Characterizing and Validating Stripe Rust Resistance Loci in US Pacific Northwest Winter Wheat Accessions (Triticum aestivum L.) by Genome-wide Association and Linkage Mapping

Fungal Disease Tolerance with a Focus on Wheat: A Review

In this paper, an extensive review of the literature is provided examining the significance of tolerance to fungal diseases in wheat amidst the escalating global demand for wheat and threats from environmental shifts and pathogen movements. The current comprehensive reliance on agrochemicals for disease management poses risks to food safety and the environment, exacerbated by the emergence of fungicide resistance. While resistance traits in wheat can offer some protection, these traits do not guarantee the complete absence of losses during periods of vigorous or moderate disease development. Furthermore, the introduction of individual resistance genes into wheat monoculture exerts selection pressure on pathogen populations. These disadvantages can be addressed or at least mitigated with the cultivation of tolerant varieties of wheat. Research in this area has shown that certain wheat varieties, susceptible to severe infectious diseases, are still capable of achieving high yields. Through the analysis of the existing literature, this paper explores the manifestations and quantification of tolerance in wheat, discussing its implications for integrated disease management and breeding strategies. Additionally, this paper addresses the ecological and evolutionary aspects of tolerance in the pathogen-plant host system, emphasizing its potential to enhance wheat productivity and sustainability.

Characterization and Molecular Mapping of a Gene Conferring High-Temperature Adult-Plant Resistance to Stripe Rust Originally from Aegilops ventricosa

Wheat near-isogenic line AvSYr17NIL carrying Yr17, originally from Aegilops ventricosa for all-stage resistance to Puccinia striiformis f. sp. tritici, also shows nonrace-specific, high-temperature adult-plant (HTAP) resistance to the stripe rust pathogen. To separate and identify the HTAP resistance gene, seeds of AvSYr17NIL were treated with ethyl methanesulfonate. Mutant lines with only HTAP resistance were obtained, and one of the lines, M1225, was crossed with the susceptible recurrent parent Avocet S (AvS). Field responses of the F₂ plants and F₃ lines, together with the parents, were recorded at the adult-plant stage in Pullman and Mount Vernon, WA under natural P. striiformis f. sp. tritici infection. The parents and the F₄ population were phenotyped with a Yr17-virulent P. striiformis f. sp. tritici race in the adult-plant stage under the high-temperature profile in the greenhouse. The phenotypic results were confirmed by testing the F₅ population in the field under natural P. striiformis f. sp. tritici infection. The F₂ data indicated a single recessive gene, temporarily named YrM1225, for HTAP resistance. The F₄ lines were genotyped with Kompetitive allele-specific PCR markers converted from single-nucleotide polymorphism markers polymorphic between M1225 and AvS. The HTAP resistance gene was mapped on the short arm of chromosome 2A in an interval of 7.5 centimorgans using both linkage and quantitative trait locus mapping approaches. The separation of the HTAP resistance gene from Yr17 should improve the understanding and utilization of the different types of resistance.

Genome-Wide Association and Genomic Prediction for Stripe Rust Resistance in Synthetic-Derived Wheats

Stripe rust caused by Puccnina striiformis (Pst) is an economically important disease attacking wheat all over the world. Identifying and deploying new genes for Pst resistance is an economical and long-term strategy for controlling Pst. A genome-wide association study (GWAS) using single nucleotide polymorphisms (SNPs) and functional haplotypes were used to identify loci associated with stripe rust resistance in synthetic-derived (SYN-DER) wheats in four environments. In total, 92 quantitative trait nucleotides (QTNs) distributed over 65 different loci were associated with resistance to Pst at seedling and adult plant stages. Nine additional loci were discovered by the linkage disequilibrium-based haplotype-GWAS approach. The durable rust-resistant gene Lr34/Yr18 provided resistance in all four environments, and against all the five Pst races used in this study. The analysis identified several SYN-DER accessions that carried major genes: either Yr24/Yr26 or Yr32. New loci were also identified on chr2B, chr5B, and chr7D, and 14 QTNs and three haplotypes identified on the D-genome possibly carry new alleles of the known genes contributed by the Ae. tauschii founders. We also evaluated eleven different models for genomic prediction of Pst resistance, and a prediction accuracy up to 0.85 was achieved for an adult plant resistance, however, genomic prediction for seedling resistance remained very low. A meta-analysis based on a large number of existing GWAS would enhance the identification of new genes and loci for stripe rust resistance in wheat. The genetic framework elucidated here for stripe rust resistance in SYN-DER identified the novel loci for resistance to Pst assembled in adapted genetic backgrounds.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.

