Characterization of polyploid wheat genomic diversity using a high‐density 90 000 single nucleotide polymorphism array

Genome-wide analysis for root and leaf architecture traits associated with drought tolerance at the seedling stage in a highly ecologically diverse wheat population

Drought stress occurred at early growth stages in wheat affecting the following growth stages. Therefore, selecting promising drought-tolerant genotypes with highly adapted traits at the seedling stage is an important task for wheat breeders and geneticists. Few research efforts were conducted on the genetic control for drought-adaptive traits at the seedling stage in wheat. In this study, a set of 146 highly diverse spring wheat core collections representing 28 different countries was evaluated under drought stress at the seedling stage. All genotypes were exposed to drought stress for 13 days by water withholding. Leaf traits including seedling length, leaf wilting, days to wilting, leaf area, and leaf rolling were scored. Moreover, root traits such as root length, maximum width, emergence angle, tip angle, and number of roots were scored. Considerable significant genetic variation was found among all genotypes tested in these experiments. The heritability estimates ranged from 0.74 (leaf witling) to 0.99 (root tip angle). A set of nine genotypes were selected and considered drought-tolerant genotypes. Among all leaf traits, shoot length had significant correlations with all root traits under drought stress. The 146 genotypes were genotyped using the Infinium Wheat 15 K single nucleotide polymorphism (SNP) array and diversity arrays technology (DArT) marker platform. The result of genotyping revealed 12,999 SNPs and 2150 DArT markers which were used to run a genome-wide association study (GWAS). The results of GWAS revealed 169 markers associated with leaf and root traits under drought stress. Out of the 169 markers, 82 were considered major quantitative trait loci (QTL). The GWAS revealed 95 candidate genes were identified with 53 genes showing evidence for drought tolerance in wheat, while the remaining candidate genes were considered novel. No shared markers were found between leaf and root traits. The results of the study provided mapping novel markers associated with new root traits at the seedling stage. Also, the selected genotypes from different countries could be employed in future wheat breeding programs not only for improving adaptive drought-tolerant traits but also for expanding genetic diversity.

QTL analysis of native Fusarium head blight and deoxynivalenol resistance in 'D8006W'/'Superior', soft white winter wheat population

BACKGROUND

Fusarium head blight (FHB), caused by Fusarium graminearum, is a major disease of wheat in North America. FHB infection causes fusarium damaged kernels (FDKs), accumulation of deoxynivalenol (DON) in the grain, and a reduction in quality and grain yield. Inheritance of FHB resistance is complex and involves multiple genes. The objective of this research was to identify QTL associated with native FHB and DON resistance in a 'D8006W'/'Superior', soft white winter wheat population.

RESULTS

Phenotyping was conducted in replicated FHB field disease nurseries across multiple environments and included assessments of morphological and FHB related traits. Parental lines had moderate FHB resistance, however, the population showed transgressive segregation. A 1913.2 cM linkage map for the population was developed with SNP markers from the wheat 90 K Infinium iSelect SNP array. QTL analysis detected major FHB resistance QTL on chromosomes 2D, 4B, 5A, and 7A across multiple environments, with resistance from both parents. Trait specific unique QTL were detected on chromosomes 1A (visual traits), 5D (FDK), 6B (FDK and DON), and 7D (DON). The plant height and days to anthesis QTL on chromosome 2D coincided with Ppd-D1 and were linked with FHB traits. The plant height QTL on chromosome 4B was also linked with FHB traits; however, the Rht-B1 locus did not segregate in the population.

CONCLUSIONS

This study identified several QTL, including on chromosome 2D linked with Ppd-D1, for FHB resistance in a native winter wheat germplasm.

Dissecting the genetic basis of resistance to Soil-borne cereal mosaic virus (SBCMV) in durum wheat by bi-parental mapping and GWAS

Soil-borne cereal mosaic virus (SBCMV), the causative agent of wheat mosaic, is a Furovirus challenging wheat production all over Europe. Differently from bread wheat, durum wheat shows greater susceptibility and stronger yield penalties, so identification and genetic characterization of resistance sources are major targets for durum genetics and breeding. The Sbm1 locus providing high level of resistance to SBCMV was mapped in bread wheat to the 5DL chromosome arm (Bass in Genome 49:1140-1148, 2006). This excluded the direct use of Sbm1 for durum wheat improvement. Only one major QTL has been mapped in durum wheat, namely QSbm.ubo-2B, on the 2BS chromosome region coincident with Sbm2, already known in bread wheat as reported (Bayles in HGCA Project Report, 2007). Therefore, QSbm.ubo-2B = Sbm2 is considered a pillar for growing durum in SBCMV-affected areas. Herein, we report the fine mapping of Sbm2 based on bi-parental mapping and GWAS, using the Infinium 90 K SNP array and high-throughput KASP®. Fine mapping pointed out a critical haploblock of 3.2 Mb defined by concatenated SNPs successfully converted to high-throughput KASP® markers coded as KUBO. The combination of KUBO-27, wPt-2106-ASO/HRM, KUBO-29, and KUBO-1 allows unequivocal tracing of the Sbm2-resistant haplotype. The interval harbors 52 high- and 41 low-confidence genes, encoding 17 cytochrome p450, three receptor kinases, two defensins, and three NBS-LRR genes. These results pave the way for Sbm2 positional cloning. Importantly, the development of Sbm2 haplotype tagging KASP® provides a valuable case study for improving efficacy of the European variety testing system and, ultimately, the decision-making process related to varietal characterization and choice.

Fine mapping of stem rust resistance derived from soft red winter wheat cultivar AGS2000 to an NLR gene cluster on chromosome 6D

The Puccinia graminis f. sp. tritici (Pgt) Ug99-emerging virulent races present a major challenge to global wheat production. To meet present and future needs, new sources of resistance must be found. Identification of markers that allow tracking of resistance genes is needed for deployment strategies to combat highly virulent pathogen races. Field evaluation of a DH population located a QTL for stem rust (Sr) resistance, QSr.nc-6D from the breeding line MD01W28-08-11 to the distal region of chromosome arm 6DS where Sr resistance genes Sr42, SrCad, and SrTmp have been identified. A locus for seedling resistance to Pgt race TTKSK was identified in a DH population and an RIL population derived from the cross AGS2000 × LA95135. The resistant cultivar AGS2000 is in the pedigree of MD01W28-08-11 and our results suggest that it is the source of Sr resistance in this breeding line. We exploited published markers and exome capture data to enrich marker density in a 10 Mb region flanking QSr.nc-6D. Our fine mapping in heterozygous inbred families identified three markers co-segregating with resistance and delimited QSr.nc-6D to a 1.3 Mb region. We further exploited information from other genome assemblies and identified collinear regions of 6DS harboring clusters of NLR genes. Evaluation of KASP assays corresponding to our co-segregating SNP suggests that they can be used to track this Sr resistance in breeding programs. However, our results also underscore the challenges posed in identifying genes underlying resistance in such complex regions in the absence of genome sequence from the resistant genotypes.

Origin and evolution of the bread wheat D genome

Bread wheat (Triticum aestivum) is a globally dominant crop and major source of calories and proteins for the human diet. Compared with its wild ancestors, modern bread wheat shows lower genetic diversity, caused by polyploidisation, domestication and breeding bottlenecks1,2. Wild wheat relatives represent genetic reservoirs, and harbour diversity and beneficial alleles that have not been incorporated into bread wheat. Here we establish and analyse extensive genome resources for Tausch's goatgrass (Aegilops tauschii), the donor of the bread wheat D genome. Our analysis of 46 Ae. tauschii genomes enabled us to clone a disease resistance gene and perform haplotype analysis across a complex disease resistance locus, allowing us to discern alleles from paralogous gene copies. We also reveal the complex genetic composition and history of the bread wheat D genome, which involves contributions from genetically and geographically discrete Ae. tauschii subpopulations. Together, our results reveal the complex history of the bread wheat D genome and demonstrate the potential of wild relatives in crop improvement.

Development of a next generation SNP genotyping array for wheat

High-throughput genotyping arrays have provided a cost-effective, reliable and interoperable system for genotyping hexaploid wheat and its relatives. Existing, highly cited arrays including our 35K Wheat Breeder's array and the Illumina 90K array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in wheat sequencing has given us access to a vast pool of SNP diversity, whilst technological improvements have allowed us to fit significantly more probes onto a 384-well format Axiom array than previously possible. Here we describe a novel Axiom genotyping array, the 'Triticum aestivum Next Generation' array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins 'Core Collection'. We used a novel haplotype optimization approach to select SNPs with the highest combined varietal discrimination and a design iteration step to test and replace SNPs which failed to convert to reliable markers. The final design with 43 372 SNPs contains a combination of haplotype-optimized novel SNPs and legacy cross-platform markers. We show that this design has an improved distribution of SNPs compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous array. We also demonstrate the improved performance of TaNGv1.1 for Genome-wide association studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available with supporting marker annotations and initial genotyping results freely available.

Genome-wide association study identifies loci and candidate genes for RVA parameters in wheat (Triticum aestivum L.)

