Molecular mapping for resistance to pea rust caused by Uromyces fabae (Pers.) de-Bary

Rust (Uromyces viciae-fabae Pers. de-Bary) of Pea (Pisum sativum L.): Present Status and Future Resistance Breeding Opportunities

Uromyces viciae-fabae Pers. de-Bary is an important fungal pathogen causing rust in peas (Pisum sativum L.). It is reported in mild to severe forms from different parts of the world where the pea is grown. Host specificity has been indicated in this pathogen in the field but has not yet been established under controlled conditions. The uredinial states of U. viciae-fabae are infective under temperate and tropical conditions. Aeciospores are infective in the Indian subcontinent. The genetics of rust resistance was reported qualitatively. However, non-hypersensitive resistance responses and more recent studies emphasized the quantitative nature of pea rust resistance. Partial resistance/slow rusting had been described as a durable resistance in peas. Such resistance is of the pre-haustorial type and expressed as longer incubation and latent period, poor infection efficiency, a smaller number of aecial cups/pustules, and lower units of AUDPC (Area Under Disease Progress Curve). Screening techniques dealing with slow rusting should consider growth stages and environment, as both have a significant influence on the disease scores. Our knowledge about the genetics of rust resistance is increasing, and now molecular markers linked with gene/QTLs (Quantitative Trait Loci) of rust resistance have been identified in peas. The mapping efforts conducted in peas came out with some potent markers associated with rust resistance, but they must be validated under multi-location trails before use in the marker-assisted selection of rust resistance in pea breeding programs.

Combining NDVI and Bacterial Blight Score to Predict Grain Yield in Field Pea

Field pea is the most commonly grown temperate pulse crop, with close to 15 million tons produced globally in 2020. Varieties improved through breeding are important to ensure ongoing improvements in yield and disease resistance. Genomic selection (GS) is a modern breeding approach that could substantially improve the rate of genetic gain for grain yield, and its deployment depends on the prediction accuracy (PA) that can be achieved. In our study, four yield trials representing breeding lines' advancement stages of the breeding program (S0, S1, S2, and S3) were assessed with grain yield, aerial high-throughput phenotyping (normalized difference vegetation index, NDVI), and bacterial blight disease scores (BBSC). Low-to-moderate broad-sense heritability (0.31-0.71) and narrow-sense heritability (0.13-0.71) were observed, as the estimated additive and non-additive genetic components for the three traits varied with the different models fitted. The genetic correlations among the three traits were high, particularly in the S0-S2 stages. NDVI and BBSC were combined to investigate the PA for grain yield by univariate and multivariate GS models, and multivariate models showed higher PA than univariate models in both cross-validation and forward prediction methods. A 6-50% improvement in PA was achieved when multivariate models were deployed. The highest PA was indicated in the forward prediction scenario when the training population consisted of early generation breeding stages with the multivariate models. Both NDVI and BBSC are commonly used traits that could be measured in the early growth stage; however, our study suggested that NDVI is a more useful trait to predict grain yield with high accuracy in the field pea breeding program, especially in diseased trials, through its incorporation into multivariate models.

Genomics Enabled Breeding Strategies for Major Biotic Stresses in Pea (Pisum sativum L.)

Pea (Pisum sativum L.) is one of the most important and productive cool season pulse crops grown throughout the world. Biotic stresses are the crucial constraints in harnessing the potential productivity of pea and warrant dedicated research and developmental efforts to utilize omics resources and advanced breeding techniques to assist rapid and timely development of high-yielding multiple stress-tolerant-resistant varieties. Recently, the pea researcher's community has made notable achievements in conventional and molecular breeding to accelerate its genetic gain. Several quantitative trait loci (QTLs) or markers associated with genes controlling resistance for fusarium wilt, fusarium root rot, powdery mildew, ascochyta blight, rust, common root rot, broomrape, pea enation, and pea seed borne mosaic virus are available for the marker-assisted breeding. The advanced genomic tools such as the availability of comprehensive genetic maps and linked reliable DNA markers hold great promise toward the introgression of resistance genes from different sources to speed up the genetic gain in pea. This review provides a brief account of the achievements made in the recent past regarding genetic and genomic resources' development, inheritance of genes controlling various biotic stress responses and genes controlling pathogenesis in disease causing organisms, genes/QTLs mapping, and transcriptomic and proteomic advances. Moreover, the emerging new breeding approaches such as transgenics, genome editing, genomic selection, epigenetic breeding, and speed breeding hold great promise to transform pea breeding. Overall, the judicious amalgamation of conventional and modern omics-enabled breeding strategies will augment the genetic gain and could hasten the development of biotic stress-resistant cultivars to sustain pea production under changing climate. The present review encompasses at one platform the research accomplishment made so far in pea improvement with respect to major biotic stresses and the way forward to enhance pea productivity through advanced genomic tools and technologies.