Breeding With Major and Minor Genes: Genomic Selection for Quantitative Disease Resistance

Disease resistance in plants is mostly quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs that are needed to select both major and minor genes for resistance. In this study, stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) of wheat (Triticum aestivum L.) was used as a model for quantitative disease resistance. The quantitative nature of stripe rust is usually phenotyped with two disease traits, infection type (IT) and disease severity (SEV). We compared two types of training populations composed of 2,630 breeding lines (BLs) phenotyped in single-plot trials from 4 years (2016-2020) and 475 diversity panel (DP) lines from 4 years (2013-2016), both across two locations. We also compared the accuracy of models using four different major gene markers and genome-wide association study (GWAS) markers as fixed effects. The prediction models used 31,975 markers that are replicated 50 times using a 5-fold cross-validation. We then compared GS models using a marker-assisted selection (MAS) to compare the prediction accuracy of the markers alone and in combination. GS models had higher accuracies than MAS and reached an accuracy of 0.72 for disease SEV. The major gene and GWAS markers had only a small to nil increase in the prediction accuracy more than the base GS model, with the highest accuracy increase of 0.03 for the major markers and 0.06 for the GWAS markers. There was a statistical increase in the accuracy using the disease SEV trait, BLs, population type, and combining years. There was also a statistical increase in the accuracy using the major markers in the validation sets as the mean accuracy decreased. The inclusion of fixed effects in low prediction scenarios increased the accuracy up to 0.06 for GS models using significant GWAS markers. Our results indicate that GS can accurately predict quantitative disease resistance in the presence of major and minor genes.

Genome-wide association analysis of stripe rust resistance in modern Chinese wheat

BACKGROUND

Stripe rust (yellow rust) is a significant disease for bread wheat (Triticum aestivum L.) worldwide. A genome-wide association study was conducted on 240 Chinese wheat cultivars and elite lines genotyped with the wheat 90 K single nucleotide polymorphism (SNP) arrays to decipher the genetic architecture of stripe rust resistance in Chinese germplasm.

RESULTS

Stripe rust resistance was evaluated at the adult plant stage in Pixian and Xindu in Sichuan province in the 2015-2016 cropping season, and in Wuhan in Hubei province in the 2013-2014, 2016-2017 and 2018-2019 cropping seasons. Twelve stable loci for stripe rust resistance were identified by GWAS using TASSEL and GAPIT software. These loci were distributed on chromosomes 1B, 1D, 2A, 2B, 3A, 3B, 4B (3), 4D, 6D, and 7B and explained 3.6 to 10.3% of the phenotypic variation. Six of the loci corresponded with previously reported genes/QTLs, including Sr2/Yr30/Lr27, while the other six (QYr.hbaas-1BS, QYr.hbaas-2BL, QYr.hbaas-3AL, QYr.hbaas-4BL.3, QYr.hbaas-4DL, and QYr.hbaas-6DS) are probably novel. The results suggest high genetic diversity for stripe rust resistance in this population. The resistance alleles of QYr.hbaas-2AS, QYr.hbaas-3BS, QYr.hbaas-4DL, and QYr.hbaas-7BL were rare in the present panel, indicating their potential use in breeding for stripe rust resistance in China. Eleven penta-primer amplification refractory mutation system (PARMS) markers were developed from SNPs significantly associated with seven mapped QTLs. Twenty-seven genes were predicted for mapped QTLs. Six of them were considered as candidates for their high relative expression levels post-inoculation.

CONCLUSION

The resistant germplasm, mapped QTLs, and PARMS markers developed in this study are resources for enhancing stripe rust resistance in wheat breeding.

