Translational Genomics in Legumes Allowed Placing In Silico 5460 Unigenes on the Pea Functional Map and Identified Candidate Genes in Pisum sativum L.

To identify genes involved in phenotypic traits, translational genomics from highly characterized model plants to poorly characterized crop plants provides a valuable source of markers to saturate a zone of interest as well as functionally characterized candidate genes. In this paper, an integrated view of the pea genetic map was developed. A series of gene markers were mapped and their best reciprocal homologs were identified on M. truncatula, L. japonicus, soybean, and poplar pseudomolecules. Based on the syntenic relationships uncovered between pea and M. truncatula, 5460 pea Unigenes were tentatively placed on the consensus map. A new bioinformatics tool, http://www.thelegumeportal.net/pea_mtr_translational_toolkit, was developed that allows, for any gene sequence, to search its putative position on the pea consensus map and hence to search for candidate genes among neighboring Unigenes. As an example, a promising candidate gene for the hypernodulation mutation nod3 in pea was proposed based on the map position of the likely homolog of Pub1, a M. truncatula gene involved in nodulation regulation. A broader view of pea genome evolution was obtained by revealing syntenic relationships between pea and sequenced genomes. Blocks of synteny were identified which gave new insights into the evolution of chromosome structure in Papillionoids and Eudicots. The power of the translational genomics approach was underlined

Compétition de deux brassicacées adventices dans une culture de blé d'hiver

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Determinism and genetic diversity of pea intercropping ability in peawheat association

National audienceIntercropping pea (Pisum sativum) with cereals stabilizes the yield and overall quality of the products in the context of low-input cropping conditions thanks to a more efficient use of resources through interspecific competitive interactions [1] . In order to develop this cropping system which increases the sustainability of agroecosystems, we have launched a survey of pea genetic variability for intercropping ability. Field experiments were carried out over two years using 11 phenotypically diverse pea genotypes associated with two contrasted wheat varieties (height, precocity). We characterized several functional architecture and phenology traits potentially impacting the interactions among the two partners and with the environment. Indicators of the competition between the two partners for the light (interception of the incident radiation and periodic measurements of heights and closing velocity) and the soil resources (root biomass, nitrogen budget) were measured throughout the cropping cycle. We identified the most determining traits in terms of success of the association. Success was mainly evaluated by the yield components and the quality of the seeds, but also by lodging, biomass of weeds and impact on microbial diversity. The most critical traits for the success of pea/wheat association will be investigated by a genome wide association study (GWAS) in a collection of 170 pea accessions genotyped using 13K to 2M SNP markers from recent pea genomic resources [2] and cultivated in association with the same two contrasted wheat varieties. References: [1] Hauggard-Nielsen, H. et al., (2001). Evaluating pea and barley cultivars fo

Caractérisation fonctionnelle des partenaires de l’association pois/blés pour l’optimisation d’agro-écosystèmes durables via le levier génétique

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

National audiencePea 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

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

International audienceAvoidance 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

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

International audiencePea 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

Complementary approaches towards the discovery of genes controlling yield in pea

International audiencePea is one of the most important grain legumes in the world. Improving pea yield is a critical breedingtarget in the current context of consumers’ increasing demand for plant proteins for food and feed. Becauseof its polygenic nature and the impact of the environment, breeding for higher yield is challenging. Weinvestigated the genetic determinism of yield (SW), seed number (SN) and thousand seed weight (TSW) usingboth linkage and linkage-disequilibrium approaches.Nine interconnected mapping populations, representing a total of 1,213 recombinant inbred lineswere phenotyped for SW, SN and TSW in six different field environments. These lines were genotyped usingthe GenoPea 13.2K SNP Array [1]. A multi-population quantitative trait loci (QTL) analysis [2] identified 19 QTLfor SW, 18 QTL for SN and 36 QTL for TSW. From this first QTL analysis, a metaQTL analysis [3] detected 27metaQTL and reduced confidence intervals.In addition, two panels of conventional winter pea (376 accessions) and spring pea (300 accessions)were phenotyped for the same traits in seven different field environments. These accessions were genotypedby re-sequencing after exome capture [4]. A Genome Wide Association analysis [5] detected markerssignificantly associated with the 3 traits.The combination of these two genetic approaches highlighted common regions on the pea genomethat represent genomic regions consistently involved in controling yield and its components in pea. Theseresults represent an important step towards marker assisted breeding programs for yield improvement

Complementary approaches towards the discovery of genes controlling yield in pea

International audiencePea is one of the most important grain legumes in the world. Improving pea yield is a critical breedingtarget in the current context of consumers’ increasing demand for plant proteins for food and feed. Becauseof its polygenic nature and the impact of the environment, breeding for higher yield is challenging. Weinvestigated the genetic determinism of yield (SW), seed number (SN) and thousand seed weight (TSW) usingboth linkage and linkage-disequilibrium approaches.Nine interconnected mapping populations, representing a total of 1,213 recombinant inbred lineswere phenotyped for SW, SN and TSW in six different field environments. These lines were genotyped usingthe GenoPea 13.2K SNP Array [1]. A multi-population quantitative trait loci (QTL) analysis [2] identified 19 QTLfor SW, 18 QTL for SN and 36 QTL for TSW. From this first QTL analysis, a metaQTL analysis [3] detected 27metaQTL and reduced confidence intervals.In addition, two panels of conventional winter pea (376 accessions) and spring pea (300 accessions)were phenotyped for the same traits in seven different field environments. These accessions were genotypedby re-sequencing after exome capture [4]. A Genome Wide Association analysis [5] detected markerssignificantly associated with the 3 traits.The combination of these two genetic approaches highlighted common regions on the pea genomethat represent genomic regions consistently involved in controling yield and its components in pea. Theseresults represent an important step towards marker assisted breeding programs for yield improvement

The Pea genome and after …

International audienceHaving a genome sequence available is a critical step towards unravelling functional diversity andestablishing genome-enabled breeding. The recently generated pea genome sequence represents a great toolfor genomicists, geneticists and breeders not only for the pea community but also for legume research. In thegenome project, re-sequencing data revealed the considerable diversity present in the Pisum genus. In thePeaMUST project, an unprecedented effort was made to genotype large pea collections using the exomecapture technology. This high-density SNP data was exploited in genome-wide association studies (GWAS) ona large number of traits related to yield, as well as response to biotic and abiotic stresses. Comparative GWASand meta-QTL analysis identified important putative loci involved in the control of yield and its components inpea. Furthermore, genomic selection strategies have been developed in order to tackle complex traits such asyield regularity and improve selection efficiency. We will present snapshots of these results and discusspotential transfer of knowledge from pea to related crops