Statistical modeling of phenotypic, pedigree and genomic information for improved genetic evaluation in modern plant breeding : a case study with sorghum

Autores
Velazco, Julio Gabriel
Año de publicación
2020
Idioma
inglés
Tipo de recurso
tesis doctoral
Estado
versión publicada
Colaborador/a o director/a de tesis
van Eeuwijk, Fred
Malosetti, M.
Descripción
Tesis para obtener el grado de Doctor of Philosophy, de la Wageningen University, en marzo de 2020
Global climate change and food insecurity are major concerns of the 21st century. Agricultural production should increase by 60–110% to meet the projected food demands of the word population by 2050 (McGuire 2012). However, the rates of global crop production are still far below the mentioned requirements and most studies predict a future decline in grain yield of major crops due to climate change (Ray et al. 2013; Wiltshire et al. 2013). Rainfed farming systems are drastically affected by climatic conditions, with water scarcity and increasing temperature being the most important limiting factors for crop productivity and, ultimately, for food security worldwide (Daryanto et al. 2013). Efforts to ensure food supply will require accelerating the development of climate resilient crop varieties. This is particularly necessary for crops that provide staple food grain in developing countries and semi-arid regions of the world. Plant breeding can play a crucial role in enhancing crop productivity and adaptation to climate change. The main goal of breeding programs is to efficiently identify and select the best-performing genotypes as potential cultivars or as parental material to improve crop performance in future generations (Falconer and Mackay 1996; Bernardo 2010). For this, new selection techniques based on modern approaches to quantitative genetics have to be adopted by breeding programs in order to accelerate genetic progress. Advances in high-throughput genotyping technologies and the increasing cost-effective access to high-density genomic data have facilitated the adoption of a novel form of marker-assisted selection known as genomic selection (GS). This genetic evaluation method has already revolutionized animal breeding over the past decade and is gaining momentum in crop breeding. In GS, phenotypic and genome-wide marker data from a reference (or training) population is used to predict genetic merit of selection candidates that have only been genotyped but not phenotyped (Meuwissen et al. 2001). As a result, selection efficiency can potentially increase, reducing phenotyping costs and generation interval. Moreover, additional opportunities for GS in crops are provided by current developments in high throughput phenotyping technologies. The success in the incorporation of genomics as breeding tool depends on an appropriate statistical analysis of the phenotypic and genetic data generated in crop breeding programs.
EEA Pergamino
Fil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda
Materia
Sorgos
Mejora Genética
Forrajes
Fitomejoramiento
Sorghum Grain
Genetic Gain
Forage
Plant Breeding
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
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spelling Statistical modeling of phenotypic, pedigree and genomic information for improved genetic evaluation in modern plant breeding : a case study with sorghumVelazco, Julio GabrielSorgosMejora GenéticaForrajesFitomejoramientoSorghum GrainGenetic GainForagePlant BreedingTesis para obtener el grado de Doctor of Philosophy, de la Wageningen University, en marzo de 2020Global climate change and food insecurity are major concerns of the 21st century. Agricultural production should increase by 60–110% to meet the projected food demands of the word population by 2050 (McGuire 2012). However, the rates of global crop production are still far below the mentioned requirements and most studies predict a future decline in grain yield of major crops due to climate change (Ray et al. 2013; Wiltshire et al. 2013). Rainfed farming systems are drastically affected by climatic conditions, with water scarcity and increasing temperature being the most important limiting factors for crop productivity and, ultimately, for food security worldwide (Daryanto et al. 2013). Efforts to ensure food supply will require accelerating the development of climate resilient crop varieties. This is particularly necessary for crops that provide staple food grain in developing countries and semi-arid regions of the world. Plant breeding can play a crucial role in enhancing crop productivity and adaptation to climate change. The main goal of breeding programs is to efficiently identify and select the best-performing genotypes as potential cultivars or as parental material to improve crop performance in future generations (Falconer and Mackay 1996; Bernardo 2010). For this, new selection techniques based on modern approaches to quantitative genetics have to be adopted by breeding programs in order to accelerate genetic progress. Advances in high-throughput genotyping technologies and the increasing cost-effective access to high-density genomic data have facilitated the adoption of a novel form of marker-assisted selection known as genomic selection (GS). This genetic evaluation method has already revolutionized animal breeding over the past decade and is gaining momentum in crop breeding. In GS, phenotypic and genome-wide marker data from a reference (or training) population is used to predict genetic merit of selection candidates that have only been genotyped but not phenotyped (Meuwissen et al. 2001). As a result, selection efficiency can potentially increase, reducing phenotyping costs and generation interval. Moreover, additional opportunities for GS in crops are provided by current developments in high throughput phenotyping technologies. The success in the incorporation of genomics as breeding tool depends on an appropriate statistical analysis of the phenotypic and genetic data generated in crop breeding programs.EEA PergaminoFil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; HolandaWageningen University, the Netherlandsvan Eeuwijk, FredMalosetti, M.2020-06-18T11:14:58Z2020-06-18T11:14:58Z2020-03info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_db06info:ar-repo/semantics/tesisDoctoralapplication/pdfhttp://hdl.handle.net/20.500.12123/7432https://research.wur.nl/en/publications/statistical-modeling-of-phenotypic-pedigree-and-genomic-informatihttps://research.wur.nl/en/publications/statistical-modeling-of-phenotypic-pedigree-and-genomic-informati978-94-6395-279-8https://doi.org/10.18174/511730enginfo:eu-repo/semantics/restrictedAccessreponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuaria2025-09-29T13:44:58Zoai:localhost:20.500.12123/7432instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:44:58.394INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Statistical modeling of phenotypic, pedigree and genomic information for improved genetic evaluation in modern plant breeding : a case study with sorghum
