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.

Starch phosphorylation in potato tubers is influenced by allelic variation in the genes encoding glucan water dikinase, starch branching enzymes I and II, and starch synthase III.

Starch phosphorylation is an important aspect of plant metabolism due to its role in starch degradation. Moreover, the degree of phosphorylation of starch determines its physicochemical properties and is therefore relevant for industrial uses of starch. Currently, starch is chemically phosphorylated to increase viscosity and paste stability. Potato cultivars with elevated starch phosphorylation would make this process unnecessary, thereby bestowing economic and environmental benefits. Starch phosphorylation is a complex trait which has been previously shown by antisense gene repression to be influenced by a number of genes including those involved in starch synthesis and degradation. We have used an association mapping approach to discover genetic markers associated with the degree of starch phosphorylation. A diverse collection of 193 potato lines was grown in replicated field trials, and the levels of starch phosphorylation at the C6 and C3 positions of the glucosyl residues were determined by mass spectrometry of hydrolyzed starch from tubers. In addition, the potato lines were genotyped by amplicon sequencing and microsatellite analysis, focusing on candidate genes known to be involved in starch synthesis. As potato is an autotetraploid, genotyping included determination of allele dosage. Significant associations (p < 0.001) were found with SNPs in the glucan water dikinase (GWD), starch branching enzyme I (SBEI) and the starch synthase III (SSIII) genes, and with a SSR allele in the SBEII gene. SNPs in the GWD gene were associated with C6 phosphorylation, whereas polymorphisms in the SBEI and SBEII genes were associated with both C6 and C3 phosphorylation and the SNP in the SSIII gene was associated with C3 phosphorylation. These allelic variants have potential as genetic markers for starch phosphorylation in potato.