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

• Published in: Nature plants Vol. 2; no. 10; p. 16150
• Main Authors: Yu, Xiaoqing, Li, Xianran, Guo, Tingting, Zhu, Chengsong, Wu, Yuye, Mitchell, Sharon E., Roozeboom, Kraig L., Wang, Donghai, Wang, Ming Li, Pederson, Gary A., Tesso, Tesfaye T., Schnable, Patrick S., Bernardo, Rex, Yu, Jianming
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.10.2016; Nature Publishing Group
• Subjects: 45; 45/23; 45/43; 631/208; 631/449; Accuracy; analytical methods; Biomass; biomass production; Biomedical and Life Sciences; cost effectiveness; Empirical analysis; Gene banks; Genomics; Genotypes; Genotyping; genotyping by sequencing; Germplasm; germplasm evaluation; high-throughput nucleotide sequencing; Life Sciences; Model accuracy; Nucleotides; Optimization; phenotype; Plant Sciences; prediction; Prediction models; Reliability analysis; Single-nucleotide polymorphism; Sorghum; Sorghum (Poaceae); Training
• ISSN: 2055-0278; 2055-0278
• DOI: 10.1038/nplants.2016.150

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. 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 (GBS), we genetically characterized this reference set with 340,496 single nucleotide polymorphisms (SNPs). A set of 299 accessions was 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 gene banks. A proof-of-concept study developed and validated a high-accuracy model to predict traits based on genotypes using data from a set of sorghum accessions, demonstrating a global strategy to assess and utilize the valuable germplasms in gene banks.