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This article in TPG

  1. Vol. 5 No. 2, p. 51-61
    OPEN ACCESS
     
    Received: Feb 8, 2012
    Published: July, 2012


    * Corresponding author(s): mes12@cornell.edu
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doi:10.3835/plantgenome2012.02.0001

Evaluation of Genomic Prediction Methods for Fusarium Head Blight Resistance in Wheat

  1. Jessica Rutkoskia,
  2. Jared Bensonb,
  3. Yi Jiaa,
  4. Gina Brown-Guedirac,
  5. Jean-Luc Janninkd and
  6. Mark Sorrells a
  1. a Dep. of Plant Breeding and Genetics, 240 Emerson Hall, Cornell Univ., Ithaca, NY 14853
    b Dep. of Crop Science, North Carolina State Univ., Raleigh, NC 27695
    c USDA-ARS, Plant Science Research Unit, Raleigh, NC 27695
    d USDA-ARS, R.W. Holley Center for Agriculture and Health, Cornell Univ., Ithaca, NY 14853

Abstract

Fusarium head blight (FHB) resistance is quantitative and difficult to evaluate. Genomic selection (GS) could accelerate FHB resistance breeding. We used U.S. cooperative FHB wheat nursery data to evaluate GS models for several FHB resistance traits including deoxynivalenol (DON) levels. For all traits we compared the models: ridge regression (RR), Bayesian LASSO (BL), reproducing kernel Hilbert spaces (RKHS) regression, random forest (RF) regression, and multiple linear regression (MLR) (fixed effects). For DON, we evaluated additional prediction methods including bivariate RR models, phenotypes for correlated traits, and RF regression models combining markers and correlated phenotypes as predictors. Additionally, for all traits, we compared different marker sets including genomewide markers, FHB quantitative trait loci (QTL) targeted markers, and both sets combined. Genomic selection accuracies were always higher than MLR accuracies, RF and RKHS regression were often the most accurate methods, and for DON, marker plus trait RF regression was more accurate than all other methods. For all traits except DON, using QTL targeted markers alone led to lower accuracies than using genomewide markers. This study indicates that cooperative FHB nursery data can be useful for GS, and prior information about correlated traits and QTL could be used to improve accuracies in some cases.

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