The methodology of constructing a high-density grapevine consensus map: integrating data from three mapping populations using the Vitis18K SNP chip with a reference genome sequence

The improvement of grapevine through biotechnology requires the elucidation of molecular determinants of traits of interest. This can be achieved by studying the association between molecular markers, such as SNPs, and target traits. The Vitis18K SNP chip provides an inexpensive genotyping tool allowing genome-wide marker analysis across populations. However, data analysis in linkage map construction for large data sets can be time consuming or require extensive coding skills. Different algorithms can facilitate faster analyses, but often at the cost of number of markers mapped, or accuracy. Moreover, most linkage maps are based on single mapping populations, but a consensus map obtained from multiple populations can increase marker density and provide insight into marker order conservation. In this study, a workflow was created which combines the genetic data of the mapping populations with a reference genome sequence to construct linkage maps. Applying this workflow, high-density maps were created for three populations, deriving from the well-known wine cultivars ‘Cabernet Sauvignon’, ‘Corvina’ and ‘Rhine Riesling’, the lesser known ‘Deckrot’ and a table grape selection, G1-7720. Furthermore, the challenges and solutions in building a consensus map using a dense data set are described. Using the graph-based theory, a consensus map was constructed from the three mapping populations, which contains 6,697 markers with an inter-locus gap distance of 0.53 cM, and shows even higher collinearity to the reference genome assembly compared to individual population maps. To conclude, the workflow described here helps to construct linkage maps faster and more accurately, whilst the consensus map provides a useful tool for identifying molecular determinants associated with traits. This information is useful for researchers who want to use the Vitis18K SNP chip, and also showcases how data can be integrated for further understanding of the grapevine genome.