Genomic Selection for Ascochyta Blight Resistance in Pea.

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.

Association mapping of starch chain length distribution and amylose content in pea (Pisum sativum L.) using carbohydrate metabolism candidate genes.

BACKGROUND: Although starch consists of large macromolecules composed of glucose units linked by α-1,4-glycosidic linkages with α-1,6-glycosidic branchpoints, variation in starch structural and functional properties is found both within and between species. Interest in starch genetics is based on the importance of starch in food and industrial processes, with the potential of genetics to provide novel starches. The starch metabolic pathway is complex but has been characterized in diverse plant species, including pea. RESULTS: To understand how allelic variation in the pea starch metabolic pathway affects starch structure and percent amylose, partial sequences of 25 candidate genes were characterized for polymorphisms using a panel of 92 diverse pea lines. Variation in the percent amylose composition of extracted seed starch and (amylopectin) chain length distribution, one measure of starch structure, were characterized for these lines. Association mapping was undertaken to identify polymorphisms associated with the variation in starch chain length distribution and percent amylose, using a mixed linear model that incorporated population structure and kinship. Associations were found for polymorphisms in seven candidate genes plus Mendel's r locus (which conditions the round versus wrinkled seed phenotype). The genes with associated polymorphisms are involved in the substrate supply, chain elongation and branching stages of the pea carbohydrate and starch metabolic pathways. CONCLUSIONS: The association of polymorphisms in carbohydrate and starch metabolic genes with variation in amylopectin chain length distribution and percent amylose may help to guide manipulation of pea seed starch structural and functional properties through plant breeding.

Ascochyta blight disease of pea (Pisum sativum L.): defence-related candidate genes associated with QTL regions and identification of epistatic QTL.

KEY MESSAGE: Advances have been made in our understanding of Ascochyta blight resistance genetics through mapping candidate genes associated with QTL regions and demonstrating the importance of epistatic interactions in determining resistance. Ascochyta blight disease of pea (Pisum sativum L.) is economically significant with worldwide distribution. The causal pathogens are Didymella pinodes, Phoma medicaginis var pinodella and, in South Australia, P. koolunga. This study aimed to identify candidate genes that map to quantitative trait loci (QTL) for Ascochyta blight field disease resistance and to explore the role of epistatic interactions. Candidate genes associated with QTL were identified beginning with 101 defence-related genes from the published literature. Synteny between pea and Medicago truncatula was used to narrow down the candidates for mapping. Fourteen pea candidate sequences were mapped in two QTL mapping populations, A26 × Rovar and A88 × Rovar. QTL peaks, or the intervals containing QTL peaks, for the Asc2.1, Asc4.2, Asc4.3 and Asc7.1 QTL were defined by four of these candidate genes, while another three candidate genes occurred within 1.0 LOD confidence intervals. Epistasis involving QTL × background marker and background marker × background marker interactions contributed to the disease response phenotypes observed in the two mapping populations. For each population, five pairwise interactions exceeded the 5% false discovery rate threshold. Two candidate genes were involved in significant pairwise interactions. Markers in three genomic regions were involved in two or more epistatic interactions. Therefore, this study has identified pea defence-related sequences that are candidates for resistance determination, and that may be useful for marker-assisted selection. The demonstration of epistasis informs breeders that the architecture of this complex quantitative resistance includes epistatic interactions with non-additive effects.

Identification of Mendel's white flower character.

BACKGROUND: The genetic regulation of flower color has been widely studied, notably as a character used by Mendel and his predecessors in the study of inheritance in pea. METHODOLOGY/PRINCIPAL FINDINGS: We used the genome sequence of model legumes, together with their known synteny to the pea genome to identify candidate genes for the A and A2 loci in pea. We then used a combination of genetic mapping, fast neutron mutant analysis, allelic diversity, transcript quantification and transient expression complementation studies to confirm the identity of the candidates. CONCLUSIONS/SIGNIFICANCE: We have identified the pea genes A and A2. A is the factor determining anthocyanin pigmentation in pea that was used by Gregor Mendel 150 years ago in his study of inheritance. The A gene encodes a bHLH transcription factor. The white flowered mutant allele most likely used by Mendel is a simple G to A transition in a splice donor site that leads to a mis-spliced mRNA with a premature stop codon, and we have identified a second rare mutant allele. The A2 gene encodes a WD40 protein that is part of an evolutionarily conserved regulatory complex.

Validation of quantitative trait loci for Ascochyta blight resistance in pea ( Pisum sativum L.), using populations from two crosses.

Resistance to Ascochyta blight of pea was genetically characterized by mapping quantitative trait loci (QTLs) using two crosses, 3147-A26 (A26, partially resistant) x cultivar Rovar (susceptible) and 3148-A88 (A88, partially resistant) x Rovar, with the aim of developing an increased understanding of the genetics of resistance and of identifying linked molecular markers that may be used to develop resistant germplasm. Molecular linkage maps for both crosses were aligned so that the results of QTL mapping could be compared. Ascochyta blight disease severity in response to natural epidemics was measured in field trials conducted in Western Australia and New Zealand. Eleven putative QTLs for Ascochyta blight resistance were identified from the A26 x Rovar population and 14 putative QTLs from the A88 x Rovar population. Six QTLs were associated with the same genomic regions in both populations. These QTLs reside on linkage groups II, III, IV, V, and VII (two QTLs). The severity of Ascochyta blight disease symptoms on pea increases during field epidemics as plants mature; therefore, QTLs for plant reproductive maturity were mapped. Six QTLs were detected for plant maturity in the A26 x Rovar population, while five plant maturity QTLs were mapped in the A88 x Rovar population. QTLs for plant maturity coincide with Ascochyta blight resistance QTLs in four genomic regions, on linkage groups II (two regions), III, and V. The plant maturity and Ascochyta blight resistance QTLs on III were linked in repulsion phase. Therefore, the coincidence of these QTLs may be explained by linkage of distinct loci for the two traits. The QTLs on linkage groups II and V were linked in coupling phase; therefore, linked QTLs for resistance and maturity may be present in these regions, or the Ascochyta blight resistance QTLs detected in these regions are the result of pleiotropic effects of plant-maturity genetic loci.