Genomic prediction – new tool in soybean breeding
Аутори
Đorđević, VukĆeran, Marina
Miladinović, Jegor
Balešević-Tubić, Svetlana
Petrović, Kristina
Ranđelović, Predrag
Marinković, Jelena
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Yield and most of the agronomical important traits are quantitatively inherited, influenced
by many loci with a small effect and affected by environmental conditions. When dealing
with the improvement of quantitative traits, it is not particularly useful to perform the
selection by using few major-effect loci as in traditional marker-assisted selection, but to
simultaneously use genome-wide molecular markers able to capture all small effect loci
influencing a trait.
The training population consisted of 227 diverse soybean linesthat were used for genomic
prediction model development. Training population was evaluated for yield at three
consecutive years. DNA of each genotype was sequencing on Illumina HiSeq 2500, using
GBS Discovery Pipeline for SNP calling. Prediction ability was evaluated using six
mathematical models, including parametric and non-parametric and were validated on
three different levels: self-prediction, cross-validation (5-fold) and external validation
(hi...storical data).
Overall, genomic prediction ability for soybean yield was relatively high (0.60) and the
results indicate a modest influence of mathematical model and marker number on the
prediction ability using cross-validation and external validation. However, model had
variable ability to predict phenotypic performance in separate environments, with
especially high prediction ability in years not impacted by yield-limiting factors, when the
genetic potential was fully achieved. Improvement of model performance in cross-validation
and external validation was achieved by increasing the phenotyping intensity that must
reflect the target environment variability.
Obtained results indicate that genomic prediction can be integrating part of breeding
process as useful tool that can increase breeding efficiency and decrees breeding time.
Particular implementations are diverse, from germplasm screening and parental choice to
the forward breeding and direct section based on genomic prediction.
Кључне речи:
soybean / genomic prediction / yield / modelИзвор:
Book of Abstracts, 6th Congress of the Serbian Genetic Society, 13-17 October 2019, Vrnjačka Banja, 2019, 185-185Издавач:
- Belgrade : Serbian Genetic Society
Колекције
Институција/група
FiVeRTY - CONF AU - Đorđević, Vuk AU - Ćeran, Marina AU - Miladinović, Jegor AU - Balešević-Tubić, Svetlana AU - Petrović, Kristina AU - Ranđelović, Predrag AU - Marinković, Jelena PY - 2019 UR - http://fiver.ifvcns.rs/handle/123456789/2755 AB - Yield and most of the agronomical important traits are quantitatively inherited, influenced by many loci with a small effect and affected by environmental conditions. When dealing with the improvement of quantitative traits, it is not particularly useful to perform the selection by using few major-effect loci as in traditional marker-assisted selection, but to simultaneously use genome-wide molecular markers able to capture all small effect loci influencing a trait. The training population consisted of 227 diverse soybean linesthat were used for genomic prediction model development. Training population was evaluated for yield at three consecutive years. DNA of each genotype was sequencing on Illumina HiSeq 2500, using GBS Discovery Pipeline for SNP calling. Prediction ability was evaluated using six mathematical models, including parametric and non-parametric and were validated on three different levels: self-prediction, cross-validation (5-fold) and external validation (historical data). Overall, genomic prediction ability for soybean yield was relatively high (0.60) and the results indicate a modest influence of mathematical model and marker number on the prediction ability using cross-validation and external validation. However, model had variable ability to predict phenotypic performance in separate environments, with especially high prediction ability in years not impacted by yield-limiting factors, when the genetic potential was fully achieved. Improvement of model performance in cross-validation and external validation was achieved by increasing the phenotyping intensity that must reflect the target environment variability. Obtained results indicate that genomic prediction can be integrating part of breeding process as useful tool that can increase breeding efficiency and decrees breeding time. Particular implementations are diverse, from germplasm screening and parental choice to the forward breeding and direct section based on genomic prediction. 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 – new tool in soybean breeding EP - 185 SP - 185 UR - https://hdl.handle.net/21.15107/rcub_fiver_2755 ER -
@conference{ author = "Đorđević, Vuk and Ćeran, Marina and Miladinović, Jegor and Balešević-Tubić, Svetlana and Petrović, Kristina and Ranđelović, Predrag and Marinković, Jelena", year = "2019", abstract = "Yield and most of the agronomical important traits are quantitatively inherited, influenced by many loci with a small effect and affected by environmental conditions. When dealing with the improvement of quantitative traits, it is not particularly useful to perform the selection by using few major-effect loci as in traditional marker-assisted selection, but to simultaneously use genome-wide molecular markers able to capture all small effect loci influencing a trait. The training population consisted of 227 diverse soybean linesthat were used for genomic prediction model development. Training population was evaluated for yield at three consecutive years. DNA of each genotype was sequencing on Illumina HiSeq 2500, using GBS Discovery Pipeline for SNP calling. Prediction ability was evaluated using six mathematical models, including parametric and non-parametric and were validated on three different levels: self-prediction, cross-validation (5-fold) and external validation (historical data). Overall, genomic prediction ability for soybean yield was relatively high (0.60) and the results indicate a modest influence of mathematical model and marker number on the prediction ability using cross-validation and external validation. However, model had variable ability to predict phenotypic performance in separate environments, with especially high prediction ability in years not impacted by yield-limiting factors, when the genetic potential was fully achieved. Improvement of model performance in cross-validation and external validation was achieved by increasing the phenotyping intensity that must reflect the target environment variability. Obtained results indicate that genomic prediction can be integrating part of breeding process as useful tool that can increase breeding efficiency and decrees breeding time. Particular implementations are diverse, from germplasm screening and parental choice to the forward breeding and direct section based on genomic prediction.", 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 – new tool in soybean breeding", pages = "185-185", url = "https://hdl.handle.net/21.15107/rcub_fiver_2755" }
Đorđević, V., Ćeran, M., Miladinović, J., Balešević-Tubić, S., Petrović, K., Ranđelović, P.,& Marinković, J.. (2019). Genomic prediction – new tool in soybean breeding. in Book of Abstracts, 6th Congress of the Serbian Genetic Society, 13-17 October 2019, Vrnjačka Banja Belgrade : Serbian Genetic Society., 185-185. https://hdl.handle.net/21.15107/rcub_fiver_2755
Đorđević V, Ćeran M, Miladinović J, Balešević-Tubić S, Petrović K, Ranđelović P, Marinković J. Genomic prediction – new tool in soybean breeding. in Book of Abstracts, 6th Congress of the Serbian Genetic Society, 13-17 October 2019, Vrnjačka Banja. 2019;:185-185. https://hdl.handle.net/21.15107/rcub_fiver_2755 .
Đorđević, Vuk, Ćeran, Marina, Miladinović, Jegor, Balešević-Tubić, Svetlana, Petrović, Kristina, Ranđelović, Predrag, Marinković, Jelena, "Genomic prediction – new tool in soybean breeding" in Book of Abstracts, 6th Congress of the Serbian Genetic Society, 13-17 October 2019, Vrnjačka Banja (2019):185-185, https://hdl.handle.net/21.15107/rcub_fiver_2755 .