A major-effect genetic locus, ApRVII, controlling resistance against both adapted and non-adapted aphid biotypes in pea.

KEY MESSAGE: A genome-wide association study for pea resistance against a pea-adapted biotype and a non-adapted biotype of the aphid, Acyrthosiphon pisum, identified a genomic region conferring resistance to both biotypes. In a context of reduced insecticide use, the development of cultivars resistant to insect pests is crucial for an integrated pest management. Pea (Pisum sativum) is a crop of major importance among cultivated legumes, for the supply of dietary proteins and nitrogen in low-input cropping systems. However, yields of the pea crop have become unstable due to plant parasites. The pea aphid (Acyrthosiphon pisum) is an insect pest species forming a complex of biotypes, each one adapted to feed on one or a few related legume species. This study aimed to identify resistance to A. pisum and the underlying genetic determinism by examining a collection of 240 pea genotypes. The collection was screened against a pea-adapted biotype and a non-adapted biotype of A. pisum to characterize their resistant phenotype. Partial resistance was observed in some pea genotypes exposed to the pea-adapted biotype. Many pea genotypes were completely resistant to non-adapted biotype, but some exhibited partial susceptibility. A genome-wide association study, using pea exome-capture sequencing data, enabled the identification of the major-effect quantitative trait locus ApRVII on the chromosome 7. ApRVII includes linkage disequilibrium blocks significantly associated with resistance to one or both of the two aphid biotypes studied. Finally, we identified candidate genes underlying ApRVII that are potentially involved in plant-aphid interactions and marker haplotypes linked with aphid resistance. This study sets the ground for the functional characterization of molecular pathways involved in pea defence to the aphids but also is a step forward for breeding aphid-resistant cultivars.

Full-length de novo assembly of RNA-seq data in pea (Pisum sativum L.) provides a gene expression atlas and gives insights into root nodulation in this species.

Next-generation sequencing technologies allow an almost exhaustive survey of the transcriptome, even in species with no available genome sequence. To produce a Unigene set representing most of the expressed genes of pea, 20 cDNA libraries produced from various plant tissues harvested at various developmental stages from plants grown under contrasting nitrogen conditions were sequenced. Around one billion reads and 100 Gb of sequence were de novo assembled. Following several steps of redundancy reduction, 46 099 contigs with N50 length of 1667 nt were identified. These constitute the 'Caméor' Unigene set. The high depth of sequencing allowed identification of rare transcripts and detected expression for approximately 80% of contigs in each library. The Unigene set is now available online (http://bios.dijon.inra.fr/FATAL/cgi/pscam.cgi), allowing (i) searches for pea orthologs of candidate genes based on gene sequences from other species, or based on annotation, (ii) determination of transcript expression patterns using various metrics, (iii) identification of uncharacterized genes with interesting patterns of expression, and (iv) comparison of gene ontology pathways between tissues. This resource has allowed identification of the pea orthologs of major nodulation genes characterized in recent years in model species, as a major step towards deciphering unresolved pea nodulation phenotypes. In addition to a remarkable conservation of the early transcriptome nodulation apparatus between pea and Medicago truncatula, some specific features were highlighted. The resource provides a reference for the pea exome, and will facilitate transcriptome and proteome approaches as well as SNP discovery in pea.

Development of two major resources for pea genomics: the GenoPea 13.2K SNP Array and a high-density, high-resolution consensus genetic map.

Single nucleotide polymorphism (SNP) arrays represent important genotyping tools for innovative strategies in both basic research and applied breeding. Pea is an important food, feed and sustainable crop with a large (about 4.45 Gbp) but not yet available genome sequence. In the present study, 12 pea recombinant inbred line populations were genotyped using the newly developed GenoPea 13.2K SNP Array. Individual and consensus genetic maps were built providing insights into the structure and organization of the pea genome. Largely collinear genetic maps of 3918-8503 SNPs were obtained from all mapping populations, and only two of these exhibited putative chromosomal rearrangement signatures. Similar distortion patterns in different populations were noted. A total of 12 802 transcript-derived SNP markers placed on a 15 079-marker high-density, high-resolution consensus map allowed the identification of ohnologue-rich regions within the pea genome and the localization of local duplicates. Dense syntenic networks with sequenced legume genomes were further established, paving the way for the identification of the molecular bases of important agronomic traits segregating in the mapping populations. The information gained on the structure and organization of the genome from this research will undoubtedly contribute to the understanding of the evolution of the pea genome and to its assembly. The GenoPea 13.2K SNP Array and individual and consensus genetic maps are valuable genomic tools for plant scientists to strengthen pea as a model for genetics and physiology and enhance breeding.