The gelatinization and retrogradation characteristics of wheat starch affect the eating quality of Chinese-style food. Rapid Visco Analyzer (RVA) parameters have been widely used as important indicators to evaluate and improve the quality of wheat starch. However, the genetic basis of RVA parameters remains to be further explored. In the present study, a natural population was genotyped using 90K single nucleotide polymorphism (SNP) arrays, and the RVA parameters of this population grown in five environments were evaluated. The results showed that 22,068 high-quality SNP markers were identified and distributed unequally on the chromosomes. According to the genetic distance, 214 wheat materials were divided into four groups. Except for the pasting temperature (PTT), six parameters followed a normal distribution. Based on the general linear model, 969 significant association SNPs were detected by genome-wide association studies (GWAS), and chromosomes 7A and 2B had the most associated SNPs. Breakdown viscosity (BV) was associated with the most SNPs (n = 238), followed by PTT (n = 186), peak viscosity (PV; n = 156), trough viscosity (TV; n = 127), and final viscosity (FV; n = 126). According to the average linkage disequilibrium (LD), 33 stable quantitative trait loci (QTLs) were identified for single parameters in multiple environments, of which 12 were associated with BV, followed by peak time (PT; n = 8) and PTT (n = 7). On the other hand, 67 pleiotropic QTLs were identified for multiple parameters. Three candidate genes-TasbeIIa, TasbeI, and TassIIa-were screened for phenotyping analysis. The grain width and the weight of the TasbeIIa and TaSSIIa knockout (KO) lines were significantly lower than those of the TasbeI KO lines and the control (CK). The KO lines had smaller endosperm cells, smaller A-type starch granules, and higher amylose content. The TasbeI KO lines showed normal RVA curves, while the TasbeIIa KO lines showed flat curves. However, the TaSSIIa lines failed to paste under the RVA temperatures. Conclusively, the SNPs/QTLs significantly associated with the RVA parameters and genetic resources with novel haplotypes could be used to improve the quality of wheat starch.

Screening of CIMMYT and South Asian Bread Wheat Germplasm Reveals Marker-Trait Associations for Seedling Resistance to Septoria Nodorum Blotch

Wheat (Triticum aestivum L.) production is adversely impacted by Septoria nodorum blotch (SNB), a fungal disease caused by Parastagonospora nodorum. Wheat breeders are constantly up against this biotic challenge as they try to create resistant cultivars. The genome-wide association study (GWAS) has become an efficient tool for identifying molecular markers linked with SNB resistance. This technique is used to acquire an understanding of the genetic basis of resistance and to facilitate marker-assisted selection. In the current study, a total of 174 bread wheat accessions from South Asia and CIMMYT were assessed for SNB reactions at the seedling stage in three greenhouse experiments at CIMMYT, Mexico. The results indicated that 129 genotypes were resistant to SNB, 39 were moderately resistant, and only 6 were moderately susceptible. The Genotyping Illumina Infinium 15K Bead Chip was used, and 11,184 SNP markers were utilized to identify marker-trait associations (MTAs) after filtering. Multiple tests confirmed the existence of significant MTAs on chromosomes 5B, 5A, and 3D, and the ones at Tsn1 on 5B were the most stable and conferred the highest phenotypic variation. The resistant genotypes identified in this study could be cultivated in South Asian countries as a preventative measure against the spread of SNB. This work also identified molecular markers of SNB resistance that could be used in future wheat breeding projects.

GWAS elucidated grain yield genetics in Indian spring wheat under diverse water conditions

KEY MESSAGE

Underpinned natural variations and key genes associated with yield under different water regimes, and identified genomic signatures of genetic gain in the Indian wheat breeding program. A novel KASP marker for TKW under water stress was developed and validated. A comprehensive genome-wide association study was conducted on 300 spring wheat genotypes to elucidate the natural variations associated with grain yield and its eleven contributing traits under fully irrigated, restricted water, and simulated no water conditions. Utilizing the 35K Wheat Breeders' Array, we identified 1155 quantitative trait nucleotides (QTNs), with 207 QTNs exhibiting stability across diverse conditions. These QTNs were further delimited into 539 genomic regions using a genome-wide LD value of 3.0 Mbp, revealing pleiotropic control across traits and conditions. Sub-genome A was significantly associated with traits under irrigated conditions, while sub-genome B showed more QTNs under water stressed conditions. Favourable alleles with significantly associated QTNs were delineated, with a notable pyramiding effect for enhancing trait performance. Additionally, allele of only 921 QTNs significantly affected the population mean. Allele profiling highlighted C-306 as a most potential source of drought tolerance. Moreover, 762 genes overlapping significant QTNs were identified, narrowing down to 27 putative candidate genes overlapping 29 novel and functional SNPs expressing (≥ 0.5 tpm) relevance across various growth conditions. A new KASP assay was developed, targeting a gene TraesCS2A03G1123700 regulating thousand kernel weight under severe drought condition. Genomic selection models (GBLUP, BayesB, MxE, and R-Norm) demonstrated an average prediction accuracy of 0.06-0.58 across environments, indicating potential for trait selection. Retrospective analysis of the Indian wheat breeding program supported a genetic gain in GY at the rate of ca. 0.56% per breeding cycle, since 1960, supporting the identification of genomic signatures driving trait selection and genetic gain. These findings offer insight into improving the rate of genetic gain in wheat breeding programs globally.

Beyond pathways: Accelerated flavonoids candidate identification and novel exploration of enzymatic properties using combined mapping populations of wheat

Although forward-genetics-metabolomics methods such as mGWAS and mQTL have proven effective in providing myriad loci affecting metabolite contents, they are somehow constrained by their respective constitutional flaws such as the hidden population structure for GWAS and insufficient recombinant rate for QTL. Here, the combination of mGWAS and mQTL was performed, conveying an improved statistical power to investigate the flavonoid pathways in common wheat. A total of 941 and 289 loci were, respectively, generated from mGWAS and mQTL, within which 13 of them were co-mapped using both approaches. Subsequently, the mGWAS or mQTL outputs alone and their combination were, respectively, utilized to delineate the metabolic routes. Using this approach, we identified two MYB transcription factor encoding genes and five structural genes, and the flavonoid pathway in wheat was accordingly updated. Moreover, we have discovered some rare-activity-exhibiting flavonoid glycosyl- and methyl-transferases, which may possess unique biological significance, and harnessing these novel catalytic capabilities provides potentially new breeding directions. Collectively, we propose our survey illustrates that the forward-genetics-metabolomics approaches including multiple populations with high density markers could be more frequently applied for delineating metabolic pathways in common wheat, which will ultimately contribute to metabolomics-assisted wheat crop improvement.

Spectral-genomic chain-model approach enhances the wheat yield component prediction under the Mediterranean climate

In light of the changing climate that jeopardizes future food security, genomic selection is emerging as a valuable tool for breeders to enhance genetic gains and introduce high-yielding varieties. However, predicting grain yield is challenging due to the genetic and physiological complexities involved and the effect of genetic-by-environment interactions on prediction accuracy. We utilized a chained model approach to address these challenges, breaking down the complex prediction task into simpler steps. A diversity panel with a narrow phenological range was phenotyped across three Mediterranean environments for various morpho-physiological and yield-related traits. The results indicated that a multi-environment model outperformed a single-environment model in prediction accuracy for most traits. However, prediction accuracy for grain yield was not improved. Thus, in an attempt to ameliorate the grain yield prediction accuracy, we integrated a spectral estimation of spike number, being a major wheat yield component, with genomic data. A machine learning approach was used for spike number estimation from canopy hyperspectral reflectance captured by an unmanned aerial vehicle. The spectral-based estimated spike number was utilized as a secondary trait in a multi-trait genomic selection, significantly improving grain yield prediction accuracy. Moreover, the ability to predict the spike number based on data from previous seasons implies that it could be applied to new trials at various scales, even in small plot sizes. Overall, we demonstrate here that incorporating a novel spectral-genomic chain-model workflow, which utilizes spectral-based phenotypes as a secondary trait, improves the predictive accuracy of wheat grain yield.

Identifying the seeds of heterotic pools for Southern and Eastern Africa from global elite spring wheat germplasm

Hybrid breeding can increase the competitiveness of wheat (Triticum aestivum L.) in Sub-Saharan Africa by fostering more public-private partnerships and promoting investment by the private sector. The benefit of hybrid wheat cultivars in South Africa has previously been demonstrated but due to the high cost of hybrid seed production, hybrid breeding has not received significant attention in the past decade. Considering the renewed commitment of the private sector to establish wheat as a hybrid crop globally, coupled with significant research investment into enhancement of outcrossing of wheat, hybrid wheat breeding in Southern and Eastern Africa should be revisited. Our study aimed to identify genetically distinct germplasm groups in spring wheat that would be useful in the establishment of heterotic pools targeting this region. Multi-environment yield testing of a large panel of F1 test hybrids, generated using global elite germplasm, was carried out between 2019 and 2020 in Argentina, Africa, Europe, and Australia. We observed significant genotype by environment interactions within our testing network, confirming the distinctiveness of African trial sites. Relatively high additive genetic variance was observed highlighting the contribution of parental genotypes to the grain yield of test hybrids. We explored the genetic architecture of these parents and the genetic factors underlying the value of parents appear to be associated with their genetic subgroup, with positive marker effects distributed throughout the genome. In testcrosses, elite germplasm from the International Maize and Wheat Improvement Center (CIMMYT) appear to be complementary to the genetically distinct germplasm bred in South Africa. The feasibility of achieving genetic gain via heterotic pool establishment and divergence, and by extension the viability of hybrid cultivars in Sub-Saharan Africa, is supported by the results of our study.

