Quantitative Trait Loci Associated to Drought Adaptation in Pea (Pisum sativum L.)

Iglesias-García, Rebeca; Prats, Elena; Fondevilla, Sara; Šatović, Zlatko; Rubiales, Diego

doi:10.1007/s11105-015-0872-z

Cited by 44 publications

(14 citation statements)

References 63 publications

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“…syriacum were used in this experiment to assess their proteomic response under water stress. These cultivars were selected based on previous screenings in which Kebby, Messire, and P665 were characterized as susceptible, moderately tolerant, and tolerant to water stress, respectively.…”

Section: Methodsmentioning

confidence: 99%

Label‐free quantitative proteomic analysis of tolerance to drought in Pisum sativum

et al. 2016

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Abiotic stresses caused by adverse environmental conditions are responsible for heavy economic losses on pea crop, being drought one of the most important abiotic constraints. Development of pea cultivars well adapted to dry conditions has been one of the major tasks in breeding programs. The increasing food requirements drive the necessity to broaden the molecular basis of tolerance to drought to develop pea cultivars well adapted to dry conditions. We have used a shotgun proteomic approach (nLC-MSMS) to study the tolerance to drought in three pea genotypes that were selected based on differences in the level of water deficit tolerance. Multivariate statistical analysis of data unraveled 367 significant differences of 700 identified when genotypes and/or treatment were compared. More than half of the significantly changed proteins belong to primary metabolism and protein regulation categories. We propose different mechanisms to cope drought in the genotypes studied. Maintenance of the primary metabolism and protein protection seems a strategy for drought tolerance. On the other hand susceptibility might be related to maintenance of the homeostatic equilibrium, a very energy consuming process. Data are available via ProteomeXchange with identifier PXD004587.

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Section: Methodsmentioning

confidence: 99%

Label‐free quantitative proteomic analysis of tolerance to drought in Pisum sativum

et al. 2016

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“…Among legume crops, similar research has only been reported in cowpea (Lucas et al, 2013). However, advances have been made in mapping QTLs for resistance to abiotic stresses in pea, including for drought (Iglesias-Garcia et al, 2015), frost (Lejeune-Hénaut et al, 2008;Klein et al, 2014), and salinity (Leonforte et al, 2013). However, advances have been made in mapping QTLs for resistance to abiotic stresses in pea, including for drought (Iglesias-Garcia et al, 2015), frost (Lejeune-Hénaut et al, 2008;Klein et al, 2014), and salinity (Leonforte et al, 2013).…”

Section: Introductionmentioning

confidence: 92%

Pea Phenology: Crop Potential in a Warming Environment

et al. 2017

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One hundred and seven recombinant inbred lines (RILs) were developed from the cross of field pea (Pisum sativum L.) cultivars CDC Centennial × CDC Sage with the objectives of evaluating phenology and yield components, and to map the quantitative trait loci (QTLs) responsible for these traits. Experiments were conducted in 2013 using normal seeding date at Saskatoon and Rosthern in Saskatchewan, and in 2014 using both normal and late seeding. The late seeding date was used to expose the plots to a more heat stressful environment, analogous to that experienced in warmer regions of the North American prairies. Days to flowering termination (DTFT) was positively correlated with final seed yield under both normal and late seeding conditions. Among the yield components, pod number (PN) was most positively associated with seed yield, followed by thousand seed weight (TSW) and seed number per pod (SNPP). A genetic linkage map consisting of 1024 loci with a total coverage of 1702 cM was developed using SNP markers. Ten QTLs were found consistent over more than one environment, five for flowering traits and five for yield component traits. A stable QTL at Linkage Group 6b for days to flowering was detected over four environments. The QTLs for flowering duration, TSW and reproductive node number were different between normal and late seeding, which implies different mechanisms were involved under the contrasting environments.

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“…These three (raffinose, stachyose, verbascose) are related in terms of their biosynthesis (Peterbauer et al, 2002 ). One of the enzymes involved (raffinose synthase, Rfs) shows genetic variation that maps approximately centrally on LG III (close to PSAB124, PSAA491 and PSAC18 markers in the JI 281 × JI 399 population; close to agpS1_SNP3 on LG III in Iglesias-García et al, 2015 ). The gene encoding a second enzyme of this pathway, stachyose synthase (Sts), has been mapped to LG V in another cross (cv.…”

Section: Resultsmentioning

confidence: 99%

NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites

et al. 2018

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Nuclear magnetic resonance (NMR) spectroscopy profiling was used to provide an unbiased assessment of changes to the metabolite composition of seeds and to define genetic variation for a range of pea seed metabolites. Mature seeds from recombinant inbred lines, derived from three mapping populations for which there is substantial genetic marker linkage information, were grown in two environments/years and analyzed by non-targeted NMR. Adaptive binning of the NMR metabolite data, followed by analysis of quantitative variation among lines for individual bins, identified the main genomic regions determining this metabolic variability and the variability for selected compounds was investigated. Analysis by t-tests identified a set of bins with highly significant associations to genetic map regions, based on probability (p) values that were appreciably lower than those determined for randomized data. The correlation between bins showing high mean absolute deviation and those showing low p-values for marker association provided an indication of the extent to which the genetics of bin variation might be explained by one or a few loci. Variation in compounds related to aromatic amino acids, branched-chain amino acids, sucrose-derived metabolites, secondary metabolites and some unidentified compounds was associated with one or more genetic loci. The combined analysis shows that there are multiple loci throughout the genome that together impact on the abundance of many compounds through a network of interactions, where individual loci may affect more than one compound and vice versa. This work therefore provides a framework for the genetic analysis of the seed metabolome, and the use of genetic marker data in the breeding and selection of seeds for specific seed quality traits and compounds that have high commercial value.

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Quantitative Trait Loci Associated to Drought Adaptation in Pea (Pisum sativum L.)

Abstract: Quantitative trait loci associated to drought tolerance in pea (Pisum sativum L.).

Cited by 44 publications

References 63 publications

Label‐free quantitative proteomic analysis of tolerance to drought in Pisum sativum

Label‐free quantitative proteomic analysis of tolerance to drought in Pisum sativum

Pea Phenology: Crop Potential in a Warming Environment

NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites

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