QTL Mapping of Adult Plant Resistance to Stripe Rust in a Doubled Haploid Wheat Population

Genome-wide association study identifies novel loci and candidate genes for rust resistance in wheat (Triticum aestivum L.)

BACKGROUND

Wheat rusts are important biotic stresses, development of rust resistant cultivars through molecular approaches is both economical and sustainable. Extensive phenotyping of large mapping populations under diverse production conditions and high-density genotyping would be the ideal strategy to identify major genomic regions for rust resistance in wheat. The genome-wide association study (GWAS) population of 280 genotypes was genotyped using a 35 K Axiom single nucleotide polymorphism (SNP) array and phenotyped at eight, 10, and, 10 environments, respectively for stem/black rust (SR), stripe/yellow rust (YR), and leaf/brown rust (LR).

RESULTS

Forty-one Bonferroni corrected marker-trait associations (MTAs) were identified, including 17 for SR and 24 for YR. Ten stable MTAs and their best combinations were also identified. For YR, AX-94990952 on 1A + AX-95203560 on 4A + AX-94723806 on 3D + AX-95172478 on 1A showed the best combination with an average co-efficient of infection (ACI) score of 1.36. Similarly, for SR, AX-94883961 on 7B + AX-94843704 on 1B and AX-94883961 on 7B + AX-94580041 on 3D + AX-94843704 on 1B showed the best combination with an ACI score of around 9.0. The genotype PBW827 have the best MTA combinations for both YR and SR resistance. In silico study identifies key prospective candidate genes that are located within MTA regions. Further, the expression analysis revealed that 18 transcripts were upregulated to the tune of more than 1.5 folds including 19.36 folds (TraesCS3D02G519600) and 7.23 folds (TraesCS2D02G038900) under stress conditions compared to the control conditions. Furthermore, highly expressed genes in silico under stress conditions were analyzed to find out the potential links to the rust phenotype, and all four genes were found to be associated with the rust phenotype.

CONCLUSION

The identified novel MTAs, particularly stable and highly expressed MTAs are valuable for further validation and subsequent application in wheat rust resistance breeding. The genotypes with favorable MTA combinations can be used as prospective donors to develop elite cultivars with YR and SR resistance.

Genetic architecture of adult-plant resistance to stripe rust in bread wheat (Triticum aestivum L.) association panel

Stripe rust, caused by Puccinia striiformis f. sp. tritici, is a severe disease in wheat worldwide, including Ethiopia, causing up to 100% wheat yield loss in the worst season. The use of resistant cultivars is considered to be the most effective and durable management technique for controlling the disease. Therefore, the present study targeted the genetic architecture of adult plant resistance to yellow rust in 178 wheat association panels. The panel was phenotyped for yellow rust adult-plant resistance at three locations. Phonological, yield, yield-related, and agro-morphological traits were recorded. The association panel was fingerprinted using the genotyping-by-sequencing (GBS) platform, and a total of 6,788 polymorphic single nucleotide polymorphisms (SNPs) were used for genome-wide association analysis to identify effective yellow rust resistance genes. The marker-trait association analysis was conducted using the Genome Association and Prediction Integrated Tool (GAPIT). The broad-sense heritability for the considered traits ranged from 74.52% to 88.64%, implying the presence of promising yellow rust resistance alleles in the association panel that could be deployed to improve wheat resistance to the disease. The overall linkage disequilibrium (LD) declined within an average physical distance of 31.44 Mbp at r2 = 0.2. Marker-trait association (MTA) analysis identified 148 loci significantly (p = 0.001) associated with yellow rust adult-plant resistance. Most of the detected resistance quantitative trait loci (QTLs) were located on the same chromosomes as previously reported QTLs for yellow rust resistance and mapped on chromosomes 1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A, and 7D. However, 12 of the discovered MTAs were not previously documented in the wheat literature, suggesting that they could represent novel loci for stripe rust resistance. Zooming into the QTL regions in IWGSC RefSeq Annotation v1 identified crucial disease resistance-associated genes that are key in plants' defense mechanisms against pathogen infections. The detected QTLs will be helpful for marker-assisted breeding of wheat to increase resistance to stripe rust. Generally, the present study identified putative QTLs for field resistance to yellow rust and some important agronomic traits. Most of the discovered QTLs have been reported previously, indicating the potential to improve wheat resistance to yellow rust by deploying the QTLs discovered by marker-assisted selection.

Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat

BACKGROUND

Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance.

RESULTS

Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker-trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development.

CONCLUSION

The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.