Optimising desired gain indices to maximise selection response

Introduction

In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit.

Methods

Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits.

Results

We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction.

Discussion

Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option.

Natural allelic variation confers diversity in the regulation of flag leaf traits in wheat

Flag leaf (FL) dimension has been reported as a key ecophysiological aspect for boosting grain yield in wheat. A worldwide winter wheat panel consisting of 261 accessions was tested to examine the phenotypical variation and identify quantitative trait nucleotides (QTNs) with candidate genes influencing FL morphology. To this end, four FL traits were evaluated during the early milk stage under two growing seasons at the Leibniz Institute of Plant Genetics and Crop Plant Research. The results showed that all leaf traits (Flag leaf length, width, area, and length/width ratio) were significantly influenced by the environments, genotypes, and environments × genotypes interactions. Then, a genome-wide association analysis was performed using 17,093 SNPs that showed 10 novel QTNs that potentially play a role in modulating FL morphology in at least two environments. Further analysis revealed 8 high-confidence candidate genes likely involved in these traits and showing high expression values from flag leaf expansion until its senescence and also during grain development. An important QTN (wsnp_RFL_Contig2177_1500201) was associated with FL width and located inside TraesCS3B02G047300 at chromosome 3B. This gene encodes a major facilitator, sugar transporter-like, and showed the highest expression values among the candidate genes reported, suggesting their positive role in controlling flag leaf and potentially being involved in photosynthetic assimilation. Our study suggests that the detection of novel marker-trait associations and the subsequent elucidation of the genetic mechanism influencing FL morphology would be of interest for improving plant architecture, light capture, and photosynthetic efficiency during grain development.

Meta-QTL analysis and identification of candidate genes for multiple-traits associated with spot blotch resistance in bread wheat

In bread wheat, a literature search gave 228 QTLs for six traits, including resistance against spot blotch and the following five other related traits: (i) stay green; (ii) flag leaf senescence; (iii) green leaf area duration; (iv) green leaf area of the main stem; and (v) black point resistance. These QTLs were used for metaQTL (MQTL) analysis. For this purpose, a consensus map with 72,788 markers was prepared; 69 of the above 228 QTLs, which were suitable for MQTL analysis, were projected on the consensus map. This exercise resulted in the identification of 16 meta-QTLs (MQTLs) located on 11 chromosomes, with the PVE ranging from 5.4% (MQTL7) to 21.8% (MQTL5), and the confidence intervals ranging from 1.5 to 20.7 cM (except five MQTLs with a range of 36.1-57.8 cM). The number of QTLs associated with individual MQTLs ranged from a maximum of 17 in MQTL3 to 8 each in MQTL5 and MQTL8 and 5 each in MQTL7 and MQTL14. The 16 MQTLs, included 12 multi-trait MQTLs; one of the MQTL also overlapped a genomic region carrying the major spot blotch resistance gene Sb1. Of the total 16 MQTLs, 12 MQTLs were also validated through marker-trait associations that were available from earlier genome-wide association studies. The genomic regions associated with MQTLs were also used for the identification of candidate genes (CGs) and led to the identification of 516 CGs encoding 508 proteins; 411 of these proteins are known to be associated with resistance against several biotic stresses. In silico expression analysis of CGs using transcriptome data allowed the identification of 71 differentially expressed CGs, which were examined for further possible studies. The findings of the present study should facilitate fine-mapping and cloning of genes, enabling Marker Assisted Selection.

Functional divergences of natural variations of TaNAM-A1 in controlling leaf senescence during wheat grain filling

Leaf senescence is an essential physiological process related to grain yield potential and nutritional quality. Green leaf duration (GLD) after anthesis directly reflects the leaf senescence process and exhibits large genotypic differences in common wheat; however, the underlying gene regulatory mechanism is still lacking. Here, we identified TaNAM-A1 as the causal gene of the major loci qGLD-6A for GLD during grain filling by map-based cloning. Transgenic assays and TILLING mutant analyses demonstrated that TaNAM-A1 played a critical role in regulating leaf senescence, and also affected spike length and grain size. Furthermore, the functional divergences among the three haplotypes of TaNAM-A1 were systematically evaluated. Wheat varieties with TaNAM-A1d (containing two mutations in the coding DNA sequence of TaNAM-A1) exhibited a longer GLD and superior yield-related traits compared to those with the wild type TaNAM-A1a. All three haplotypes were functional in activating the expression of genes involved in macromolecule degradation and mineral nutrient remobilization, with TaNAM-A1a showing the strongest activity and TaNAM-A1d the weakest. TaNAM-A1 also modulated the expression of the senescence-related transcription factors TaNAC-S-7A and TaNAC016-3A. TaNAC016-3A enhanced the transcriptional activation ability of TaNAM-A1a by protein-protein interaction, thereby promoting the senescence process. Our study offers new insights into the fine-tuning of the leaf functional period and grain yield formation for wheat breeding under various geographical climatic conditions.

Characterization of Quantitative Trait Loci for Leaf Rust Resistance in the Uzbekistani Wheat Landrace Teremai Bugdai

Leaf rust, caused by Puccinia triticina, is a major cause of wheat yield losses globally, and novel leaf rust resistance genes are needed to enhance wheat leaf rust resistance. Teremai Bugdai is a landrace from Uzebekistan that is highly resistant to many races of P. triticina in the United States. To unravel leaf rust resistance loci in Teremai Bugdai, a recombinant inbred line (RIL) population of Teremai Bugdai × TAM 110 was evaluated for response to P. triticina race Pt54-1 (TNBGJ) and genotyped using single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). Quantitative trait loci (QTL) analysis using 5,130 high-quality GBS-SNPs revealed three QTLs, QLr-Stars-2DS, QLr-Stars-6BL, and QLr.Stars-7BL, for leaf rust resistance in two experiments. QLr-Stars-2DS, which is either a new Lr2 allele or a new resistance locus, was delimited to an ∼19.47-Mb interval between 46.4 and 65.9 Mb on 2DS and explained 31.3 and 33.2% of the phenotypic variance in the two experiments. QLr-Stars-6BL was mapped in an ∼84.0-kb interval between 719.48 and 719.56 Mb on 6BL, accounting for 33 to 36.8% of the phenotypic variance in two experiments. QLr.Stars-7BL was placed in a 350-kb interval between 762.41 and 762.76 Mb on 7BL and explained 4.4 to 5.3% of the phenotypic variance. Nine GBS-SNPs flanking these QTLs were converted to kompetitive allele specific PCR (KASP) markers, and these markers can be used to facilitate their introgression into locally adapted wheat lines.

Identification of a Locus for High-Temperature Adult-Plant Resistance to Stripe Rust in the Wheat Yr8 Near-Isogenic Line Through Mutagenesis and Molecular Mapping

Aegilops species are wheat relatives that harbor valuable disease resistance genes for wheat breeding. The wheat Yr8 near-isogenic line AvSYr8NIL has long been believed to carry only Yr8 for race-specific all-stage resistance to stripe rust, caused by Puccinia striiformis f. sp. tritici, derived from Aegilops comosa. However, AvSYr8NIL has been found to have high-temperature adult-plant (HTAP) resistance in our field and greenhouse tests. To confirm both HTAP and Yr8 resistance, seeds from AvSYr8NIL were treated with ethyl methanesulfonate to generate mutant lines. The mutant lines with only Yr8 (M641) and only HTAP resistance (M488) were crossed with the susceptible recurrent parent Avocet S (AvS). The F1 and F4 lines of AvS/M641 were phenotyped with Yr8-avirulent races in the seedling stage at the low-temperature (4 to 20°C) profile, while the F1, F2, F4, and F5 lines of AvS/M488 were phenotyped with Yr8-virulent races in the adult-plant stage at the high-temperature (10 to 30°C) profile. Both Yr8 and the HTAP resistance gene (YrM488) were recessive. The F4 populations of AvS/M641 and AvS/M488 were genotyped using polymorphic Kompetitive allele-specific PCR markers converted from single-nucleotide polymorphisms. Yr8 was mapped to a 0.66-cM fragment, and YrM488 was mapped to a 1.22-cM interval on chromosome 2D. The physical distance between the two resistance genes was estimated to be more than 500 Mb, indicating their distinct loci. The mutant lines with separated resistance genes would be useful in enhancing our understanding of different types of resistance and in further studying the interactions between wheat and the stripe rust pathogen.

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

Statistical sampling of missing environmental variables improves biophysical genomic prediction in wheat

KEY MESSAGE

The integration of genomic prediction with crop growth models enabled the estimation of missing environmental variables which improved the prediction accuracy of grain yield. Since the invention of whole-genome prediction (WGP) more than two decades ago, breeding programmes have established extensive reference populations that are cultivated under diverse environmental conditions. The introduction of the CGM-WGP model, which integrates crop growth models (CGM) with WGP, has expanded the applications of WGP to the prediction of unphenotyped traits in untested environments, including future climates. However, CGMs require multiple seasonal environmental records, unlike WGP, which makes CGM-WGP less accurate when applied to historical reference populations that lack crucial environmental inputs. Here, we investigated the ability of CGM-WGP to approximate missing environmental variables to improve prediction accuracy. Two environmental variables in a wheat CGM, initial soil water content (InitlSoilWCont) and initial nitrate profile, were sampled from different normal distributions separately or jointly in each iteration within the CGM-WGP algorithm. Our results showed that sampling InitlSoilWCont alone gave the best results and improved the prediction accuracy of grain number by 0.07, yield by 0.06 and protein content by 0.03. When using the sampled InitlSoilWCont values as an input for the traditional CGM, the average narrow-sense heritability of the genotype-specific parameters (GSPs) improved by 0.05, with GNSlope, PreAnthRes, and VernSen showing the greatest improvements. Moreover, the root mean square of errors for grain number and yield was reduced by about 7% for CGM and 31% for CGM-WGP when using the sampled InitlSoilWCont values. Our results demonstrate the advantage of sampling missing environmental variables in CGM-WGP to improve prediction accuracy and increase the size of the reference population by enabling the utilisation of historical data that are missing environmental records.

