Genome-Wide Association Studies in Sunflower: Towards Sclerotinia sclerotiorum and Diaporthe/Phomopsis Resistance Breeding

Abstract

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.

Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance

“…Previous studies identified various loci involved in the quantitative resistance to V. dahliae and S. sclerotiorum, mostly through comprehensive analysis in biparental mapping, association mapping, or transcriptome differential expression studies [25,26,[30][31][32]55]. In an attempt to provide a biological context to the candidate genes derived either by colocalizing with a molecular marker in GWAS studies or by being located in between markers in QTL studies, we focused on the candidates predicted to be related to defense functions in our modules of interest.…”

“…After completion and analysis of our networks, we compiled lists of candidate genes previously associated with resistance to the sunflower fungal pathogens V. dahliae and S. sclerotiorum to assess the performance of our networks in predicting gene functions. We collected three groups of genes: Group A, a list of 26 candidate genes from Filippi et al [25]; Group B, genes that were close (±1 megabase) to markers from four QTL mapping and genome-wide association studies: Zubrzycki et al [30], Filippi et al [25], Filippi et al [31], and unpublished data from Montecchia et al; and Group C, genes that were in between markers flanking QTL regions from the work of Talukder et al [32]. It is noteworthy that groups B and C employed different germplasm in their respective studies, which allows for a broader range of genetic characteristics to be captured.…”

Co-expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance

“…Previous studies identified various loci involved in the quantitative resistance to V. dahliae and S. sclerotiorum, mostly through comprehensive analysis in biparental mapping, association mapping, or transcriptome differential expression studies [25,26,[30][31][32]46] (citas). In an attempt to provide a biological context to the candidate genes derived either by co-localizing with a molecular marker in GWAS studies or by being located in between markers of QTL studies, we focused on the candidates predicted as related to defense functions in our modules of interest.…”

“…After completion and analysis of our networks, we compiled lists of candidate genes previously associated with resistance to sunflower fungal pathogens V. dahliae and S. sclerotiorum to assess the performance of our networks in predicting gene functions. We collected three groups of genes: Group A, a list of 26 candidate genes from Filippi et al (2020) [25]; Group B, genes that were close (±1 megabase) to markers from four QTL mapping and genome-wide association studies: [25,30,31]; and Group C, genes that were in between markers flanking QTL regions from one study [32]. It is noteworthy that group B and group C employed different germplasm in their respective studies which allows for a broader range of genetic characteristics to be captured.…”