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

By J. Rutkoski, Jared H. Benson, Yi Jia, G. Brown-Guedira, Jean-Luc Jannink, M. Sorrells. 2012

242 citations recorded by the source corpus.

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.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/f3add152782402bef53817b8056c175f31e3855c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Plant Science

Citations in the corpus

Genomic Prediction of Genetic Values for Resistance to Wheat Rusts (2012, background): “In Rutkoski et al. (2012), ρ reached almost 0.55 when predicting incidence, severity, and kernel quality index of Fusarium head blight in wheat using a similar number of individuals (170).”
Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.) (2016, result): “Rutkoski et al. (2012) compared several GS models and MAS for FHB resistance and concluded that GS always outperformed MAS, but treating QTL as fixed effects did not improve accuracy.”
Breeding strategies and advances in line selection for Fusarium head blight resistance in wheat (2017, background): “GS models able to capture non-additive effects were often the more accurate methods, suggesting epistatic interactions of the underlying resistance genes (Rutkoski et al. 2012; Mirdita et al. 2015).”
Perspectives for Genomic Selection Applications and Research in Plants (2015, background): “Higher accuracies for mycotoxin content in wheat, an expensive trait to phenotype with low heritability, were reported by combining markers and simple disease scores (Rutkoski et al., 2012).”