Agroecological genetics of biomass allocation in wheat uncovers genotype interactions with canopy shade and plant size

How plants distribute biomass among organs influences resource acquisition, reproduction and plant-plant interactions, and is essential in understanding plant ecology, evolution, and yield production in agriculture. However, the genetic mechanisms regulating allocation responses to the environment are largely unknown. We studied recombinant lines of wheat (Triticum spp.) grown as single plants under sunlight and simulated canopy shade to investigate genotype-by-environment interactions in biomass allocation to the leaves, stems, spikes, and grains. Size-corrected mass fractions and allometric slopes were employed to dissect allocation responses to light limitation and plant size. Size adjustments revealed light-responsive alleles associated with adaptation to the crop environment. Combined with an allometric approach, we demonstrated that polymorphism in the DELLA protein is associated with the response to shade and size. While a gibberellin-sensitive allelic effect on stem allocation was amplified when plants were shaded, size-dependent effects of this allele drive allocation to reproduction, suggesting that the ontogenetic trajectory of the plant affects the consequences of shade responses for allocation. Our approach provides a basis for exploring the genetic determinants underlying investment strategies in the face of different resource constraints and will be useful in predicting social behaviours of individuals in a crop community.

Genome-Wide Association Study to Identify Marker-Trait Associations for Seed Color in Colored Wheat (Triticum aestivum L.)

This study conducted phenotypic evaluations on a wheat F3 population derived from 155 F2 plants. Traits related to seed color, including chlorophyll a, chlorophyll b, carotenoid, anthocyanin, L*, a*, and b*, were assessed, revealing highly significant correlations among various traits. Genotyping using 81,587 SNP markers resulted in 3969 high-quality markers, revealing a genome-wide distribution with varying densities across chromosomes. A genome-wide association study using fixed and random model circulating probability unification (FarmCPU) and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) identified 11 significant marker-trait associations (MTAs) associated with L*, a*, and b*, and chromosomal distribution patterns revealed predominant locations on chromosomes 2A, 2B, and 4B. A comprehensive annotation uncovered 69 genes within the genomic vicinity of each MTA, providing potential functional insights. Gene expression analysis during seed development identified greater than 2-fold increases or decreases in expression in colored wheat for 16 of 69 genes. Among these, eight genes, including transcription factors and genes related to flavonoid and ubiquitination pathways, exhibited distinct expression patterns during seed development, providing further approaches for exploring seed coloration. This comprehensive exploration expands our understanding of the genetic basis of seed color and paves the way for informed discussions on the molecular intricacies contributing to this phenotypic trait.

Exploring the Drought Tolerant Quantitative Trait Loci in Spring Wheat

Drought-induced stress poses a significant challenge to wheat throughout its growth, underscoring the importance of identifying drought-stable quantitative trait loci (QTLs) for enhancing grain yield. Here, we evaluated 18 yield-related agronomic and physiological traits, along with their drought tolerance indices, in a recombinant inbred line population derived from the XC7 × XC21 cross. These evaluations were conducted under both non-stress and drought-stress conditions. Drought stress significantly reduced grain weight per spike and grain yield per plot. Genotyping the recombinant inbred line population using the wheat 90K single nucleotide polymorphism array resulted in the identification of 131 QTLs associated with the 18 traits. Drought stress also exerted negative impacts on grain formation and filling, directly leading to reductions in grain weight per spike and grain yield per plot. Among the identified QTLs, 43 were specifically associated with drought tolerance across the 18 traits, with 6 showing direct linkages to drought tolerance in wheat. These results provide valuable insights into the genetic mechanisms governing wheat growth and development, as well as the traits contributing to the drought tolerance index. Moreover, they serve as a theoretical foundation for the development of new wheat cultivars having exceptional drought tolerance and high yield potentials under both drought-prone and drought-free conditions.

Genetic mapping of deoxynivalenol and fusarium damaged kernel resistance in an adapted durum wheat population

BACKGROUND

Fusarium head blight (FHB) infection results in Fusarium damaged kernels (FDK) and deoxynivalenol (DON) contamination that are downgrading factors at the Canadian elevators. Durum wheat (Triticum turgidum L. var. durum Desf.) is particularly susceptible to FHB and most of the adapted Canadian durum wheat cultivars are susceptible to moderately susceptible to this disease. However, the durum line DT696 is less susceptible to FHB than commercially grown cultivars. Little is known about genetic variation for durum wheat ability to resist FDK infection and DON accumulation. This study was undertaken to map genetic loci conferring resistance to DON and FDK resistance using a SNP high-density genetic map of a DT707/DT696 DH population and to identify SNP markers useful in marker-assisted breeding. One hundred twenty lines were grown in corn spawn inoculated nurseries near Morden, MB in 2015, 2016 and 2017 and the harvested seeds were evaluated for DON. The genetic map of the population was used in quantitative trait locus analysis performed with MapQTL.6® software.

RESULTS

Four DON accumulation resistance QTL detected in two of the three years were identified on chromosomes 1 A, 5 A (2 loci) and 7 A and two FDK resistance QTL were identified on chromosomes 5 and 7 A in single environments. Although not declared significant due to marginal LOD values, the QTL for FDK on the 5 and 7 A were showing in other years suggesting their effects were real. DT696 contributed the favourable alleles for low DON and FDK on all the chromosomes. Although no resistance loci contributed by DT707, transgressive segregant lines were identified resulting in greater resistance than DT696. Breeder-friendly KASP markers were developed for two of the DON and FDK QTL detected on chromosomes 5 and 7 A. Markers flanking each QTL were physically mapped against the durum wheat reference sequence and candidate genes which might be involved in FDK and DON resistance were identified within the QTL intervals.

CONCLUSIONS

The DH lines harboring the desired resistance QTL will serve as useful resources in breeding for FDK and DON resistance in durum wheat. Furthermore, breeder-friendly KASP markers developed during this study will be useful for the selection of durum wheat varieties with low FDK and DON levels in durum wheat breeding programs.

The genetics of Ug99 stem rust resistance in spring wheat variety 'Linkert'

Wheat stem rust caused by Puccinia graminis f. sp. tritici (Pgt) threatens wheat production worldwide. The objective of this study was to characterize wheat stem rust resistance in 'Linkert', a variety with adult plant resistance effective to emerging wheat stem rust pathogen strain Ug99. Two doubled haploid (DH) populations and one recombinant inbred line (RIL) population were developed with 'Linkert' as a stem rust resistant parent. Hard red spring wheat variety 'Forefront' and genetic stock 'LMPG' were used as stem rust susceptible parents of the DH populations. Breeding line 'MN07098-6' was used as a susceptible parent of the RIL population. Both DH and RIL populations with their parents were evaluated both at the seedling stage and in the field against Pgt races. Genotyping data of the DH populations were generated using the wheat iSelect 90k SNP assay. The RIL population was genotyped by genotyping-by-sequencing. We found QTL consistently associated with wheat stem rust resistance on chromosome 2BS for the Linkert/Forefront DH population and the Linkert/MN07098-6 RIL population both in Ethiopia and Kenya. Additional reliable QTL were detected on chromosomes 5BL (125.91 cM) and 4AL (Sr7a) for the Linkert/LMPG population in Ethiopia and Kenya. Different QTL identified in the populations reflect the importance of examining the genetics of resistance in populations derived from adapted germplasm (Forefront and MN07098-6) in addition to a genetic stock (LMPG). The associated markers in this study could be used to track and select for the identified QTL in wheat breeding programs.

Genome-wide association study of drought tolerance in wheat (Triticum aestivum L.) identifies SNP markers and candidate genes

Drought stress poses a severe threat to global wheat production, necessitating an in-depth exploration of the genetic basis for drought tolerance associated traits. This study employed a 90 K SNP array to conduct a genome-wide association analysis, unravelling genetic determinants of key traits related to drought tolerance in wheat, namely plant height, root length, and root and shoot dry weight. Using the mixed linear model (MLM) method on 125 wheat accessions subjected to both well-watered and drought stress treatments, we identified 53 SNPs significantly associated with stress susceptibility (SSI) and tolerance indices (STI) for the targeted traits. Notably, chromosomes 2A and 3B stood out with ten and nine associated markers, respectively. Across 17 chromosomes, 44 unique candidate genes were pinpointed, predominantly located on the distal ends of 1A, 1B, 1D, 2A, 3A, 3B, 4A, 6A, 6B, 7A, 7B, and 7D chromosomes. These genes, implicated in diverse functions related to plant growth, development, and stress responses, offer a rich resource for future investigation. A clustering pattern emerged, notably with seven genes associated with SSI for plant height and four genes linked to both STI of plant height and shoot dry weight, converging on specific regions of chromosome arms of 2AS and 3BL. Additionally, shared genes encoding polygalacturonase, auxilin-related protein 1, peptide deformylase, and receptor-like kinase underscored the interconnectedness between plant height and shoot dry weight. In conclusion, our findings provide insights into the molecular mechanisms governing wheat drought tolerance, identifying promising genomic loci for further exploration and crop improvement strategies.

