic selection for different stress-prone growing regions

Autores
Annicchiarico, Paolo; Nazzicari, Nelson; Bouizgaren, Abdelaziz; Hayek, Taoufik; Laouar, Meriem; Cornacchione, Monica; Basigalup, Daniel Horacio; Monterrubio Martin, Cristina; Brummer, Edward Charles; Pecetti, Luciano
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Alfalfa (Medicago sativa L.) selection for stress-prone regions has high priority for sustainable crop–livestock systems. This study assessed the genomic selection (GS) ability to predict alfalfa breeding values for drought-prone agricultural sites of Algeria, Morocco, and Argentina; managed-stress (MS) environments of Italy featuring moderate or intense drought; and one Tunisian site irrigated with moderately saline water. Additional aims were to investigate genotype × environment interaction (GEI) patterns and the effect on GS predictions of three single-nucleotide polymorphism (SNP) calling procedures, 12 statistical models that exclude or incorporate GEI, and allele dosage information. Our study included 127 genotypes from a Mediterranean reference population originated from three geographically contrasting populations, genotyped via genotyping-by-sequencing and phenotyped based on multi-year biomass dry matter yield of their dense-planted half-sib progenies. The GEI was very large, as shown by 27-fold greater additive genetic variance × environment interaction relative to the additive genetic variance and low genetic correlation for progeny yield responses across environments. The predictive ability of GS (using at least 37,969 SNP markers) exceeded 0.20 for moderate MS (representing Italian stress-prone sites) and the sites of Algeria and Argentina while being quite low for the Tunisian site and intense MS. Predictions of GS were complicated by rapid linkage disequilibrium decay. The weighted GBLUP model, GEI incorporation into GS models, and SNP calling based on a mock reference genome exhibited a predictive ability advantage for some environments. Our results support the specific breeding for each target region and suggest a positive role for GS in most regions when considering the challenges associated with phenotypic selection.
EEA Santiago del Estero
Fil: Annicchiarico, Paolo. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; Italia
Fil: Nazzicari, Nelson. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; Italia
Fil: Bouizgaren, Abdelaziz. Institut National de la Recherche Agronomique du Maroc. Centres Régionaux de Marrakech et de Rabat; Marruecos
Fil: Hayek, Taoufik. Institut des Régions Arides de Médenine; Tunez
Fil: Laouar, Meriem. Ecole Nationale Supérieure Agronomique. Dép. de Productions Végétales. Laboratoire d’Amélioration Intégrative des Productions Végétales; Argelia
Fil: Cornacchione, Monica. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; Argentina
Fil: Basigalup, Daniel Horacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Grupo de Mejoramiento Genético de Alfalfa; Argentina
Fil: Monterrubio Martin, Cristina. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; Italia
Fil: Brummer, E. Charles. University of California at Davies. Depeparment of Plant Sciences. Plant Breeding Center,; Estados Unidos
Fil: Pecetti, Luciano. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; Italia
Fuente
The Plant Genome : e20264 (First published: 12 October 2022)
Materia
Medicago sativa
Selección Asistida por Marcadores
Valor Genético
Estres
Estrés de Sequia
Marker-assisted Selection
Breeding Value
Stress
Drought Stress
Alfalfa
Selección Genómica
Lucerne
Genomic Selection
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/13132
Acceder
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oai_identifier_str oai:localhost:20.500.12123/13132
network_acronym_str INTADig
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network_name_str INTA Digital (INTA)
spelling Alfalfa genomic selection for different stress-prone growing regionsAnnicchiarico, PaoloNazzicari, NelsonBouizgaren, AbdelazizHayek, TaoufikLaouar, MeriemCornacchione, MonicaBasigalup, Daniel HoracioMonterrubio Martin, CristinaBrummer, Edward CharlesPecetti, LucianoMedicago sativaSelección Asistida por MarcadoresValor GenéticoEstresEstrés de SequiaMarker-assisted SelectionBreeding ValueStressDrought StressAlfalfaSelección GenómicaLucerneGenomic SelectionAlfalfa (Medicago sativa L.) selection for stress-prone regions has high priority for sustainable crop–livestock systems. This study assessed the genomic selection (GS) ability to predict alfalfa breeding values for drought-prone agricultural sites of Algeria, Morocco, and Argentina; managed-stress (MS) environments of Italy featuring moderate or intense drought; and one Tunisian site irrigated with moderately saline water. Additional aims were to investigate genotype × environment interaction (GEI) patterns and the effect on GS predictions of three single-nucleotide polymorphism (SNP) calling procedures, 12 statistical models that exclude or incorporate GEI, and allele dosage information. Our study included 127 genotypes from a Mediterranean reference population originated from three