Genomic Selection for Predicting Fusarium Head Blight Resistance in a Wheat Breeding Program

Abstract

Genomic selection (GS) is a breeding method that uses marker-trait models to predict unobserved phenotypes. This study developed GS models for predicting traits associated with resistance to Fusarium head blight (FHB) in wheat (Triticum aestivum L.). We used genotyping-by-sequencing (GBS) to identify 5054 single-nucleotide polymorphisms (SNPs), which were then treated as predictor variables in GS analysis. We compared how the prediction accuracy of the genomic-estimated breeding values (GEBVs) was affected by (i) five genotypic imputation methods (random forest imputation [RFI], expectation maximization imputation [EMI], k-nearest neighbor imputation [kNNI], singular value decomposition imputation [SVDI], and the mean imputation [MNI]); (ii) three statistical models (ridge-regression best linear unbiased predictor [RR-BLUP], least absolute shrinkage and operator selector [LASSO], and elastic net); (iii) marker density (p = 500, 1500, 3000, and 4500 SNPs); (iv) training population (TP) size (n_TP = 96, 144, 192, and 218); (v) marker-based and pedigree-based relationship matrices; and (vi) control for relatedness in TPs and validation populations (VPs). No discernable differences in prediction accuracy were observed among imputation methods. The RR-BLUP outperformed other models in nearly all scenarios. Accuracies decreased substantially when marker number decreased to 3000 or 1500 SNPs, depending on the trait; when sample size of the training set was less than 192; when using pedigree-based instead of marker-based matrix; or when no control for relatedness was implemented. Overall, moderate to high prediction accuracies were observed in this study, suggesting that GS is a very promising breeding strategy for FHB resistance in wheat.