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ARS Home » Southeast Area » Stuttgart, Arkansas » Dale Bumpers National Rice Research Center » Research » Publications at this Location » Publication #390580

Research Project: Gene Discovery and Crop Design for Current and New Rice Management Practices and Market Opportunities

Location: Dale Bumpers National Rice Research Center

Title: Modeling phenotypic groups reflects genetic relationships between cultivars and their wild relatives in rice

Author
item GREENBERG, ANTHONY - Bayesic Research
item Edwards, Jeremy
item MCNALLY, KENNETH - International Rice Research Institute
item JUNG, JANELLE - Cornell University - New York
item KIM, HYUN-JUNG - Cornell University - New York
item NAREDO, ELIZABETH - International Rice Research Institute
item BANATICLA-HILARIO, CELESTE - International Rice Research Institute
item Eizenga, Georgia
item MCCOUCH, SUSAN - Cornell University - New York

Submitted to: Crop Science Society of America
Publication Type: Abstract Only
Publication Acceptance Date: 11/12/2021
Publication Date: 11/12/2021
Citation: Greenberg, A.J., Edwards, J., McNally, K.L., Jung, J., Kim, H., Naredo, E.B., Banaticla-Hilario, C.N., Eizenga, G.C., McCouch, S.R. 2021. Modeling phenotypic groups reflects genetic relationships between cultivars and their wild relatives in rice. Abstract. ASA,CSSA,SSSA International Annual Meeting. Salt Lake City, Utah.

Interpretive Summary:

Technical Abstract: Wild relatives of cultivated crops provide a rich gene pool that can be mined for beneficial traits. Collections of wild accessions, while often sparsely genotyped, carry passport data including heritable phenotypes. To make use of such data, we implemented a Bayesian Gaussian mixture model to infer accession groups from data on multiple traits. To test the usefulness of this approach, we applied it to accessions of the Oryza rufipogon species complex (ORSC; the wild relative of cultivated rice) that had both phenotypic and genotypic data available. We found that phenotypic groups largely reflect genotypic population structure and life habit but provide additional information that allows us to identify the effects of gene flow from cultivated O. sativa and among ORSC populations. We identify a subset of traits that effectively predict genotypic populations. An R package, MuGaMix, is available to perform these types of analyses.