Dissecting the Genetic Architecture of Aphanomyces Root Rot Resistance in Lentil by QTL Mapping and Genome-Wide Association Study

Lentil (Lens culinaris Medikus) is an important source of protein for people in developing countries. Aphanomyces root rot (ARR) has emerged as one of the most devastating diseases affecting lentil production. In this study, we applied two complementary quantitative trait loci (QTL) analysis approaches to unravel the genetic architecture underlying this complex trait. A recombinant inbred line (RIL) population and an association mapping population were genotyped using genotyping by sequencing (GBS) to discover novel single nucleotide polymorphisms (SNPs). QTL mapping identified 19 QTL associated with ARR resistance, while association mapping detected 38 QTL and highlighted accumulation of favorable haplotypes in most of the resistant accessions. Seven QTL clusters were discovered on six chromosomes, and 15 putative genes were identified within the QTL clusters. To validate QTL mapping and genome-wide association study (GWAS) results, expression analysis of five selected genes was conducted on partially resistant and susceptible accessions. Three of the genes were differentially expressed at early stages of infection, two of which may be associated with ARR resistance. Our findings provide valuable insight into the genetic control of ARR, and genetic and genomic resources developed here can be used to accelerate development of lentil cultivars with high levels of partial resistance to ARR.

Identifying Loci Conferring Resistance to Leaf and Stripe Rusts in a Spring Wheat Population (Triticum aestivum) via Genome-Wide Association Mapping

A previous genome-wide association study (GWAS) for leaf rust (caused by Puccinia triticina) resistance identified 46 resistance quantitative trait loci (QTL) in an elite spring wheat leaf rust resistance diversity panel. With the aim of characterizing the pleiotropic resistance sources to both leaf rust and stripe rust (caused by P. striiformis f. sp. tritici), stripe rust responses were tested in five U.S. environments at the adult-plant stage and to five U.S. races at the seedling stage. The data revealed balanced phenotypic distributions in this population except for the seedling response to P. striiformis f. sp. tritici race PSTv-37. GWAS for stripe rust resistance discovered a total of 21 QTL significantly associated with all-stage or field resistance on chromosomes 1B, 1D, 2B, 3B, 4A, 5A, 5B, 5D, 6A, 6B, 7A, and 7B. Previously documented pleiotropic resistance genes Yr18/Lr34 and Yr46/Lr67 and tightly linked genes Yr17-Lr37 and Yr30-Sr2-Lr27 were also detected in this population. In addition, stripe rust resistance QTL Yrswp-2B.1, Yrswp-3B, and Yrswp-7B colocated with leaf rust resistance loci 2B_3, 3B_t2, and 7B_4, respectively. Haplotype analysis uncovered that Yrswp-3B and 3B_t2 were either tightly linked genes or the same gene for resistance to both stripe and leaf rusts. Single nucleotide polymorphism markers IWB35950, IWB74350, and IWB72134 for the 3B QTL conferring resistance to both rusts should be useful in incorporating the resistance allele(s) in new cultivars.

Genetic Dissection of Snow Mold Tolerance in US Pacific Northwest Winter Wheat Through Genome-Wide Association Study and Genomic Selection

Snow mold is a yield-limiting disease of wheat in the Pacific Northwest (PNW) region of the US, where there is prolonged snow cover. The objectives of this study were to identify genomic regions associated with snow mold tolerance in a diverse panel of PNW winter wheat lines in a genome-wide association study (GWAS) and to evaluate the usefulness of genomic selection (GS) for snow mold tolerance. An association mapping panel (AMP; N = 458 lines) was planted in Mansfield and Waterville, WA in 2017 and 2018 and genotyped using the Illumina^® 90K single nucleotide polymorphism (SNP) array. GWAS identified 100 significant markers across 17 chromosomes, where SNPs on chromosomes 5A and 5B coincided with major freezing tolerance and vernalization loci. Increased number of favorable alleles was related to improved snow mold tolerance. Independent predictions using the AMP as a training population (TP) to predict snow mold tolerance of breeding lines evaluated between 2015 and 2018 resulted in a mean accuracy of 0.36 across models and marker sets. Modeling nonadditive effects improved accuracy even in the absence of a close genetic relatedness between the TP and selection candidates. Selecting lines based on genomic estimated breeding values and tolerance scores resulted in a 24% increase in tolerance. The identified genomic regions associated with snow mold tolerance demonstrated the genetic complexity of this trait and the difficulty in selecting tolerant lines using markers. GS was validated and showed potential for use in PNW winter wheat for selecting on complex traits such tolerance to snow mold.

