Genome-Wide Association Study for Multiple Biotic Stress Resistance in Synthetic Hexaploid Wheat

Signals and Machinery for Mycorrhizae and Cereal and Oilseed Interactions towards Improved Tolerance to Environmental Stresses

In the quest for sustainable agricultural practices, there arises an urgent need for alternative solutions to mineral fertilizers and pesticides, aiming to diminish the environmental footprint of farming. Arbuscular mycorrhizal fungi (AMF) emerge as a promising avenue, bestowing plants with heightened nutrient absorption capabilities while alleviating plant stress. Cereal and oilseed crops benefit from this association in a number of ways, including improved growth fitness, nutrient uptake, and tolerance to environmental stresses. Understanding the molecular mechanisms shaping the impact of AMF on these crops offers encouraging prospects for a more efficient use of these beneficial microorganisms to mitigate climate change-related stressors on plant functioning and productivity. An increased number of studies highlighted the boosting effect of AMF on grain and oil crops' tolerance to (a)biotic stresses while limited ones investigated the molecular aspects orchestrating the different involved mechanisms. This review gives an extensive overview of the different strategies initiated by mycorrhizal cereal and oilseed plants to manage the deleterious effects of environmental stress. We also discuss the molecular drivers and mechanistic concepts to unveil the molecular machinery triggered by AMF to alleviate the tolerance of these crops to stressors.

A novel QTL conferring Fusarium crown rot resistance on chromosome 2A in a wheat EMS mutant

KEY MESSAGE

A novel QTL on chromosome 2A for Fusarium crown rot resistance was identified and validated in wheat. Fusarium crown rot (FCR) is a fungal disease that causes significant yield losses in many cereal growing regions in the world. In this study, genetic analysis was conducted for a wheat EMS mutant C549 which showed stable resistance to FCR at seedling stage. A total of 10 QTL were detected on chromosomes 1A, 2A, 3B, 4A, 6B, and 7B using a population of 138 F7 recombinant inbred lines (RILs) derived from a cross between C549 and a Chinese germplasm 3642. A novel locus Qfcr.cau-2A, which accounted for up to 24.42% of the phenotypic variation with a LOD value of 12.78, was consistently detected across all six trials conducted. Furthermore, possible effects of heading date (HD) and plant height on FCR severity were also investigated in the mapping population. While plant height had no effects on FCR resistance, a weak and negative association between FCR resistance and HD was observed. A QTL for HD (Qhd.cau-2A.2) was coincident with Qfcr.cau-2A. Conditional QTL mapping indicated that although Qfcr.cau-2A and Qhd.cau-2A.2 had significant interactions, Qfcr.cau-2A remained significant after the effects of HD was removed. It is unlikely that genes underlying these two loci are same. Nevertheless, the stable expression of Qfcr.cau-2A in the validation population of 148 F7 RILs developed between C549 and its wild parent Chuannong 16 demonstrated the potential value of this locus in FCR resistance breeding programs.

Dissecting the genetic basis of Fusarium crown rot resistance in wheat by genome wide association study

KEY MESSAGE

A locus conferring Fusarium crown rot resistance was identified on chromosome arm 3DL through genome wide association study and further validated in two recombinant inbred lines populations. Fusarium crown rot (FCR) is a severe soil borne disease in many wheat growing regions of the world. In this study, we attempted to detect loci conferring FCR resistance through a new seedling inoculation assay. A total of 223 wheat accessions from different geography origins were used to assemble an association panel for GWAS analysis. Four genotypes including Heng 4332, Luwanmai, Pingan 998 and Yannong 24 showed stable resistance to FCR. A total of 54 SNPs associated with FCR resistance were identified. Among the 10 putative QTLs represented by these SNPs, seven QTLs on chromosome 2B, 3A, 3D, 4A, 7A and 7B were novel and were consistently detected in at least two of the three trials conducted. Qfcr.cau.3D-3, which was targeted by 38 SNPs clustered within a genomic region of approximately 5.57 Mb (609.12-614.69 Mb) on chromosome arm 3DL, was consistently detected in all the three trials. The effects of Qfcr.cau.3D-3 were further validated in two recombinant inbred line populations. The presence of this locus reduced FCR severity up to 21.55%. Interestingly, the collinear positions of sequences containing the four SNPs associated with two FCR loci (Qfcr.cau.3A and Qfcr.cau.3B) were within the regions of Qfcr.cau.3D-3, suggesting that genes underlying these three loci may be homologous. Our results provide useful information for improving FCR resistance in wheat.

