Raman Dhariwal

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.

The micronutrient content is a major aspect of food quality and has been under threat after a gain in production post-green revolution. Calcium (Ca) and magnesium (Mg) are the micronutrients that are cofactors for many enzymes and play a critical role in human physiology. Deciphering the accumulation of these micronutrients in wheat and the identification of QTLs associated with these elements is very significant for cutting the risk of malnutrition in humans. Here, a genome-wide association study (GWAS) of 105 lines from an elite panel of the Wheat Association Mapping Initiative (WAMI) was performed for the two cropping seasons of 2021–2022 and 2022–2023 for the grain calcium and magnesium content (GCaC and GMgC). Notably, two marker trait associations (MTAs), wsnp_Ex_c2718_5038582, Kukri_c11327_977, and RAC875_c9984_1003, were found for the GCaC, and similarly three MTAs (Tdurum_contig28802_213, wsnp_Ex_c34597_42879693, and RFL_Contig6053_3082) were identified for the GMgC in both the cropping seasons, proving their utility and non-redundancy. An MTA associated with a SNP marker (wsnp_Ex_c34597_42879718) was also identified in the two seasons and was significant for both the GCaC and GMgC. Candidate gene analysis showed the association of these MTAs with some of the very vital genes associated with activities where Ca and Mg play significant roles. Our study widens the insights on the genetic control of Ca and Mg accumulation in wheat and the utilization of this information for future breeding programs, wherein wheat improvement with enhanced Ca and Mg may be designed and conducted.

Common wheat (Triticum aestivum L.) is one of the most widely grown and consumed cereal crops, but its complicated genome makes it difficult to study how genes affect important agronomic and yield-related traits. Genome-wide association study (GWAS) is a useful method for finding specific loci that control complex agronomic and yield-related traits. The present investigation revealed significant phenotypic variability across the genotypes examined for all traits. The broad sense heritability (H²) for all traits ranged from 0.50 to 0.71 (Env1; 2021-2022) and 0.53 to 0.81 (Env2; 2022-2023). Using two environments' phenotypic data, and high-throughput single-nucleotide polymorphisms (SNPs) genotypic data of 20,996 markers, we discovered 114 grain-yield-related quantitative trait loci (QTLs) and 300 associated SNP markers. Eighty-five of the identified markers were stable, consistently detected across environments (Env1 and Env2) and combined environment (CE) data, and showed a significant association with 32 different QTLs. The trait with the most associated QTLs (28) was the number of fertile tillers (NFT), with 70 markers. This was followed by 20 QTLs for each, spike length (SL) and spikelet number per spike (SPS), with 69 and 82 SNPs, respectively. Conversely, six SNPs that exhibited association with multiple traits were also identified. Twenty-nine of the total 114 identified QTLs were located in chromosomal positions where at least one marker-trait association had been previously identified. This study has found new SNPs, and useful QTLs that may help us to understand the biological processes behind each studied trait. Further validation in various genetic backgrounds and environments is necessary to confirm the potential utility of the significant alleles found in this study for breeding wheat varieties with improved agronomic and yield-related traits.

Micronutrient content is a major aspect of food quality and has been under threat after gain in production post green revolution. Calcium (Ca) and magnesium (Mg) are the micronutrients that are cofactors for many enzymes and plays critical role in human physiology. Deciphering the accumulation of these micronutrients in wheat and identification of QTLs associated with these elements, is very significant for cutting the risk of malnutrition in humans. Here, genome wide association study (GWAS) of 105 lines from an elite panel of wheat association mapping initiative (WAMI) was done for the two cropping seasons of 2021-22 and 2022-23 for grain calcium and magnesium content (GCaC and GMgC). Notably, two marker trait associations (MTAs) wsnp_Ex_c2718_5038582, Kuk-ri_c11327_977, and RAC875_c9984_1003 were found for GCaC and similarly three MTAs (Tdurum_contig28802_213, wsnp_Ex_c34597_42879693 and RFL_Contig6053_3082) were identified for GMgC in both the cropping seasons proving their utility and non-redundancy. MTA associated with SNP marker (wsnp_Ex_c34597_42879718) was also identified in the two seasons and significant for both GCaC and GMgC. Our study widens the insights on the genetic control of Ca and Mg accumulation in wheat and utilization of this information for future breeding programs, wherein wheat improvement with enhanced Ca and Mg may be designed and conducted.

