A GWAS to identify the cereal cyst nematode (Heterodera filipjevi) resistance loci in diverse wheat prebreeding lines

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 Analyses of Seed Longevity in Capsicum annuum L. in Cold Storage Conditions

Seed longevity is the most important trait in the genebank management system. No seed can remain infinitely viable. There are 1241 accessions of Capsicum annuum L. available at the German Federal ex situ genebank at IPK Gatersleben. C. annuum (Capsicum) is the most economically important species of the genus Capsicum. So far, there is no report that has addressed the genetic basis of seed longevity in Capsicum. Here, we convened a total of 1152 Capsicum accessions that were deposited in Gatersleben over forty years (from 1976 to 2017) and assessed their longevity by analyzing the standard germination percentage after 5-40 years of storage at -15/-18 °C. These data were used to determine the genetic causes of seed longevity, along with 23,462 single nucleotide polymorphism (SNP) markers covering all of the 12 Capsicum chromosomes. Using the association-mapping approach, we identified a total of 224 marker trait associations (MTAs) (34, 25, 31, 35, 39, 7, 21 and 32 MTAs after 5-, 10-, 15-, 20-, 25-, 30-, 35- and 40-year storage intervals) on all the Capsicum chromosomes. Several candidate genes were identified using the blast analysis of SNPs, and these candidate genes are discussed.

GWAS scans of cereal cyst nematode (Heterodera avenae) resistance in Indian wheat germplasm

Significant yield losses in major cereal-growing regions around the world have been linked to cereal cyst nematodes (Heterodera spp.). Identifying and deploying natural sources of resistance is of utmost importance due to increasing concerns associated with chemical methods over the years. We screened 141 diverse wheat genotypes collected from pan-Indian wheat cultivation states for nematode resistance over two years, alongside two resistant (Raj MR1, W7984 (M6)) and two susceptible (WH147, Opata M85) checks. We performed genome-wide association analysis using four single-locus models (GLM, MLM, CMLM, and ECMLM) and three multi-locus models (Blink, FarmCPU, and MLMM). Single locus models identified nine significant MTAs (-log10 (P) > 3.0) on chromosomes 2A, 3B, and 4B whereas, multi-locus models identified 11 significant MTAs on chromosomes 1B, 2A, 3B, 3D and 4B. Single and multi-locus models identified nine common significant MTAs. Candidate gene analysis identified 33 genes like F-box-like domain superfamily, Cytochrome P450 superfamily, Leucine-rich repeat, cysteine-containing subtype Zinc finger RING/FYVE/PHD-type, etc., having a putative role in disease resistance. Such genetic resources can help to reduce the impact of this disease on wheat production. Additionally, these results can be used to design new strategies for controlling the spread of H. avenae, such as the development of resistant varieties or the use of resistant cultivars. Finally, the obtained results can also be used to identify new sources of resistance to this pathogen and develop novel control methods.

Preliminary Dissection of Grain Yield and Related Traits at Differential Nitrogen Levels in Diverse Pre-Breeding Wheat Germplasm Through Association Mapping

Development of nutrient efficient cultivars depends on effective identification and utilization of genetic variation. We characterized a set of 276 pre-breeding lines (PBLs) for several traits at different levels of nitrogen application. These PBLs originate from synthetic wheats and landraces. We witnessed significant variation in various traits among PBLs to different nitrogen doses. There was ~ 4-18% variation range in different agronomic traits in response to nitrogen application, with the highest variation for the biological yield (BY) and the harvest index. Among various agronomic traits measured, plant height, tiller number, and BY showed a positive correlation with nitrogen applications. GWAS analysis detected 182 marker-trait associations (MTAs) (at p-value < 0.001), out of which 8 MTAs on chromosomes 5D, 4A, 6A, 1B, and 5B explained more than 10% phenotypic variance. Out of all, 40 MTAs observed for differential nitrogen application response were contributed by the synthetic derivatives. Moreover, 20 PBLs exhibited significantly higher grain yield than checks and can be selected as potential donors for improved plant nitrogen use efficiency (pNUE).

A GBS-Based GWAS Analysis of Leaf and Stripe Rust Resistance in Diverse Pre-Breeding Germplasm of Bread Wheat (Triticum aestivum L.)

