Dataset Information

Genomic Selection for Ascochyta Blight Resistance in Pea.

ABSTRACT: Genomic selection (GS) is a breeding tool, which is rapidly gaining popularity for plant breeding, particularly for traits that are difficult to measure. One such trait is ascochyta blight resistance in pea (Pisum sativum L.), which is difficult to assay because it is strongly influenced by the environment and depends on the natural occurrence of multiple pathogens. Here we report a study of the efficacy of GS for predicting ascochyta blight resistance in pea, as represented by ascochyta blight disease score (ASC), and using nucleotide polymorphism data acquired through genotyping-by-sequencing. The effects on prediction accuracy of different GS models and different thresholds for missing genotypic data (which modified the number of single nucleotide polymorphisms used in the analysis) were compared using cross-validation. Additionally, the inclusion of marker × environment interactions in a genomic best linear unbiased prediction (GBLUP) model was evaluated. Finally, different ways of combining trait data from two field trials using bivariate, spatial, and single-stage analyses were compared to results obtained using a mean value. The best prediction accuracy achieved for ASC was 0.56, obtained using GBLUP analysis with a mean value for ASC and data quality threshold of 70% (i.e., missing SNP data in <30% of lines). GBLUP and Bayesian Reproducing kernel Hilbert spaces regression (RKHS) performed slightly better than the other models trialed, whereas different missing data thresholds made minimal differences to prediction accuracy. The prediction accuracies of individual, randomly selected, testing/training partitions were highly variable, highlighting the effect that the choice of training population has on prediction accuracy. The inclusion of marker × environment interactions did not increase the prediction accuracy for lines which had not been phenotyped, but did improve the results of prediction across environments. GS is potentially useful for pea breeding programs pursuing ascochyta blight resistance, both for predicting breeding values for lines that have not been phenotyped, and for providing enhanced estimated breeding values for lines for which trait data is available.

SUBMITTER: Carpenter MA

PROVIDER: S-EPMC6306417 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Genomic Selection for Ascochyta Blight Resistance in Pea.

Carpenter Margaret A MA Goulden David S DS Woods Carmel J CJ Thomson Susan J SJ Kenel Fernand F Frew Tonya J TJ Cooper Rebecca D RD Timmerman-Vaughan Gail M GM

Frontiers in plant science 20181220

Genomic selection (GS) is a breeding tool, which is rapidly gaining popularity for plant breeding, particularly for traits that are difficult to measure. One such trait is ascochyta blight resistance in pea (<i>Pisum sativum</i> L.), which is difficult to assay because it is strongly influenced by the environment and depends on the natural occurrence of multiple pathogens. Here we report a study of the efficacy of GS for predicting ascochyta blight resistance in pea, as represented by ascochyta ...[more]

PMID: 30619430

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Project description:Ascochyta blight caused by Ascochyta pisi is a major constraint to pea (Pisum sativum L.) production worldwide. Deciphering the pathogenic mechanism of A. pisi on peas will help in breeding resistant pea varieties and developing effective approaches for disease management. However, little is known about the genomic features and pathogenic factors of A. pisi. In this study, we first report that A. pisi is one of the causal agents of ascochyta blight disease of pea in China. The genome of the representative isolate A. pisi HNA23 was sequenced using PacBio and Illumina sequencing technologies. The HNA23 genome assembly is almost 41.5 Mb in size and harbors 10,796 putative protein-encoding genes. We predicted 555 carbohydrate-active enzymes (CAZymes), 1,008 secreted proteins, 74 small secreted cysteine-rich proteins (SSCPs), and 26 secondary metabolite biosynthetic gene clusters (SMGCs). A comparison of A. pisi genome features with the features of 6 other available genomes of Ascochyta species showed that CAZymes, the secretome, and SMGCs of this genus are considerably conserved. Importantly, the transcriptomes of HNA23 during infection of peas at three stages were further analyzed. We found that 245 CAZymes and 29 SSCPs were upregulated at all three tested infection stages. SMGCs were also trigged, but most of them were induced at only one stage of infection. Together, our results provide important genomic information on Ascochyta spp. and offer insights into the pathogenesis of A. pisi. IMPORTANCE Ascochyta blight is a major disease of legumes worldwide. Ascochyta pisi and other Ascochyta species have been identified as pathogens of ascochyta blight. Here, we first report that A. pisi causes ascochyta blight of pea in China, and we report the high-quality, fully annotated genome of A. pisi. Comparative genome analysis was performed to elucidate the differences and similarities among 7 Ascochyta species. We predict abundant CAZymes (569 per species), secreted proteins (851 per species), and prolific secondary metabolite gene clusters (29 per species) in these species. We identified a set of genes that may be responsible for fungal virulence based on transcriptomes in planta, including CAZymes, SSCPs, and secondary metabolites. The findings from the comparative genome analysis highlight the genetic diversity and help in understanding the evolutionary relationship of Ascochyta species. In planta transcriptome analysis provides reliable information for further investigation of the mechanism of the interaction between Ascochyta spp. and legumes.