Screening and Resistance Locus Identification of the Mutant fcrZ22 Resistant to Crown Rot Caused by Fusarium pseudograminearum

Crown rot caused by Fusarium pseudograminearum is a devastating wheat disease worldwide. In addition to yield losses, the fungi causing Fusarium crown rot (FCR) also deteriorate the quality and safety of food because of the production of mycotoxins. Planting resistant cultivars is an effective way to control FCR. However, most wheat cultivars are susceptible to FCR. Therefore, development of new sources and detection of loci for FCR resistance are necessary. In the present study, a resistant mutant, fcrZ22, was identified from an ethyl methane sulfonate (EMS)-mutagenized population of the cultivar Zhoumai 22, and then fcrZ22 was crossed with the wild type to produce an F2 population. Genetic analysis of the F2 population was carried out by the mixed inheritance model of major genes plus polygenes, and 20 resistant and 20 susceptible plants were selected to assemble mixed pools. Combining 660K SNP arrays, the resistance loci were detected by bulked segregant analysis. The resistance to FCR caused by F. pseudograminearum in the F2 population was in accordance with the "mixed model with two major genes of additive-epistasis effect + additive-dominant polygenes," and the heritability of the major gene was 0.92. Twenty-one loci were detected, which were located on 10 chromosomes, namely, 1B (1), 1D (1), 2A (3), 1B (1), 3A (3), 3B (3), 4A (2), 5A (2), 7A (3), and 7B (2). Among the 21 loci, eight were new loci for FCR resistance. This is the first report of detecting loci for FCR resistance from a mutant. The results of the present study provided excellent germplasm resources for breeding wheat cultivars with FCR resistance and laid the foundation for fine mapping of FCR resistance loci.

Identification of Key Gene Network Modules and Hub Genes Associated with Wheat Response to Biotic Stress Using Combined Microarray Meta-analysis and WGCN Analysis

Wheat (Triticum aestivum) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising solutions to handle losses caused by biotic stress factors, a comprehensive understanding of molecular mechanisms and identifying key genes is a critical step to obtaining success. Here, a network-based meta-analysis approach based on two main statistical methods was used to identify key genes and molecular mechanisms of the wheat response to biotic stress. A total of 163 samples (21,792 genes) from 10 datasets were analyzed. Fisher Z test based on the p-value and REM method based on effect size resulted in 533 differentially expressed genes (p < 0.001 and FDR < 0.001). WGCNA analysis using a dynamic tree-cutting algorithm was used to construct a co-expression network and three significant modules were detected. The modules were significantly enriched by 16 BP terms and 4 KEGG pathways (Benjamini-Hochberg FDR < 0.001). A total of nine hub genes (a top 1.5% of genes with the highest degree) were identified from the constructed network. The identification of DE genes, gene-gene co-expressing network, and hub genes may contribute to uncovering the molecular mechanisms of the wheat response to biotic stress.

Unlocking the molecular basis of wheat straw composition and morphological traits through multi-locus GWAS

BACKGROUND

Rapid reductions in emissions from fossil fuel burning are needed to curb global climate change. Biofuel production from crop residues can contribute to reducing the energy crisis and environmental deterioration. Wheat is a renewable source for biofuels owing to the low cost and high availability of its residues. Thus, identifying candidate genes controlling these traits is pivotal for efficient biofuel production. Here, six multi-locus genome-wide association (ML-GWAS) models were applied using 185 tetraploid wheat accessions to detect quantitative trait nucleotides (QTNs) for fifteen traits associated with biomass composition.