geographically contrasting populations, genotyped via genotyping-by-sequencing and phenotyped based on multi-year biomass dry matter yield of their dense-planted half-sib progenies. The GEI was very large, as shown by 27-fold greater additive genetic variance × environment interaction relative to the additive genetic variance and low genetic correlation for progeny yield responses across environments. The predictive ability of GS (using at least 37,969 SNP markers) exceeded 0.20 for moderate MS (representing Italian stress-prone sites) and the sites of Algeria and Argentina while being quite low for the Tunisian site and intense MS. Predictions of GS were complicated by rapid linkage disequilibrium decay. The weighted GBLUP model, GEI incorporation into GS models, and SNP calling based on a mock reference genome exhibited a predictive ability advantage for some environments. Our results support the specific breeding for each target region and suggest a positive role for GS in most regions when considering the challenges associated with phenotypic selection.EEA Santiago del EsteroFil: Annicchiarico, Paolo. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; ItaliaFil: Nazzicari, Nelson. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; ItaliaFil: Bouizgaren, Abdelaziz. Institut National de la Recherche Agronomique du Maroc. Centres Régionaux de Marrakech et de Rabat; MarruecosFil: Hayek, Taoufik. Institut des Régions Arides de Médenine; TunezFil: Laouar, Meriem. Ecole Nationale Supérieure Agronomique. Dép. de Productions Végétales. Laboratoire d’Amélioration Intégrative des Productions Végétales; ArgeliaFil: Cornacchione, Monica. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; ArgentinaFil: Basigalup, Daniel Horacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Grupo de Mejoramiento Genético de Alfalfa; ArgentinaFil: Monterrubio Martin, Cristina. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; ItaliaFil: Brummer, E. Charles. University of California at Davies. Depeparment of Plant Sciences. Plant Breeding Center,; Estados UnidosFil: Pecetti, Luciano. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; ItaliaWiley2022-10-17T14:04:18Z2022-10-17T14:04:18Z2022-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/13132https://acsess.onlinelibrary.wiley.com/doi/10.1002/tpg2.202641940-3372https://doi.org/10.1002/tpg2.20264The Plant Genome : e20264 (First published: 12 October 2022)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-23T11:18:08Zoai:localhost:20.500.12123/13132instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-23 11:18:09.326INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Alfalfa genomic selection for different stress-prone growing regions
title Alfalfa genomic selection for different stress-prone growing regions
spellingShingle Alfalfa genomic selection for different stress-prone growing regions
Annicchiarico, Paolo
Medicago sativa
Selección Asistida por Marcadores
Valor Genético
Estres
Estrés de Sequia
Marker-assisted Selection
Breeding Value
Stress
Drought Stress
Alfalfa
Selección Genómica
Lucerne
Genomic Selection
title_short Alfalfa genomic selection for different stress-prone growing regions
title_full Alfalfa genomic selection for different stress-prone growing regions
title_fullStr Alfalfa genomic selection for different stress-prone growing regions
title_full_unstemmed Alfalfa genomic selection for different stress-prone growing regions
title_sort Alfalfa genomic selection for different stress-prone growing regions
dc.creator.none.fl_str_mv Annicchiarico, Paolo
Nazzicari, Nelson
Bouizgaren, Abdelaziz
Hayek, Taoufik
Laouar, Meriem
Cornacchione, Monica
Basigalup, Daniel Horacio
Monterrubio Martin, Cristina
Brummer, Edward Charles
Pecetti, Luciano
author Annicchiarico, Paolo
author_facet Annicchiarico, Paolo
Nazzicari, Nelson
Bouizgaren, Abdelaziz
Hayek, Taoufik
Laouar, Meriem
Cornacchione, Monica
Basigalup, Daniel Horacio
Monterrubio Martin, Cristina
Brummer, Edward Charles
Pecetti, Luciano
author_role author
author2 Nazzicari, Nelson
Bouizgaren, Abdelaziz
Hayek, Taoufik
Laouar, Meriem
Cornacchione, Monica
Basigalup, Daniel Horacio
Monterrubio Martin, Cristina
Brummer, Edward Charles
Pecetti, Luciano
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Medicago sativa
Selección Asistida por Marcadores
Valor Genético
Estres
Estrés de Sequia
Marker-assisted Selection
Breeding Value
Stress
Drought Stress
Alfalfa
Selección Genómica
Lucerne
Genomic Selection
topic Medicago sativa
Selección Asistida por Marcadores
Valor Genético
Estres
Estrés de Sequia
Marker-assisted Selection
Breeding Value
Stress
Drought Stress
Alfalfa
Selección Genómica
Lucerne
Genomic Selection