Development and Assessment of SNP Genotyping Arrays for Citrus and Its Close Relatives

Rapid advancements in technologies provide various tools to analyze fruit crop genomes to better understand genetic diversity and relationships and aid in breeding. Genome-wide single nucleotide polymorphism (SNP) genotyping arrays offer highly multiplexed assays at a relatively low cost per data point. We report the development and validation of 1.4M SNP Axiom® Citrus HD Genotyping Array (Citrus 15AX 1 and Citrus 15AX 2) and 58K SNP Axiom® Citrus Genotyping Arrays for Citrus and close relatives. SNPs represented were chosen from a citrus variant discovery panel consisting of 41 diverse whole-genome re-sequenced accessions of Citrus and close relatives, including eight progenitor citrus species. SNPs chosen mainly target putative genic regions of the genome and are accurately called in both Citrus and its closely related genera while providing good coverage of the nuclear and chloroplast genomes. Reproducibility of the arrays was nearly 100%, with a large majority of the SNPs classified as the most stringent class of markers, "PolyHighResolution" (PHR) polymorphisms. Concordance between SNP calls in sequence data and array data average 98%. Phylogenies generated with array data were similar to those with comparable sequence data and little affected by 3 to 5% genotyping error. Both arrays are publicly available.

A conditional mutation in a wheat (Triticum aestivum L.) gene regulating root morphology

KEY MESSAGE

Characterisation and genetic mapping of a key gene defining root morphology in bread wheat. Root morphology is central to plants for the efficient uptake up of soil water and mineral nutrients. Here we describe a conditional mutant of hexaploid wheat (Triticum aestivum L.) that when grown in soil with high Ca2+ develops a larger rhizosheath accompanied with shorter roots than the wild type. In wheat, rhizosheath size is a reliable surrogate for root hair length and this was verified in the mutant which possessed longer root hairs than the wild type when grown in high Ca2+ soil. We named the mutant Stumpy and showed it to be due to a single semi-dominant mutation. The short root phenotype at high Ca2+ was due to reduced cellular elongation which might also explain the long root hair phenotype. Analysis of root cell walls showed that the polysaccharide composition of Stumpy roots is remodelled when grown at non-permissive (high) Ca2+ concentrations. The mutation mapped to chromosome 7B and sequencing of the 7B chromosomes in both wild type and Stumpy identified a candidate gene underlying the Stumpy mutation. As part of the process to determine whether the candidate gene was causative, we identified wheat lines in a Cadenza TILLING population with large rhizosheaths but accompanied with normal root length. This finding illustrates the potential of manipulating the gene to disconnect root length from root hair length as a means of developing wheat lines with improved efficiency of nutrient and water uptake. The Stumpy mutant will be valuable for understanding the mechanisms that regulate root morphology in wheat.

GenoBaits®WheatplusEE: a targeted capture sequencing panel for quick and accurate identification of wheat-Thinopyrum derivatives

KEY MESSAGE

A total of 90,000 capture probes derived from wheat and Thinopyrum elongatum were integrated into one chip, which served as an economical genotype for explorating Thinopyrumspecies and their derivatives. Thinopyrum species play a crucial role as a source of new genetic variations for enhancing wheat traits, including resistance to both abiotic and biotic factors. Accurate identification of exogenous chromosome(s) or chromosome segments or genes is essential following the introduction of alien genetic material into wheat, but this task remains challenging. This study aimed to develop a high-resolution wheat-Thinopyrum elongatum array, named GenoBaits®WheatplusEE, to trace alien genetic information by genotyping using a target sequencing system. This GenoBaits®WheatplusEE array included 90,000 capture probes derived from two species and integrated into one chip, with 10,000 and 80,000 originating from wheat and Th. elongatum, respectively. The capture probes were strategically positioned in genes and evenly distributed across the genome, facilitating the development of a roadmap for identifying each alien gene. The array was applied to the high-throughput identification of the alien chromosomes or segments in Thinopyrum and distantly related species and their derivatives. Our results demonstrated that the GenoBaits®WheatplusEE array could be used for direct identification of the breakpoint of alien segments, determine copy number of alien chromosomes, and reveal variations in wheat chromosomes by a single round of target sequencing of the sample. Additionally, we could efficiently and cost-effectively genotype, supporting the exploration of subgenome composition, phylogenetic relationships, and polymorphisms in essential genes (e.g., Fhb7 gene) among Thinopyrum species and their derivatives. We hope that GenoBaits®WheatplusEE will become a widely adopted tool for exporting wild germplasm for wheat improvement in the future.

Heat Stress-Tolerant Quantitative Trait Loci Identified Using Backcrossed Recombinant Inbred Lines Derived from Intra-Specifically Diverse Aegilops tauschii Accessions

In the face of climate change, bringing more useful alleles and genes from wild relatives of wheat is crucial to develop climate-resilient varieties. We used two populations of backcrossed recombinant inbred lines (BIL1 and BIL2), developed by crossing and backcrossing two intra-specifically diverse Aegilops tauschii accessions from lineage 1 and lineage 2, respectively, with the common wheat cultivar 'Norin 61'. This study aimed to identify quantitative trait loci (QTLs) associated with heat stress (HS) tolerance. The two BILs were evaluated under heat stress environments in Sudan for phenology, plant height (PH), grain yield (GY), biomass (BIO), harvest index (HI), and thousand-kernel weight (TKW). Grain yield was significantly correlated with BIO and TKW under HS; therefore, the stress tolerance index (STI) was calculated for these traits as well as for GY. A total of 16 heat-tolerant lines were identified based on GY and STI-GY. The QTL analysis performed using inclusive composite interval mapping identified a total of 40 QTLs in BIL1 and 153 QTLs in BIL2 across all environments. We detected 39 QTLs associated with GY-STI, BIO-STI, and TKW-STI in both populations (14 in BIL1 and 25 in BIL2). The QTLs associated with STI were detected on chromosomes 1A, 3A, 5A, 2B, 4B, and all the D-subgenomes. We found that QTLs were detected only under HS for GY on chromosome 5A, TKW on 3B and 5B, PH on 3B and 4B, and grain filling duration on 2B. The higher number of QTLs identified in BIL2 for heat stress tolerance suggests the importance of assessing the effects of intraspecific variation of Ae. tauschii in wheat breeding as it could modulate the heat stress responses/adaptation. Our study provides useful genetic resources for uncovering heat-tolerant QTLs for wheat improvement for heat stress environments.

Genetic Analysis of Adaptive Traits in Spring Wheat in Northeast China

The dissection of the genetic architecture and the detection of the loci for adaptive traits are important for marker-assisted selection (MAS) for breeding. A spring wheat diversity panel with 251 cultivars, mainly from China, was obtained to conduct a genome-wide association study (GWAS) to detect the new loci, including the heading date (HD), maturating date (MD), plant height (PH), and lodging resistance (LR). In total, 41 loci existing in all 21 chromosomes, except for 4A and 6B, were identified, and each explained 4.3-18.9% of the phenotypic variations existing in two or more environments. Of these, 13 loci are overlapped with the known genes or quantitative trait loci (QTLs), whereas the other 28 are likely to be novel. The 1A locus (296.9-297.7 Mb) is a multi-effect locus for LR and PH, whereas the locus on chromosome 6D (464.5-471.0 Mb) affects both the HD and MD. Furthermore, four candidate genes for adaptive traits were identified, involved in cell division, signal transduction, and plant development. Additionally, two competitive, allele-specific PCR (KASP) markers, Kasp_2D_PH for PH and Kasp_6D_HD for HD, were developed and validated in another 162 spring wheat accessions. Our study uncovered the genetic basis of adaptive traits and provided the associated SNPs and varieties with more favorable alleles for wheat MAS breeding.

Identification of Sr67, a new gene for stem rust resistance in KU168-2 located close to the Sr13 locus in wheat

KEY MESSAGE

Sr67 is a new stem rust resistance gene that represents a new resource for breeding stem rust resistant wheat cultivars Re-appearance of stem rust disease, caused by the fungal pathogen Puccinia graminis f. sp. tritici (Pgt), in different parts of Europe emphasized the need to develop wheat varieties with effective resistance to local Pgt populations and exotic threats. A Kyoto University wheat (Triticum aestivum L.) accession KU168-2 was reported to carry good resistance to leaf and stem rust. To identify the genomic region associated with the KU168-2 stem rust resistance, a genetic study was conducted using a doubled haploid (DH) population from the cross RL6071 × KU168-2. The DH population was phenotyped with three Pgt races (TTKSK, TPMKC, and QTHSF) and genotyped using the Illumina 90 K wheat SNP array. Linkage mapping showed the resistance to all three Pgt races was conferred by a single stem rust resistance (Sr) gene on chromosome arm 6AL, associated with Sr13. Presently, four Sr13 resistance alleles have been reported. Sr13 allele-specific KASP and STARP markers, and sequencing markers all showed null alleles in KU168-2. KU168-2 showed a unique combination of seedling infection types for five Pgt races (TTKSK, QTHSF, RCRSF, TMRTF, and TPMKC) compared to Sr13 alleles. The phenotypic uniqueness of the stem rust resistance gene in KU168-2 and null alleles for Sr13 allele-specific markers showed the resistance was conferred by a new gene, designated Sr67. Since Sr13 is less effective in hexaploid background, Sr67 will be a good source of stem rust resistance in bread wheat breeding programs.

