BELIS is creating and coordinating a network of legume breeders and users, gathering members of the project teams and external actors interested in legume breeding. BELIS Network brings together the legume breeding community around themes of shared interest, and identifies areas of technical collaboration on species, target traits, breeding methodologies, protocols for phenotyping and genotyping, registration procedures, technical training and knowledge exchange. The Network also facilitates business collaborations for carrying out benchmarking activities aimed at policy-makers (such as regulatory institutions and national and EU managers) to promote and request support for legume breeding activities. 

The BELIS network, gathering private and public breeders, actors of scientific research, extension services, registration offices as well as seed, food and feed industries is the first gateway to disseminate and exploit the BELIS activities and results. If you are a professional involved in the legume breeding value chain, and you are interested in receiving more information about BELIS activities and results and joining our Network, subscribe to BELIS Newsletter to stay tuned to the project.

Faba bean is a key protein feed and food worldwide that still requires accurate genomic tools to facilitate molecular marker-assisted breeding. Efficient quantitative trait locus (QTL) mapping in faba bean is restricted by the low or medium density of most of the available genetic maps. In this study, a recombinant inbred line faba bean population including 124 lines from the cross Vf6 x Vf27, highly segregating for autofertility, flowering time, plant architecture, dehiscence, and yield-related traits, was genotyped using the ‘Vfaba_v2’ SNP array. Genotypic data were used to generate a high-density genetic map that, after quality control and filtering, included 2,296 SNP markers. The final map consisted of 1,674 bin markers distributed across the six faba bean chromosomes, covering 2,963.87 cM with an average marker distance of 1.77 cM. A comparison of the physical and genetic maps revealed a good correspondence between chromosomes and linkage groups. QTL analysis of 66 segregating traits, previously phenotyped in different environments and years, identified 99 significant QTLs corresponding to 35 of the traits. Most QTLs were stable over the years and QTLs for highly correlated traits were mapped to the same or adjacent genomic regions. Colocalization of QTLs occurred in 13 major regions, joining three or more overlapping QTLs. Some of the pleiotropic QTL regions, especially in chromosome VI, shared the same significant marker for different traits related to pollen quantity and size, number of ovules per ovary, seeds per pod, and pod set. Finally, several putative candidate genes for yield-related traits, recently identified using a genome-wide association study, fall inside the colocalizing groups described in this study, indicating that, apart from refining the position of the QTLs and the detection of candidates, the dense new map provides a valuable tool for validation of causative loci derived from association studies and will help advance breeding programs in this crop. [Read more]

Rust diseases are a major concern in legume production worldwide causing heavy losses especially in developing countries that depend on grain legumes as staple food. Fungal species from Uromyces, Phakopsora and Puccinia genera are the main causal agents of the various legume rust diseases. They induce up to 100% yield losses on susceptible cultivars and are emerging as a substantial threat to global food security. Developing durable resistance to rust has thus become a critical breeding objective alongside efforts to improve cultural and disease management practices. This review specifically focuses on the recent advances in understanding and enhancing genetic rust resistance across diverse legume crops. Key topics covered include: (i) the diversity and host range of the rust species affecting legumes; (ii) the disease management strategies from cultural practices to chemical control; (iii) the available screening methods for identifying new sources of resistance; (iv) the genetic basis of resistance, encompassing both major resistance genes and quantitative trait loci; (v) insights into gene regulation and effector molecules leading to legume-rust interactions; and (vi) emerging genomic-assisted breeding techniques that can accelerate the development of durable rust resistance in legumes. Overall, this review highlights the progress made to date and the remaining challenges in sustainably managing rust diseases across diverse legume crops through integrated approaches spanning pathogen biology, advanced phenotyping, genetic resistance, and molecular breeding. [Read more]

