Exploration of selective genotyping and selective phenotyping for optimization of soybean genomic prediction models
Аутори
Ćeran, MarinaĐorđević, Vuk
Miladinović, Jegor
Ranđelović, Predrag
Vasiljević, Marjana
Jaćimović, Simona
Đukić, Vojin
Остала ауторства
Vollmann, JohannVasiljević, Marjana
Rittler, Leopold
Miladinović, Jegor
Murphy-Bokern, Donal
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
The accuracy of genomic selection can be affected by several factors including trait architecture and heritability, marker density, linkage disequilibrium between markers and trait loci, statistical models, training population size, composition, and population structure. The selection of a minimal and optimal marker set with high prediction accuracy as an alternative to reduce genotyping costs, computational time, and multicollinearity for the genomic selection is a challenging task. Furthermore, optimal training population size is mostly determined empirically, by random sampling a whole set of genotypes, which may not reflect the true relationships in the population and may lead to the loss of rare genotypes and alleles. Selective phenotyping could reduce the number of genotypes tested in the field while preserving the genetic diversity of the initial population. This study aimed to evaluate different methods of selective genotyping and phenotyping on the accuracy of genomic predicti...on for soybean yield.
Кључне речи:
selective genotyping / selective phenotyping / soybean / genomic prediction modelsИзвор:
Abstracts, 11th World Soybean Research Conference (WSRC 11), Soybean Research for Sustainable Development, Vienna, 18-23 June 2023, 2023, 153-153Издавач:
- Vienna : University of Natural Resources and Life Sciences (BOKU)
Финансирање / пројекти:
Колекције
Институција/група
FiVeRTY - CONF AU - Ćeran, Marina AU - Đorđević, Vuk AU - Miladinović, Jegor AU - Ranđelović, Predrag AU - Vasiljević, Marjana AU - Jaćimović, Simona AU - Đukić, Vojin PY - 2023 UR - http://fiver.ifvcns.rs/handle/123456789/3648 AB - The accuracy of genomic selection can be affected by several factors including trait architecture and heritability, marker density, linkage disequilibrium between markers and trait loci, statistical models, training population size, composition, and population structure. The selection of a minimal and optimal marker set with high prediction accuracy as an alternative to reduce genotyping costs, computational time, and multicollinearity for the genomic selection is a challenging task. Furthermore, optimal training population size is mostly determined empirically, by random sampling a whole set of genotypes, which may not reflect the true relationships in the population and may lead to the loss of rare genotypes and alleles. Selective phenotyping could reduce the number of genotypes tested in the field while preserving the genetic diversity of the initial population. This study aimed to evaluate different methods of selective genotyping and phenotyping on the accuracy of genomic prediction for soybean yield. PB - Vienna : University of Natural Resources and Life Sciences (BOKU) C3 - Abstracts, 11th World Soybean Research Conference (WSRC 11), Soybean Research for Sustainable Development, Vienna, 18-23 June 2023 T1 - Exploration of selective genotyping and selective phenotyping for optimization of soybean genomic prediction models EP - 153 SP - 153 DO - 10.5281/zenodo.7974681 ER -
@conference{ author = "Ćeran, Marina and Đorđević, Vuk and Miladinović, Jegor and Ranđelović, Predrag and Vasiljević, Marjana and Jaćimović, Simona and Đukić, Vojin", year = "2023", abstract = "The accuracy of genomic selection can be affected by several factors including trait architecture and heritability, marker density, linkage disequilibrium between markers and trait loci, statistical models, training population size, composition, and population structure. The selection of a minimal and optimal marker set with high prediction accuracy as an alternative to reduce genotyping costs, computational time, and multicollinearity for the genomic selection is a challenging task. Furthermore, optimal training population size is mostly determined empirically, by random sampling a whole set of genotypes, which may not reflect the true relationships in the population and may lead to the loss of rare genotypes and alleles. Selective phenotyping could reduce the number of genotypes tested in the field while preserving the genetic diversity of the initial population. This study aimed to evaluate different methods of selective genotyping and phenotyping on the accuracy of genomic prediction for soybean yield.", publisher = "Vienna : University of Natural Resources and Life Sciences (BOKU)", journal = "Abstracts, 11th World Soybean Research Conference (WSRC 11), Soybean Research for Sustainable Development, Vienna, 18-23 June 2023", title = "Exploration of selective genotyping and selective phenotyping for optimization of soybean genomic prediction models", pages = "153-153", doi = "10.5281/zenodo.7974681" }
Ćeran, M., Đorđević, V., Miladinović, J., Ranđelović, P., Vasiljević, M., Jaćimović, S.,& Đukić, V.. (2023). Exploration of selective genotyping and selective phenotyping for optimization of soybean genomic prediction models. in Abstracts, 11th World Soybean Research Conference (WSRC 11), Soybean Research for Sustainable Development, Vienna, 18-23 June 2023 Vienna : University of Natural Resources and Life Sciences (BOKU)., 153-153. https://doi.org/10.5281/zenodo.7974681
Ćeran M, Đorđević V, Miladinović J, Ranđelović P, Vasiljević M, Jaćimović S, Đukić V. Exploration of selective genotyping and selective phenotyping for optimization of soybean genomic prediction models. in Abstracts, 11th World Soybean Research Conference (WSRC 11), Soybean Research for Sustainable Development, Vienna, 18-23 June 2023. 2023;:153-153. doi:10.5281/zenodo.7974681 .
Ćeran, Marina, Đorđević, Vuk, Miladinović, Jegor, Ranđelović, Predrag, Vasiljević, Marjana, Jaćimović, Simona, Đukić, Vojin, "Exploration of selective genotyping and selective phenotyping for optimization of soybean genomic prediction models" in Abstracts, 11th World Soybean Research Conference (WSRC 11), Soybean Research for Sustainable Development, Vienna, 18-23 June 2023 (2023):153-153, https://doi.org/10.5281/zenodo.7974681 . .