Article

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

  • Nature Plants 2, Article number: 16150 (2016)
  • doi:10.1038/nplants.2016.150
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Abstract

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.

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Acknowledgements

This work was supported by the Agriculture and Food Research Initiative competitive grant (2011-03587) from the USDA National Institute of Food and Agriculture, by the National Science Foundation grant IOS-1238142, by the Kansas State University Center for Sorghum Improvement, by the Iowa State University Raymond F. Baker Center for Plant Breeding and by the Iowa State University Plant Science Institute. We appreciate K. Mayfield, L. Lambright and S. Staggenborg from Chromatin for conducting experiments at Lubbock, Texas.

Author information

Affiliations

  1. Iowa State University, Ames, Iowa 50011, USA

    • Xiaoqing Yu
    • , Xianran Li
    • , Tingting Guo
    • , Chengsong Zhu
    • , Patrick S. Schnable
    •  & Jianming Yu
  2. Kansas State University, Manhattan, Kansas 66506, USA

    • Yuye Wu
    • , Kraig L. Roozeboom
    • , Donghai Wang
    •  & Tesfaye T. Tesso
  3. Cornell University, Ithaca, New York 14853, USA

    • Sharon E. Mitchell
  4. US Department of Agriculture, Agricultural Research Service (USDA-ARS), Griffin, Georgia 30223, USA

    • Ming Li Wang
    •  & Gary A. Pederson
  5. University of Minnesota, St. Paul, Minnesota 55108, USA

    • Rex Bernardo

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Contributions

J.Y., M.L.W., G.A.P., T.T.T., P.S.S. and R.B. conceived and designed the experiments. X.Y., X.L., T.G., C.Z., Y.W., K.L.R., M.L.W. and J.Y. performed the experiments. X.Y., X.L. and C.Z. analysed the data. S.E.M., K.L.R., D.W., M.L.W., G.A.P., T.T.T., P.S.S. and R.B. contributed materials/analysis tools. X.Y., X.L., T.G. and J.Y. wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Jianming Yu.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Figures 1-12 and Supplementary Tables 1-3.

Excel files

  1. 1.

    Supplementary Data Set

    Trait data for the 299-accession training set, the 200-accession validation set, and the 45-accession validation set.