Addition of a breeding database in the Genome Database for Rosaceae

Breeding programs produce large datasets that require efficient management systems to keep track of performance, pedigree, geographical and image-based data. With the development of DNA-based screening technologies, more breeding programs perform genotyping in addition to phenotyping for performance evaluation. The integration of breeding data with other genomic and genetic data is instrumental for the refinement of marker-assisted breeding tools, enhances genetic understanding of important crop traits and maximizes access and utility by crop breeders and allied scientists. Development of new infrastructure in the Genome Database for Rosaceae (GDR) was designed and implemented to enable secure and efficient storage, management and analysis of large datasets from the Washington State University apple breeding program and subsequently expanded to fit datasets from other Rosaceae breeders. The infrastructure was built using the software Chado and Drupal, making use of the Natural Diversity module to accommodate large-scale phenotypic and genotypic data. Breeders can search accessions within the GDR to identify individuals with specific trait combinations. Results from Search by Parentage lists individuals with parents in common and results from Individual Variety pages link to all data available on each chosen individual including pedigree, phenotypic and genotypic information. Genotypic data are searchable by markers and alleles; results are linked to other pages in the GDR to enable the user to access tools such as GBrowse and CMap. This breeding database provides users with the opportunity to search datasets in a fully targeted manner and retrieve and compare performance data from multiple selections, years and sites, and to output the data needed for variety release publications and patent applications. The breeding database facilitates efficient program management. Storing publicly available breeding data in a database together with genomic and genetic data will further accelerate the cross-utilization of diverse data types by researchers from various disciplines.