Registration of Four Near‐Isogenic Soybean Lines of G00‐3213 for Resistance to Asian Soybean Rust

King, Zachary R.; Harris, Diana; Wood, E. Dale; Buck, James W.; Boerma, H. R.; Li, Zenglu

doi:10.3198/jpr2015.04.0027crg

Cited by 8 publications

(4 citation statements)

References 44 publications

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“…Materials in the TS ranged from maturity groups (MGs) VI through VIII. These materials had been used to develop cultivars for commercial release in the Southern United States (Boerma et al, 2012;Boerma et al, 2016;King et al, 2016;Li et al, 2020).…”

Section: Plant Materialsmentioning

confidence: 99%

Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max)

et al. 2023

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Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean’s profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross’s offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding.

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Section: Plant Materialsmentioning

confidence: 99%

Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max)

et al. 2023

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“…These cultivars are currently used in combination with fungicide applications and management practices to reduce disease pressure and help maintain genetic resistance. Although R genes have been introgressed into elite soybean cultivars in the United States (Boerma et al ., 2011 ; Diers et al ., 2014 ; King et al ., 2016b ), and germplasm harbouring one or more R genes are available, the deployment of commercial soybean varieties resistant to SBR has not yet taken place, in part due to the challenge of obtaining high‐yield commercial lines with native Rpp genes and the fact that currently, SBR is not a major concern for soybean production in the United States (Childs et al ., 2018a ). Sojapar R24, a commercially available cultivar harbouring Rpp4 , was launched by the Institute of Agricultural Biotechnology (INBIO) in 2016 in Paraguay ( https://www.inbio.org.py/sojapar/sojapar‐r24/ ).…”

Section: Approaches For Improving Sbr Resistance I...mentioning

confidence: 99%

Soybean‐Phakopsora pachyrhizi interactions: towards the development of next‐generation disease‐resistant plants

Chicowski,

Bredow,

Utiyama

et al. 2023

Plant Biotechnology Journal

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SummarySoybean rust (SBR), caused by the obligate biotrophic fungus Phakopsora pachyrhizi, is a devastating foliar disease threatening soybean production. To date, no commercial cultivars conferring durable resistance to SBR are available. The development of long‐lasting SBR resistance has been hindered by the lack of understanding of this complex pathosystem, encompassing challenges posed by intricate genetic structures in both the host and pathogen, leading to a gap in the knowledge of gene‐for‐gene interactions between soybean and P. pachyrhizi. In this review, we focus on recent advancements and emerging technologies that can be used to improve our understanding of the P. pachyrhizi‐soybean molecular interactions. We further explore approaches used to combat SBR, including conventional breeding, transgenic approaches and RNA interference, and how advances in our understanding of plant immune networks, the availability of new molecular tools, and the recent sequencing of the P. pachyrhizi genome could be used to aid in the development of better genetic resistance against SBR. Lastly, we discuss the research gaps of this pathosystem and how new technologies can be used to shed light on these questions and to develop durable next‐generation SBR‐resistant soybean plants.

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“…(Hyuuga) and Rpp5 into MG II and MG IV elite backgrounds (Diers et al., ). Additional NILs released by the University of Georgia with Rpp1, Rpp2, Rpp3 and Rpp4 backcrossed into a MG VII elite background have also been developed (King et al., ). Furthermore, a breeding line was released in 2007 by the Georgia Agricultural Experiment Stations that incorporated the Rpp?…”

Section: Current Status Of Sbr Resistance Breedingmentioning

confidence: 99%

Breeding soybeans with resistance to soybean rust (Phakopsora pachyrhizi)

Childs

Buck

2018

Plant Breeding

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Soybean rust, caused by the fungal pathogen Phakopsora pachyrhizi, continues to be a global threat to soybean production, decreasing productivity and increasing the pesticide burden of cropping systems. However, breeders now have access to resistance genes that map to at least seven independent loci which can help protect crops against soybean rust infection. Efficient greenhouse screening protocols have been developed, and low-cost SNP genotyping technology is available for markerassisted selection and backcrossing of resistance to Phakopsora pachyrhizi (Rpp) loci.Soybean breeders can now employ these technologies for the development of highyielding soybean cultivars with two, three, or even four pyramided Rpp genes. Such cultivars should provide resistance against the most virulent P. pachyrhizi populations and would be of great help to both large-scale growers in the Americas and subsistence farmers in developing countries. We hope that a better understanding of the history and unique characteristics of P. pachyrhizi, the discovery of Rpp resistance alleles and the latest molecular breeding techniques will empower breeders across the globe to develop cultivars with durable resistance. K E Y W O R D Sgermplasm improvement, plant pathogen, rust resistance, soybean

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Registration of Four Near‐Isogenic Soybean Lines of G00‐3213 for Resistance to Asian Soybean Rust

Cited by 8 publications

References 44 publications

Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max)

Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine max)

Soybean‐Phakopsora pachyrhizi interactions: towards the development of next‐generation disease‐resistant plants

Breeding soybeans with resistance to soybean rust (Phakopsora pachyrhizi)

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