Reyazul Rouf Mir

Additional file 1: Supplementary Table 1. QTLs utilized in the present study for meta-analysis. Supplementary Table 2. Detailed information on MQTLs identified during the present study. Supplementary Table 3. Singletons detected during the present study. Supplementary Table 4. QTL hotspots detected during the present study. Supplementary Table 5. Validation of MQTLs with significant SNPs or MTAs identified in earlier GWAS. Supplementary Table 6. MQTLs co-localizing with known major rust resistance genes. Supplementary Table 7. Gene models available from hcMQTL regions. Supplementary Table 8. Differentially expressed candidate genes identified within the hcMQTL regions. Supplementary Table 9. Comparison of our study with the study previously published by Jan et al. [54]. Supplementary Table 10. Details on recently identified QTLs and their association with the MQTLs identified during the present study. Supplementary Table 11. Markers available from different genetic maps utilized for the construction of consensus map (shared markers are highlighted with dark red colour). Supplementary Table 12. Summary of genome-wide association studies considered in the current study.

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.