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1.  Genome-wide association mapping of partial resistance to Aphanomyces euteiches in pea 
BMC Genomics  2016;17:124.
Genome-wide association (GWA) mapping has recently emerged as a valuable approach for refining the genetic basis of polygenic resistance to plant diseases, which are increasingly used in integrated strategies for durable crop protection. Aphanomyces euteiches is a soil-borne pathogen of pea and other legumes worldwide, which causes yield-damaging root rot. Linkage mapping studies reported quantitative trait loci (QTL) controlling resistance to A. euteiches in pea. However the confidence intervals (CIs) of these QTL remained large and were often linked to undesirable alleles, which limited their application in breeding. The aim of this study was to use a GWA approach to validate and refine CIs of the previously reported Aphanomyces resistance QTL, as well as identify new resistance loci.
A pea-Aphanomyces collection of 175 pea lines, enriched in germplasm derived from previously studied resistant sources, was evaluated for resistance to A. euteiches in field infested nurseries in nine environments and with two strains in climatic chambers. The collection was genotyped using 13,204 SNPs from the recently developed GenoPea Infinium® BeadChip.
GWA analysis detected a total of 52 QTL of small size-intervals associated with resistance to A. euteiches, using the recently developed Multi-Locus Mixed Model. The analysis validated six of the seven previously reported main Aphanomyces resistance QTL and detected novel resistance loci. It also provided marker haplotypes at 14 consistent QTL regions associated with increased resistance and highlighted accumulation of favourable haplotypes in the most resistant lines. Previous linkages between resistance alleles and undesired late-flowering alleles for dry pea breeding were mostly confirmed, but the linkage between loci controlling resistance and coloured flowers was broken due to the high resolution of the analysis. A high proportion of the putative candidate genes underlying resistance loci encoded stress-related proteins and others suggested that the QTL are involved in diverse functions.
This study provides valuable markers, marker haplotypes and germplasm lines to increase levels of partial resistance to A. euteiches in pea breeding.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-016-2429-4) contains supplementary material, which is available to authorized users.
PMCID: PMC4761183  PMID: 26897486
Root rot; Plant disease resistance; GWAS; Pea (Pisum sativum); Quantitative trait loci; Marker haplotype; Candidate genes
2.  De novo construction of a “Gene-space” for diploid plant genome rich in repetitive sequences by an iterative Process of Extraction and Assembly of NGS reads (iPEA protocol) with limited computing resources 
BMC Research Notes  2016;9:81.
The continuing increase in size and quality of the “short reads” raw data is a significant help for the quality of the assembly obtained through various bioinformatics tools. However, building a reference genome sequence for most plant species remains a significant challenge due to the large number of repeated sequences which are problematic for a whole-genome quality de novo assembly. Furthermore, for most SNP identification approaches in plant genetics and breeding, only the “Gene-space” regions including the promoter, exon and intron sequences are considered.
We developed the iPea protocol to produce a de novo Gene-space assembly by reconstructing, in an iterative way, the non-coding sequence flanking the Unigene cDNA sequence through addition of next-generation DNA-seq data. The approach was elaborated with the large diploid genome of pea (Pisumsativum L.), rich in repetitive sequences. The final Gene-space assembly included 35,400 contigs (97 Mb), covering 88 % of the 40,227 contigs (53.1 Mb) of the PsCam_low-copy Unigen set. Its accuracy was validated by the results of the built GenoPea 13.2 K SNP Array.
The iPEA protocol allows the reconstruction of a Gene-space based from RNA-Seq and DNA-seq data with limited computing resources.
Electronic supplementary material
The online version of this article (doi:10.1186/s13104-016-1903-z) contains supplementary material, which is available to authorized users.
PMCID: PMC4750290  PMID: 26864345
Gene-space; Unigene; Next-generation sequencing NGS; Assembly; Iterative process; Limited computing resources
3.  Genetic diversity and trait genomic prediction in a pea diversity panel 
BMC Genomics  2015;16(1):105.
Pea (Pisum sativum L.), a major pulse crop grown for its protein-rich seeds, is an important component of agroecological cropping systems in diverse regions of the world. New breeding challenges imposed by global climate change and new regulations urge pea breeders to undertake more efficient methods of selection and better take advantage of the large genetic diversity present in the Pisum sativum genepool. Diversity studies conducted so far in pea used Simple Sequence Repeat (SSR) and Retrotransposon Based Insertion Polymorphism (RBIP) markers. Recently, SNP marker panels have been developed that will be useful for genetic diversity assessment and marker-assisted selection.
A collection of diverse pea accessions, including landraces and cultivars of garden, field or fodder peas as well as wild peas was characterised at the molecular level using newly developed SNP markers, as well as SSR markers and RBIP markers. The three types of markers were used to describe the structure of the collection and revealed different pictures of the genetic diversity among the collection. SSR showed the fastest rate of evolution and RBIP the slowest rate of evolution, pointing to their contrasted mode of evolution. SNP markers were then used to predict phenotypes -the date of flowering (BegFlo), the number of seeds per plant (Nseed) and thousand seed weight (TSW)- that were recorded for the collection. Different statistical methods were tested including the LASSO (Least Absolute Shrinkage ans Selection Operator), PLS (Partial Least Squares), SPLS (Sparse Partial Least Squares), Bayes A, Bayes B and GBLUP (Genomic Best Linear Unbiased Prediction) methods and the structure of the collection was taken into account in the prediction. Despite a limited number of 331 markers used for prediction, TSW was reliably predicted.
