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Title: Genomic selection of biomass yield in a global collection of one thousand sorghum accessions

item YU, XIAOQING - Iowa State University
item LI, XIANRAN - Iowa State University
item YU, JIANMING - Iowa State University
item WU, YUYE - Kansas State University
item ROOZEBOOM, KRAIG - Kansas State University
item WANG, DONGHAI - Kansas State University
item TESSO, TESFAYE - Kansas State University
item MITCHELL, SHARON - Cornell University
item BERNARDO, REX - University Of Minnesota
item Wang, Ming
item Pederson, Gary

Submitted to: Meeting Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 6/2/2014
Publication Date: 8/6/2014
Citation: Yu, X., Li, X., Yu, J., Wu, Y., Roozeboom, K.L., Wang, D., Tesso, T.T., Mitchell, S.E., Bernardo, R., Wang, M.L., Pederson, G.A. 2014. Genomic selection of biomass yield in a global collection of one thousand sorghum accessions.[abstract] 4th Annual Meeting of the National Association of Plant Breeders, August 5-8, 2014. Minneapolis, MN. Paper No. 69.

Interpretive Summary:

Technical Abstract: The need for a large, sustainable supply of biomass in lignocelluloses-based biofuel production requires the development of dedicated bioenergy crops. Sorghum (Sorghum bicolor) has been identified as a key lignocellulosic biofuel species in the United States. The objectives in this study were to determine: 1) How to tap into the vast plant germplasm collections for biomass crop improvement? 2) How to increase the information contained in genotypic and phenotypic data for the selected germplasm? 3) How robust are the various genomic prediction models for biomass traits? In summary of the results, large sample size of the training set allowed for accurate prediction. Traits with high repeatability show high prediction accuracies. Cross-validation runs indicate that various models could provide moderate to high (0.3-0.8) prediction accuracies for biomass traits, which will be validated with empirical experiments. Genotype by sequencing, selective phenotyping, and genomic prediction could form an efficient pipeline to evaluate germplasm resources for crop improvement.