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Oat is uniquely valued among grain crops for the health-promoting composition of its seeds. Enhancing the ability of breeders to select for higher concentrations or new combinations of compounds will increase the value of the crop and ensure its continued role in sustainable cropping systems. In collaboration with Jean-Luc Jannink (USDA-ARS; Cornell University), Mark Sorrells (Cornell University), Kevin Smith (University of Minnesota), Melanie Caffe-Treml (South Dakota State University), and Lucia Gutierrez (University of Wisconsin-Madison), our project will generate detailed information on seed composition and its genetic control in global diversity and elite North American oat panels, and develop and evaluate methods enabling breeders to leverage that information for selection decision support and to discover new mutations affecting composition. The specific objectives of the project are: i) to identify metabolites and gene transcripts that are hubs in networks of these features in a global oat diversity panel, ii) to evaluate methods to incorporate this information in genomic evaluation and determine its value in selecting improved progeny in an elite Upper-Midwest panel, and iii) to sequence an oat TILLING population at sites suggested by this analysis and characterize new mutations for their impact on seed composition. The project will elucidate genetic drivers of oat seed composition to show how to increase accuracy of composition prediction. It will also deliver new alleles affecting composition. The project fits well with program priorities as data and methodological outputs will be published in the user-friendly environment of the online breeding research database T3/Oat. Specifically all data sets will be available and coupled to network analysis tools to analyze them or other similar data, thereby implementing systems-level predictive modeling for seed composition in oat.

Funding: USDA-ARS and USDA-NIFA-AFRI 2017-67007-26502.