Genomic prediction in maize breeding
Аутори
Stanisavljević, DušanZorić, Miroslav
Mitrović, Bojan
Perišić, Milica
Nastasić, Aleksandra
Babić, Milosav
Bekavac, Goran
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
In recent years the availability of cheap genome-wide markers resulted in a novel approach in maize breeding called genomic prediction (GP). Genomic prediction is a special form of marker assisted breeding, in which genetic markers covering the whole genome are used for estimation genomic breeding value of individuals under selection. Genomic estimated breeding value (GEBV) of an individual represents the sum of effects associated with all the marker alleles present in an individual and included in the GP model applied to the population under selection. The GP is based on training (TP) and breeding (BP) populations. The TP is used for training of the GP model and for estimation of the marker effects required for estimation of GEBV of the individuals in the BP. The BP is the population subjected to GP for identification of the superior lines for use as parents for new hybrid combinations. In real maize breeding programs, successful implementation of the GP is based on data from multi-en...vironment trials. Thus, appropriate modeling and statistical approaches are required to deal with the complexity of the multi-environment grain yield data coupled with genomic and environmental data to speed-up maize breeding.
Кључне речи:
maize / breeding / genomic prediction / genomic estimated breeding value / training populations / breeding populationsИзвор:
Book of Abstracts, 6th Congress of the Serbian Genetic Society, 13-17 October 2019, Vrnjačka Banja, 2019, 227-227Издавач:
- Belgrade : Serbian Genetic Society
Колекције
Институција/група
FiVeRTY - CONF AU - Stanisavljević, Dušan AU - Zorić, Miroslav AU - Mitrović, Bojan AU - Perišić, Milica AU - Nastasić, Aleksandra AU - Babić, Milosav AU - Bekavac, Goran PY - 2019 UR - http://fiver.ifvcns.rs/handle/123456789/3060 AB - In recent years the availability of cheap genome-wide markers resulted in a novel approach in maize breeding called genomic prediction (GP). Genomic prediction is a special form of marker assisted breeding, in which genetic markers covering the whole genome are used for estimation genomic breeding value of individuals under selection. Genomic estimated breeding value (GEBV) of an individual represents the sum of effects associated with all the marker alleles present in an individual and included in the GP model applied to the population under selection. The GP is based on training (TP) and breeding (BP) populations. The TP is used for training of the GP model and for estimation of the marker effects required for estimation of GEBV of the individuals in the BP. The BP is the population subjected to GP for identification of the superior lines for use as parents for new hybrid combinations. In real maize breeding programs, successful implementation of the GP is based on data from multi-environment trials. Thus, appropriate modeling and statistical approaches are required to deal with the complexity of the multi-environment grain yield data coupled with genomic and environmental data to speed-up maize breeding. PB - Belgrade : Serbian Genetic Society C3 - Book of Abstracts, 6th Congress of the Serbian Genetic Society, 13-17 October 2019, Vrnjačka Banja T1 - Genomic prediction in maize breeding EP - 227 SP - 227 UR - https://hdl.handle.net/21.15107/rcub_fiver_3060 ER -
@conference{ author = "Stanisavljević, Dušan and Zorić, Miroslav and Mitrović, Bojan and Perišić, Milica and Nastasić, Aleksandra and Babić, Milosav and Bekavac, Goran", year = "2019", abstract = "In recent years the availability of cheap genome-wide markers resulted in a novel approach in maize breeding called genomic prediction (GP). Genomic prediction is a special form of marker assisted breeding, in which genetic markers covering the whole genome are used for estimation genomic breeding value of individuals under selection. Genomic estimated breeding value (GEBV) of an individual represents the sum of effects associated with all the marker alleles present in an individual and included in the GP model applied to the population under selection. The GP is based on training (TP) and breeding (BP) populations. The TP is used for training of the GP model and for estimation of the marker effects required for estimation of GEBV of the individuals in the BP. The BP is the population subjected to GP for identification of the superior lines for use as parents for new hybrid combinations. In real maize breeding programs, successful implementation of the GP is based on data from multi-environment trials. Thus, appropriate modeling and statistical approaches are required to deal with the complexity of the multi-environment grain yield data coupled with genomic and environmental data to speed-up maize breeding.", publisher = "Belgrade : Serbian Genetic Society", journal = "Book of Abstracts, 6th Congress of the Serbian Genetic Society, 13-17 October 2019, Vrnjačka Banja", title = "Genomic prediction in maize breeding", pages = "227-227", url = "https://hdl.handle.net/21.15107/rcub_fiver_3060" }
Stanisavljević, D., Zorić, M., Mitrović, B., Perišić, M., Nastasić, A., Babić, M.,& Bekavac, G.. (2019). Genomic prediction in maize breeding. in Book of Abstracts, 6th Congress of the Serbian Genetic Society, 13-17 October 2019, Vrnjačka Banja Belgrade : Serbian Genetic Society., 227-227. https://hdl.handle.net/21.15107/rcub_fiver_3060
Stanisavljević D, Zorić M, Mitrović B, Perišić M, Nastasić A, Babić M, Bekavac G. Genomic prediction in maize breeding. in Book of Abstracts, 6th Congress of the Serbian Genetic Society, 13-17 October 2019, Vrnjačka Banja. 2019;:227-227. https://hdl.handle.net/21.15107/rcub_fiver_3060 .
Stanisavljević, Dušan, Zorić, Miroslav, Mitrović, Bojan, Perišić, Milica, Nastasić, Aleksandra, Babić, Milosav, Bekavac, Goran, "Genomic prediction in maize breeding" in Book of Abstracts, 6th Congress of the Serbian Genetic Society, 13-17 October 2019, Vrnjačka Banja (2019):227-227, https://hdl.handle.net/21.15107/rcub_fiver_3060 .