Turbo-Charging the Genebanks with Genomic Prediction.

The 7.4 million plant accessions in genebanks are still largely underutilized due to various resource constraints, but current genomic, phenomic, and analytic technologies are enabling us to mine this natural heritage on a wide scale. 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 based on passport data. With high throughput genotyping-by-sequencing, we genetically characterized this reference set with 340,496 SNPs. A set of 299 accessions were then 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 genotype and phenotype data of this training set revealed high prediction accuracy for biomass yield. Empirical validation with 200 new accessions chosen from the reference set confirmed high prediction accuracy. The potential to apply the prediction models to broader genetic contexts was also verified 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 worldwide.