Genome-Wide Association Analysis Unravels New Quantitative Trait Loci (QTLs) for Eight Lodging Resistance Constituent Traits in Rice (Oryza sativa L.)

Lodging poses a significant challenge to rice yield, prompting the need to identify elite alleles for lodging resistance traits to improve cultivated rice varieties. In this study, a natural population of 518 rice accessions was examined to identify elite alleles associated with plant height (PH), stem diameter (SD), stem anti-thrust (AT/S), and various internode lengths (first (FirINL), second (SecINL), third (ThirINL), fourth (ForINL), and fifth (FifINL) internode lengths). A total of 262 SSR markers linked to these traits were uncovered through association mapping in two environmental conditions. Phenotypic evaluations revealed striking differences among cultivars, and genetic diversity assessments showed polymorphisms across the accessions. Favorable alleles were identified for PH, SD, AT/S, and one to five internode lengths, with specific alleles displaying considerable effects. Noteworthy alleles include RM6811-160 bp on chromosome 6 (which reduces PH) and RM161-145 bp on chromosome 5 (which increases SD). The study identified a total of 42 novel QTLs. Specifically, seven QTLs were identified for PH, four for SD, five for AT/S, five for FirINL, six for SecINL, five for ThirINL, six for ForINL, and four for FifINL. QTLs qAT/S-2, qPH2.1, qForINL2.1, and qFifINL exhibited the most significant phenotypic variance (PVE) of 3.99% for the stem lodging trait. AT/S, PH, ForINL, and FifINL had additive effects of 5.31 kPa, 5.42 cm, 4.27 cm, and 4.27 cm, respectively, offering insights into eight distinct cross-combinations for enhancing each trait. This research suggests the potential for crossbreeding superior parents based on stacked alleles, promising improved rice cultivars with enhanced lodging resistance to meet market demands.

Genome-wide association study of common resistance to rust species in tetraploid wheat

Rusts of the genus Puccinia are wheat pathogens. Stem (black; Sr), leaf (brown; Lr), and stripe (yellow; Yr) rust, caused by Puccinia graminis f. sp. tritici (Pgt), Puccinia triticina (Pt), and Puccinia striiformis f. sp. tritici (Pst), can occur singularly or in mixed infections and pose a threat to wheat production globally in terms of the wide dispersal of their urediniospores. The development of durable resistant cultivars is the most sustainable method for controlling them. Many resistance genes have been identified, characterized, genetically mapped, and cloned; several quantitative trait loci (QTLs) for resistance have also been described. However, few studies have considered resistance to all three rust pathogens in a given germplasm. A genome-wide association study (GWAS) was carried out to identify loci associated with resistance to the three rusts in a collection of 230 inbred lines of tetraploid wheat (128 of which were Triticum turgidum ssp. durum) genotyped with SNPs. The wheat panel was phenotyped in the field and subjected to growth chamber experiments across different countries (USA, Mexico, Morocco, Italy, and Spain); then, a mixed linear model (MLM) GWAS was performed. In total, 9, 34, and 5 QTLs were identified in the A and B genomes for resistance to Pgt, Pt, and Pst, respectively, at both the seedling and adult plant stages. Only one QTL on chromosome 4A was found to be effective against all three rusts at the seedling stage. Six QTLs conferring resistance to two rust species at the adult plant stage were mapped: three on chromosome 1B and one each on 5B, 7A, and 7B. Fifteen QTLs conferring seedling resistance to two rusts were mapped: five on chromosome 2B, three on 7B, two each on 5B and 6A, and one each on 1B, 2A, and 7A. Most of the QTLs identified were specific for a single rust species or race of a species. Candidate genes were identified within the confidence intervals of a QTL conferring resistance against at least two rust species by using the annotations of the durum (cv. 'Svevo') and wild emmer wheat ('Zavitan') reference genomes. The 22 identified loci conferring resistance to two or three rust species may be useful for breeding new and potentially durable resistant wheat cultivars.

Genome-Wide Association Study of Preharvest Sprouting in Wheat

Genome-wide association study (GWAS) is widely used to characterize genes or quantitative trait loci (QTLs) associated with preharvest sprouting and seed dormancy. GWAS can identify both previously discovered and novel QTLs across diverse genetic panels. The high-throughput SNP arrays or next-generation sequencing technologies have facilitated the identification of numerous genetic markers, thereby significantly enhancing the resolution of GWAS. Although various methods have been developed, the fundamental principles underlying these techniques remain constant. Here, we provide a basic technological flow to perform seed dormancy assay, followed by GWAS using population structure control, and compared it with previous identified QTLs and genes.

Sr65: a widely effective gene for stem rust resistance in wheat

KEY MESSAGE

Sr65 in chromosome 1A of Indian wheat landrace Hango-2 is a potentially useful all-stage resistance gene that currently protects wheat from stem rust in Australia, India, Africa and Europe. Stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), threatened global wheat production with the appearance of widely virulent races that included TTKSK and TTRTF. Indian landrace Hango-2 showed resistance to Pgt races in India and Australia. Screening of a Hango-2/Avocet 'S' (AvS) recombinant inbred line population identified two stem rust resistance genes, a novel gene (temporarily named as SrH2) from Hango-2 and Sr26 from AvS. A mapping population segregating for SrH2 alone was developed from two recombinant lines. SrH2 was mapped on the short arm of chromosome 1A, where it was flanked by KASP markers KASP_7944 (proximal) and KASP_12147 (distal). SrH2 was delimited to an interval of 1.8-2.3 Mb on chromosome arm 1AS. The failure to detect candidate genes through MutRenSeq and comparative genomic analysis with the pan-genome dataset indicated the necessity to generate a Hango-2 specific assembly for detecting the gene sequence linked with SrH2 resistance. MutRenSeq however enabled identification of SrH2-linked KASP marker sunCS_265. Markers KASP_12147 and sunCS_265 showed 92% and 85% polymorphism among an Australian cereal cultivar diversity panel and can be used for marker-assisted selection of SrH2 in breeding programs. The effectiveness of SrH2 against Pgt races from Europe, Africa, India, and Australia makes it a valuable resource for breeding stem rust-resistant wheat cultivars. Since no wheat-derived gene was previously located in chromosome arm 1AS, SrH2 represents a new locus and named as SR65.

Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic-assisted selection

Improved selection of wheat varieties with high end-use quality contributes to sustainable food systems by ensuring productive crops are suitable for human consumption end-uses. Here, we investigated the genetic control and genomic prediction of milling and baking quality traits in a panel of 379 historic and elite, high-quality UK bread wheat (Triticum eastivum L.) varieties and breeding lines. Analysis of the panel showed that genetic diversity has not declined over recent decades of selective breeding while phenotypic analysis found a clear trend of increased loaf baking quality of modern milling wheats despite declining grain protein content. Genome-wide association analysis identified 24 quantitative trait loci (QTL) across all quality traits, many of which had pleiotropic effects. Changes in the frequency of positive alleles of QTL over recent decades reflected trends in trait variation and reveal where progress has historically been made for improved baking quality traits. It also demonstrates opportunities for marker-assisted selection for traits such as Hagberg falling number and specific weight that do not appear to have been improved by recent decades of phenotypic selection. We demonstrate that applying genomic prediction in a commercial wheat breeding program for expensive late-stage loaf baking quality traits outperforms phenotypic selection based on early-stage predictive quality traits. Finally, trait-assisted genomic prediction combining both phenotypic and genomic selection enabled slightly higher prediction accuracy, but genomic prediction alone was the most cost-effective selection strategy considering genotyping and phenotyping costs per sample.

Mining Aegilops tauschii genetic diversity in the background of bread wheat revealed a novel QTL for seed dormancy

Due to the low genetic diversity in the current wheat germplasm, gene mining from wild relatives is essential to develop new wheat cultivars that are more resilient to the changing climate. Aegilops tauschii, the D-genome donor of bread wheat, is a great gene source for wheat breeding; however, identifying suitable genes from Ae. tauschii is challenging due to the different morphology and the wide intra-specific variation within the species. In this study, we developed a platform for the systematic evaluation of Ae. tauschii traits in the background of the hexaploid wheat cultivar 'Norin 61' and thus for the identification of QTLs and genes. To validate our platform, we analyzed the seed dormancy trait that confers resistance to preharvest sprouting. We used a multiple synthetic derivative (MSD) population containing a genetic diversity of 43 Ae. tauschii accessions representing the full range of the species. Our results showed that only nine accessions in the population provided seed dormancy, and KU-2039 from Afghanistan had the highest level of seed dormancy. Therefore, 166 backcross inbred lines (BILs) were developed by crossing the synthetic wheat derived from KU-2039 with 'Norin 61' as the recurrent parent. The QTL mapping revealed one novel QTL, Qsd.alrc.5D, associated with dormancy explaining 41.7% of the phenotypic variation and other five unstable QTLs, two of which have already been reported. The Qsd.alrc.5D, identified for the first time within the natural variation of wheat, would be a valuable contribution to breeding after appropriate validation. The proposed platform that used the MSD population derived from the diverse Ae. tauschii gene pool and recombinant inbred lines proved to be a valuable platform for mining new and important QTLs or alleles, such as the novel seed dormancy QTL identified here. Likewise, such a platform harboring genetic diversity from wheat wild relatives could be a useful source for mining agronomically important traits, especially in the era of climate change and the narrow genetic diversity within the current wheat germplasm.

