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This article in TPG

  1. Vol. 8 No. 1
    unlockOPEN ACCESS
     
    Received: Sept 08, 2014
    Accepted: Jan 05, 2015
    Published: March 13, 2015


    * Corresponding author(s): jer263@cornell.edu
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doi:10.3835/plantgenome2014.09.0046

Efficient Use of Historical Data for Genomic Selection: A Case Study of Stem Rust Resistance in Wheat

  1. J. Rutkoski *a,
  2. R. P. Singhb,
  3. J. Huerta-Espinobc,
  4. S. Bhavanid,
  5. J. Polande,
  6. J. L. Janninkf and
  7. M. E. Sorrellsg
  1. a International Programs in the College of Agriculture and Life Sciences, and Plant Breeding and Genetics Section in the School of Integrative Plant Science, 240 Emerson Hall, Cornell Univ., Ithaca, NY 14853, USA, and International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 El Batan, Mexico
    b International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 El Batan, Mexico
    c Campo Experimental Valle de México INIFAP, Apdo. Postal 10, 56230 Chapingo, Edo de México, Mexico
    d CIMMYT, ICRAF House, United Nations Ave., Gigiri, Village Market-00621, Nairobi, Kenya
    e Wheat Genetics Resource Center, Dep. of Plant Pathology and Dep. of Agronomy, Kansas State Univ. (KSU), 4011 Throckmorton Hall, Manhattan, KS 66506, USA
    f USDA–ARS and Plant Breeding and Genetics Section in the School of Integrative Plant Science, 240 Emerson Hall, Cornell Univ., Ithaca, NY 14853, USA
    g Plant Breeding and Genetics Section in the School of Integrative Plant Science, 240 Emerson Hall Cornell Univ., Ithaca, NY 14853, USA

Abstract

Genomic selection (GS) is a methodology that can improve crop breeding efficiency. To implement GS, a training population (TP) with phenotypic and genotypic data is required to train a statistical model used to predict genotyped selection candidates (SCs). A key factor impacting prediction accuracy is the relationship between the TP and the SCs. This study used empirical data for quantitative adult plant resistance to stem rust of wheat (Triticum aestivum L.) to investigate the utility of a historical TP (TPH) compared with a population-specific TP (TPPS), the potential for TPH optimization, and the utility of TPH data when close relative data is available for training. We found that, depending on the population size, a TPPS was 1.5 to 4.4 times more accurate than a TPH, and TPH optimization based on the mean of the generalized coefficient of determination or prediction error variance enabled the selection of subsets that led to significantly higher accuracy than randomly selected subsets. Retaining historical data when data on close relatives were available lead to a 11.9% increase in accuracy, at best, and a 12% decrease in accuracy, at worst, depending on the heritability. We conclude that historical data could be used successfully to initiate a GS program, especially if the dataset is very large and of high heritability. Training population optimization would be useful for the identification of TPH subsets to phenotype additional traits. However, after model updating, discarding historical data may be warranted. More studies are needed to determine if these observations represent general trends.

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