Evaluation of genomic prediction methods for fusarium head blight resistance in wheat

Jessica Rutkoski, Jared Benson, Yi Jia, Gina Brown-Guedira, Jean Luc Jannink, Mark Sorrells

Research output: Contribution to journalArticle

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 boThsets 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.

Original languageEnglish (US)
Pages (from-to)51-61
Number of pages11
JournalPlant Genome
Volume5
Issue number2
DOIs
StatePublished - Dec 1 2012
Externally publishedYes

Fingerprint

Fusarium head blight
Fusarium
Triticum
deoxynivalenol
genomics
marker-assisted selection
wheat
prediction
Quantitative Trait Loci
quantitative trait loci
Nurseries
cooperatives
Linear Models
methodology
Phenotype
phenotype
seeds
Breeding
Forests
breeding

ASJC Scopus subject areas

  • Genetics
  • Agronomy and Crop Science
  • Plant Science

Cite this

Evaluation of genomic prediction methods for fusarium head blight resistance in wheat. / Rutkoski, Jessica; Benson, Jared; Jia, Yi; Brown-Guedira, Gina; Jannink, Jean Luc; Sorrells, Mark.

In: Plant Genome, Vol. 5, No. 2, 01.12.2012, p. 51-61.

Research output: Contribution to journalArticle

Rutkoski, Jessica ; Benson, Jared ; Jia, Yi ; Brown-Guedira, Gina ; Jannink, Jean Luc ; Sorrells, Mark. / Evaluation of genomic prediction methods for fusarium head blight resistance in wheat. In: Plant Genome. 2012 ; Vol. 5, No. 2. pp. 51-61.
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