The 7.4 million plant accessions in gene banks are largely underutilized due to various resource constraints, but current genomic and analytic technologies are enabling us to mine this natural heritage. Following the successful proof-of-concept implementation of this grand scheme in our USDA-funded biomass sorghum genomics and phenomics project, we plan to generalize this integrative strategy to a broader context. The goal is to take advantage of recent developments in high throughput sequencing, genotyping-by-sequencing, selective phenotyping (training set design), and genome-wide prediction into germplasm characterization. Using a genome-wide prediction index generated from a purposely selected set of accessions most representative to the inference space, genome-wide prediction for all remaining accessions will be generated. Validation is then carried out to demonstrate the prediction accuracy, and as an added benefit, to update the genome-wide prediction index for the next round of prediction. In the second proof-of-concept study, we worked on predicting and validating the microphenotypes of a diverse set of maize inbred lines. We specifically designed six validation sets to answer the question of how reliability may help in candidate selection to balance the need of identifying the best candidates and exploring the diversity. Our findings suggest that double selection considering both prediction and reliability can be implemented in choosing selection candidates for phenotyping when exploring new diversity is desired.

PI: Yu, Jianming.
Funding: The Agriculture and Food Research Initiative competitive grant (2011-03587) from the USDA National Institute of Food and Agriculture, the National Science Foundation grant IOS-1238142, the Kansas State University Center for Sorghum Improvement, the Iowa State University Raymond F. Baker Center for Plant Breeding, and the Iowa State University Plant Sciences Institute.

Related Publications:

• Yu, X., X. Li, T. Guo, C. Zhu, Y. Wu, S.E. Mitchell, K.L. Roozeboom, D. Wang, M.L. Wang, G.A. Pederson, T.T. Tesso, P.S. Schnable, R. Bernardo, and J. Yu*. 2016. Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nature Plants 2:16150.
• Yu, X., S. Leiboff, X. Li, T. Guo, N. Ronning, X. Zhang, G.J. Muehlbauer, M.C.P. Timmermans, P.S. Schnable, M.J. Scanlon, and J. Yu*. 2020. Genomic prediction of maize micro-phenotypes provides insights for optimizing selection and mining diversity. Plant Biotechnology Journal (online first).