Yellow (YR) and leaf (LR) rusts caused by Puccinia striiformis f. sp. tritici (Pst) and Puccinia triticina, respectively, are of utmost importance to wheat producers because of their qualitative and quantitative effect on yield. The search for new loci resistant to both rusts is an ongoing challenge faced by plant breeders and pathologists. Our investigation was conducted on a subset of 168 pre-breeding lines (PBLs) to identify the resistant germplasm against the prevalent local races of LR and YR under field conditions followed by its genetic mapping. Our analysis revealed a range of phenotypic responses towards both rusts. We identified 28 wheat lines with immune response and 85 resistant wheat genotypes against LR, whereas there were only eight immune and 52 resistant genotypes against YR. A GWAS (genome-wide association study) identified 190 marker-trait associations (MTAs), where 120 were specific to LR and 70 were specific to YR. These MTAs were confined to 86 quantitative trait loci (QTLs), where 50 QTLs carried MTAs associated with only LR, 29 QTLs carried MTAs associated with YR, and seven QTLs carried MTAs associated with both LR and YR. Possible candidate genes at the site of these QTLs are discussed. Overall, 70 PBLs carried all seven LR/YR QTLs. Furthermore, there were five PBLs with less than five scores for both LR and YR carrying positive alleles of all seven YR/LR QTLs, which are fit to be included in a breeding program for rust resistance induction.

QTL mapping for resistance against cereal cyst nematode (Heterodera avenae Woll.) in wheat (Triticum aestivum L.)

The resistance to cereal cyst nematode (Heterodera avenae Woll.) in wheat (Triticum aestivum L.) was studied using 114 doubled haploid lines from a novel ITMI mapping population. These lines were screened for nematode infestation in a controlled environment for two years. QTL-mapping analyses were performed across two years (Y1 and Y2) as well as combining two years (CY) data. On the 114 lines that were screened, a total of 2,736 data points (genotype, batch or years, and replication combinations) were acquired. For QTL analysis, 12,093 markers (11,678 SNPs and 415 SSRs markers) were used, after filtering the genotypic data, for the QTL mapping. Composite interval mapping, using Haley-Knott regression (hk) method in R/QTL, was used for QTL analysis. In total, 19 QTLs were detected out of which 13 were novel and six were found to be colocalized or nearby to previously reported Cre genes, QTLs or MTAs for H. avenae or H. filipjevi. Nine QTLs were detected across all three groups (Y1, Y2 and CY) including a significant QTL "QCcn.ha-2D" on chromosome 2D that explains 23% of the variance. This QTL colocalized with a previously identified Cre3 locus. Novel QTL, QCcn.ha-2A, detected in the present study could be the possible unreported homeoloci to QCcn.ha-2D, QCcn.ha-2B.1 and QCcn.ha-2B.2. Six significant digenic epistatic interactions were also observed. In addition, 26 candidate genes were also identified including genes known for their involvement in PPNs (plant parasitic nematodes) resistance in different plant species. In-silico expression of putative candidate genes showed differential expression in roots during specific developmental stages. Results obtained in the present study are useful for wheat breeding to generate resistant genetic resources against H. avenae.

Genetic Insight Into the Insect Resistance in Bread Wheat Exploiting the Untapped Natural Diversity

Climate change is an undeniable threat to sustainable wheat production in the future as an increased temperature will significantly increase grain loss due to the increased number of generations per season of multivoltine species that are detrimental to plants. Among insects, orange wheat blossom midge (OWBM), yellow wheat blossom midge (YWBM), saddle gall midge (SGM), thrips, and frit fly (FF) are important wheat pests in the European environments, which can be managed by the development of resistant cultivars. This involves the identification, confirmation, and incorporation of insect resistance sources into new high-yielding cultivars. We used two diverse and unrelated wheat [winter wheat (WW) and spring wheat (SW)] panels to associate single-nucleotide polymorphism (SNP) markers with the mentioned pests using the tools of association mapping. All in all, a total of 645 and 123 significant associations were detected in WW and SW, respectively, which were confined to 246 quantitative trait loci. Many candidate genes were identified using the BLAST analysis of the sequences of associated SNPs. Some of them are involved in controlling the physical structures of plants such as stomatal immunity and closure, cuticular wax in leaf blade, whereas others are involved in the production of certain enzymes in response to biotic and abiotic stresses. To our knowledge, this is the first detailed investigation that deals with YWBM, SGM, thrips, and FF resistance genetics using the natural variation in wheat. The reported germplasm is also readily available to breeders across the world that can make rational decisions to breed for the pest resilience of their interest by including the resistant genotypes being reported.