Association Mapping of Lathyrus sativus Disease Response to Uromyces pisi Reveals Novel Loci Underlying Partial Resistance

Uromyces pisi ([Pers.] D.C.) Wint. is an important foliar biotrophic pathogen infecting grass pea (Lathyrus sativus L.), compromising their yield stability. To date, few efforts have been made to assess the natural variation in grass pea resistance and to identify the resistance loci operating against this pathogen, limiting its efficient breeding exploitation. To overcome this knowledge gap, the genetic architecture of grass pea resistance to U. pisi was investigated using a worldwide collection of 182 accessions through a genome-wide association approach. The response of the grass pea collection to rust infection under controlled conditions and at the seedling stage did not reveal any hypersensitive response but a continuous variation for disease severity, with the identification of promising sources of partial resistance. A panel of 5,651 high-quality single-nucleotide polymorphism (SNP) markers previously generated was used to test for SNP-trait associations, based on a mixed linear model accounting for population structure. We detected seven SNP markers significantly associated with U. pisi disease severity, suggesting that partial resistance is oligogenic. Six of the associated SNP markers were located in chromosomes 4 and 6, while the remaining SNP markers had no known chromosomal position. Through comparative mapping with the pea reference genome, a total of 19 candidate genes were proposed, encoding for leucine-rich repeat, NB-ARC domain, and TGA transcription factor family, among others. Results presented in this study provided information on the availability of partial resistance in grass pea germplasm and advanced our understanding of the molecular mechanisms of quantitative resistance to rust in grass pea. Moreover, the detected associated SNP markers constitute promising genomic targets for the development of molecular tools to assist disease resistance precision breeding.

Omics resources and omics-enabled approaches for achieving high productivity and improved quality in pea (Pisum sativum L.)

Pea (Pisum sativum L.), a cool-season legume crop grown in more than 85 countries, is the second most important grain legume and one of the major green vegetables in the world. While pea was historically studied as the genetic model leading to the discovery of the laws of genetics, pea research has lagged behind that of other major legumes in the genomics era, due to its large and complex genome. The evolving climate change and growing population have posed grand challenges to the objective of feeding the world, making it essential to invest research efforts to develop multi-omics resources and advanced breeding tools to support fast and continuous development of improved pea varieties. Recently, the pea researchers have achieved key milestones in omics and molecular breeding. The present review provides an overview of the recent important progress including the development of genetic resource databases, high-throughput genotyping assays, reference genome, genes/QTLs responsible for important traits, transcriptomic, proteomic, and phenomic atlases of various tissues under different conditions. These multi-faceted resources have enabled the successful implementation of various markers for monitoring early-generation populations as in marker-assisted backcrossing breeding programs. The emerging new breeding approaches such as CRISPR, speed breeding, and genomic selection are starting to change the paradigm of pea breeding. Collectively, the rich omics resources and omics-enable breeding approaches will enhance genetic gain in pea breeding and accelerate the release of novel pea varieties to meet the elevating demands on productivity and quality.

A High-Density Integrated DArTseq SNP-Based Genetic Map of Pisum fulvum and Identification of QTLs Controlling Rust Resistance

Pisum fulvum, a wild relative of pea is an important source of allelic diversity to improve the genetic resistance of cultivated species against fungal diseases of economic importance like the pea rust caused by Uromyces pisi. To unravel the genetic control underlying resistance to this fungal disease, a recombinant inbred line (RIL) population was generated from a cross between two P. fulvum accessions, IFPI3260 and IFPI3251, and genotyped using Diversity Arrays Technology. A total of 9,569 high-quality DArT-Seq and 8,514 SNPs markers were generated. Finally, a total of 12,058 markers were assembled into seven linkage groups, equivalent to the number of haploid chromosomes of P. fulvum and P. sativum. The newly constructed integrated genetic linkage map of P. fulvum covered an accumulated distance of 1,877.45 cM, an average density of 1.19 markers cM^-1 and an average distance between adjacent markers of 1.85 cM. The composite interval mapping revealed three QTLs distributed over two linkage groups that were associated with the percentage of rust disease severity (DS%). QTLs UpDSII and UpDSIV were located in the LGs II and IV respectively and were consistently identified both in adult plants over 3 years at the field (Córdoba, Spain) and in seedling plants under controlled conditions. Whenever they were detected, their contribution to the total phenotypic variance varied between 19.8 and 29.2. A third QTL (UpDSIV.2) was also located in the LGIVand was environmentally specific as was only detected for DS % in seedlings under controlled conditions. It accounted more than 14% of the phenotypic variation studied. Taking together the data obtained in the study, it could be concluded that the expression of resistance to fungal diseases in P. fulvum originates from the resistant parent IFPI3260.

