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
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/7432
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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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12.559606 |