QTL analysis of frost damage in pea suggests different mechanisms involved in frost tolerance.

KEY MESSAGE: Avoidance mechanisms and intrinsic resistance are complementary strategies to improve winter frost tolerance and yield potential in field pea. The development of the winter pea crop represents a major challenge to expand plant protein production in temperate areas. Breeding winter cultivars requires the combination of freezing tolerance as well as high seed productivity and quality. In this context, we investigated the genetic determinism of winter frost tolerance and assessed its genetic relationship with yield and developmental traits. Using a newly identified source of frost resistance, we developed a population of recombinant inbred lines and evaluated it in six environments in Dijon and Clermont-Ferrand between 2005 and 2010. We developed a genetic map comprising 679 markers distributed over seven linkage groups and covering 947.1 cM. One hundred sixty-one quantitative trait loci (QTL) explaining 9-71 % of the phenotypic variation were detected across the six environments for all traits measured. Two clusters of QTL mapped on the linkage groups III and one cluster on LGVI reveal the genetic links between phenology, morphology, yield-related traits and frost tolerance in winter pea. QTL clusters on LGIII highlighted major developmental gene loci (Hr and Le) and the QTL cluster on LGVI explained up to 71 % of the winter frost damage variation. This suggests that a specific architecture and flowering ideotype defines frost tolerance in winter pea. However, two consistent frost tolerance QTL on LGV were independent of phenology and morphology traits, showing that different protective mechanisms are involved in frost tolerance. Finally, these results suggest that frost tolerance can be bred independently to seed productivity and quality.

New consistent QTL in pea associated with partial resistance to Aphanomyces euteiches in multiple French and American environments.

Partial resistances, often controlled by quantitative trait loci (QTL), are considered to be more durable than monogenic resistances. Therefore, a precursor to developing efficient breeding programs for polygenic resistance to pathogens should be a greater understanding of genetic diversity and stability of resistance QTL in plants. In this study, we deciphered the diversity and stability of resistance QTL to Aphanomyces euteiches in pea towards pathogen variability, environments and scoring criteria, from two new sources of partial resistance (PI 180693 and 552), effective in French and USA infested fields. Two mapping populations of 178 recombinant inbred lines each, derived from crosses between 552 or PI 180693 (partially resistant) and Baccara (susceptible), were used to identify QTL for Aphanomyces root rot resistance in controlled and in multiple French and USA field conditions using several resistance criteria. We identified a total of 135 additive-effect QTL corresponding to 23 genomic regions and 13 significant epistatic interactions associated with partial resistance to A. euteiches in pea. Among the 23 additive-effect genomic regions identified, five were consistently detected, and showed highly stable effects towards A. euteiches strains, environments, resistance criteria, condition tests and RIL populations studied. These results confirm the complexity of inheritance of partial resistance to A. euteiches in pea and provide good bases for the choice of consistent QTL to use in marker-assisted selection schemes to increase current levels of resistance to A. euteiches in pea breeding programs.

Meta-analysis of QTL reveals the genetic control of yield-related traits and seed protein content in pea.

Pea is one of the most important grain legume crops in temperate regions worldwide. Improving pea yield is a critical breeding target. Nine inter-connected pea recombinant inbred line populations were evaluated in nine environments at INRAE Dijon, France and genotyped using the GenoPea 13.2 K SNP array. Each population has been evaluated in two to four environments. A multi-population Quantitative Trait Loci (QTL) analysis for seed weight per plant (SW), seed number per plant (SN), thousand seed weight (TSW) and seed protein content (SPC) was done. QTL were then projected on the multi-population consensus map and a meta-analysis of QTL was performed. This analysis identified 17 QTL for SW, 16 QTL for SN, 35 QTL for TSW and 21 QTL for SPC, shedding light on trait relationships. These QTL were resolved into 27 metaQTL. Some of them showed small confidence intervals of less than 2 cM encompassing less than one hundred underlying candidate genes. The precision of metaQTL and the potential candidate genes reported in this study enable their use for marker-assisted selection and provide a foundation towards map-based identification of causal polymorphisms.