Combined linkage analysis and association mapping identifies genomic regions associated with yield-related and drought-tolerance traits in wheat (Triticum aestivum L.)

KEY MESSAGE

Combined linkage analysis and association mapping identified genomic regions associated with yield and drought tolerance, providing information to assist breeding for high yield and drought tolerance in wheat. Wheat (Triticum aestivum L.) is one of the most widely grown food crops and provides adequate amounts of protein to support human health. Drought stress is the most important abiotic stress constraining yield during the flowering and grain development periods. Precise targeting of genomic regions underlying yield- and drought tolerance-responsive traits would assist in breeding programs. In this study, two water treatments (well-watered, WW, and rain-fed water stress, WS) were applied, and five yield-related agronomic traits (plant height, PH; spike length, SL; spikelet number per spike, SNPS; kernel number per spike, KNPS; thousand kernel weight, TKW) and drought response values (DRVs) were used to characterize the drought sensitivity of each accession. Association mapping was performed on an association panel of 304 accessions, and linkage analysis was applied to a doubled haploid (DH) population of 152 lines. Eleven co-localized genomic regions associated with yield traits and DRV were identified in both populations. Many previously cloned key genes were located in these regions. In particular, a TKW-associated region on chromosome 2D was identified using both association mapping and linkage analysis and a key candidate gene, TraesCS2D02G142500, was detected based on gene annotation and differences in expression levels. Exonic SNPs were analyzed by sequencing the full length of TraesCS2D02G142500 in the association panel, and a rare haplotype, Hap-2, which reduced TKW to a lesser extent than Hap-1 under drought stress, and the Hap-2 varieties presented drought-insensitive. Altogether, this study provides fundamental insights into molecular targets for high yield and drought tolerance in wheat.

Meta-QTL analysis in wheat: progress, challenges and opportunities

Wheat, an important cereal crop globally, faces major challenges due to increasing global population and changing climates. The production and productivity are challenged by several biotic and abiotic stresses. There is also a pressing demand to enhance grain yield and quality/nutrition to ensure global food and nutritional security. To address these multifaceted concerns, researchers have conducted numerous meta-QTL (MQTL) studies in wheat, resulting in the identification of candidate genes that govern these complex quantitative traits. MQTL analysis has successfully unraveled the complex genetic architecture of polygenic quantitative traits in wheat. Candidate genes associated with stress adaptation have been pinpointed for abiotic and biotic traits, facilitating targeted breeding efforts to enhance stress tolerance. Furthermore, high-confidence candidate genes (CGs) and flanking markers to MQTLs will help in marker-assisted breeding programs aimed at enhancing stress tolerance, yield, quality and nutrition. Functional analysis of these CGs can enhance our understanding of intricate trait-related genetics. The discovery of orthologous MQTLs shared between wheat and other crops sheds light on common evolutionary pathways governing these traits. Breeders can leverage the most promising MQTLs and CGs associated with multiple traits to develop superior next-generation wheat cultivars with improved trait performance. This review provides a comprehensive overview of MQTL analysis in wheat, highlighting progress, challenges, validation methods and future opportunities in wheat genetics and breeding, contributing to global food security and sustainable agriculture.

Genome-Wide Association Study Reveals Novel Powdery Mildew Resistance Loci in Bread Wheat

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.

Unraveling the genetic basis of grain number-related traits in a wheat-Agropyron cristatum introgressed line through high-resolution linkage mapping

BACKGROUND

Grain number per spike (GNS) is a pivotal determinant of grain yield in wheat. Pubing 3228 (PB3228), a wheat-Agropyron cristatum germplasm, exhibits a notably higher GNS.

RESULTS

In this study, we developed a recombinant inbred line (RIL) population derived from PB3228/Gao8901 (PG-RIL) and constructed a high-density genetic map comprising 101,136 loci, spanning 4357.3 cM using the Wheat 660 K SNP array. The genetic map demonstrated high collinearity with the wheat assembly IWGSC RefSeq v1.0. Traits related to grain number and spikelet number per spike were evaluated in seven environments for quantitative trait locus (QTL) analysis. Five environmentally stable QTLs were detected in at least three environments. Among these, two major QTLs, QGns-4A.2 and QGns-1A.1, associated with GNS, exhibited positive alleles contributed by PB3228. Further, the conditional QTL analysis revealed a predominant contribution of PB3228 to the GNS QTLs, with both grain number per spikelet (GNSL) and spikelet number per spike (SNS) contributing to the overall GNS trait. Four kompetitive allele-specific PCR (KASP) markers that linked to QGns-4A.2 and QGns-1A.1 were developed and found to be effective in verifying the QTL effect within a diversity panel. Compared to previous studies, QGns-4A.2 exhibited stability across different trials, while QGns-1A.1 represents a novel QTL. The results from unconditional and conditional QTL analyses are valuable for dissecting the genetic contribution of the component traits to GNS at the individual QTL level and for understanding the genetic basis of the superior grain number character in PB3228. The KASP markers can be utilized in marker-assisted selection for enhancing GNS.

CONCLUSIONS

Five environmentally stable QTLs related to grain number and spikelet number per spike were identified. PB3228 contributed to the majority of the QTLs associated with GNS.

Genetic diversity, linkage disequilibrium, and population structure of tetraploid wheat landraces originating from Europe and Asia

BACKGROUND

Durum wheat is one of the most important crops, especially in the Mediterranean region. Insight into the genetic diversity of germplasm can improve the breeding program management in various traits. This study was done using single nucleotide polymorphisms (SNP) markers to characterize the genetic distinctiveness and differentiation of tetraploid wheat landraces collected from nine European and Asian countries. A sum of 23,334 polymorphic SNPs was detected in 126 tetraploid wheat landraces in relation to the reference genome.

RESULTS

The number of identified SNPs was 11,613 and 11,721 in A and B genomes, respectively. The highest and lowest diversity was on 6B and 6 A chromosomes, respectively. Structure analysis classified the landraces into two distinct subpopulations (K = 2). Evaluating the principal coordinate analysis (PCoA) and weighted pair-group method using arithmetic averages (WPGMA) clustering results demonstrated that landraces (99.2%) are categorized into one of the two chief subpopulations. Therefore, the grouping pattern did not clearly show the presence of a clear pattern of relationships between genetic diversity and their geographical derivation. Part of this result could be due to the historical exchange between different germplasms. Although the result did not separate landraces based on their region of origin, the landraces collected from Iran were classified into the same group and cluster. Analysis of molecular variance (AMOVA) also confirmed the results of population structure. Finally, Durum wheat landraces in some countries, including Turkey, Russia, Ukraine, and Afghanistan, were highly diverse, while others, including Iran and China, were low-diversity.

CONCLUSION

The recent study concluded that the 126 tetraploid wheat genotypes and their GBS-SNP markers are very appropriate for quantitative trait loci (QTLs) mapping and genome-wide association studies (GWAS). The core collection comprises two distinct subpopulations. Subpopulation II genotypes are the most diverse genotypes, and if they possess desired traits, they may be used in future breeding programs. The degree of diversity in the landraces of countries can provide the ground for the improvement of new cultivars with international cooperation. linkage disequilibrium (LD) hotspot distribution across the genome was investigated, which provides useful information about the genomic regions that contain intriguing genes.

Mapping of QTLs and meta-QTLs for Heterodera avenae Woll. resistance in common wheat (Triticum aestivum L.)

BACKGROUND

In hexaploid wheat, quantitative trait loci (QTL) and meta-QTL (MQTL) analyses were conducted to identify genomic regions controlling resistance to cereal cyst nematode (CCN), Heterodera avenae. A mapping population comprising 149 RILs derived from the cross HUW 468 × C 306 was used for composite interval mapping (CIM) and inclusive composite interval mapping (ICIM).

RESULTS

Eight main effect QTLs on three chromosomes (1B, 2A and 3A) were identified using two repeat experiments. One of these QTLs was co-localized with a previously reported wheat gene Cre5 for resistance to CCN. Seven important digenic epistatic interactions (PVE = 5% or more) were also identified, each involving one main effect QTL and another novel E-QTL. Using QTLs earlier reported in literature, two meta-QTLs were also identified, which were also used for identification of 57 candidate genes (CGs). Out of these, 29 CGs have high expression in roots and encoded the following proteins having a role in resistance to plant parasitic nematodes (PPNs): (i) NB-ARC,P-loop containing NTP hydrolase, (ii) Protein Kinase, (iii) serine-threonine/tyrosine-PK, (iv) protein with leucine-rich repeat, (v) virus X resistance protein-like, (vi) zinc finger protein, (vii) RING/FYVE/PHD-type, (viii) glycosyl transferase, family 8 (GT8), (ix) rubisco protein with small subunit domain, (x) protein with SANT/Myb domain and (xi) a protein with a homeobox.

