Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola (Brassica napus L.).

Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015-2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding.

Quantifying the colour loss of green field pea (Pisum sativum L.) due to bleaching.

Post-harvest change in the colour of green field pea (Pisum sativum L.) is undesirable as this impacts the visual quality and market value of the seed. To date, there is no standard, objective method to determine bleaching. Therefore, the aim of this study was to develop an objective method for scoring bleaching based on colour reflectance spectra, measured both by spectrophotometer and multispectral Image Analysis (IA). Green field pea seeds were sorted into samples of uniform colour and these were used to train the model. Spectra calculated from multispectral images (with colour bands at 405,470,530,590,660 and 850nm) were matched to the spectrophotometer output through multiple linear regression. All spectra were transformed to emphasize the wavelength regions most impacted during bleaching, following which two critical reflectance values were scaled to a single bleaching score. The bleaching assessment method was tested in a time-course experiment comprising seeds from five green-pea genotypes stored for six months. Each sample was divided into two so that half of the seeds were stored in the dark and the remainder were exposed to controlled light to exaggerate bleaching. Throughout this period, the samples were imaged at six-weekly intervals. Assessment of bleaching by the IA method agreed well with spectrophotometer measurements, achieving a Lin's concordance statistic of 0.99 and 0.96 for the calibration and time-course samples respectively. The IA method proved more versatile because assessments could be made on individual seeds enabling the computation of bleaching uniformity within each sample. This method captured differences between genotypes in the extent, rate and uniformity of bleaching. All genotypes exhibited susceptibility to bleaching when stored under the controlled light conditions. Excell was observed to be the most susceptible genotype with the greatest bleaching-rate and OZB1308 displayed the most colour-stability.

Genomic Prediction Using Prior Quantitative Trait Loci Information Reveals a Large Reservoir of Underutilised Blackleg Resistance in Diverse Canola (Brassica napus L.) Lines.

Genomic prediction is becoming a popular plant breeding method to predict the genetic merit of lines. While some genomic prediction results have been reported in canola, none have been evaluated for blackleg disease. Here, we report genomic prediction for seedling emergence, survival rate, and internal infection), using 532 Spring and Winter canola lines. These lines were phenotyped in two replicated blackleg disease nurseries grown at Wickliffe and Green Lake, Victoria, Australia. A transcriptome genotyping-by-sequencing approach revealed 98,054 single nucleotide polymorphisms (SNPs) after quality control. We assessed various genomic prediction scenarios based on Genomic Best Linear Unbiased Prediction (GBLUP), BayesR and BayesRC, which can make use of prior quantitative trait loci information, via cross-validation. Clustering based on genomic relationships showed that Winter and Spring lines were genetically distinct, indicating limited gene flow between sets. Genetic correlations within traits between Spring and Winter lines ranged from 0.68 and 0.90 (mean = 0.76). Based on GBLUP in the whole population, moderate to high genomic prediction accuracies were achieved within environments (0.35-0.74) and were reduced across environments (0.28-0.58). Prediction accuracy within the Spring set ranged from 0.30-0.69, and from 0.19-0.71 within the Winter set. The BayesR model resulted in slightly lower accuracy to GBLUP. The proportion of genetic variance explained by known blackleg quantitative trait loci (QTL) was < 30%, indicating that there is a large reservoir of genetic variation in blackleg traits that remains to be discovered, but can be captured with genomic prediction. However, providing prior information of known QTL in the BayesRC method resulted in an increased prediction accuracy for survival and internal infection, particularly with Spring lines. Overall, these promising results indicate that genomic prediction will be a valuable tool to make use of all genetic variation to improve blackleg resistance in canola.

Is floral structure a reliable indicator of breeding system in the Brassicaceae?

This study investigated the usefulness of floral characters as a potential indicator of breeding system in the Brassicaceae. Initially, pod set, seed set and pollen tube growth experiments were carried out to confirm the breeding systems of 53 lines representing 25 different cultivated and weedy species from the Brassicaceae. The results of the pod set tests clearly differentiated between self-compatible and self-incompatible species. Floral characters were then evaluated on one or more lines of each of the 25 species. Fourteen floral characters were evaluated including, flower diameter, Cruden's outcrossing index, timing and direction of dehiscence and pollen-ovule ratio. Significant differences between species were evident in all of the floral characteristics evaluated. Flower diameter was generally larger in self-incompatible species than self-compatible species and pollen/ovule ratio was generally higher in self-incompatible species than self-compatible species. However, none of the floral characteristics was able to clearly differentiate the self-compatible and self-incompatible species and allow prediction of the breeding system with absolute confidence. The floral characteristic which was most effective at differentiating the two groups was anther direction at dehiscence.

Discriminant Analysis of Defective and Non-Defective Field Pea (Pisum sativum L.) into Broad Market Grades Based on Digital Image Features.

Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to achieve 87% and 77% accuracy in market grade classification of calibration and validation sample-sets respectively. Following these results, we used the same colour, shape and size traits to develop an LDA model which correctly classified over 97% of all validation samples as defective or non-defective.

Evidence from genome-wide simple sequence repeat markers for a polyphyletic origin and secondary centers of genetic diversity of Brassica juncea in China and India.

The oilseed Brassica juncea is an important crop with a long history of cultivation in India and China. Previous studies have suggested a polyphyletic origin of B. juncea and more than one migration from the primary to secondary centers of diversity. We investigated molecular genetic diversity based on 99 simple sequence repeat markers in 119 oilseed B. juncea varieties from China, India, Europe, and Australia to test whether molecular differentiation follows Vavilov's proposal of secondary centers of diversity in India and China. Two distinct groups were identified by markers in the A genome, and the same two groups were confirmed by markers in the B genome. Group 1 included accessions from central and western India, in addition to those from eastern China. Group 2 included accessions from central and western China, as well as those from northern and eastern India. European and Australian accessions were found only in Group 2. Chinese accessions had higher allelic diversity per accession (Group 1) and more private alleles per accession (Groups 1 and 2) than those from India. The marker data and geographic distribution of Groups 1 and 2 were consistent with two independent migrations of B. juncea from its center of origin in the Middle East and neighboring regions along trade routes to western China and northern India, followed by regional adaptation. Group 1 migrated further south and west in India, and further east in China, than Group 2. Group 2 showed diverse agroecological adaptation, with yellow-seeded spring-sown types in central and western China and brown-seeded autumn-sown types in India.