Intelligent reprogramming of wheat for enhancement of fungal and nematode disease resistance using advanced molecular techniques

Wheat (Triticum aestivum L.) diseases are major factors responsible for substantial yield losses worldwide, which affect global food security. For a long time, plant breeders have been struggling to improve wheat resistance against major diseases by selection and conventional breeding techniques. Therefore, this review was conducted to shed light on various gaps in the available literature and to reveal the most promising criteria for disease resistance in wheat. However, novel techniques for molecular breeding in the past few decades have been very fruitful for developing broad-spectrum disease resistance and other important traits in wheat. Many types of molecular markers such as SCAR, RAPD, SSR, SSLP, RFLP, SNP, and DArT, etc., have been reported for resistance against wheat pathogens. This article summarizes various insightful molecular markers involved in wheat improvement for resistance to major diseases through diverse breeding programs. Moreover, this review highlights the applications of marker assisted selection (MAS), quantitative trait loci (QTL), genome wide association studies (GWAS) and the CRISPR/Cas-9 system for developing disease resistance against most important wheat diseases. We also reviewed all reported mapped QTLs for bunts, rusts, smuts, and nematode diseases of wheat. Furthermore, we have also proposed how the CRISPR/Cas-9 system and GWAS can assist breeders in the future for the genetic improvement of wheat. If these molecular approaches are used successfully in the future, they can be a significant step toward expanding food production in wheat crops.

Genome-wide association study for resistance in bread wheat (Triticum aestivum L.) to stripe rust (Puccinia striiformis f. sp. tritici) races in Argentina

BACKGROUND

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating diseases of the wheat crop. It causes significant reductions in both grain yield and grain quality. In recent years, new and more virulent races have overcome many of the known resistance genes in Argentinian germplasm. In order to identify loci conferring resistance to the local races of Pst for effective utilization in future breeding programs, a genome-wide association study (GWAS) was performed using a collection of 245 bread wheat lines genotyped with 90 K SNPs.

RESULTS

To search for adult plant resistance (APR) the panel was evaluated for disease severity (DS) and area under disease progress curve (AUDPC) in field trials during two years under natural infection conditions. To look for seedling or all-stage resistance (ASR) the panel was evaluated to determine infection type (IT) under greenhouse conditions against two prevalent races in Argentina. The phenotypic data showed that the panel possessed enough genetic variability for searching for sources of resistance to Pst. Significant correlations between years were observed for Pst response in the field and high heritability values were found for DS (H² = 0.89) and AUDPC (H² = 0.93). Based on GWAS, eight markers associated with Pst resistance (FDR < 0.01) were identified, of these, five were associated with ASR (on chromosomes 1B, 2A, 3A and 5B) and three with APR (on chromosomes 3B and 7A). These markers explained between 2% and 32.62% of the phenotypic variation. Five of the markers corresponded with previously reported Yr genes/QTL, while the other three (QYr.Bce.1B.sd.1, QYr.Bce.3A.sd and QYr.Bce.3B.APR.2) might be novel resistance loci.

CONCLUSION

Our results revealed high genetic variation for resistance to Argentinian stripe rust races in the germplasm used here. It constitutes a very promising step towards the improvement of Pst resistance of bread wheat in Argentina. Also, the identification of new resistance loci would represent a substantial advance for diversifying the current set of resistance genes and to advance in the improvement of the durable resistance to the disease.

Smart breeding approaches in post-genomics era for developing climate-resilient food crops

Improving the crop traits is highly required for the development of superior crop varieties to deal with climate change and the associated abiotic and biotic stress challenges. Climate change-driven global warming can trigger higher insect pest pressures and plant diseases thus affecting crop production sternly. The traits controlling genes for stress or disease tolerance are economically imperative in crop plants. In this scenario, the extensive exploration of available wild, resistant or susceptible germplasms and unraveling the genetic diversity remains vital for breeding programs. The dawn of next-generation sequencing technologies and omics approaches has accelerated plant breeding by providing the genome sequences and transcriptomes of several plants. The availability of decoded plant genomes offers an opportunity at a glance to identify candidate genes, quantitative trait loci (QTLs), molecular markers, and genome-wide association studies that can potentially aid in high throughput marker-assisted breeding. In recent years genomics is coupled with marker-assisted breeding to unravel the mechanisms to harness better better crop yield and quality. In this review, we discuss the aspects of marker-assisted breeding and recent perspectives of breeding approaches in the era of genomics, bioinformatics, high-tech phonemics, genome editing, and new plant breeding technologies for crop improvement. In nutshell, the smart breeding toolkit in the post-genomics era can steadily help in developing climate-smart future food crops.