Cicer arietinum is a significant legume crop cultivated mainly in short-season environments, where early-flowering is a desirable trait to overcome terminal constraints. Despite its agricultural significance, the genetic control of flowering time in chickpea is not fully understood. In this study, we developed, phenotyped, re-sequenced and genetically characterized a pair of near-isogenic lines (NILs) with contrasting days to flowering to identify candidate gene variants potentially associated with flowering time. In addition to days to flowering, noticeable differences in multiple shoot architecture traits were observed between the NILs. The resequencing data confirms that the NILs developed in this study serve as appropriate plant materials, effectively constraining genetic variation to specific regions and thereby establishing a valuable resource for future genetic and functional investigations in chickpea research. Leveraging bioinformatics tools and public genomic datasets, we identified homologs of flowering-related genes from Arabidopsis thaliana, including ELF3 and, for the first time in chickpea, MED16 and STO/BBX24, with variants among the NILs. Analysis of the allelic distribution of these genes revealed their preservation within chickpea diversity and their potential association with flowering time. Variants were also identified in members of the ERF and ARF gene families. Furthermore, in silico expression analysis was conducted elucidating their putative roles in flowering. While the gene CaELF3a is identified as a prominent candidate, this study also exposes new targets in chickpea, such as CaMED16b and LOC101499101 (BBX24-like), homologs of flowering-related genes in Arabidopsis, as well as ERF12 and ARF2. The in silico expression characterization and genetic variability analysis performed could contribute to their use as specific markers for chickpea breeding programs. This study lays the groundwork for future investigations utilizing this plant material, promising further insights into the complex mechanisms governing flowering time in chickpea. [Read more]

Little resistance to the pea weevil insect pest (Bruchus pisorum) is available in pea (Pisum sativum) cultivars, highlighting the need to search for sources of resistance in Pisum germplasm and to decipher the genetic basis of resistance. To address this need, we screened the response to pea weevil in a Pisum germplasm collection (324 accession, previously genotyped) under field conditions over four environments. Significant variation for weevil seed infestation (SI) was identified, with resistance being frequent in P. fulvum, followed by P. sativum ssp. elatius, P. abyssinicum, and P. sativum ssp. humile. SI tended to be higher in accessions with lighter seed color. SI was also affected by environmental factors, being favored by high humidity during flowering and hampered by warm winter temperatures and high evapotranspiration during and after flowering. Merging the phenotypic and genotypic data allowed genome-wide association studies (GWAS) yielding 73 markers significantly associated with SI. Through the GWAS models, 23 candidate genes were found associated with weevil resistance, highlighting the interest of five genes located on chromosome 6. These included gene 127136761 encoding squalene epoxidase; gene 127091639 encoding a transcription factor MYB SRM1; gene 127097033 encoding a 60S ribosomal protein L14; gene 127092211, encoding a BolA-like family protein, which, interestingly, was located within QTL BpLD.I, earlier described as conferring resistance to weevil in pea; and gene 127096593 encoding a methyltransferase. These associated genes offer valuable potential for developing pea varieties resistant to Bruchus spp. and efficient utilization of genomic resources through marker-assisted selection (MAS). [Read more]

Genomic selection (GS) has become an indispensable tool in modern plant breeding, particularly for complex traits. This study aimed to assess the efficacy of GS in predicting rust (Uromyces pisi) resistance in pea (Pisum sativum), using a panel of 320 pea accessions and a set of 26,045 Silico-Diversity Arrays Technology (Silico-DArT) markers. We compared the prediction abilities of different GS models and explored the impact of incorporating marker × environment (M×E) interaction as a covariate in the GBLUP (genomic best linear unbiased prediction) model. The analysis included phenotyping data from both field and controlled conditions. We assessed the predictive accuracies of different cross-validation strategies and compared the efficiency of using single traits versus a multi-trait index, based on factor analysis and ideotype-design (FAI-BLUP), which combines traits from controlled conditions. The GBLUP model, particularly when modified to include M×E interactions, consistently outperformed other models, demonstrating its suitability for traits affected by complex genotype-environment interactions (GEI). The best predictive ability (0.635) was achieved using the FAI-BLUP approach within the Bayesian Lasso (BL) model. The inclusion of M×E interactions significantly enhanced prediction accuracy across diverse environments in GBLUP models, although it did not markedly improve predictions for non-phenotyped lines. These findings underscore the variability of predictive abilities due to GEI and the effectiveness of multi-trait approaches in addressing complex traits. Overall, our study illustrates the potential of GS, especially when employing a multi-trait index like FAI-BLUP and accounting for M×E interactions, in pea breeding programs focused on rust resistance. [Read more]

