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In perennial plants and especially those propagated through cuttings, several genotypes can coexist in a single individual, thus leading to chimeras. When the variant induces a noticeable phenotype modification, it can lead to a new cultivar. Viticulture already took economic advantage of this natural phenomenon: for instance, the berry skin of ‘Pinot gris’ derived from ‘Pinot noir’ by the selection of a chimera. Chimeras could also impact other crucial traits without being visually identified. Periclinal chimera where the variant has entirely colonized a cell layer is the most stable and can be propagated through cuttings. In grapevine, two functional cell layers are present in leaves, L1 and L2. However, lateral roots are formed from the L2 cell layer only. Thus, comparing DNA sequences of roots and leaves could allow chimera detection. In this study we used new generation Hifi long reads sequencing and recent bioinformatics tools applied to ‘Merlot’ to detect periclinal chimeras. Sequencing of ‘Magdeleine Noire des Charentes’ and ‘Cabernet franc’, the parents of ‘Merlot’, allowed haplotype resolved assembly. Pseudomolecules were built with few contigs, in some occasions only one per chromosome. This high resolution allowed haplotype comparison. Annotation from PN40024 was transferred to all pseudomolecules. Through variant detection, periclinal chimeras were found on both haplotypes. These results open new perspectives on chimera detection, which is an important resource to improve cultivars through clonal selection or breed new ones. Detailed results will be presented and discussed. [ABSTRACT FROM AUTHOR]

Through multiple vegetative propagation cycles, clones accumulate mutations in somatic cells that are at the origin of clonal phenotypic diversity in grape. Clonal diversity provided clones such as Cabernet-Sauvignon N°470, Chardonnay Nu 548 and Pinot noir N° 777 which all produce wines of superior quality. The economic impact of clonal selection is therefore very high: since approx. 95% of the grapevines produced in French nurseries originate from the French clonal selection. In this study we provide the first broad description of polymorphism in different clones of a single grapevine cultivar, Pinot noir, in the context of vegetative propagation. Genome sequencing was performed using 454 GS-FLX methodology without a priori, in order to identify and quantify for the first time molecular polymorphisms responsible for clonal variability in grapevine. New generation sequencing (NGS) was used to compare a large portion of the genome of three Pinot noir clones selected for their phenotypic differences. Reads obtained with NGS and the sequence of Pinot noir ENTAV-INRA® 115 sequenced by Velasco et al., were aligned on the PN40024 reference sequence. We then searched for molecular polymorphism between clones. Three types of polymorphism (SNPs, Indels, mobile elements) were found but insertion polymorphism generated by mobile elements of many families displayed the highest mutational event with respect to clonal variation. Mobile elements inducing insertion polymorphism in the genome of Pinot noir were identified and classified and a list is presented in this study as potential markers for the study of clonal variation. Among these, the dynamic of four mobile elements with a high polymorphism level were analyzed and insertion polymorphism was confirmed in all the Pinot clones registered in France. [ABSTRACT FROM AUTHOR]

The genome of modern sugarcane cultivars is highly polyploid (~12x), aneuploid, of interspecific origin, and contains 10 Gb of DNA. Its size and complexity represent a major challenge for the isolation of agronomically important genes. Here we report on the first attempt to isolate a gene from sugarcane by map-based cloning, targeting a durable major rust resistance gene (Bru1). We describe the genomic strategies that we have developed to overcome constraints associated with high polyploidy in the successive steps of map-based cloning approaches, including diploid/polyploid syntenic shuttle mapping with two model diploid species (sorghum and rice) and haplotype-specific chromosome walking. Their applications allowed us (i) to develop a high-resolution map including markers at 0.28 and 0.14cM on both sides and 13 markers cosegregating with Bru1 and (ii) to develop a physical map of the target haplotype that still includes two gaps at this stage due to the discovery of an insertion specific to this haplotype. These approaches will pave the way for the development of future map-based cloning approaches for sugarcane and other complex polyploid species. [ABSTRACT FROM AUTHOR]

Grapevine (Vitis vinifera) breeding reaches a critical point. New cultivars are released every year with resistance to powdery and downy mildews. However, the traditional process remains time-consuming, taking 20–25 years, and demands the evaluation of new traits to enhance grapevine adaptation to climate change. Until now, the selection process has relied on phenotypic data and a limited number of molecular markers for simple genetic traits such as resistance to pathogens, without a clearly defined ideotype, and was carried out on a large scale. To accelerate the breeding process and address these challenges, we investigated the use of genomic prediction, a methodology using molecular markers to predict genotypic values. In our study, we focused on 2 existing grapevine breeding programs: Rosé wine and Cognac production. In these programs, several families were created through crosses of emblematic and interspecific resistant varieties to powdery and downy mildews. Thirty traits were evaluated for each program, using 2 genomic prediction methods: Genomic Best Linear Unbiased Predictor and Least Absolute Shrinkage Selection Operator. The results revealed substantial variability in predictive abilities across traits, ranging from 0 to 0.9. These discrepancies could be attributed to factors such as trait heritability and trait characteristics. Moreover, we explored the potential of across-population genomic prediction by leveraging other grapevine populations as training sets. Integrating genomic prediction allowed us to identify superior individuals for each program, using multivariate selection index method. The ideotype for each breeding program was defined collaboratively with representatives from the wine-growing sector. [ABSTRACT FROM AUTHOR]