Wheat powdery mildew occurs as a devastating infection caused by the pathogenic fungi Blumeeria graminis f. sp. tritici (Bgt). The management of disease becomes much more effective when host resistance methods are employed. The WL711-2U/2B line served as the introgressed line to identify genes linked with powdery mildew resistance. This allowed us to look into the potential resistance components in response to powdery mildew infection. The resistance status of the line towards powdery mildew developed from a single dominant gene named PmAT. Bulked segregant RNA-Seq (BSR-Seq) together with previous QTL-Seq SNP markers helped identify strong candidate regions in chromosome arm 2B extending from 531114848 bp to 530235618 bp. The LOD score from this region reached 72.4 with a PVE% of 64.7, thus proving its main role in resistance. The physical position was subsequently locked up, which was different from the previously identified genes on the same chromosome arm in its position, suggesting that it is most likely a new Pm gene . The QTL interval contained five potential genes that we identified during this research. The five candidate genes directs the protein production, which performs functions linked to the pentatricopeptide repeat family as well as B-box-type zinc finger domain, P-loop-containing nucleoside triphosphate hydrolase, and plant peroxidase. Highlight: The identification of a new wheat powdery mildew resistance gene ( PmAT ) using a combination of BSR-Seq and QTL-Seq analysis, which located five candidate genes specifically on chromosome 2B.

Pre-harvest sprouting (PHS) is a complex abiotic stress caused by multiple exogenous and endogenous variables that results in random but significant quality and yield loss at the terminal crop stage in more than half of the wheat-producing areas of the world. Systematic research over more than five decades suggests that addressing this challenge requires tools beyond the traditional genetic manipulation approach. Previous molecular studies indicate a possible role of epigenetics in the regulation of seed dormancy and PHS in crops, especially through RNA-directed DNA methylation (RdDM) pathways mediated by Argonaute (AGO) proteins. In this study, we explore the role of the AGO802B gene associated with PHS resistance in wheat, through the presence of a SINE retrotransposon insertion. The current study found the SINE insertion at 3′UTR of the TaAGO802B present in 73.2% of 41 cultivars analyzed and in 92.6% of the resistant cultivar subset. The average expression of TaAGO802B in cultivars with the SINE insertion was 73.3% lower than in cultivars without insertion. This study also indicated a significant positive correlation between the PHS score and methylation levels in the cultivars. The resistant cultivars with the SINE insertion recorded 54.7% lower methylation levels than susceptible cultivars. Further analysis of a DH population (Sadash × P2711) reveals that SINE insertion co-segregates with PHS resistance. This sets forth the SINE insertion in TaAGO802B as a genetic marker for screening wheat germplasm and as an efficient tool for breeding PHS-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 (2020–2021, 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.

Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from −0.11 to −0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a devastating disease of wheat (Triticum aestivum L.). Deployment of resistant wheat cultivars is one of the best strategies to mitigate disease‐associated risks. The genotype P2711 possesses effective stripe rust resistance under field conditions in western Canada, however, the genetic basis of this resistance is unknown. To identify resistance, a recombinant inbred line (RIL) population comprised of 252 RILs was developed from the cross AAC Cameron/P2711. This population was evaluated for stripe rust severity at the adult plant stage in Creston, BC (in 2018, 2019, and 2020) and Lethbridge, AB (in 2018 and 2020), and was genotyped using the wheat 90K iSelect single nucleotide polymorphism (SNP) assay. A high‐density genetic map comprised of 8,915 markers was constructed covering all the wheat chromosomes. Four resistance quantitative trait loci (QTLs) were contributed by P2711 and three from AAC Cameron. QYr.lrdc‐2A.1, corresponding to the Yr17 gene, was the most stable QTL and detected in four out of five environments, whereas QYr.lrdc‐2B was the second most stable QTL. These two QTL along with QYr.lrdc‐5A showed a significant reduction in stripe rust severity when present together. Where most of the QTLs detected in both locations, QYr.lrdc‐1A.1 and QYr.lrdc‐1A.2, both were detected only in Creston, BC. Stable QTLs on chromosome 2A, 2B, and 5A, and their closely associated markers identified in this study could be utilized in marker‐assisted selection for stripe rust resistant cultivar development.