Pea is an important food and feed crop and a valuable component of low-input farming systems. Improving resistance to biotic and abiotic stresses is a major breeding target to enhance yield potential and regularity. Genomic selection has lately emerged as a promising technique to increase the accuracy and gain of marker-based selection. It uses genome-wide molecular marker data to predict the breeding values of candidate lines to selection. A collection of 339 genetic resource accessions (CRB339) was subjected to high-density genotyping using the GenoPea 13.2K SNP Array. Genomic prediction accuracy was evaluated for thousand seed weight (TSW), the number of seeds per plant (NSeed), and the date of flowering (BegFlo). Mean cross-environment prediction accuracies reached 0.83 for TSW, 0.68 for NSeed, and 0.65 for BegFlo. For each trait, the statistical method, the marker density, and/or the training population size and composition used for prediction were varied to investigate their effects on prediction accuracy: the effect was large for the size and composition of the training population but limited for the statistical method and marker density. Maximizing the relatedness between individuals in the training and test sets, through the CDmean-based method, significantly improved prediction accuracies. A cross-population cross-validation experiment was further conducted using the CRB339 collection as a training population set and 9 recombinant inbred lines populations as test set. Prediction quality was high with mean Q² of 0.44 for TSW and 0.59 for BegFlo. Results are discussed in the light of current efforts to develop genomic selection strategies in pea.

Summary: The afila (af) mutation causes the replacement of leaflets by a branched mass of tendrils in the compound leaves of pea – Pisum sativum L. This mutation was first described in 1953, and several reports of spontaneous af mutations and induced mutants with a similar phenotype exist. Despite widespread introgression into breeding material, the nature of af and the origin of the alleles used remain unknown. Here, we combine comparative genomics with reverse genetic approaches to elucidate the genetic determinants of af. We also investigate haplotype diversity using a set of AfAf and afaf cultivars and breeding lines and molecular markers linked to seven consecutive genes. Our results show that deletion of two tandemly arranged genes encoding Q‐type Cys(2)His(2) zinc finger transcription factors, PsPALM1a and PsPALM1b, is responsible for the af phenotype in pea. Eight haplotypes were identified in the af‐harbouring genomic region on chromosome 2. These haplotypes differ in the size of the deletion, covering more or less genes. Diversity at the af locus is valuable for crop improvement and sheds light on the history of pea breeding for improved standing ability. The results will be used to understand the function of PsPALM1a/b and to transfer the knowledge for innovation in related crops.

Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emission and loss of biodiversity. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value. Faba bean ( Vicia faba L.) has a high yield potential and is well-suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has grown to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, though with significant copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association (GWA) analysis to dissect the genetic basis of hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate improvement of sustainable protein production across Mediterranean, subtropical, and northern temperate agro-ecological zones.

Background & Summary: In addition to being the model plant used by Mendel ¹ to establish genetic laws, pea ( Pisum sativum L., 2n=14) is a major pulse crop cultivated in many temperate regions of the world. In order to face new challenges imposed particularly by global climate change and new regulations targeted at reducing chemical inputs, pea breeders have to take advantage of the genetic diversity present in the Pisum genepool to develop improved, resilient varieties. The aim of this study was to assess the genetic diversity of a pea germplasm collection and allow genome-wide association studies using this collection. To be able to perform genome-wide association approaches with high resolution, genotyping with a large set of genetic markers such as Single Nucleotide Polymorphism (SNP) markers well-spread over the genome is required. Rapid advances in second-generation sequencing technologies and the development of bioinformatic tools have revolutionized the access to and the characterization of available genetic diversity. High-density, high-throughput genotyping has been possible for a large number of species, including those with large and complex genomes ² such as pea (2n=14) which genome size is estimated to be 4.45 Gb ³ . In this study, which is part of the PeaMUST project ⁴ , we used a target capture technology based on pea transcriptome sequences to generate exome-enriched genomic libraries that were further subjected to Illumina sequencing in paired-end mode. This methodology was chosen because whole-genome resequencing is relatively expensive for species with large genomes and because capturing genetic variations in repeated non-coding regions is difficult to achieve or to interpret ⁵ . Whole-exome sequencing represented an interesting alternative that focused on coding regions only ^6,7 . Mapping the obtained reads on the reference pea genome sequence enabled the discovery of an abundant set of SNPs. The development of this resource is a crucial cornerstone in research and breeding projects towards boosting the improvement of pea production and quality.

Cool-season grain legumes are mostly grown over spring and summer due to poor frost tolerance. However, fall-sown varieties often provide higher yields, earlier harvests and avoid late-season drought and heat. Understanding the genetic determinism and molecular basis of frost tolerance is therefore crucial for developing high-performing winter varieties. This study aimed to (1) investigate the genetic architecture of frost tolerance in Vicia faba L. using 247 accessions phenotyped under four field environments, and (2) explore the conservation of frost tolerance loci in cool-season legumes using the OrthoLegKB translational research database. A genome-wide association study identified nineteen V. faba genomic regions with a high density of markers significantly associated with frost tolerance, on all chromosomes. Mapping of frost tolerance QTL from V. faba and related species obtained from the literature onto their respective reference genomes and their integration into OrthoLegKB revealed synteny of major QTL across V. faba , Pisum sativum , and/or Medicago truncatula , particularly near clusters of CBF/DREB1 genes. Frost tolerance QTL at the P. sativum Le locus, which controls internode length, were also syntenic with a frost tolerance QTL in V. faba . Synteny between frost tolerance QTL and those controlling phenology and physiology was found at other loci, suggesting pleiotropy. Finally, expression data from P. sativum and C. arietinum accessions grown under low temperature were considered as information source to highlight potential candidate genes underlying the conserved QTL. Overall, these results provide a valuable resource for understanding and improving frost tolerance in V. faba and other cool-season legumes, including orphan crops by knowledge transfer. The use of OrthoLegKB to explore the genetic and molecular determinism of target traits across species is worth generalising.