Quality Control Analysis

Of the various challenges facing genebank managers and genetic resources scientists³³, taxonomic misclassification (misidentification or misnaming) and mislabeling are the major sources of errors at every genebank, which accounted from 3% to 28% of the errors reported in the literature^{10,28,34–38}. Such types of errors restrict effective use of germplasm for correct purpose in various ways, including difficulty in determining “true-to-type” accessions and /varieties that have been used as parents in crop improvement programs, developing populations for gene discovery and molecular breeding, and other genetic studies³².

In one of our studies²⁸, we found out that nearly 3% of the 3,134 rice accessions conserved at the RBCA was either taxonomically misclassified or mislabeled. To develop genotyping quality control (QC) methods for routine use in our operations, we identified 332 species-and sub-species specific diagnostic DArTseq-SNPs that discriminated O. glaberrima, O. barthii, O. longistaminata, O. sativa spp. indica and O. sativa spp. japonica.. Once SNPs of interest have been identified via GBS, DArTseq, and whole-genome sequencing, they must be converted to uniplex genotyping platforms that run one marker at a time, such as Kompetitive allele-specific PCR (KASP) and validated. Next-generation sequencing technologies are cost-effective and high throughput for applications that require high-density markers, while uniplex assays are cost-effective for marker-assisted selection (MAS) and QC analysis^39,40. For more results, please read our open-access full research paper from Molecular Breeding.

We have converted and validated a subset of the DArTseq-based diagnostic SNPs into KASP assays and developed three panels of 10-36 KASP SNPs for QC analysis. These panels provide users a flexible, rapid turnaround and cost-effective tool that would help ongoing efforts in facilitating germplasm curation and management of rice collection.