Genome-wide association mapping for eyespot disease in US Pacific Northwest winter wheat

Eyespot, caused by the soil-borne necrotrophic fungi Oculimacula yallundae and O. acuformis, is a disease of major economic significance for wheat, barley and rye. Pacific Northwest (PNW) winter wheat (Triticum aestivum L.) grown in areas of high rainfall and moderate winters is most vulnerable to infection. The objective of this research was to identify novel genomic regions associated with eyespot resistance in winter wheat adapted to the PNW. Two winter wheat panels of 469 and 399 lines were compiled for one of the first genome-wide association studies (GWAS) of eyespot resistance in US winter wheat germplasm. These panels were genotyped with the Infinium 9K and 90K iSelect SNP arrays. Both panels were phenotyped for disease resistance in a two-year field study and in replicated growth chamber trials. Growth chamber trials were used to evaluate the genetic resistance of O. acuformis and O. yallundae species separately. Best linear unbiased predictors (BLUPs) were calculated across all field and growth chamber environments. A total of 73 marker-trait associations (MTAs) were detected on nine different chromosomes (1A, 2A, 2B, 4A, 5A, 5B, 7A, 7B and 7D) that were significantly associated (p-value <0.001) with eyespot resistance in Panel A, and 19 MTAs on nine different chromosomes (1A, 1B, 2A, 2D, 3B, 5A, 5B, 7A, and 7B) in Panel B. The most significant SNPs were associated with Pch1 and Pch2 resistance genes on the long arms of chromosome 7D and 7A. Most of the novel MTAs appeared to have a minor effect on reducing eyespot disease. Nevertheless, eyespot disease scores decreased as the number of resistance alleles increased. Seven SNP markers, significantly associated with reducing eyespot disease across environments and in the absence and presence of Pch1 were identified. These markers were located on chromosomes 2A (IWB8331), 5A (IWB73709), 5B (IWB47298), 7AS (IWB47160), 7B (IWB45005) and two SNPs (Ex_c44379_2509 and IAAV4340) had unknown map positions. The additive effect of the MTAs explained most of the remaining phenotypic variation not accounted for by Pch1 or Pch2. This study provides breeders with adapted germplasm and novel sources of eyespot resistance to be used in the development of superior cultivars with increased eyespot resistance.

Genome-Wide Association Mapping for Tolerance to Preharvest Sprouting and Low Falling Numbers in Wheat

Preharvest sprouting (PHS), the germination of grain on the mother plant under cool and wet conditions, is a recurring problem for wheat farmers worldwide. α-amylase enzyme produced during PHS degrades starch resulting in baked good with poor end-use quality. The Hagberg-Perten Falling Number (FN) test is used to measure this problem in the wheat industry, and determines how much a farmer's wheat is discounted for PHS damage. PHS tolerance is associated with higher grain dormancy. Thus, breeding programs use germination-based assays such as the spike-wetting test to measure PHS susceptibility. Association mapping identified loci associated with PHS tolerance in U.S. Pacific Northwest germplasm based both on FN and on spike-wetting test data. The study was performed using a panel of 469 white winter wheat cultivars and elite breeding lines grown in six Washington state environments, and genotyped for 15,229 polymorphic markers using the 90k SNP Illumina iSelect array. Marker-trait associations were identified using the FarmCPU R package. Principal component analysis was directly and a kinship matrix was indirectly used to account for population structure. Nine loci were associated with FN and 34 loci associated with PHS based on sprouting scores. None of the QFN.wsu loci were detected in multiple environments, whereas six of the 34 QPHS.wsu loci were detected in two of the five environments. There was no overlap between the QTN detected based on FN and PHS, and there was little correlation between the two traits. However, both traits appear to be PHS-related since 19 of the 34 QPHS.wsu loci and four of the nine QFN.wsu loci co-localized with previously published dormancy and PHS QTL. Identification of these loci will lead to a better understanding of the genetic architecture of PHS and will help with the future development of genomic selection models.