|Lee, Hoonsoo - Chungnam National University|
|Oh, Mirae - National Institute Of Animal Science|
|Tarpley, Lee - Texas A&M University|
|Lee, Kangjin - National Institute Of Horticultural & Herbal Science (NIHHS)|
Submitted to: Rice Technical Working Group Meeting Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 1/15/2020
Publication Date: 2/26/2020
Citation: Barnaby, J.Y., Huggins, T.D., Lee, H., McClung, A.M., Pinson, S.R., Oh, M., Bauchan, G.R., Tarpley, L., Lee, K., Kim, M.S., Edwards, J. 2020. Differentiating sub-population, production environment and grain chalk by hyperspectral imaging. Rice Technical Working Group Meeting Proceedings.
Technical Abstract: Rice grain quality influences crop value and is important to growers, millers, and processors as well as consumers. Grain quality in rice is determined by multiple factors including starch composition, cooking quality, and grain size, shape, and translucency (chalky appearance). High grain chalk causes grain breakage during milling and loss of crop value impacting domestic and export markets. Molecular markers are sought as tools for marker-assisted selection (MAS) in rice breeding for traits like grain quality that are complex, difficult to phenotype and are influenced by the environment. Furthermore, Genome-Wide Association mapping Studies (GWAS) have been used in rice to map a wide range of traits. One of the bottlenecks, however, in mapping of genes for grain quality traits is the intensive labor, time, and expense required to phenotype the diversity of physicochemical traits impacting rice quality. High throughput Vis/NIR spectroscopy phenotyping is a rapid analytical tool that assesses samples by utilizing visible and invisible regions of the spectrum. The aim of this study was to determine if Vis/NIR hyperspectral imaging of whole grain rice can discern differences in rice sub-population structure and production environment, as well as grain quality traits. Whole grain (brown) rice samples from the USDA mini-core collection grown in multiple locations were evaluated using hyperspectral imaging and compared with results from standard grain quality phenotyping. Loci associated with hyperspectral values were mapped in the mini-core with 3.2 million SNPs in a genome-wide association study (GWAS). Our results show that visible and near infra-red (Vis/NIR) spectroscopy can classify rice according to sub-population and production environment based on differences in physicochemical properties. The 702-900 nm range of the NIR spectrum was associated with the undesirable chalky grain trait. GWAS revealed that grain chalk and hyperspectral variation share genomic regions containing several plausible candidate genes for grain chalkiness. Hyperspectral quantification of grain chalk was validated using a segregating bi-parental mapping population. These results indicate that Vis/NIR can be used for non-destructive high throughput phenotyping of grain chalk and, potentially, other grain quality properties.