TY  - JOUR
AU  - Ćeran, Marina
AU  - Miladinović, Dragana
AU  - Đorđević, Vuk
AU  - Trkulja, Dragana
AU  - Radanović, Aleksandra
AU  - Glogovac, Svetlana
AU  - Kondić-Špika, Ankica
PY  - 2024
UR  - http://fiver.ifvcns.rs/handle/123456789/4477
AB  - Global agricultural productivity and food security are threatened by climate change, the growing world population, and the difficulties posed by the pandemic era. To overcome these challenges and meet food requirements, breeders have applied and implemented different advanced techniques that accelerate plant development and increase crop selection effectiveness. However, only two or three generations could be advanced annually using these approaches. Speed breeding (SB) is an innovative and promising technology to develop new varieties in a shorter time, utilizing the manipulation of controlled environmental conditions. This strategy can reduce the generation length from 2.5 to 5 times compared to traditional methods and accelerate generation advancement and crop improvement, accommodating multiple generations of crops per year. Beside long breeding cycles, SB can address other challenges related to traditional breeding, such as response to environmental conditions, disease and pest management, genetic uniformity, and improving resource efficiency. Combining genomic approaches such as marker-assisted selection, genomic selection, and genome editing with SB offers the capacity to further enhance breeding efficiency by reducing breeding cycle time, enabling early phenotypic assessment, efficient resource utilization, and increasing selection accuracy and genetic gain per year. Genomics-assisted SB holds the potential to revolutionize plant breeding by significantly accelerating the identification and selection of desirable genetic traits, expediting the development of improved crop varieties crucial for addressing global agricultural challenges.
PB  - Frontiers Media S.A.
T2  - Frontiers in Sustainable Food Systems
T1  - Genomics-assisted speed breeding for crop improvement: present and future
SP  - 1383302
VL  - 8
DO  - 10.3389/fsufs.2024.1383302
ER  - 

@article{
author = "Ćeran, Marina and Miladinović, Dragana and Đorđević, Vuk and Trkulja, Dragana and Radanović, Aleksandra and Glogovac, Svetlana and Kondić-Špika, Ankica",
year = "2024",
abstract = "Global agricultural productivity and food security are threatened by climate change, the growing world population, and the difficulties posed by the pandemic era. To overcome these challenges and meet food requirements, breeders have applied and implemented different advanced techniques that accelerate plant development and increase crop selection effectiveness. However, only two or three generations could be advanced annually using these approaches. Speed breeding (SB) is an innovative and promising technology to develop new varieties in a shorter time, utilizing the manipulation of controlled environmental conditions. This strategy can reduce the generation length from 2.5 to 5 times compared to traditional methods and accelerate generation advancement and crop improvement, accommodating multiple generations of crops per year. Beside long breeding cycles, SB can address other challenges related to traditional breeding, such as response to environmental conditions, disease and pest management, genetic uniformity, and improving resource efficiency. Combining genomic approaches such as marker-assisted selection, genomic selection, and genome editing with SB offers the capacity to further enhance breeding efficiency by reducing breeding cycle time, enabling early phenotypic assessment, efficient resource utilization, and increasing selection accuracy and genetic gain per year. Genomics-assisted SB holds the potential to revolutionize plant breeding by significantly accelerating the identification and selection of desirable genetic traits, expediting the development of improved crop varieties crucial for addressing global agricultural challenges.",
publisher = "Frontiers Media S.A.",
journal = "Frontiers in Sustainable Food Systems",
title = "Genomics-assisted speed breeding for crop improvement: present and future",
pages = "1383302",
volume = "8",
doi = "10.3389/fsufs.2024.1383302"
}

Ćeran, M., Miladinović, D., Đorđević, V., Trkulja, D., Radanović, A., Glogovac, S.,& Kondić-Špika, A.. (2024). Genomics-assisted speed breeding for crop improvement: present and future. in Frontiers in Sustainable Food Systems
Frontiers Media S.A.., 8, 1383302.
https://doi.org/10.3389/fsufs.2024.1383302

Ćeran M, Miladinović D, Đorđević V, Trkulja D, Radanović A, Glogovac S, Kondić-Špika A. Genomics-assisted speed breeding for crop improvement: present and future. in Frontiers in Sustainable Food Systems. 2024;8:1383302.
doi:10.3389/fsufs.2024.1383302 .

Ćeran, Marina, Miladinović, Dragana, Đorđević, Vuk, Trkulja, Dragana, Radanović, Aleksandra, Glogovac, Svetlana, Kondić-Špika, Ankica, "Genomics-assisted speed breeding for crop improvement: present and future" in Frontiers in Sustainable Food Systems, 8 (2024):1383302,
https://doi.org/10.3389/fsufs.2024.1383302 . .