A community resource for exploring and utilizing genetic diversity in the USDA pea single plant plus collection

Globally, pea (Pisum sativum L.) is an important temperate legume crop for food, feed and fodder, and many breeding programs develop cultivars adapted to these end-uses. In order to assist pea development efforts, we assembled the USDA Pea Single Plant Plus Collection (PSPPC), which contains 431 P. sativum accessions with morphological, geographic and taxonomic diversity. The collection was characterized genetically in order to maximize its value for trait mapping and genomics-assisted breeding. To that end, we used genotyping-by-sequencing-a cost-effective method for de novo single-nucleotide polymorphism (SNP) marker discovery-to generate 66 591 high-quality SNPs. These data facilitated the identification of accessions divergent from mainstream breeding germplasm that could serve as sources of novel, favorable alleles. In particular, a group of accessions from Central Asia appear nearly as diverse as a sister species, P. fulvum, and subspecies, P. sativum subsp. elatius. PSPPC genotypes can be paired with new and existing phenotype data for trait mapping; as proof-of-concept, we localized Mendel's A gene controlling flower color to its known position. We also used SNP data to define a smaller core collection of 108 accessions with similar levels of genetic diversity as the entire PSPPC, resulting in a smaller germplasm set for research screening and evaluation under limited resources. Taken together, the results presented in this study along with the release of a publicly available SNP data set comprise a valuable resource for supporting worldwide pea genetic improvement efforts.

Genomic Tools in Pea Breeding Programs: Status and Perspectives

Pea (Pisum sativum L.) is an annual cool-season legume and one of the oldest domesticated crops. Dry pea seeds contain 22-25% protein, complex starch and fiber constituents, and a rich array of vitamins, minerals, and phytochemicals which make them a valuable source for human consumption and livestock feed. Dry pea ranks third to common bean and chickpea as the most widely grown pulse in the world with more than 11 million tons produced in 2013. Pea breeding has achieved great success since the time of Mendel's experiments in the mid-1800s. However, several traits still require significant improvement for better yield stability in a larger growing area. Key breeding objectives in pea include improving biotic and abiotic stress resistance and enhancing yield components and seed quality. Taking advantage of the diversity present in the pea genepool, many mapping populations have been constructed in the last decades and efforts have been deployed to identify loci involved in the control of target traits and further introgress them into elite breeding materials. Pea now benefits from next-generation sequencing and high-throughput genotyping technologies that are paving the way for genome-wide association studies and genomic selection approaches. This review covers the significant development and deployment of genomic tools for pea breeding in recent years. Future prospects are discussed especially in light of current progress toward deciphering the pea genome.

Validation of SSR markers associated with rust (Uromyces fabae) resistance in pea (Pisum sativum L.)

Pea rust is a devastating disease of peas especially in the sub-tropical regions of the world and greatly influenced by the environmental conditions during disease development. Molecular markers associated with pea rust resistance would be useful in marker assisted selection (MAS). Utility of molecular markers associated with the pea rust resistance were evaluated in 30 diverse pea genotypes using four SSR markers (AA446 and AA505 flanking the major QTL Qruf; AD146 and AA416 flanking the minor QTL, Qruf1). QTL, Qruf flanking markers were able to identify all the resistant genotypes when used together, except Pant P 31. While, SSR markers AD146 and AA416 flanking the minor QTL, Qruf1 were able to identify all the pea resistant genotypes used for validation, except for HUDP-11 by AD146 and Pant P 31 by AA416. Similarly, SSR markers AA446 and AA505 were able to identify all the susceptible pea genotypes, except IPFD 99-13, HFP 9415 and S- 143. SSR markers AD146 and AA416 were together able to identify all the pea susceptible genotypes used for validation, except KPMR 526, KPMR 632 and IPFD 99-13. On the basis of marker allele analysis it may be concluded that SSR markers (AA446, AA505, AD146 and AA416) can be used in MAS of pea rust resistance.

Allelic diversity in the transcriptomes of contrasting rust-infected genotypes of Lathyrus sativus, a lasting resource for smart breeding

BACKGROUND

Grass pea (Lathyrus sativus L.) is a valuable resource for potentially durable partial resistance to rust. To gain insight into the resistance mechanism and identify potential resistance genes, we generated the first comprehensive transcriptome assemblies from control and Uromyces pisi inoculated leafs of a susceptible and a partially rust-resistant grass pea genotype by RNA-seq.