RESULTS

Among the 470 QTNs, only 72 identified by at least two models were considered as reliable. Among these latter, 16 also showed a significant effect on the corresponding trait (p.value < 0.05). Candidate genes survey carried out within 4 Mb flanking the QTNs, revealed putative biological functions associated with lipid transfer and metabolism, cell wall modifications, cell cycle, and photosynthesis. Four genes encoded as Cellulose Synthase (CeSa), Anaphase promoting complex (APC/C), Glucoronoxylan 4-O Methyltransferase (GXM) and HYPONASTIC LEAVES1 (HYL1) might be responsible for an increase in cellulose, and natural and acid detergent fiber (NDF and ADF) content in tetraploid wheat. In addition, the SNP marker RFL_Contig3228_2154 associated with the variation in stem solidness (Q.Scsb-3B) was validated through two molecular methods (High resolution melting; HRM and RNase H²-dependent PCR; rhAMP).

CONCLUSIONS

The study provides new insights into the genetic basis of biomass composition traits on tetraploid wheat. The application of six ML-GWAS models on a panel of diverse wheat genotypes represents an efficient approach to dissect complex traits with low heritability such as wheat straw composition. The discovery of genes/genomic regions associated with biomass production and straw quality parameters is expected to accelerate the development of high-yielding wheat varieties useful for biofuel production.

Genetic networks underlying salinity tolerance in wheat uncovered with genome-wide analyses and selective sweeps

A genetic framework underpinning salinity tolerance at reproductive stage was revealed by genome-wide SNP markers and major adaptability genes in synthetic-derived wheats, and trait-associated loci were used to predict phenotypes. Using wild relatives of crops to identify genes related to improved productivity and resilience to climate extremes is a prioritized area of crop genetic improvement. High salinity is a widespread crop production constraint, and development of salt-tolerant cultivars is a sustainable solution. We evaluated a panel of 294 wheat accessions comprising synthetic-derived wheat lines (SYN-DERs) and modern bread wheat advanced lines under control and high salinity conditions at two locations. The GWAS analysis revealed a quantitative genetic framework of more than 200 loci with minor effect underlying salinity tolerance at reproductive stage. The significant trait-associated SNPs were used to predict phenotypes using a GBLUP model, and the prediction accuracy (r²) ranged between 0.57 and 0.74. The r² values for flag leaf weight, days to flowering, biomass, and number of spikes per plant were all above 0.70, validating the phenotypic effects of the loci discovered in this study. Furthermore, the germplasm sets were compared to identify selection sweeps associated with salt tolerance loci in SYN-DERs. Six loci associated with salinity tolerance were found to be differentially selected in the SYN-DERs (12.4 Mb on chromosome (chr)1B, 7.1 Mb on chr2A, 11.2 Mb on chr2D, 200 Mb on chr3D, 600 Mb on chr6B, and 700.9 Mb on chr7B). A total of 228 reported markers and genes, including 17 well-characterized genes, were uncovered using GWAS and EigenGWAS. A linkage disequilibrium (LD) block on chr5A, including the Vrn-A1 gene at 575 Mb and its homeologs on chr5D, were strongly associated with multiple yield-related traits and flowering time under salinity stress conditions. The diversity panel was screened with more than 68 kompetitive allele-specific PCR (KASP) markers of functional genes in wheat, and the pleiotropic effects of superior alleles of Rht-1, TaGASR-A1, and TaCwi-A1 were revealed under salinity stress. To effectively utilize the extensive genetic information obtained from the GWAS analysis, a genetic interaction network was constructed to reveal correlations among the investigated traits. The genetic network data combined with GWAS, selective sweeps, and the functional gene survey provided a quantitative genetic framework for identifying differentially retained loci associated with salinity tolerance in wheat.