dc.description.none.fl_txt_mv Alfalfa (Medicago sativa L.) selection for stress-prone regions has high priority for sustainable crop–livestock systems. This study assessed the genomic selection (GS) ability to predict alfalfa breeding values for drought-prone agricultural sites of Algeria, Morocco, and Argentina; managed-stress (MS) environments of Italy featuring moderate or intense drought; and one Tunisian site irrigated with moderately saline water. Additional aims were to investigate genotype × environment interaction (GEI) patterns and the effect on GS predictions of three single-nucleotide polymorphism (SNP) calling procedures, 12 statistical models that exclude or incorporate GEI, and allele dosage information. Our study included 127 genotypes from a Mediterranean reference population originated from three geographically contrasting populations, genotyped via genotyping-by-sequencing and phenotyped based on multi-year biomass dry matter yield of their dense-planted half-sib progenies. The GEI was very large, as shown by 27-fold greater additive genetic variance × environment interaction relative to the additive genetic variance and low genetic correlation for progeny yield responses across environments. The predictive ability of GS (using at least 37,969 SNP markers) exceeded 0.20 for moderate MS (representing Italian stress-prone sites) and the sites of Algeria and Argentina while being quite low for the Tunisian site and intense MS. Predictions of GS were complicated by rapid linkage disequilibrium decay. The weighted GBLUP model, GEI incorporation into GS models, and SNP calling based on a mock reference genome exhibited a predictive ability advantage for some environments. Our results support the specific breeding for each target region and suggest a positive role for GS in most regions when considering the challenges associated with phenotypic selection.
EEA Santiago del Estero
Fil: Annicchiarico, Paolo. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; Italia
Fil: Nazzicari, Nelson. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; Italia
Fil: Bouizgaren, Abdelaziz. Institut National de la Recherche Agronomique du Maroc. Centres Régionaux de Marrakech et de Rabat; Marruecos
Fil: Hayek, Taoufik. Institut des Régions Arides de Médenine; Tunez
Fil: Laouar, Meriem. Ecole Nationale Supérieure Agronomique. Dép. de Productions Végétales. Laboratoire d’Amélioration Intégrative des Productions Végétales; Argelia
Fil: Cornacchione, Monica. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santiago del Estero; Argentina
Fil: Basigalup, Daniel Horacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Grupo de Mejoramiento Genético de Alfalfa; Argentina
Fil: Monterrubio Martin, Cristina. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; Italia
Fil: Brummer, E. Charles. University of California at Davies. Depeparment of Plant Sciences. Plant Breeding Center,; Estados Unidos
Fil: Pecetti, Luciano. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria. Centro di Ricerca Zootecnia e Acquacoltura; Italia
description Alfalfa (Medicago sativa L.) selection for stress-prone regions has high priority for sustainable crop–livestock systems. This study assessed the genomic selection (GS) ability to predict alfalfa breeding values for drought-prone agricultural sites of Algeria, Morocco, and Argentina; managed-stress (MS) environments of Italy featuring moderate or intense drought; and one Tunisian site irrigated with moderately saline water. Additional aims were to investigate genotype × environment interaction (GEI) patterns and the effect on GS predictions of three single-nucleotide polymorphism (SNP) calling procedures, 12 statistical models that exclude or incorporate GEI, and allele dosage information. Our study included 127 genotypes from a Mediterranean reference population originated from three geographically contrasting populations, genotyped via genotyping-by-sequencing and phenotyped based on multi-year biomass dry matter yield of their dense-planted half-sib progenies. The GEI was very large, as shown by 27-fold greater additive genetic variance × environment interaction relative to the additive genetic variance and low genetic correlation for progeny yield responses across environments. The predictive ability of GS (using at least 37,969 SNP markers) exceeded 0.20 for moderate MS (representing Italian stress-prone sites) and the sites of Algeria and Argentina while being quite low for the Tunisian site and intense MS. Predictions of GS were complicated by rapid linkage disequilibrium decay. The weighted GBLUP model, GEI incorporation into GS models, and SNP calling based on a mock reference genome exhibited a predictive ability advantage for some environments. Our results support the specific breeding for each target region and suggest a positive role for GS in most regions when considering the challenges associated with phenotypic selection.
publishDate 2022
dc.date.none.fl_str_mv 2022-10-17T14:04:18Z
2022-10-17T14:04:18Z
2022-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/13132
https://acsess.onlinelibrary.wiley.com/doi/10.1002/tpg2.20264
1940-3372
https://doi.org/10.1002/tpg2.20264
url http://hdl.handle.net/20.500.12123/13132
https://acsess.onlinelibrary.wiley.com/doi/10.1002/tpg2.20264
https://doi.org/10.1002/tpg2.20264
identifier_str_mv 1940-3372
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv The Plant Genome : e20264 (First published: 12 October 2022)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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