Ramandeep Kaur

Powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is a major foliar disease of bread wheat (Triticum aestivum L.). Genomic prediction (GP) can accelerate breeding for this trait, but conventional, machine-learning (ML), and deep-learning (DL) methods have rarely been compared side by side. Using the Wheat Association Mapping Initiative (WAMI) panel (n = 286), phenotyped for powdery mildew severity over three years (DS1–DS3) and genotyped with the Illumina 90K SNP array, we compared 30 GP models under per-year and genotype-grouped pooled cross-validation. Broad-sense heritability ranged from 0.65 to 0.89. In the pooled, leakage-safe analysis, six Bayesian-alphabet models clustered at the top (r = 0.291–0.299), too close to identify a single best model; partial least squares (PLS) regression was the strongest non-Bayesian model, and elastic net was the strongest ML model within each year. Within-year accuracy under random 5-fold cross-validation was higher (up to r ~ 0.70 in DS2) but may be optimistic, as folds were not stratified by genomic relatedness. Accuracy showed limited, non-monotonic sensitivity to marker density and increased, though not strictly monotonically, with training population size. Leave-one-year-out (LOYO) accuracy was near zero (mean r = 0.009); fitting year as a fixed effect did not improve it, and cross-year genetic correlations were low (0.08–0.15), indicating limited shared genetic control across years. DL results apply only to the architectures and sample size tested. With pooled accuracy near r = 0.30, these models suit within-season prediction in this panel, whereas reliable cross-season application is not supported by the present results and will require broader multi-environment training and/or season-specific recalibration.

Powdery mildew (PM), caused by the fungal pathogen Blumeria graminis f. sp. tritici (Bgt), significantly threatens global bread wheat production. Although the use of resistant cultivars is an effective strategy for managing PM, currently available wheat cultivars lack sufficient levels of resistance. To tackle this challenge, we conducted a comprehensive genome-wide association study (GWAS) using a diverse panel of 286 bread wheat genotypes. Over three consecutive years (2020–2021, 2021–2022, and 2022–2023), these genotypes were extensively evaluated for PM severity under field conditions following inoculation with virulent Bgt isolates. The panel was previously genotyped using the Illumina 90K Infinium iSelect assay to obtain genome-wide single-nucleotide polymorphism (SNP) marker coverage. By applying FarmCPU, a multilocus mixed model, we identified a total of 113 marker–trait associations (MTAs) located on chromosomes 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, and 7B at a significance level of p ≤ 0.001. Notably, four novel MTAs on chromosome 6B were consistently detected in 2020–2021 and 2021–2022. Furthermore, within the confidence intervals of the identified SNPs, we identified 96 candidate genes belonging to different proteins including 12 disease resistance/host–pathogen interaction-related protein families. Among these, protein kinases, leucine-rich repeats, and zinc finger proteins were of particular interest due to their potential roles in PM resistance. These identified loci can serve as targets for breeding programs aimed at developing disease-resistant wheat cultivars.

Powdery mildew (PM), caused by the fungal pathogen Blumeria graminis f. sp. tritici (Bgt), significantly threatens global bread wheat production. Although the use of resistant cultivars is an effective strategy for managing PM, currently available wheat cultivars lack sufficient levels of resistance. To tackle this challenge, we conducted a comprehensive genome-wide association study (GWAS) using a diverse panel of 286 bread wheat genotypes. Over three consecutive years (2020-21, 2021-22, and 2022-23), these genotypes were extensively evaluated for PM severity under field conditions following inoculation with virulent Bgt isolates. The panel was previously genotyped using the Illumina 90K SNP Infinium iSelect SNP assay to obtain genome-wide SNP marker coverage. By applying FarmCPU, a multilocus mixed model, we identified a total of 113 MTAs located on chromosomes 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, and 7B at a significance level of p≤0.001. Notably, four novel MTAs on chromosome 6B were consistently detected in 2020-21 and 2021-22 environments. Furthermore, within the confidence intervals of the identified SNPs, we identified 96 candidate genes belonging to different proteins including 12 disease resistance/host-pathogen interactions related protein families. Among these, protein kinases, leucine-rich repeats, and zinc finger proteins, were of particular interest due to their potential roles in PM resistance. These identified loci can serve as targets for breeding programs aimed at developing disease-resistant wheat cultivars.