Genomic Selection Paves Way for the Identification of Rust Disease Resistant Genotypes in Bread Wheat (Triticum aestivum).

In the last two decades, genomic prediction (GP) or Genomic Selection (GS) methods have been widely adopted in various plant and animal breeding programs globally. GP/GS is a promising method that employs genomic markers to calculate genomic-estimated breeding values (GEBVs) to select best individuals. To evaluate the performance of different genomic selection (GS) models, we examined six different models namely, ridge regression (RR), least absolute shrinkage and selection operator (LASSO), genomic best linear unbiased prediction (GBLUP), elastic net (EN), reproducing kernel Hilbert spacing (RKHS), and random forest (RF) models, for seedling and adult plant resistance to leaf, stem and stripe rust of wheat using a panel of 347 wheat germplasm accessions. The GBLUP and RF models performed noticeably better than the other GS models, with mean predictive abilities of 0.5 and 0.4 for seedling resistance and 0.4 and 0.3 for adult plant resistance (APR) for leaf and stem rust, respectively. Unfortunately, except for a few environments, the performance of GP models in the current study is quite low for stripe rust for both seedling and APR. The outcomes of this study revealed the capability of GP to be applied for breeding initiatives aimed at developing wheat varieties resistant to rust diseases. Moreover, based on favorable allele analysis we also identified a total of 2 lines (CRP-165/42, HGP1-470) that showed resistance to most of the pathotypes at seedling and adult plant stage to all three rusts. These lines can serve as valuable resources for future breeding programs focused on rust resistance. Keywords: GS; GEBVs; leaf rust; stem rust; stripe rust; seedling resistance; APR