title Statistical modeling of phenotypic, pedigree and genomic information for improved genetic evaluation in modern plant breeding : a case study with sorghum
spellingShingle Statistical modeling of phenotypic, pedigree and genomic information for improved genetic evaluation in modern plant breeding : a case study with sorghum
Velazco, Julio Gabriel
Sorgos
Mejora Genética
Forrajes
Fitomejoramiento
Sorghum Grain
Genetic Gain
Forage
Plant Breeding
title_short Statistical modeling of phenotypic, pedigree and genomic information for improved genetic evaluation in modern plant breeding : a case study with sorghum
title_full Statistical modeling of phenotypic, pedigree and genomic information for improved genetic evaluation in modern plant breeding : a case study with sorghum
title_fullStr Statistical modeling of phenotypic, pedigree and genomic information for improved genetic evaluation in modern plant breeding : a case study with sorghum
title_full_unstemmed Statistical modeling of phenotypic, pedigree and genomic information for improved genetic evaluation in modern plant breeding : a case study with sorghum
title_sort Statistical modeling of phenotypic, pedigree and genomic information for improved genetic evaluation in modern plant breeding : a case study with sorghum
dc.creator.none.fl_str_mv Velazco, Julio Gabriel
author Velazco, Julio Gabriel
author_facet Velazco, Julio Gabriel
author_role author
dc.contributor.none.fl_str_mv van Eeuwijk, Fred
Malosetti, M.
dc.subject.none.fl_str_mv Sorgos
Mejora Genética
Forrajes
Fitomejoramiento
Sorghum Grain
Genetic Gain
Forage
Plant Breeding
topic Sorgos
Mejora Genética
Forrajes
Fitomejoramiento
Sorghum Grain
Genetic Gain
Forage
Plant Breeding
dc.description.none.fl_txt_mv Tesis para obtener el grado de Doctor of Philosophy, de la Wageningen University, en marzo de 2020
Global climate change and food insecurity are major concerns of the 21st century. Agricultural production should increase by 60–110% to meet the projected food demands of the word population by 2050 (McGuire 2012). However, the rates of global crop production are still far below the mentioned requirements and most studies predict a future decline in grain yield of major crops due to climate change (Ray et al. 2013; Wiltshire et al. 2013). Rainfed farming systems are drastically affected by climatic conditions, with water scarcity and increasing temperature being the most important limiting factors for crop productivity and, ultimately, for food security worldwide (Daryanto et al. 2013). Efforts to ensure food supply will require accelerating the development of climate resilient crop varieties. This is particularly necessary for crops that provide staple food grain in developing countries and semi-arid regions of the world. Plant breeding can play a crucial role in enhancing crop productivity and adaptation to climate change. The main goal of breeding programs is to efficiently identify and select the best-performing genotypes as potential cultivars or as parental material to improve crop performance in future generations (Falconer and Mackay 1996; Bernardo 2010). For this, new selection techniques based on modern approaches to quantitative genetics have to be adopted by breeding programs in order to accelerate genetic progress. Advances in high-throughput genotyping technologies and the increasing cost-effective access to high-density genomic data have facilitated the adoption of a novel form of marker-assisted selection known as genomic selection (GS). This genetic evaluation method has already revolutionized animal breeding over the past decade and is gaining momentum in crop breeding. In GS, phenotypic and genome-wide marker data from a reference (or training) population is used to predict genetic merit of selection candidates that have only been genotyped but not phenotyped (Meuwissen et al. 2001). As a result, selection efficiency can potentially increase, reducing phenotyping costs and generation interval. Moreover, additional opportunities for GS in crops are provided by current developments in high throughput phenotyping technologies. The success in the incorporation of genomics as breeding tool depends on an appropriate statistical analysis of the phenotypic and genetic data generated in crop breeding programs.
EEA Pergamino
Fil: Velazco, Julio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Sección Forrajeras; Argentina. Wageningen University and Research . Biometris – Mathematical and Statistical Methods; Holanda
description Tesis para obtener el grado de Doctor of Philosophy, de la Wageningen University, en marzo de 2020
publishDate 2020
dc.date.none.fl_str_mv 2020-06-18T11:14:58Z
2020-06-18T11:14:58Z
2020-03
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_db06
info:ar-repo/semantics/tesisDoctoral
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/7432
https://research.wur.nl/en/publications/statistical-modeling-of-phenotypic-pedigree-and-genomic-informati
https://research.wur.nl/en/publications/statistical-modeling-of-phenotypic-pedigree-and-genomic-informati
978-94-6395-279-8
https://doi.org/10.18174/511730
url http://hdl.handle.net/20.500.12123/7432
https://research.wur.nl/en/publications/statistical-modeling-of-phenotypic-pedigree-and-genomic-informati
https://doi.org/10.18174/511730
identifier_str_mv 978-94-6395-279-8
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wageningen University, the Netherlands
publisher.none.fl_str_mv Wageningen University, the Netherlands
dc.source.none.fl_str_mv 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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