Genome-wide association mapping identifies common bunt (Tilletia caries) resistance loci in bread wheat (Triticum aestivum) accessions of the USDA National Small Grains Collection

Association mapping and phenotypic analysis of a diversity panel of 238 bread wheat accessions highlights differences in resistance against common vs. dwarf bunt and identifies genotypes valuable for bi-parental crosses. Common bunt caused by Tilletia caries and T. laevis was successfully controlled by seed dressings with systemic fungicides for decades, but has become a renewed threat to wheat yield and quality in organic agriculture where such treatments are forbidden. As the most efficient way to address this problem is the use of resistant cultivars, this study aims to broaden the spectrum of resistance sources available for breeders by identifying resistance loci against common bunt in bread wheat accessions of the USDA National Small Grains Collection. We conducted three years of artificially inoculated field trials to assess common bunt infection levels in a diversity panel comprising 238 wheat accessions for which data on resistance against the closely related pathogen Tilletia controversa causing dwarf bunt was already available. Resistance levels against common bunt were higher compared to dwarf bunt with 99 accessions showing [Formula: see text] 1% incidence. Genome-wide association mapping identified six markers significantly associated with common bunt incidence in regions already known to confer resistance on chromosomes 1A and 1B and novel loci on 2B and 7A. Our results show that resistance against common and dwarf bunt is not necessarily controlled by the same loci but we identified twenty accessions with high resistance against both diseases. These represent valuable new resources for research and breeding programs since several bunt races have already been reported to overcome known resistance genes.

Polyploidy as a Fundamental Phenomenon in Evolution, Development, Adaptation and Diseases

DNA replication during cell proliferation is 'vertical' copying, which reproduces an initial amount of genetic information. Polyploidy, which results from whole-genome duplication, is a fundamental complement to vertical copying. Both organismal and cell polyploidy can emerge via premature cell cycle exit or via cell-cell fusion, the latter giving rise to polyploid hybrid organisms and epigenetic hybrids of somatic cells. Polyploidy-related increase in biological plasticity, adaptation, and stress resistance manifests in evolution, development, regeneration, aging, oncogenesis, and cardiovascular diseases. Despite the prevalence in nature and importance for medicine, agri- and aquaculture, biological processes and epigenetic mechanisms underlying these fundamental features largely remain unknown. The evolutionarily conserved features of polyploidy include activation of transcription, response to stress, DNA damage and hypoxia, and induction of programs of morphogenesis, unicellularity, and longevity, suggesting that these common features confer adaptive plasticity, viability, and stress resistance to polyploid cells and organisms. By increasing cell viability, polyploidization can provide survival under stressful conditions where diploid cells cannot survive. However, in somatic cells it occurs at the expense of specific function, thus promoting developmental programming of adult cardiovascular diseases and increasing the risk of cancer. Notably, genes arising via evolutionary polyploidization are heavily involved in cancer and other diseases. Ploidy-related changes of gene expression presumably originate from chromatin modifications and the derepression of bivalent genes. The provided evidence elucidates the role of polyploidy in evolution, development, aging, and carcinogenesis, and may contribute to the development of new strategies for promoting regeneration and preventing cardiovascular diseases and cancer.

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.

Genome-Wide Association Study for Resistance to Tan Spot in Synthetic Hexaploid Wheat

Synthetic hexaploid wheat (SHW) has shown effective resistance to a diversity of diseases and insects, including tan spot, which is caused by Pyrenophora tritici-repentis, being an important foliar disease that can attack all types of wheat and several grasses. In this study, 443 SHW plants were evaluated for their resistance to tan spot under controlled environmental conditions. Additionally, a genome-wide association study was conducted by genotyping all entries with the DArTSeq technology to identify marker-trait associations for tan spot resistance. Of the 443 SHW plants, 233 showed resistant and 183 moderately resistant reactions, and only 27 were moderately susceptible or susceptible to tan spot. Durum wheat (DW) parents of the SHW showed moderately susceptible to susceptible reactions. A total of 30 significant marker-trait associations were found on chromosomes 1B (4 markers), 1D (1 marker), 2A (1 marker), 2D (2 markers), 3A (4 markers), 3D (3 markers), 4B (1 marker), 5A (4 markers), 6A (6 markers), 6B (1 marker) and 7D (3 markers). Increased resistance in the SHW in comparison to the DW parents, along with the significant association of resistance with the A and B genome, supported the concept of activating epistasis interaction across the three wheat genomes. Candidate genes coding for F-box and cytochrome P450 proteins that play significant roles in biotic stress resistance were identified for the significant markers. The identified resistant SHW lines can be deployed in wheat breeding for tan spot resistance.