RESULTS

134,914 contigs, shared by both libraries, were used to analyse their differential expression in response to rust infection. Functional annotation grouped 60.4% of the contigs present in plant databases (37.8% of total) to 33 main functional categories, being "protein", "RNA", "signalling", "transport" and "stress" the most represented. Transcription profiles revealed considerable differences in regulation of major phytohormone signalling pathways: whereas Salicylic and Abscisic Acid pathways were up-regulated in the resistant genotype, Jasmonate and Ethylene pathways were down-regulated in the susceptible one. As potential Resistance-genes we identified a mildew resistance locus O (MLO)-like gene, and MLO-related transcripts. Also, several pathogenesis-related genes were up-regulated in the resistant and exclusively down regulated in the susceptible genotype. Pathogen effectors identified in both inoculated libraries, as e.g. the rust Rtp1 transcript, may be responsible for the down-regulation of defence-related transcripts. The two genotypes contained 4,892 polymorphic contigs with SNPs unevenly distributed between different functional categories. Protein degradation (29.7%) and signalling receptor kinases (8.2%) were the most diverged, illustrating evolutionary adaptation of grass pea to the host/pathogens arms race.

CONCLUSIONS

The vast array of novel, resistance-related genomic information we present here provides a highly valuable resource for future smart breeding approaches in this hitherto under-researched, valuable legume crop.

Marker-assisted introgression of a QTL region to improve rust resistance in three elite and popular varieties of peanut (Arachis hypogaea L.)

Successful introgression of a major QTL for rust resistance, through marker-assisted backcrossing, in three popular Indian peanut cultivars generated several promising introgression lines with enhanced rust resistance and higher yield. Leaf rust, caused by Puccinia arachidis Speg, is one of the major devastating diseases in peanut (Arachis hypogaea L.). One QTL region on linkage group AhXV explaining upto 82.62 % phenotypic variation for rust resistance was validated and introgressed from cultivar 'GPBD 4' into three rust susceptible varieties ('ICGV 91114', 'JL 24' and 'TAG 24') through marker-assisted backcrossing (MABC). The MABC approach employed a total of four markers including one dominant (IPAHM103) and three co-dominant (GM2079, GM1536, GM2301) markers present in the QTL region. After 2-3 backcrosses and selfing, 200 introgression lines (ILs) were developed from all the three crosses. Field evaluation identified 81 ILs with improved rust resistance. Those ILs had significantly increased pod yields (56-96 %) in infested environments compared to the susceptible parents. Screening of selected 43 promising ILs with 13 markers present on linkage group AhXV showed introgression of the target QTL region from the resistant parent in 11 ILs. Multi-location field evaluation of these ILs should lead to the release of improved varieties. The linked markers may be used in improving rust resistance in peanut breeding programmes.

QTL meta-analysis provides a comprehensive view of loci controlling partial resistance to Aphanomyces euteiches in four sources of resistance in pea

BACKGROUND

Development of durable plant genetic resistance to pathogens through strategies of QTL pyramiding and diversification requires in depth knowledge of polygenic resistance within the available germplasm. Polygenic partial resistance to Aphanomyces root rot, caused by Aphanomyces euteiches, one of the most damaging pathogens of pea worldwide, was previously dissected in individual mapping populations. However, there are no data available regarding the diversity of the resistance QTL across a broader collection of pea germplasm. In this study, we performed a meta-analysis of Aphanomyces root rot resistance QTL in the four main sources of resistance in pea and compared their genomic localization with genes/QTL controlling morphological or phenological traits and with putative candidate genes.

RESULTS

Meta-analysis, conducted using 244 individual QTL reported previously in three mapping populations (Puget x 90-2079, Baccara x PI180693 and Baccara x 552) and in a fourth mapping population in this study (DSP x 90-2131), resulted in the identification of 27 meta-QTL for resistance to A. euteiches. Confidence intervals of meta-QTL were, on average, reduced four-fold compared to mean confidence intervals of individual QTL. Eleven consistent meta-QTL, which highlight seven highly consistent genomic regions, were identified. Few meta-QTL specificities were observed among mapping populations, suggesting that sources of resistance are not independent. Seven resistance meta-QTL, including six of the highly consistent genomic regions, co-localized with six of the meta-QTL identified in this study for earliness and plant height and with three morphological genes (Af, A, R). Alleles contributing to the resistance were often associated with undesirable alleles for dry pea breeding. Candidate genes underlying six main meta-QTL regions were identified using colinearity between the pea and Medicago truncatula genomes.

CONCLUSIONS

QTL meta-analysis provided an overview of the moderately low diversity of loci controlling partial resistance to A. euteiches in four main sources of resistance in pea. Seven highly consistent genomic regions with potential use in marker-assisted-selection were identified. Confidence intervals at several main QTL regions were reduced and co-segregation among resistance and morphological/phenological alleles was identified. Further work will be required to identify the best combinations of QTL for durably increasing partial resistance to A. euteiches.