Validation of marker-assisted selection in soybean breeding program for pod shattering resistance

Kim, Jimin; Kim, Kyung-Hye; Jung, Jiyeong; Kang, Beom Kyu; Lee, Juseok; Ha, Bo‐Keun; Kang, Sung-Taeg

doi:10.1007/s10681-020-02703-w

Cited by 12 publications

(10 citation statements)

References 21 publications

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“…The prediction accuracy of the KASP marker was very high, with 81.7% to 88.2% in the RIL populations, and 91.3% to 95.5% in the varieties and elite lines. The lower prediction accuracy in the RIL populations may be due to a recombination in the RILs (although they were F 7:8 ), and these results were similar to the results of Kim et al [31]. The prediction accuracy of this study is comparable to a previous study [31].…”

Section: Discussionsupporting

confidence: 89%

“…The SNPs within Glyma.16g141600 on chromosome 16 also have significant associations with podshattering [32]. However, for a highly accurate selection of soybean genotypes, we need additional molecular markers for pod-shattering tolerance [31]. Therefore, we wanted to confirm the candidate genes and develop additional allele-specific markers related to the pod-shattering tolerance from an elite cultivar DW with strong tolerance to pod-shattering.…”

Section: Discussionmentioning

confidence: 99%

“…In soybean, KASP assays have been employed for a few traits, including soybean cyst nematode resistance [26,27], frogeye leaf spot resistance [28], and Fusarium graminearum resistance [29]. However, there are few reports of selection markers for the pod-shattering tolerance in soybean [30,31]. We designed SNP and/or InDel markers and validated them in populations with diverse genetic backgrounds.…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of SNP and InDel Markers for Pod-Shattering Tolerance in Soybean

Seo

Dhungana

Kang

et al. 2022

IJMS

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Pod-shattering causes a significant yield loss in many soybean cultivars. Shattering-tolerant cultivars provide the most effective approach to minimizing this loss. We developed molecular markers for pod-shattering and validated them in soybeans with diverse genetic backgrounds. The genes Glyma.16g141200, Glyma.16g141500, and Glyma.16g076600, identified in our previous study by quantitative trait locus (QTL) mapping and whole-genome resequencing, were selected for marker development. The whole-genome resequencing of three parental lines (one shattering-tolerant and two shattering-susceptible) identified single nucleotide polymorphism (SNP) and/or insertion/deletion (InDel) regions within or near the selected genes. Two SNPs and one InDel were converted to Kompetitive Allele-Specific PCR (KASP) and InDel markers, respectively. The accuracy of the markers was examined in the two recombinant inbred line populations used for the QTL mapping, as well as the 120 varieties and elite lines, through allelic discrimination and phenotyping by the oven-drying method. Both types of markers successfully discriminated the pod shattering-tolerant and shattering-susceptible genotypes. The prediction accuracy, which was as high as 90.9% for the RILs and was 100% for the varieties and elite lines, also supported the accuracy and usefulness of these markers. Thus, the markers can be used effectively for genetic and genomic studies and the marker-assisted selection for pod-shattering tolerance in soybean.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development and Validation of SNP and InDel Markers for Pod-Shattering Tolerance in Soybean

Seo

Dhungana

Kang

et al. 2022

IJMS

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“…In other words, GAB is facilitated by the identification of molecular genomic markers linked to QTLs or genes underlying agronomic traits of interest, which are then utilized as useful tools for molecular breeding ( Singh R.K. et al, 2020 ; Sinha et al, 2021 ). To that end, several GAB approaches have been deployed in various crop improvement programs, including marker-assisted backcrossing (MABC) to enhance β-carotene content in maize ( Qutub et al, 2021 ); marker-assisted recurrent selection (MARS) to improve crown rot ( Fusarium pseudograminearum ) resistance in bread wheat ( Rahman et al, 2020 ) and pod shattering resistance in soybean ( Kim et al, 2020 ); as well as genomic selection (GS) to improve rice blast ( Magnaporthe oryzae ) resistance ( Huang et al, 2019 ) and maize drought tolerance ( Shikha et al, 2017 ). Meanwhile, molecular marker based applications such as gene linkage and quantitative trait loci (QTL) mapping have become more feasible owing to the recent advances in genotyping platforms and statistical genomics ( Kulwal, 2018 ).…”

Section: Genomics and Pan-genomicsmentioning

confidence: 99%

Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value

et al. 2021

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Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the “omics” technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands.

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“…The MAS (includes MABC and MARS) utilizes molecular markers known to be associated with a particular trait/phenotype to identify a desirable individual carrying favorable allele for the trait of interest (Jiang, 2013). However, MAS is applicable only for the major‐effect genes (Bhat et al, 2016); for example, most of the successful MAS‐based studies carried out earlier in soybean involved mainly the major‐effect QTLs/genes that govern large portion of phenotypic variation for the trait of interest (see details in Concibido et al, 1996; Gupta et al, 2017; Kim et al, 2020; Saghai Maroof et al, 2008; Viganó et al, 2018). In case of minor genes, which contribute only a small invisible phenotypic variation for the complex trait, MAS has not led to successful results in soybean (Bhat et al, 2016; Zhang et al, 2015).…”

Section: Genomics‐assisted Breedingmentioning

confidence: 99%

High‐throughput NGS‐based genotyping and phenotyping: Role in genomics‐assisted breeding for soybean improvement

Bhat

2021

Legume Science

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Soybean is an important food crop that provides edible protein and oil for human and animal nutrition. Conventional phenotypic‐based breeding approaches have made significant contribution in the last century by developing many improved soybean varieties. However, due to the longer time taken to develop a variety, low genetic gain per unit time, and adverse environmental influence of phenotypic‐based selection, conventional approaches are not sufficient to maintain pace with growing population and climate change. In this context, the recent method of genotypic selection, that is, genomics‐assisted breeding (GAB) is considered a promising approach to address the challenges in soybean breeding. However, to harness the true potential of GAB in soybean improvement, the great coverage and precision in the genotyping and phenotyping are required. Previously, a huge gap was observed between the discovery and practical use of quantitative trait loci (QTLs) in soybean improvement. It has been suggested that low marker density and manually collected phenotypes are the major reasons for this failure. Hence, high‐throughput genotyping (HTG) providing higher genome‐wide marker density, as well as accurate and precise phenotyping using high‐throughput digital phenotyping (HTP) platforms, can significantly increase the success of QTL and candidate gene identification in soybean. These approaches can greatly increase the practical utility of GAB in soybean and also offer a faster characterization of germplasm and breeding materials. This review provides the detailed information on how the recent innovations in genomics and phenomics can assists in improving the efficiency and potential of GAB in soybean improvement.

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Validation of marker-assisted selection in soybean breeding program for pod shattering resistance

Cited by 12 publications

References 21 publications

Development and Validation of SNP and InDel Markers for Pod-Shattering Tolerance in Soybean

Development and Validation of SNP and InDel Markers for Pod-Shattering Tolerance in Soybean

Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value

High‐throughput NGS‐based genotyping and phenotyping: Role in genomics‐assisted breeding for soybean improvement

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