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.

Genetic dissection of grain architecture-related traits in a winter wheat population

BACKGROUND

The future productivity of wheat (T. aestivum L.) as the most grown crop worldwide is of utmost importance for global food security. Thousand kernel weight (TKW) in wheat is closely associated with grain architecture-related traits, e.g. kernel length (KL), kernel width (KW), kernel area (KA), kernel diameter ratio (KDR), and factor form density (FFD). Discovering the genetic architecture of natural variation in these traits, identifying QTL and candidate genes are the main aims of this study. Therefore, grain architecture-related traits in 261 worldwide winter accessions over three field-year experiments were evaluated.

RESULTS

Genome-wide association analysis using 90K SNP array in FarmCPU model revealed several interesting genomic regions including 17 significant SNPs passing false discovery rate threshold and strongly associated with the studied traits. Four of associated SNPs were physically located inside candidate genes within LD interval e.g. BobWhite_c5872_589 (602,710,399 bp) found to be inside TraesCS6A01G383800 (602,699,767-602,711,726 bp). Further analysis reveals the four novel candidate genes potentially involved in more than one grain architecture-related traits with a pleiotropic effects e.g. TraesCS6A01G383800 gene on 6A encoding oxidoreductase activity was associated with TKW and KA. The allelic variation at the associated SNPs showed significant differences betweeen the accessions carying the wild and mutated alleles e.g. accessions carying C allele of BobWhite_c5872_589, TraesCS6A01G383800 had significantly higher TKW than the accessions carying T allele. Interestingly, these genes were highly expressed in the grain-tissues, demonstrating their pivotal role in controlling the grain architecture.

CONCLUSIONS

These results are valuable for identifying regions associated with kernel weight and dimensions and potentially help breeders in improving kernel weight and architecture-related traits in order to increase wheat yield potential and end-use quality.

Lessons from a GWAS study of a wheat pre-breeding program: pyramiding resistance alleles to Fusarium crown rot

Much has been published on QTL detection for complex traits using bi-parental and multi-parental crosses (linkage analysis) or diversity panels (GWAS studies). While successful for detection, transferability of results to real applications has proven more difficult. Here, we combined a QTL detection approach using a pre-breeding populations which utilized intensive phenotypic selection for the target trait across multiple plant generations, combined with rapid generation turnover (i.e. "speed breeding") to allow cycling of multiple plant generations each year. The reasoning is that QTL mapping information would complement the selection process by identifying the genome regions under selection within the relevant germplasm. Questions to answer were the location of the genomic regions determining response to selection and the origin of the favourable alleles within the pedigree. We used data from a pre-breeding program that aimed at pyramiding different resistance sources to Fusarium crown rot into elite (but susceptible) wheat backgrounds. The population resulted from a complex backcrossing scheme involving multiple resistance donors and multiple elite backgrounds, akin to a MAGIC population (985 genotypes in total, with founders, and two major offspring layers within the pedigree). A significant increase in the resistance level was observed (i.e. a positive response to selection) after the selection process, and 17 regions significantly associated with that response were identified using a GWAS approach. Those regions included known QTL as well as potentially novel regions contributing resistance to Fusarium crown rot. In addition, we were able to trace back the sources of the favourable alleles for each QTL. We demonstrate that QTL detection using breeding populations under selection for the target trait can identify QTL controlling the target trait and that the frequency of the favourable alleles was increased as a response to selection, thereby validating the QTL detected. This is a valuable opportunistic approach that can provide QTL information that is more easily transferred to breeding applications.

Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning

Inferring trait networks from a large volume of genetically correlated diverse phenotypes such as yield, architecture, and disease resistance can provide information on the manner in which complex phenotypes are interrelated. However, studies on statistical methods tailored to multidimensional phenotypes are limited, whereas numerous methods are available for evaluating the massive number of genetic markers. Factor analysis operates at the level of latent variables predicted to generate observed responses. The objectives of this study were to illustrate the manner in which data-driven exploratory factor analysis can map observed phenotypes into a smaller number of latent variables and infer a genomic latent factor network using 45 agro-morphological, disease, and grain mineral phenotypes measured in synthetic hexaploid wheat lines (Triticum aestivum L.). In total, eight latent factors including grain yield, architecture, flag leaf-related traits, grain minerals, yellow rust, two types of stem rust, and leaf rust were identified as common sources of the observed phenotypes. The genetic component of the factor scores for each latent variable was fed into a Bayesian network to obtain a trait structure reflecting the genetic interdependency among traits. Three directed paths were consistently identified by two Bayesian network algorithms. Flag leaf-related traits influenced leaf rust, and yellow rust and stem rust influenced grain yield. Additional paths that were identified included flag leaf-related traits to minerals and minerals to architecture. This study shows that data-driven exploratory factor analysis can reveal smaller dimensional common latent phenotypes that are likely to give rise to numerous observed field phenotypes without relying on prior biological knowledge. The inferred genomic latent factor structure from the Bayesian network provides insights for plant breeding to simultaneously improve multiple traits, as an intervention on one trait will affect the values of focal phenotypes in an interrelated complex trait system.

Genomics-Assisted Breeding for Quantitative Disease Resistances in Small-Grain Cereals and Maize

Generating genomics-driven knowledge opens a way to accelerate the resistance breeding process by family or population mapping and genomic selection. Important prerequisites are large populations that are genomically analyzed by medium- to high-density marker arrays and extensive phenotyping across locations and years of the same populations. The latter is important to train a genomic model that is used to predict genomic estimated breeding values of phenotypically untested genotypes. After reviewing the specific features of quantitative resistances and the basic genomic techniques, the possibilities for genomics-assisted breeding are evaluated for six pathosystems with hemi-biotrophic fungi: Small-grain cereals/Fusarium head blight (FHB), wheat/Septoria tritici blotch (STB) and Septoria nodorum blotch (SNB), maize/Gibberella ear rot (GER) and Fusarium ear rot (FER), maize/Northern corn leaf blight (NCLB). Typically, all quantitative disease resistances are caused by hundreds of QTL scattered across the whole genome, but often available in hotspots as exemplified for NCLB resistance in maize. Because all crops are suffering from many diseases, multi-disease resistance (MDR) is an attractive aim that can be selected by specific MDR QTL. Finally, the integration of genomic data in the breeding process for introgression of genetic resources and for the improvement within elite materials is discussed.

Marker-Trait Associations for Enhancing Agronomic Performance, Disease Resistance, and Grain Quality in Synthetic and Bread Wheat Accessions in Western Siberia

Exploiting genetically diverse lines to identify genes for improving crop performance is needed to ensure global food security. A genome-wide association study (GWAS) was conducted using 46,268 SNP markers on a diverse panel of 143 hexaploid bread and synthetic wheat to identify potential genes/genomic regions controlling agronomic performance (yield and 26 yield-related traits), disease resistance, and grain quality traits. From phenotypic evaluation, we found large genetic variation among the 35 traits and recommended five lines having a high yield, better quality, and multiple disease resistance for direct use in a breeding program. From a GWAS, we identified a total of 243 significant marker-trait associations (MTAs) for 35 traits that explained up to 25% of the phenotypic variance. Of these, 120 MTAs have not been reported in the literature and are potentially novel MTAs. In silico gene annotation analysis identified 116 MTAs within genes and of which, 21 MTAs were annotated as a missense variant. Furthermore, we were able to identify 23 co-located multi-trait MTAs that were also phenotypically correlated to each other, showing the possibility of simultaneous improvement of these traits. Additionally, most of the co-located MTAs were within genes. We have provided genomic fingerprinting for significant markers with favorable and unfavorable alleles in the diverse set of lines for developing elite breeding lines from useful trait-integration. The results from this study provided a further understanding of genetically complex traits and would facilitate the use of diverse wheat accessions for improving multiple traits in an elite wheat breeding program.