Identification of candidate genes for LepR1 resistance against Leptosphaeria maculans in Brassica napus.

Utilising resistance (R) genes, such as LepR1, against Leptosphaeria maculans, the causal agent of blackleg in canola (Brassica napus), could help manage the disease in the field and increase crop yield. Here we present a genome wide association study (GWAS) in B. napus to identify LepR1 candidate genes. Disease phenotyping of 104 B. napus genotypes revealed 30 resistant and 74 susceptible lines. Whole genome re-sequencing of these cultivars yielded over 3 million high quality single nucleotide polymorphisms (SNPs). GWAS in mixed linear model (MLM) revealed a total of 2,166 significant SNPs associated with LepR1 resistance. Of these SNPs, 2108 (97%) were found on chromosome A02 of B. napus cv. Darmor bzh v9 with a delineated LepR1_mlm1 QTL at 15.11-26.08 Mb. In LepR1_mlm1, there are 30 resistance gene analogs (RGAs) (13 nucleotide-binding site-leucine rich repeats (NLRs), 12 receptor-like kinases (RLKs), and 5 transmembrane-coiled-coil (TM-CCs)). Sequence analysis of alleles in resistant and susceptible lines was undertaken to identify candidate genes. This research provides insights into blackleg resistance in B. napus and assists identification of the functional LepR1 blackleg resistance gene.

Modeling first order additive × additive epistasis improves accuracy of genomic prediction for sclerotinia stem rot resistance in canola.

The fungus Sclerotinia sclerotiorum infects hundreds of plant species including many crops. Resistance to this pathogen in canola (Brassica napus L. subsp. napus) is controlled by numerous quantitative trait loci (QTL). For such polygenic traits, genomic prediction may be useful for breeding as it can capture many QTL at once while also considering nonadditive genetic effects. Here, we test application of common regression models to genomic prediction of S. sclerotiorum resistance in canola in a diverse panel of 218 plants genotyped at 24,634 loci. Disease resistance was scored by infection with an aggressive isolate and monitoring over 3 wk. We found that including first-order additive × additive epistasis in linear mixed models (LMMs) improved accuracy of breeding value estimation between 3 and 40%, depending on method of assessment, and correlation between phenotypes and predicted total genetic values by 14%. Bayesian models performed similarly to or worse than genomic relationship matrix-based models for estimating breeding values or overall phenotypes from genetic values. Bayesian ridge regression, which is most similar to the genomic relationship matrix-based approach in the amount of shrinkage it applies to marker effects, was the most accurate of this family of models. This confirms several studies indicating the highly polygenic nature of sclerotinia stem rot resistance. Overall, our results highlight the use of simple epistasis terms for prediction of breeding values and total genetic values for a complex disease resistance phenotype in canola.

Genotyping for Species Identification and Diversity Assessment Using Double-Digest Restriction Site-Associated DNA Sequencing (ddRAD-Seq).

Genotyping-by-sequencing (GBS) is a powerful approach for studying the genetic diversity of legume species. By using restriction enzymes or other methods to generate a reduced representation of the genome for sequencing, GBS can provide genome-wide single nucleotide polymorphisms (SNP) for diversity analysis at high throughput and low cost. Here we describe a novel double-digest restriction site-associated DNA sequencing (ddRAD-seq) approach. We also describe the downstream bioinformatic analysis of the sequencing data, including alignment to a reference genome, de novo assembly, SNP calling, phylogenetic analysis, and structure analysis.

Recent Findings Unravel Genes and Genetic Factors Underlying Leptosphaeria maculans Resistance in Brassica napus and Its Relatives.

Among the Brassica oilseeds, canola (Brassica napus) is the most economically significant globally. However, its production can be limited by blackleg disease, caused by the fungal pathogen Lepstosphaeria maculans. The deployment of resistance genes has been implemented as one of the key strategies to manage the disease. Genetic resistance against blackleg comes in two forms: qualitative resistance, controlled by a single, major resistance gene (R gene), and quantitative resistance (QR), controlled by numerous, small effect loci. R-gene-mediated blackleg resistance has been extensively studied, wherein several genomic regions harbouring R genes against L. maculans have been identified and three of these genes were cloned. These studies advance our understanding of the mechanism of R gene and pathogen avirulence (Avr) gene interaction. Notably, these studies revealed a more complex interaction than originally thought. Advances in genomics help unravel these complexities, providing insights into the genes and genetic factors towards improving blackleg resistance. Here, we aim to discuss the existing R-gene-mediated resistance, make a summary of candidate R genes against the disease, and emphasise the role of players involved in the pathogenicity and resistance. The comprehensive result will allow breeders to improve resistance to L. maculans, thereby increasing yield.