Genomic prediction contributing to a promising global strategy to turbocharge gene banks

Authors: YU, XIAOQING - Iowa State University; LI, XIANRAN - Iowa State University; GUO, TINGTING - Iowa State University; ZHU, CHENGSONG - Iowa State University; WU, YUYE - Kansas State University; MITCHELL, SHARON - Cornell University; ROOZEBOOM, KRAIG - Kansas State University; WANG, DONGHAI - Kansas State University; Wang, Ming; Pederson, Gary; TESSO, TESFAYE - Kansas State University; SCHNABLE, PATRICK - Iowa State University; BERNADO, REX - University Of Minnesota; YU, JIANMING - Iowa State University

Submitted to: Nature Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/31/2016
Publication Date: 10/3/2016
Publication URL: https://handle.nal.usda.gov/10113/5661761
Citation: Yu, X., Li, X., Guo, T., Zhu, C., Wu, Y., Mitchell, S., Roozeboom, K., Wang, D., Wang, M.L., Pederson, G.A., Tesso, T., Schnable, P., Bernado, R., Yu, J. 2016. Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nature Plants. doi: 10/1038/NPLANTS.2016.150.

Interpretive Summary: The 7.4 million plant accessions in genebanks are largely underutilized due to various resource constraints, but current genomic and analytic technologies are enabling us to mine this natural heritage. Here we report a proof-of-concept study to integrate genomic prediction into a broad germplasm evaluation process. First, a set of 962 biomass sorghum accessions were chosen as a reference set by germplasm curators. With high throughput genotyping-by-sequencing, we genetically characterized this reference set with 340,496 SNPs. A set of 299 accessions were selected as the training set to represent the overall diversity of the reference set, and we phenotypically characterized the training set for biomass yield and other related traits. Cross validation with multiple analytical methods using the data of this training set indicated high prediction accuracy for biomass yield. Empirical experiments with a 200-accession validation set chosen from the reference set confirmed high prediction accuracy. The potential to apply the prediction model to broader genetic contexts was also examined with an independent population. Detailed analyses on prediction reliability provided new insights into strategy optimization. The success of this project illustrates that a global, cost-effective strategy may be designed to assess the vast amount of valuable germplasm archived in 1,750 genebanks.

Technical Abstract: The 7.4 million plant accessions in genebanks are largely underutilized due to various resource constraints, but current genomic and analytic technologies are enabling us to mine this natural heritage. Here we report a proof-of-concept study to integrate genomic prediction into a broad germplasm evaluation process. First, a set of 962 biomass sorghum accessions were chosen as a reference set by germplasm curators. With high throughput genotyping-by-sequencing, we genetically characterized this reference set with 340,496 SNPs. A set of 299 accessions were selected as the training set to represent the overall diversity of the reference set, and we phenotypically characterized the training set for biomass yield and other related traits. Cross validation with multiple analytical methods using the data of this training set indicated high prediction accuracy for biomass yield. Empirical experiments with a 200-accession validation set chosen from the reference set confirmed high prediction accuracy. The potential to apply the prediction model to broader genetic contexts was also examined with an independent population. Detailed analyses on prediction reliability provided new insights into strategy optimization. The success of this project illustrates that a global, cost-effective strategy may be designed to assess the vast amount of valuable germplasm archived in 1,750 genebanks.