Transcriptional Reprogramming of Pea Leaves at Early Reproductive Stages.

Pea (Pisum sativum L.) is an important source of dietary proteins. Nutrient recycling from leaves contributes to the accumulation of seed proteins and is a pivotal determinant of protein yields in this grain legume. The aim of this study was to unveil the transcriptional regulations occurring in pea leaves before the sharp decrease in chlorophyll breakdown. As a prelude to this study, a time-series analysis of 15N translocation at the whole plant level was performed, which indicated that nitrogen recycling among organs was highly dynamic during this period and varied depending on nitrate availability. Leaves collected on vegetative and reproductive nodes were further analyzed by transcriptomics. The data revealed extensive transcriptome changes in leaves of reproductive nodes during early seed development (from flowering to 14 days after flowering), including an up-regulation of genes encoding transporters, and particularly of sulfate that might sustain sulfur metabolism in leaves of the reproductive part. This developmental period was also characterized by a down-regulation of cell wall-associated genes in leaves of both reproductive and vegetative nodes, reflecting a shift in cell wall structure. Later on, 27 days after flowering, genes potentially switching the metabolism of leaves toward senescence were pinpointed, some of which are related to ribosomal RNA processing, autophagy, or transport systems. Transcription factors differentially regulated in leaves between stages were identified and a gene co-expression network pointed out some of them as potential regulators of the above-mentioned biological processes. The same approach was conducted in Medicago truncatula to identify shared regulations with this wild legume species. Altogether the results give a global view of transcriptional events in leaves of legumes at early reproductive stages and provide a valuable resource of candidate genes that could be targeted by reverse genetics to improve nutrient remobilization and/or delay catabolic processes leading to senescence.

A reference genome for pea provides insight into legume genome evolution.

We report the first annotated chromosome-level reference genome assembly for pea, Gregor Mendel's original genetic model. Phylogenetics and paleogenomics show genomic rearrangements across legumes and suggest a major role for repetitive elements in pea genome evolution. Compared to other sequenced Leguminosae genomes, the pea genome shows intense gene dynamics, most likely associated with genome size expansion when the Fabeae diverged from its sister tribes. During Pisum evolution, translocation and transposition differentially occurred across lineages. This reference sequence will accelerate our understanding of the molecular basis of agronomically important traits and support crop improvement.

Unleashing meiotic crossovers in crops.

Improved plant varieties are important in our attempts to face the challenges of a growing human population and limited planet resources. Plant breeding relies on meiotic crossovers to combine favourable alleles into elite varieties1. However, meiotic crossovers are relatively rare, typically one to three per chromosome2, limiting the efficiency of the breeding process and related activities such as genetic mapping. Several genes that limit meiotic recombination were identified in the model species Arabidopsis thaliana2. Mutation of these genes in Arabidopsis induces a large increase in crossover frequency. However, it remained to be demonstrated whether crossovers could also be increased in crop species hybrids. We explored the effects of mutating the orthologues of FANCM3, RECQ44 or FIGL15 on recombination in three distant crop species, rice (Oryza sativa), pea (Pisum sativum) and tomato (Solanum lycopersicum). We found that the single recq4 mutation increases crossovers about three-fold in these crops, suggesting that manipulating RECQ4 may be a universal tool for increasing recombination in plants. Enhanced recombination could be used with other state-of-the-art technologies such as genomic selection, genome editing or speed breeding6 to enhance the pace and efficiency of plant improvement.

Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy.

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 (GS) 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 nine recombinant inbred lines populations as test set. Prediction quality was high with mean Q (2) of 0.44 for TSW and 0.59 for BegFlo. Results are discussed in the light of current efforts to develop GS strategies in pea.