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ARS Home » Southeast Area » Stuttgart, Arkansas » Dale Bumpers National Rice Research Center » Research » Publications at this Location » Publication #343815

Research Project: Genomic Approaches and Genetic Resources for Improving Rice Yield and Grain Quality

Location: Dale Bumpers National Rice Research Center

Title: GENOME WIDE ASSOCIATION STUDY DISSECTING GENETIC ARCHITECTURE OF GRAIN PHYSICOCHEMICAL TRAITS IN RICE

Author
item Barnaby, Jinyoung
item Huggins, Trevis
item Mcclung, Anna
item Mcclung, Anna
item Pinson, Shannon
item Edwards, Jeremy
item Tarpley, Lee - Texas A&M Agrilife
item Kim, Moon
item Lee, Hoonsoo - Us Forest Service (FS)
item Oh, Mirae - Us Forest Service (FS)

Submitted to: Mid Atlantic Plant Molecular Biology Society Conference
Publication Type: Abstract Only
Publication Acceptance Date: 8/15/2017
Publication Date: 8/15/2017
Citation: Barnaby, J.Y., Huggins, T.D., McClung, A.M., Pinson, S.R., Edwards, J., Tarpley, L., Kim, M.S., Lee, H., Oh, M. 2017. Genome wide association study dissecting genetic architecture of grain physicochemical traits in rice. 34th Annual Mid Atlantic Plant Molecular Biology Society Conference . p.15. Program Booklet. http://wp.towson.edu/mapmbs/files/2017/08/34th-MAPMBS-program-2017-26ysoam.pdf

Interpretive Summary:

Technical Abstract: Given the rapid advances in genomic technologies, phenotyping has become the bottleneck for revealing gene-trait relationships. Therefore, developing a means to rapidly and accurately phenotype thousands of genotypes can allow us to more fully utilize the genomic data that is currently available. A hyperspectral imaging system is a high-throughput phenotyping tool that has been used to evaluate grain quality components such as protein, fat, starch, antioxidants, etc. This platform provides extensive phenotypic data; however, utilization and interpretation of the data is largely unexplored. The USDA Minicore rice germplasm collection contains 220 varieties originating from around the world, includes members of the 5 subpopulations of O. sativa, and has a genomic dataset of 3.3 million SNP markers. The objective of this study is to determine if hyperspectral imaging and SNP array data can be used to elucidate quantitatively inherited grain quality traits in rice, and to conduct genome-wide association mapping to identify SNPs and candidate genes associated with physicochemical grain traits. A wavelength range of 600-700 nm of visible NIR (VisNIR) spectroscopy was significantly associated with the grain chalk phenotype, and the same genetic loci associated with chalk were validated using NIR spectral data and SNP information using a bi-parental mapping population segregating for grain chalk. Furthermore, multi-spectral phenotypes differed by growing environment as observed by short-wave IR (SWIR) and fourier-transform IR (FTIR), but not by VisNIR indicating detection of environmental impacts based on spectral regions. These results indicate the value of using hyperspectral imaging as a means of non-destructive high throughput phenotyping for grain physicochemical traits.