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1.  Gene-based SNP discovery and genetic mapping in pea 
Key message
Gene-based SNPs were identified and mapped in pea using five recombinant inbred line populations segregating for traits of agronomic importance.
Abstract
Pea (Pisum sativum L.) is one of the world’s oldest domesticated crops and has been a model system in plant biology and genetics since the work of Gregor Mendel. Pea is the second most widely grown pulse crop in the world following common bean. The importance of pea as a food crop is growing due to its combination of moderate protein concentration, slowly digestible starch, high dietary fiber concentration, and its richness in micronutrients; however, pea has lagged behind other major crops in harnessing recent advances in molecular biology, genomics and bioinformatics, partly due to its large genome size with a large proportion of repetitive sequence, and to the relatively limited investment in research in this crop globally. The objective of this research was the development of a genome-wide transcriptome-based pea single-nucleotide polymorphism (SNP) marker platform using next-generation sequencing technology. A total of 1,536 polymorphic SNP loci selected from over 20,000 non-redundant SNPs identified using deep transcriptome sequencing of eight diverse Pisum accessions were used for genotyping in five RIL populations using an Illumina GoldenGate assay. The first high-density pea SNP map defining all seven linkage groups was generated by integrating with previously published anchor markers. Syntenic relationships of this map with the model legume Medicago truncatula and lentil (Lens culinaris Medik.) maps were established. The genic SNP map establishes a foundation for future molecular breeding efforts by enabling both the identification and tracking of introgression of genomic regions harbouring QTLs related to agronomic and seed quality traits.
Electronic supplementary material
The online version of this article (doi:10.1007/s00122-014-2375-y) contains supplementary material, which is available to authorized users.
doi:10.1007/s00122-014-2375-y
PMCID: PMC4180032  PMID: 25119872
2.  Transcriptome sequencing for high throughput SNP development and genetic mapping in Pea 
BMC Genomics  2014;15:126.
Background
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.
Results
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.
Conclusions
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.
doi:10.1186/1471-2164-15-126
PMCID: PMC3925251  PMID: 24521263
Pisum sativum; Medicago truncatula; Next generation sequencing; Genetic diversity; Composite genetic map; Synteny; Marker assisted selection
3.  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, 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.
doi:10.1534/g3.111.000349
PMCID: PMC3276132  PMID: 22384322
Pisum sativum; functional consensus map; synteny; model legume species; translational genomics
4.  Highly-multiplexed SNP genotyping for genetic mapping and germplasm diversity studies in pea 
BMC Genomics  2010;11:468.
Background
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.
Results
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.
Conclusion
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.
doi:10.1186/1471-2164-11-468
PMCID: PMC3091664  PMID: 20701750
5.  UTILLdb, a Pisum sativum in silico forward and reverse genetics tool 
Genome Biology  2008;9(2):R43.
UTILLdb is a database of phenotypic and sequence information on mutant genes from a reference Pisum sativum EMS-mutant population.
The systematic characterization of gene functions in species recalcitrant to Agrobacterium-based transformation, like Pisum sativum, remains a challenge. To develop a high throughput forward and reverse genetics tool in pea, we have constructed a reference ethylmethane sulfonate mutant population and developed a database, UTILLdb, that contains phenotypic as well as sequence information on mutant genes. UTILLdb can be searched online for TILLING alleles, through the BLAST tool, or for phenotypic information about mutants by keywords.
doi:10.1186/gb-2008-9-2-r43
PMCID: PMC2374714  PMID: 18302733

Results 1-5 (5)