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Ultrahigh-density linkage map for cultivated cucumber (Cucumis sativus L.) using a single-nucleotide polymorphism genotyping array

Mor Rubinstein, Mark Katzenellenbogen, Ravit Eshed, Ada Rozen, Nurit Katzir, Marivi Colle, Luming Yang, Rebecca Grumet, Yiqun Weng, Amir Sherman, Ron Ophir
Plos One 2015 v.10 no.4 pp. e0124101
Cucumis sativus, chromosome aberrations, chromosome mapping, cucumbers, genetic markers, genetic recombination, genome, genome-wide association study, genotyping, inbred lines, linkage groups, loci, microsatellite repeats, quantitative trait loci, single nucleotide polymorphism
With the low cost of single nucleotide polymorphism (SNP) discovery, use of SNP markers for SNP array development is becoming more affordable. The SNP array is a very useful tool for high throughput genotyping and has a number of applications such as genome-wide association studies (GWAS). Since the first cucumber genome draft was reported, genetic maps were constructed mainly based on low throughput markers like the simple-sequence repeats (SSRs) or sequence-related amplified polymorphism. In this study we developed the first cucumber genotyping array with 32,864 SNPs. These markers cover the cucumber genome every 2.1 kb. A training set was developed with parental and F1 hybridizations signals. The training set was validated with Fluidigm technology and had 98% concordance. The application of this genotyping array was illustrated by constructing a SNP-based cucumber genetic map with recombinant inbred lines (RILs) from the Gy14 × 9930. A total of 11,156 SNPs in seven linkage groups were placed on the genetic map. The markers’ collinearity between the genetic map and assembled draft genomes of both Gy14 and 9930 was high (R2=0.97). We used the resulting linkage map to investigate chromosomal rearrangement, regional recombination rate, and specific regions with segregation distortions. Finally, we showed that the array is applicable to other cucumber variants. We suggest herein a genotyping array together with the training set would be a powerful tool in applications such as quantitative-trait loci (QTL) analysis and GWAS.