Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Fikere M

PROVIDER: S-EPMC7356366 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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

Fikere Mulusew M Barbulescu Denise M DM Malmberg M Michelle MM Maharjan Pankaj P Salisbury Phillip A PA Kant Surya S Panozzo Joe J Norton Sally S Spangenberg German C GC Cogan Noel O I NOI Daetwyler Hans D HD

Plants (Basel, Switzerland) 20200605 6

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 Nucleo ...[more]

PMID: 32517116

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Project description:Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable potential for pre-selection of promising hybrid combinations prior to resource-intensive field testing over multiple locations and years.

Project description:Sustainable canola production is essential to meet growing human demands for vegetable oil, biodiesel, and meal for stock feed markets. Blackleg, caused by the fungal pathogen, Leptosphaeria maculans is a devastating disease that can lead to significant yield loss in many canola production regions worldwide. Breakdown of race-specific resistance to L. maculans in commercial cultivars poses a constant threat to the canola industry. To identify new alleles, especially for quantitative resistance (QR), we analyzed 177 doubled haploid (DH) lines derived from an RP04/Ag-Outback cross. DH lines were evaluated for QR under field conditions in three experiments conducted at Wagga Wagga (2013, 2014) and Lake Green (2015), and under shade house conditions using the 'ascospore shower' test. DH lines were also characterized for qualitative R gene-mediated resistance via cotyledon tests with two differential single spore isolates, IBCN17 and IBCN76, under glasshouse conditions. Based on 18,851 DArTseq markers, a linkage map representing 2,019 unique marker bins was constructed and then utilized for QTL detection. Marker regression analysis identified 22 significant marker associations for resistance, allowing identification of two race-specific resistance R genes, Rlm3 and Rlm4, and 21 marker associations for QR loci. At least three SNP associations for QR were repeatedly detected on chromosomes A03, A07 and C04 across phenotyping environments. Physical mapping of markers linked with these consistent QR loci on the B. napus genome assembly revealed their localization in close proximity of the candidate genes of B. napus BnaA03g26760D (A03), BnaA07g20240D (A07) and BnaC04g02040D (C04). Annotation of these candidate genes revealed their association with protein kinase and jumonji proteins implicated in defense resistance. Both Rlm3 and Rlm4 genes identified in this DH population did not show any association with resistance loci detected under either field and/or shade house conditions (ascospore shower) suggesting that both genes are ineffective in conferring resistance to L. maculans in Australian field conditions. Taken together, our study identified sequence-based molecular markers for dissecting R and QR loci to L. maculans in a canola DH population from the RP04/Ag-Outback cross.

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Dataset Information

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

Publications

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

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