Analysis of multi-environment yield performance of faba bean (Vacia Faba L.) genotypes using AMMI model

This study was carried out to determine the yield performance of 16 faba bean genotypes across 12 environments at Highlands of Bale, Southeastern Ethiopia in the 2004-2006 growing season. The experimental layout was randomized complete block design with four replications. Yield relative to environmental maximum (YREM) implies that the closer the YREM value to 1, the better performance of the genotype. The performance of the genotypes was generally good. Eighty-one percent [81.25%] (13 genotypes) had YREM of more than 50% while only 18.75% of the genotypes Ace. No. 25041, Ace. No. 25042 and Ace. No. 25194 achieved less than 50% of their potential. In contrast Ace. No. 25194, Ace. No. 25042 and Ace. No. 25041 had the lowest YREM. Regression coefficient plotted against the mean yield indicates that cvs Degaga (G13) and Shalo (G14) are the most stable and adapted to most of the environments. Ace. No. 25041 (G4) is below average stability by specifically adapted to high yielding environments. AMMI analysis of variance indicated that the yield performance of genotypes were under the major environmental effects of genotypes by environmental interaction (GEI). All principal components axis were significant at

Meta-analysis of GWAS in canola blackleg (Leptosphaeria maculans) disease traits demonstrates increased power from imputed whole-genome sequence

Blackleg disease causes yield losses in canola (Brassica napus L.). To identify resistance genes and genomic regions, genome-wide association studies (GWAS) of 585 diverse winter and spring canola accessions were performed using imputed whole-genome sequence (WGS) and transcriptome genotype-by-sequencing (GBSt). Blackleg disease phenotypes were collected across three years in six trials. GWAS were performed in several ways and their respective power was judged by the number of significant single nucleotide polymorphisms (SNP), the false discovery rate (FDR), and the percentage of SNP that validated in additional field trials in two subsequent years. WGS GWAS with 1,234,708 million SNP detected a larger number of significant SNP, achieved a lower FDR and a higher validation rate than GBSt with 64,072 SNP. A meta-analysis combining survival and average internal infection resulted in lower FDR but also lower validation rates. The meta-analysis GWAS identified 79 genomic regions (674 SNP) conferring potential resistance to L. maculans. While several GWAS signals localised in regions of known Rlm genes, fifty-three new potential resistance regions were detected. Seventeen regions had underlying genes with putative functions related to disease defence or stress response in Arabidopsis thaliana. This study provides insight into the genetic architecture and potential molecular mechanisms underlying canola L. maculans resistance

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

Data_Sheet_1_Multi-environment genomic prediction for soluble solids content in peach (Prunus persica).docx

Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual’s narrow-sense and broad-sense heritability for SSC were high (0.57–0.73 and 0.66–0.80, respectively), with 19–32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.</p

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