Reliable In Silico Identification Of Sequence Polymorphisms And Their Application For Extending The Genetic Map Of Sugar Beet (Beta Vulgaris)

Molecular markers are a highly valuable tool for creating genetic maps. Like in many other crops, sugar bee Beta vulgar's L. breeding is increasingly supported by the application of such genetic markers. Single nucleotide polymorphism (SNP) based markers have a high potential for automated analysis and high-throughout genotyping. We developed a workflow that uses Sanger and 2nd-generation sequence data for detection, evaluation and verification of new transcript-associated SNPs from sugar beet. RNAseq data from one parent of an established mapping population were produced by 454-FLX sequencing and compared to Sanger ESTs derived from the other parent. The workflow established or SNP detection considers the quality values of both types of reads, provides polymorphic alignments as well as selection criteria for reliable SNP detection and allows painless generation of new genetic markers within genes. We obtained a total of 14,323 genic SNPs and InDels. According to empirically optimised settings for the quality parameters, we classified these SNPs into four usability categories. Validation of a subset of the in silico genotyping the mapping population indicated a high success rate of the SNP detection Finally, a total of 307 new markers were integrated with existing data into a new genetic map of sugar beet which offers proved resolution and the integration of terminal markers.