Development of SNP Genotyping Assays for Seed Composition Traits in Soybean

Gunvant Patil, Juhi Chaudhary, Tri D. Vuong, Brian Jenkins, Dan Qiu, Suhas Kadam, Grover J. Shannon, Henry T. Nguyen

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

Seed composition is one of the most important determinants of the economic values in soybean. The quality and quantity of different seed components, such as oil, protein, and carbohydrates, are crucial ingredients in food, feed, and numerous industrial products. Soybean researchers have successfully developed and utilized a diverse set of molecular markers for seed trait improvement in soybean breeding programs. It is imperative to design and develop molecular assays that are accurate, robust, high-Throughput, cost-effective, and available on a common genotyping platform. In the present study, we developed and validated KASP (Kompetitive allele-specific polymerase chain reaction) genotyping assays based on previously known functional mutant alleles for the seed composition traits, including fatty acids, oligosaccharides, trypsin inhibitor, and lipoxygenase. These assays were validated on mutant sources as well as mapping populations and precisely distinguish the homozygotes and heterozygotes of the mutant genes. With the obvious advantages, newly developed KASP assays in this study can substitute the genotyping assays that were previously developed for marker-Assisted selection (MAS). The functional gene-based assay resource developed using common genotyping platform will be helpful to accelerate efforts to improve soybean seed composition traits.

Original languageEnglish
Article number6572969
JournalInternational Journal of Plant Genomics
Volume2017
DOIs
StatePublished - 2017

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    Patil, G., Chaudhary, J., Vuong, T. D., Jenkins, B., Qiu, D., Kadam, S., Shannon, G. J., & Nguyen, H. T. (2017). Development of SNP Genotyping Assays for Seed Composition Traits in Soybean. International Journal of Plant Genomics, 2017, [6572969]. https://doi.org/10.1155/2017/6572969