CONCLUSION

Identification and selection of resistance loci with additive and epistatic effect along with two MQTL and associated CGs, identified in the present study may prove useful for understanding the molecular basis of resistance against H. avenae in wheat and for marker-assisted selection (MAS) for breeding CCN resistant wheat cultivars.

Genetic control and prospects of predictive breeding for European winter wheat's Zeleny sedimentation values and Hagberg-Perten falling number

KEY MESSAGE

Sedimentation values and falling number in the last decades have helped maintain high baking quality despite rigorous selection for grain yield in wheat. Allelic combinations of major loci sustained the bread-making quality while improving grain yield. Glu-D1, Pinb-D1, and non-gluten proteins are associated with sedimentation values and falling number in European wheat. Zeleny sedimentation values (ZSV) and Hagberg-Perten falling number (HFN) are among the most important parameters that help determine the baking quality classes of wheat and, thus, influence the monetary benefits for growers. We used a published data set of 372 European wheat varieties evaluated in replicated field trials in multiple environments. ZSV and HFN traits hold a wide and significant genotypic variation and high broad-sense heritability. The genetic correlations revealed positive and significant associations of ZSV and HFN with each other, grain protein content (GPC) and grain hardness; however, they were all significantly negatively correlated with grain yield. Besides, GPC appeared to be the major predictor for ZSV and HFN. Our genome-wide association analyses based on high-quality SSR, SNP, and candidate gene markers revealed a strong quantitative genetic nature of ZSV and HFN by explaining their total genotypic variance as 41.49% and 38.06%, respectively. The association of known Glutenin (Glu-1) and Puroindoline (Pin-1) with ZSV provided positive analytic proof of our studies. We report novel candidate loci associated with globulins and albumins-the non-gluten monomeric proteins in wheat. In addition, predictive breeding analyses for ZSV and HFN suggest using genomic selection in the early stages of breeding programs with an average prediction accuracy of 81 and 59%, respectively.

Wheat Grains as a Sustainable Source of Protein for Health

Protein deficiency is recognized among the major global health issues with an underestimation of its importance. Genetic biofortification is a cost-effective and sustainable strategy to overcome global protein malnutrition. This study was designed to focus on protein-dense grains of wheat (Triticum aestivum L.) and identify the genes governing grain protein content (GPC) that improve end-use quality and in turn human health. Genome-wide association was applied using the 90k iSELECT Infinium and 35k Affymetrix arrays with GPC quantified by using a proteomic-based technique in 369 wheat genotypes over three field-year trials. The results showed significant natural variation among bread wheat genotypes that led to detecting 54 significant quantitative trait nucleotides (QTNs) surpassing the false discovery rate (FDR) threshold. These QTNs showed contrasting effects on GPC ranging from -0.50 to +0.54% that can be used for protein content improvement. Further bioinformatics analyses reported that these QTNs are genomically linked with 35 candidate genes showing high expression during grain development. The putative candidate genes have functions in the binding, remobilization, or transport of protein. For instance, the promising QTN AX-94727470 on chromosome 6B increases GPC by +0.47% and is physically located inside the gene TraesCS6B02G384500 annotated as Trehalose 6-phosphate phosphatase (T6P), which can be employed to improve grain protein quality. Our findings are valuable for the enhancement of protein content and end-use quality in one of the major daily food resources that ultimately improve human nutrition.

Introgression of the bread wheat D genome encoded Lr34/Yr18/Sr57/Pm38/Ltn1 adult plant resistance gene into Triticum turgidum (durum wheat)

KEY MESSAGE

Lack of function of a D-genome adult plant resistance gene upon introgression into durum wheat. The wheat Lr34/Yr18/Sr57/Pm38/Ltn1 adult plant resistance gene (Lr34), located on chromosome arm 7DS, provides broad spectrum, partial, adult plant resistance to leaf rust, stripe rust, stem rust and powdery mildew. It has been used extensively in hexaploid bread wheat (AABBDD) and conferred durable resistance for many decades. These same diseases also occur on cultivated tetraploid durum wheat and emmer wheat but transfer of D genome sequences to those subspecies is restricted due to very limited intergenomic recombination. Herein we have introgressed the Lr34 gene into chromosome 7A of durum wheat. Durum chromosome substitution line Langdon 7D(7A) was crossed to Cappelli ph1c, a mutant derivative of durum cultivar Cappelli homozygous for a deletion of the chromosome pairing locus Ph1. Screening of BC1F2 plants and their progeny by KASP and PCR markers, 90 K SNP genotyping and cytology identified 7A chromosomes containing small chromosome 7D fragments encoding Lr34. However, in contrast to previous transgenesis experiments in durum wheat, resistance to wheat stripe rust was not observed in either Cappelli/Langdon 7D(7A) or Bansi durum plants carrying this Lr34 encoding segment due to low levels of Lr34 gene expression.

KEY MESSAGE

Genome-wide linkage mapping of root system architecture-related traits in common wheat (Triticum aestivum L.)

Identifying loci for root system architecture (RSA) traits and developing available markers are crucial for wheat breeding. In this study, RSA-related traits, including total root length (TRL), total root area (TRA), and number of root tips (NRT), were evaluated in the Doumai/Shi4185 recombinant inbred line (RIL) population under hydroponics. In addition, both the RILs and parents were genotyped using the wheat 90K single-nucleotide polymorphism (SNP) array. In total, two quantitative trait loci (QTLs) each for TRL (QTRL.caas-4A.1 and QTRL.caas-4A.2), TRA (QTRA.caas-4A and QTRA.caas-4D), and NRT (QNRT.caas-5B and QNRT.caas-5D) were identified and each explaining 5.94%-9.47%, 6.85%-7.10%, and 5.91%-10.16% phenotypic variances, respectively. Among these, QTRL.caas-4A.1 and QTRA.caas-4A overlapped with previous reports, while QTRL.caas-4A.2, QTRA.caas-4D, QNRT.caas-5B, and QNRT.caas-5D were novel. The favorable alleles of QTRL.caas-4A.1, QTRA.caas-4A, and QTRA.caas-5B were contributed by Doumai, whereas the favorable alleles of QTRL.caas-4A.2, QTRA.caas-4D, and QTRA.caas-5D originated from Shi 4185. Additionally, two competitive allele-specific PCR (KASP) markers, Kasp_4A_RL (QTRA.caas-4A) and Kasp_5D_RT (QNRT.caas-5D), were developed and validated in 165 wheat accessions. This study provides new loci and available KASP markers, accelerating wheat breeding for higher yields.

Multi-locus genome-wide association studies reveal the genetic architecture of Fusarium head blight resistance in durum wheat

Durum wheat is more susceptible to Fusarium head blight (FHB) than other types or classes of wheat. The disease is one of the most devastating in wheat; it reduces yield and end-use quality and contaminates the grain with fungal mycotoxins such as deoxynivalenol (DON). A panel of 265 Canadian and European durum wheat cultivars, as well as breeding and experimental lines, were tested in artificially inoculated field environments (2019-2022, inclusive) and two greenhouse trials (2019 and 2020). The trials were assessed for FHB severity and incidence, visual rating index, Fusarium-damaged kernels, DON accumulation, anthesis or heading date, maturity date, and plant height. In addition, yellow pigment and protein content were analyzed for the 2020 field season. To capture loci underlying FHB resistance and related traits, GWAS was performed using single-locus and several multi-locus models, employing 13,504 SNPs. Thirty-one QTL significantly associated with one or more FHB-related traits were identified, of which nine were consistent across environments and associated with multiple FHB-related traits. Although many of the QTL were identified in regions previously reported to affect FHB, the QTL QFhb-3B.2, associated with FHB severity, incidence, and DON accumulation, appears to be novel. We developed KASP markers for six FHB-associated QTL that were consistently detected across multiple environments and validated them on the Global Durum Panel (GDP). Analysis of allelic diversity and the frequencies of these revealed that the lines in the GDP harbor between zero and six resistance alleles. This study provides a comprehensive assessment of the genetic basis of FHB resistance and DON accumulation in durum wheat. Accessions with multiple favorable alleles were identified and will be useful genetic resources to improve FHB resistance in durum breeding programs through marker-assisted recurrent selection and gene stacking.

ShinyCore: An R/Shiny program for establishing core collection based on single nucleotide polymorphism data

BACKGROUND

Managing and investigating all available genetic resources are challenging. As an alternative, breeders and researchers use core collection-a representative subset of the entire collection. A good core is characterized by high genetic diversity and low repetitiveness. Among the several available software, GenoCore uses a coverage criterion that does not require computationally expensive distance-based metrics.

RESULTS

ShinyCore is a new method to select a core collection through two phases. The first phase uses the coverage criterion to quickly attain a fixed coverage, and the second phase uses a newly devised score (referred to as the rarity score) to further enhance diversity. It can attain a fixed coverage faster than a currently available algorithm devised for the coverage criterion, so it will benefit users who have big data. ShinyCore attains the minimum coverage specified by a user faster than GenoCore, and it then seeks to add entries with the rarest allele for each marker. Therefore, measures of genetic diversity and distance can be improved.

CONCLUSION

Although GenoCore is a fast algorithm, its implementation is difficult for those unfamiliar with R, ShinyCore can be easily implemented in Shiny with RStudio and an interactive web applet is available for those who are not familiar with programming languages.