Utilizing the Diversity of Wild Soybeans in China for Accelerating Soybean Breeding in the Genome Era

2016 ◽  
pp. 71-80
Author(s):  
Y Li ◽  
R Chang ◽  
L Qiu
Keyword(s):  
Soybean Breeding

Identification of novel haplotype of a cyst nematode resistance gene, GmSNAP18 in soybean [Glycine max (L.) Merr.]

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Ik-Young Choi ◽  
Prakash Basnet ◽  
Hana Yoo ◽  
Neha Samir Roy ◽  
Rahul Vasudeo Ramekar ◽  
...  
Keyword(s):  
Soybean Cyst Nematode ◽  
Gene Families ◽  
Nematode Resistance ◽  
Cyst Nematode ◽  
Soybean Breeding ◽  
Resistant Varieties ◽  
Soybean Genotypes ◽  
Glycine Max L ◽  
Scn Resistance

Soybean cyst nematode (SCN) is one of the most damaging pest of soybean. Discovery and characterization of the genes involved in SCN resistance are important in soybean breeding. Soluble NSF attachment protein (SNAP) genes are related to SCN resistance in soybean. SNAP genes include five gene families, and 2 haplotypes of exons 6 and 9 of SNAP18 are considered resistant to the SCN. In present study the haplotypes of GmSNAP18 were surveyed and chacterized in a total of 60 diverse soybean genotypes including Korean cultivars, landraces, and wild-types. The target region of exons 6 and 9 in GmSNAP18 region was amplified and sequenced to examine nucleotide variation. Characterization of 5 haplotypes identified in present study for the GmSNAP18 gene revealed two haplotypes as resistant, 1 as susceptible and two as novel. A total of twelve genotypes showed resistant haplotypes, and 45 cultivars were found susceptible. Interestingly, the two novel haplotypes were present in 3 soybean lines. The information provided here about the haplotypic variation of GmSNAP18 gene can be further explored for soybean breeding to develop resistant varieties.

Genetic Gain of Soybean Breeding for Yield in Henan Summer Soybean Zone over the Last 30 Years

2016 ◽  
Vol 42 (7) ◽  
pp. 1009
Author(s):  
Zhong-Wen HUANG ◽  
Xin-Juan XU ◽  
Wei WANG ◽  
Pei-Pei MEI
Keyword(s):  
Genetic Gain ◽  
Soybean Breeding

Grafting as a Tool in Soybean Breeding 1

Crop Science ◽  
1977 ◽  
Vol 17 (1) ◽  
pp. 181-183 ◽  
Author(s):  
R. A. S. Kiihl ◽  
E. E. Hartwig ◽  
T. C. Kilen
Keyword(s):  
Soybean Breeding

Molecular Marker Satt481 is Associated with Iron-Deficiency Chlorosis Resistance in a Soybean Breeding Population

Crop Science ◽  
2005 ◽  
Vol 45 (6) ◽  
pp. 2394-2399 ◽  
Author(s):  
Dirk V. Charlson ◽  
Theodore B. Bailey ◽  
Silvia R. Cianzio ◽  
Randy C. Shoemaker
Keyword(s):  
Iron Deficiency ◽  
Molecular Marker ◽  
Breeding Population ◽  
Iron Deficiency Chlorosis ◽  
Soybean Breeding ◽  
Chlorosis Resistance

scholarly journals Comparison and Application of Non-Destructive NIR Evaluations of Seed Protein and Oil Content in Soybean Breeding

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 77 ◽  
Author(s):  
Guo-Liang Jiang
Keyword(s):  
Plant Breeding ◽  
Oil Content ◽  
Near Infrared ◽  
Regression Equations ◽  
Single Plant ◽  
Crop Species ◽  
Soybean Breeding ◽  
Plant Breeding Program ◽  
Significant Difference ◽  
Non Destructive

A plant breeding program needs to evaluate a large number of materials for different traits within a limited time. Near-infrared (NIR) spectroscopy has been used to quickly determine seed composition in various crop species. In this study, we compared whole-seed evaluations of protein and oil content by NIR methods in soybean [Glycine max (L.) Merr.], and then discussed the application to plant breeding. The differences among the entries tested were highly significant in both traits for each method used. No significant difference but high correlation and consistency existed between DA 7250 and wet-chemistry methods. Compared with DA 7250, ZX-50 exhibited, to some extent, differences or errors. The differences of ZX-50 methods were found to be correlated with seed sizes and could be corrected using regression equations formulated for bias calculation. After correction, the differences in the predictions between DA 7250 and ZX-50 methods were insignificant. Similar to DA 7250, ZX-50 methods exhibited a high repeatability (> 98%) of the predictions. By validation with 760 bulk samples of different seed types and 240 single-plant samples, it further demonstrated that as a non-destructive, fast and cost-efficient method, ZX-50 NIR analysis with an appropriate bias correction could be used in soybean breeding, specifically suitable for single plant selection based on whole seeds.

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