Aphanomyces root rot (ARR), caused by Aphanomyces euteiches, is one of the most devastating diseases that affect the production of peas. Several control strategies such as crop rotation, biocontrol, and fungicides have been proposed, but none provides a complete solution. Therefore, the deployment of resistant cultivars is fundamental. ARR resistance breeding is hampered by the moderate levels of resistance identified so far. The available screening protocols require post-inoculation root assessment, which is destructive, time-consuming, and tedious. In an attempt to address these limitations, we developed a non-destructive screening protocol based on foliar symptoms and used it to identify new sources of resistance in a Pisum spp. germplasm collection. Accessions were root inoculated separately with two A. euteiches isolates, and leaf symptoms were assessed at 5, 10, 14, 17, and 20 days after inoculation (DAI). Although the majority of accessions exhibited high levels of susceptibility, thirty of them exhibited moderate resistance. These thirty accessions were selected for a second experiment, in which they were inoculated with both A. euteiches isolates at two inoculum doses. The objective of this second trial was to confirm the resistance of these accessions by evaluating root and biomass loss, as well as foliar symptoms, and to compare root and foliar evaluations. As a result, a high correlation (R2 = 0.75) between foliar and root evaluations was observed, validating the foliar evaluation method. Notably, accessions from P.s. subsp. humile exhibited the lowest symptomatology across all evaluation methods, representing valuable genetic resources for breeding programs aimed at developing pea varieties resistant to ARR. [Read more]

The changing climate could expand northwards in Europe the autumn sowing of cool-season grain legumes, to take advantage of milder winters and to escape the increasing risk of terminal drought. Greater frost tolerance is a key breeding target, also because sudden frosts following mild-temperature periods may produce high winter mortality of insufficiently acclimated plants. The increasing year-to-year climate variation hinders the field-based selection for frost tolerance. This study focused on pea and white lupin with the objectives of (i) optimizing an easy-to-build, high-throughput phenotyping platform for frost tolerance assessment with respect to optimal freezing temperatures, and (ii) verifying the consistency of genotype plant mortality responses across platform and field conditions. The platform was a 13.6 m2 freezing chamber with programmable temperature in the range of −15 °C to 25 °C. The study included 11 genotypes per species with substantial variation for field-based winter plant survival. Plant seedlings were evaluated under four freezing temperature treatments, i.e. −7, −9, −11, and −13 °C, after a 15-day acclimation period at 4 °C. Genotype plant mortality and lethal temperature corresponding to 50% mortality (LT50) were assessed at the end of a regrowth period, whereas biomass injury was observed through a visual score based on the amount of necrosis and mortality after recovery and regrowth. On average, pea displayed higher frost tolerance than white lupin (mean LT50 of −12.8 versus −11.0 °C). Genotype LT50 values ranged from −11.6 to −14.5 °C for pea, and from −10.0 to −12.0 °C for lupin. The freezing temperature that maximized the genotype mortality variation was −13 °C for pea and −11 °C for lupin. Genotype mortality at these temperatures exhibited high correlations with LT50 values (0.91 for pea; 0.94 for lupin) and the biomass injury score (0.98 for pea; 0.97 for lupin). Frost tolerance responses in the platform showed a good consistency with field-based winter survival of the genotypes. Our study indicates the reliability of genotype frost tolerance assessment under artificial conditions for two cool-season grain legumes, offering a platform that could be valuable for crop improvement as well as for genomics and ecophysiological research. [Read more]