|ALI, M - University Of Missouri System|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/28/2020
Publication Date: 2/10/2020
Citation: Huggins, T.D., McClung, A.M., Edwards, J., Jia, M.H., Bockelman, H.E., Ali, M.L., Eizenga, G.C. 2020. Efficient accession management and characterization of the USDA-ARS rice germplasm collection through phenotyping and genotyping. Meeting Abstract.
Technical Abstract: Genebanks are an important source of genetic diversity for food crops world-wide and offer valuable information that can be used by plant breeders to improve agricultural productivity and nutritional quality. Collections of major crop species maintained by genebanks often have over 10,000 accessions, thus there is the potential to generate “big data” that captures a wide range of genotypic and phenotypic diversity across all accessions. There will be abundant opportunities for artificial intelligence (AI) to enable high-throughput phenotyping and mining of the collections for novel genetic diversity. The USDA-ARS National Small Grains Collection (NSGC) currently maintains over 19,000 accessions of cultivated Asian rice (Oryza sativa L.), 193 African cultivated rice (O. glaberrima Steud.) and 54 Oryza wild species. In this study, subsets of the rice collection were genotyped and phenotyped to better characterize the accessions and to evaluate redundancy. We phenotyped a random set of 1,993 Rice-NSGC accessions, for two phenological traits, plant height, 13 morphological traits, six grain quality traits, four production traits, resistance to three diseases, and two stress-related traits and genotyped these accessions with 11 fingerprint markers (FPM), one subspecies marker, and 14 trait specific markers (TSM). The TSM were used to validate phenotypic data for fragrance, pericarp color, blast disease resistance, leaf and hull pubescence, apparent amylose content, starch pasting properties and gelatinization temperature, and plant height. The markers classified accessions by species, subspecies, subpopulation, bran color, aroma, and in some cases by variety. Even with the limited number of markers, the genotyping was adequate for differentiating varieties and predicting phenotypes. Some traits such as plant height, yield, disease detection, and stress detection have been adapted to AI across species. Continued efforts to genotype more accessions with additional trait specific markers will help breeders select valuable germplasm from the NSGC for use in variety development.