The development of marker assisted selection has not reached its full potential in pea until now. This paper shows that the high-throughput SNP arrays that are being developed will most probably allow for a more efficient selection in this species.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-1266-1) contains supplementary material, which is available to authorized users.
PMCID: PMC4355348  PMID: 25765216
4.  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 Q2 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.
PMCID: PMC4648083  PMID: 26635819
pea (Pisum sativum L.); GenoPea 13.2K SNP Array; genomic selection; marker density; training set; prediction accuracy
5.  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.
PMCID: PMC4661580  PMID: 26640470
pea (Pisum sativum L.); breeding targets; genetic diversity; genomic resources; genotyping platforms; genetic maps; QTL and association mapping
6.  Unexpectedly low nitrogen acquisition and absence of root architecture adaptation to nitrate supply in a Medicago truncatula highly branched root mutant 
Journal of Experimental Botany  2014;65(9):2365-2380.
Physiological and developmental analyses provide evidence that the highly branched root architecture of a mutant results from systemic regulation by its nitrogen status, possibly involving glutamine or asparagine signals.
To complement N2 fixation through symbiosis, legumes can efficiently acquire soil mineral N through adapted root architecture. However, root architecture adaptation to mineral N availability has been little studied in legumes. Therefore, this study investigated the effect of nitrate availability on root architecture in Medicago truncatula and assessed the N-uptake potential of a new highly branched root mutant, TR185. The effects of varying nitrate supply on both root architecture and N uptake were characterized in the mutant and in the wild type. Surprisingly, the root architecture of the mutant was not modified by variation in nitrate supply. Moreover, despite its highly branched root architecture, TR185 had a permanently N-starved phenotype. A transcriptome analysis was performed to identify genes differentially expressed between the two genotypes. This analysis revealed differential responses related to the nitrate acquisition pathway and confirmed that N starvation occurred in TR185. Changes in amino acid content and expression of genes involved in the phenylpropanoid pathway were associated with differences in root architecture between the mutant and the wild type.
PMCID: PMC4036509  PMID: 24706718
Amino acids; highly branched root mutant; Medicago truncatula; nitrogen acquisition; nitrogen limitation; phenylpropanoid; root architecture.
7.  Transcriptome sequencing for high throughput SNP development and genetic mapping in Pea 
BMC Genomics  2014;15:126.
Pea has a complex genome of 4.3 Gb for which only limited genomic resources are available to date. Although SNP markers are now highly valuable for research and modern breeding, only a few are described and used in pea for genetic diversity and linkage analysis.
We developed a large resource by cDNA sequencing of 8 genotypes representative of modern breeding material using the Roche 454 technology, combining both long reads (400 bp) and high coverage (3.8 million reads, reaching a total of 1,369 megabases). Sequencing data were assembled and generated a 68 K unigene set, from which 41 K were annotated from their best blast hit against the model species Medicago truncatula. Annotated contigs showed an even distribution along M. truncatula pseudochromosomes, suggesting a good representation of the pea genome. 10 K pea contigs were found to be polymorphic among the genetic material surveyed, corresponding to 35 K SNPs.
We validated a subset of 1538 SNPs through the GoldenGate assay, proving their ability to structure a diversity panel of breeding germplasm. Among them, 1340 were genetically mapped and used to build a new consensus map comprising a total of 2070 markers. Based on blast analysis, we could establish 1252 bridges between our pea consensus map and the pseudochromosomes of M. truncatula, which provides new insight on synteny between the two species.
Our approach created significant new resources in pea, i.e. the most comprehensive genetic map to date tightly linked to the model species M. truncatula and a large SNP resource for both academic research and breeding.
PMCID: PMC3925251  PMID: 24521263
Pisum sativum; Medicago truncatula; Next generation sequencing; Genetic diversity; Composite genetic map; Synteny; Marker assisted selection
8.  Translational Genomics in Legumes Allowed Placing In Silico 5460 Unigenes on the Pea Functional Map and Identified Candidate Genes in Pisum sativum L. 
G3: Genes|Genomes|Genetics  2011;1(2):93-103.
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,, 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.
PMCID: PMC3276132  PMID: 22384322
Pisum sativum; functional consensus map; synteny; model legume species; translational genomics
9.  Highly-multiplexed SNP genotyping for genetic mapping and germplasm diversity studies in pea 
BMC Genomics  2010;11:468.
Single Nucleotide Polymorphisms (SNPs) can be used as genetic markers for applications such as genetic diversity studies or genetic mapping. New technologies now allow genotyping hundreds to thousands of SNPs in a single reaction.
In order to evaluate the potential of these technologies in pea, we selected a custom 384-SNP set using SNPs discovered in Pisum through the resequencing of gene fragments in different genotypes and by compiling genomic sequence data present in databases. We then designed an Illumina GoldenGate assay to genotype both a Pisum germplasm collection and a genetic mapping population with the SNP set.
We obtained clear allelic data for more than 92% of the SNPs (356 out of 384). Interestingly, the technique was successful for all the genotypes present in the germplasm collection, including those from species or subspecies different from the P. sativum ssp sativum used to generate sequences. By genotyping the mapping population with the SNP set, we obtained a genetic map and map positions for 37 new gene markers.
Our results show that the Illumina GoldenGate assay can be used successfully for high-throughput SNP genotyping of diverse germplasm in pea. This genotyping approach will simplify genotyping procedures for association mapping or diversity studies purposes and open new perspectives in legume genomics.
PMCID: PMC3091664  PMID: 20701750

Results 1-9 (9)