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Abstract Background Sclerotinia Head Rot (SHR) is one of the most damaging diseases of sunflower in Europe, Argentina, and USA, causing average yield reductions of 10 to 20 %, but leading to total production loss under favorable environmental conditions for the pathogen. Association Mapping (AM) is a promising choice for Quantitative Trait Locus (QTL) mapping, as it detects relationships between phenotypic variation and gene polymorphisms in existing germplasm without development of mapping populations. This article reports the identification of QTL for resistance to SHR based on candidate gene AM. Results A collection of 94 sunflower inbred lines were tested for SHR under field conditions using assisted inoculation with the fungal pathogen Sclerotinia sclerotiorum. Given that no biological mechanisms or biochemical pathways have been clearly identified for SHR, 43 candidate genes were selected based on previous transcript profiling studies in sunflower and Brassica napus infected with S. sclerotiorum. Associations among SHR incidence and haplotype polymorphisms in 16 candidate genes were tested using Mixed Linear Models (MLM) that account for population structure and kinship relationships. This approach allowed detection of a significant association between the candidate gene HaRIC_B and SHR incidence (P Conclusions These results suggest that AM will be useful in dissecting other complex traits in sunflower, thus providing a valuable tool to assist in crop breeding.

Diseases caused by necrotrophic fungi, such as the cosmopolitan Sclerotinia sclerotiorum and the Diaporthe/Phomopsis complex, are among the most destructive diseases of sunflower worldwide. The lack of complete resistance combined with the inefficiency of chemical control makes assisted breeding the best strategy for disease control. In this work, we present an integrated genome-wide association (GWA) study investigating the response of a diverse panel of sunflower inbred lines to both pathogens. Phenotypic data for Sclerotinia head rot (SHR) consisted of five disease descriptors (disease incidence, DI; disease severity, DS; area under the disease progress curve for DI, AUDPCI, and DS, AUDPCS; and incubation period, IP). Two disease descriptors (DI and DS) were evaluated for two manifestations of Diaporthe/Phomopsis: Phomopsis stem canker (PSC) and Phomopsis head rot (PHR). In addition, a principal component (PC) analysis was used to derive transformed phenotypes as inputs to a univariate GWA (PC-GWA). Genotypic data comprised a panel of 4269 single nucleotide polymorphisms (SNP), generated via genotyping-by-sequencing. The GWA analysis revealed 24 unique marker-trait associations for SHR, 19 unique marker-trait associations for Diaporthe/Phomopsis diseases, and 7 markers associated with PC1 and PC2. No common markers were found for the response to the two pathogens. Nevertheless, epistatic interactions were identified between markers significantly associated with the response to S. sclerotiorum and Diaporthe/Phomopsis. This suggests that, while the main determinants of resistance may differ for the two pathogens, there could be an underlying common genetic basis. The exploration of regions physically close to the associated markers yielded 364 genes, of which 19 were predicted as putative disease resistance genes. This work presents the first simultaneous evaluation of two manifestations of Diaporthe/Phomopsis in sunflower, and undertakes a comprehensive GWA study by integrating PSC, PHR, and SHR data. The multiple regions identified, and their exploration to identify candidate genes, contribute not only to the understanding of the genetic basis of resistance, but also to the development of tools for assisted breeding.

We report a comprehensive primary metabolite profiling of sunflower (Helianthus annuus) genotypes displaying contrasting behavior to Sclerotinia sclerotiorum infection. Applying a GC-MS-based metabolite profiling approach, we were able to identify differential patterns involving a total of 63 metabolites including major and minor sugars and sugar alcohols, organic acids, amino acids, fatty acids and few soluble secondary metabolites in the sunflower capitulum, the main target organ of pathogen attack. Metabolic changes and disease incidence of the two contrasting genotypes were determined throughout the main infection period (R5.2-R6). Both point-by-point and non-parametric statistical analyses showed metabolic differences between genotypes as well as interaction effects between genotype and time after inoculation. Network correlation analyses suggested that these metabolic changes were synchronized in a time-dependent manner in response to the pathogen. Concerted differential metabolic changes were detected to a higher extent in the susceptible, rather than the resistant genotype, thereby allowing differentiation of modules composed by intermediates of the same pathway which are highly interconnected in the susceptible line but not in the resistant one. Evaluation of these data also demonstrated a genotype specific regulation of distinct metabolic pathways, suggesting the importance of detection of metabolic patterns rather than specific metabolite changes when looking for metabolic markers differentially responding to pathogen infection. In summary, the GC-MS strategy developed in this study was suitable for detection of differences in carbon primary metabolism in sunflower capitulum, a tissue which is the main entry point for this and other pathogens which cause great detrimental impact on crop yield., (2009 Elsevier Ltd. All rights reserved.)