TY  - JOUR
AU  - Ćeran, Marina
AU  - Đorđević, Vuk
AU  - Miladinović, Jegor
AU  - Vasiljević, Marjana
AU  - Đukić, Vojin
AU  - Ranđelović, Predrag
AU  - Jaćimović, Simona
PY  - 2024
UR  - http://fiver.ifvcns.rs/handle/123456789/4471
AB  - To overcome the different challenges to food security caused by a growing population andclimate change, soybean (Glycine max (L.) Merr.) breeders are creating novel cultivars that have thepotential to improve productivity while maintaining environmental sustainability. Genomic selection(GS) is an advanced approach that may accelerate the rate of genetic gain in breeding using genome-wide molecular markers. The accuracy of genomic selection can be affected by trait architectureand heritability, marker density, linkage disequilibrium, statistical models, and training set. Theselection of a minimal and optimal marker set with high prediction accuracy can lower genotypingcosts, computational time, and multicollinearity. Selective phenotyping could reduce the numberof genotypes tested in the field while preserving the genetic diversity of the initial population. Thisstudy aimed to evaluate different methods of selective genotyping and phenotyping on the accuracyof genomic prediction for soybean yield. The evaluation was performed on three populations:recombinant inbred lines, multifamily diverse lines, and germplasm collection. Strategies adoptedfor marker selection were as follows: SNP (single nucleotide polymorphism) pruning, estimation ofmarker effects, randomly selected markers, and genome-wide association study. Reduction of thenumber of genotypes was performed by selecting a core set from the initial population based onmarker data, yet maintaining the original population’s genetic diversity. Prediction ability using allmarkers and genotypes was different among examined populations. The subsets obtained by themodel-based strategy can be considered the most suitable for marker selection for all populations. Theselective phenotyping based on makers in all cases had higher values of prediction ability comparedto minimal values of prediction ability of multiple cycles of random selection, with the highestvalues of prediction obtained using AN approach and 75% population size. The obtained resultsindicate that selective genotyping and phenotyping hold great potential and can be integrated astools for improving or retaining selection accuracy by reducing genotyping or phenotyping costs forgenomic selection.
PB  - Basel : MDPI
T2  - Plants
T1  - Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity
IS  - 7
SP  - 975
VL  - 13
DO  - 10.3390/plants13070975
ER  - 

@article{
author = "Ćeran, Marina and Đorđević, Vuk and Miladinović, Jegor and Vasiljević, Marjana and Đukić, Vojin and Ranđelović, Predrag and Jaćimović, Simona",
year = "2024",
abstract = "To overcome the different challenges to food security caused by a growing population andclimate change, soybean (Glycine max (L.) Merr.) breeders are creating novel cultivars that have thepotential to improve productivity while maintaining environmental sustainability. Genomic selection(GS) is an advanced approach that may accelerate the rate of genetic gain in breeding using genome-wide molecular markers. The accuracy of genomic selection can be affected by trait architectureand heritability, marker density, linkage disequilibrium, statistical models, and training set. Theselection of a minimal and optimal marker set with high prediction accuracy can lower genotypingcosts, computational time, and multicollinearity. Selective phenotyping could reduce the numberof genotypes tested in the field while preserving the genetic diversity of the initial population. Thisstudy aimed to evaluate different methods of selective genotyping and phenotyping on the accuracyof genomic prediction for soybean yield. The evaluation was performed on three populations:recombinant inbred lines, multifamily diverse lines, and germplasm collection. Strategies adoptedfor marker selection were as follows: SNP (single nucleotide polymorphism) pruning, estimation ofmarker effects, randomly selected markers, and genome-wide association study. Reduction of thenumber of genotypes was performed by selecting a core set from the initial population based onmarker data, yet maintaining the original population’s genetic diversity. Prediction ability using allmarkers and genotypes was different among examined populations. The subsets obtained by themodel-based strategy can be considered the most suitable for marker selection for all populations. Theselective phenotyping based on makers in all cases had higher values of prediction ability comparedto minimal values of prediction ability of multiple cycles of random selection, with the highestvalues of prediction obtained using AN approach and 75% population size. The obtained resultsindicate that selective genotyping and phenotyping hold great potential and can be integrated astools for improving or retaining selection accuracy by reducing genotyping or phenotyping costs forgenomic selection.",
publisher = "Basel : MDPI",
journal = "Plants",
title = "Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity",
number = "7",
pages = "975",
volume = "13",
doi = "10.3390/plants13070975"
}

Ćeran, M., Đorđević, V., Miladinović, J., Vasiljević, M., Đukić, V., Ranđelović, P.,& Jaćimović, S.. (2024). Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity. in Plants
Basel : MDPI., 13(7), 975.
https://doi.org/10.3390/plants13070975

Ćeran M, Đorđević V, Miladinović J, Vasiljević M, Đukić V, Ranđelović P, Jaćimović S. Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity. in Plants. 2024;13(7):975.
doi:10.3390/plants13070975 .

Ćeran, Marina, Đorđević, Vuk, Miladinović, Jegor, Vasiljević, Marjana, Đukić, Vojin, Ranđelović, Predrag, Jaćimović, Simona, "Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity" in Plants, 13, no. 7 (2024):975,
https://doi.org/10.3390/plants13070975 . .