Project description:Didymella pinodes is the principal causal agent of ascochyta blight, one of the most important fungal diseases of pea (Pisum sativum) worldwide. Understanding its host specificity has crucial implications in epidemiology and management; however, this has not been clearly delineated yet. In this study we attempt to clarify the host range of D. pinodes and to compare it with that of other close Didymella spp. D. pinodes was very virulent on pea accessions, although differences in virulence were identified among isolates. On the contrary, studied isolates of D. fabae, D. rabiei, and D. lentil showed a reduced ability to infect pea not causing macroscopically visible symptoms on any of the pea accessions tested. D. pinodes isolates were also infective to some extend on almost all species tested including species such as Hedysarum coronarium, Lathyrus sativus, Lupinus albus, Medicago spp., Trifolium spp., Trigonella foenum-graecum, and Vicia articulata which were not mentioned before as hosts of D. pinodes. On the contrary, D. lentil and D. rabiei were more specific, infecting only lentil and chickpea, respectively. D. fabae was intermediate, infecting mainly faba bean, but also slightly other species such as Glycine max, Phaseolus vulgaris, Trifolium spp., Vicia sativa, and V. articulata. DNA sequence analysis of the nuclear ribosomal internal transcribed spacer region (ITS) was performed to confirm identity of the isolates studies and to determine phylogenetic relationship among the Didymella species, revealing the presence of two clearly distinct clades. Clade one was represented by two supported subclusters including D. fabae isolates as well as D. rabiei with D. lentil isolates. Clade two was the largest and included all the D. pinodes isolates as well as Phoma medicaginis var. pinodella. Genetic distance between D. pinodes and the other Didymella spp. isolates was not correlated with overall differences in pathogenicity. Based on evidences presented here, D. pinodes is not specialized on pea and its host range is larger than that of D. fabae, D. lentil, and D. rabiei. This has relevant implications in epidemiology and control as these species might act as alternative hosts for D. pinodes.

Project description:Ascochyta blight of lentil is an important fungal disease in many lentil-producing regions of the world causing major yield and grain quality losses. Quick shifts in aggressiveness of the population of the causal agent Ascochyta lentis mandates developing germplasm with novel and durable resistance. In the absence of complete resistance, lentil genotypes CDC Robin and 964a-46 have frequently been used as sources of partial resistance to ascochyta blight and carry non-allelic ascochyta blight resistance genes. RNA-seq analysis was conducted to identify differences in the transcriptome of CDC Robin, 964a-46 and the susceptible check Eston after inoculation with A. lentis. Candidate defense genes differentially expressed among the genotypes had hypothetical functions in various layers of plant defense, including pathogen recognition, phytohormone signaling pathways and downstream defense responses. CDC Robin and 964a-46 activated cell surface receptors (e.g. receptor like kinases) tentatively associated with pathogen-associated molecular patterns (PAMP) recognition and nucleotide-binding site leucine-rich repeat (NBS-LRR) receptors associated with intracellular effector recognition upon A. lentis infection, and differed in their activation of salicylic acid, abscisic acid and jasmonic acid / ethylene signal transduction pathways. These differences were reflected in the differential expression of downstream defense responses such as pathogenesis-related proteins, and genes associated with the induction of cell death and cell-wall reinforcement. A significant correlation between expression levels of a selection of genes based on quantitative real-time PCR and their expression levels estimated through RNA-seq demonstrated the technical and analytical accuracy of RNA-seq for identification of genes differentially expressed among genotypes. The presence of different resistance mechanisms in 964a-46 and CDC Robin indicates their value for pyramiding gene leading to more durable resistance to ascochyta blight.

Project description:BackgroundA thorough verification of the ability of genomic selection (GS) to predict estimated breeding values for pea (Pisum sativum L.) grain yield is pending. Prediction for different environments (inter-environment prediction) has key importance when breeding for target environments featuring high genotype × environment interaction (GEI). The interest of GS would increase if it could display acceptable prediction accuracies in different environments also for germplasm that was not used in model training (inter-population prediction).ResultsSome 306 genotypes belonging to three connected RIL populations derived from paired crosses between elite cultivars were genotyped through genotyping-by-sequencing and phenotyped for grain yield, onset of flowering, lodging susceptibility, seed weight and winter plant survival in three autumn-sown environments of northern or central Italy. The large GEI for grain yield and its pattern (implying larger variation across years than sites mainly due to year-to-year variability for low winter temperatures) encouraged the breeding for wide adaptation. Wider within-population than between-population variation was observed for nearly all traits, supporting GS application to many lines of relatively few elite RIL populations. Bayesian Lasso without structure imputation and 1% maximum genotype missing rate (including 6058 polymorphic SNP markers) was selected for GS modelling after assessing different GS models and data configurations. On average, inter-environment predictive ability using intra-population predictions reached 0.30 for yield, 0.65 for onset of flowering, 0.64 for seed weight, and 0.28 for lodging susceptibility. Using inter-population instead of intra-population predictions reduced the inter-environment predictive ability to 0.19 for grain yield, 0.40 for onset of flowering, 0.28 for seed weight, and 0.22 for lodging susceptibility. A comparison of GS vs phenotypic selection (PS) based on predicted genetic gains per unit time for same selection costs suggested greater efficiency of GS for all traits under various selection scenarios. For yield, the advantage in predicted efficiency of GS over PS was at least 80% using intra-population predictions and 20% using inter-population predictions. A genome-wide association study confirmed the highly polygenic control of most traits.ConclusionsGenome-enabled predictions can increase the efficiency of pea line selection for wide adaptation to Italian environments relative to phenotypic selection.

Dataset Information

Genomic Selection for Ascochyta Blight Resistance in Pea.

Publications

Genomic Selection for Ascochyta Blight Resistance in Pea.

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