Mapping Resistance to Argentinean Fusarium (Graminearum) Head Blight Isolates in Wheat

Fusarium head blight (FHB) of wheat, caused by Fusarium graminearum (Schwabe), is a destructive disease worldwide, reducing wheat yield and quality. To accelerate the improvement of scab tolerance in wheat, we assessed the International Triticeae Mapping Initiative mapping population (ITMI/MP) for Type I and II resistance against a wide population of Argentinean isolates of F. graminearum. We discovered a total of 27 additive QTLs on ten different (2A, 2D, 3B, 3D, 4B, 4D, 5A, 5B, 5D and 6D) wheat chromosomes for Type I and Type II resistances explaining a maximum of 15.99% variation. Another four and two QTLs for thousand kernel weight in control and for Type II resistance, respectively, involved five different chromosomes (1B, 2D, 6A, 6D and 7D). Furthermore, three, three and five QTLs for kernel weight per spike in control, for Type I resistance and for Type II resistance, correspondingly, involved ten chromosomes (2A, 2D, 3B, 4A, 5A, 5B, 6B, 7A, 7B, 7D). We were also able to detect five and two epistasis pairs of QTLs for Type I and Type II resistance, respectively, in addition to additive QTLs that evidenced that FHB resistance in wheat is controlled by a complex network of additive and epistasis QTLs.

Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods

In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.

Genetic basis of some physiological traits and yield in early and late sowing conditions in bread wheat (Triticum aestivum L.)

The rise in human population necessitates the use of all available tools to enhance wheat productivity. In this regard, pre-breeding has mobilized novel underutilized genetic variation into breeding programs. However, this germplasm needs to be characterized for its efficient utilization. This investigation was initiated to evaluate the early and late sown wheat pre-breeding germplasm for physiology- and yield-related traits and to associate the mapped SNPs using association mapping approach. Our results indicate that the germplasm performed better in early sowing in comparison to late planting where grain yield (Yd) was found positively correlated with water use efficiency (WUE), heading time, and chlorophyll contents (Chl). We discovered a total of 210 associations involving 155 SNPs. Taking into consideration either early or late sowing and the mean values, only 12 marker traits were associated with trait germination, plant height, stomatal conductance, transpiration rate, Chl, carotenoids, and Yd. Our correlations and mapping results indicate that higher WUE along with Chl can be targeted as indirect physiological markers to enhance wheat yield.

Genome-Wide Association Study Uncover the Genetic Architecture of Salt Tolerance-Related Traits in Common Wheat (Triticum aestivum L.)

Soil salinity is a serious threat to wheat yield affecting sustainable agriculture. Although salt tolerance is important for plant establishment at seedling stage, its genetic architecture remains unclear. In the present study, we have evaluated eight salt tolerance-related traits at seedling stage and identified the loci for salt tolerance by genome-wide association study (GWAS). This GWAS panel comprised 317 accessions and was genotyped with the wheat 90 K single-nucleotide polymorphism (SNP) chip. In total, 37 SNPs located at 16 unique loci were identified, and each explained 6.3 to 18.6% of the phenotypic variations. Among these, six loci were overlapped with previously reported genes or quantitative trait loci, whereas the other 10 were novel. Besides, nine loci were detected for two or more traits, indicating that the salt-tolerance genetic architecture is complex. Furthermore, five candidate genes were identified for salt tolerance-related traits, including kinase family protein, E3 ubiquitin-protein ligase-like protein, and transmembrane protein. SNPs identified in this study and the accessions with more favorable alleles could further enhance salt tolerance in wheat breeding. Our results are useful for uncovering the genetic mechanism of salt tolerance in wheat at seeding stage.