The development of a new genetic test for grapevine cultivars using a computational genomics approach

Due to the sale and consumption of wine and table grapes, the grapevine is an important crop for many countries, including Canada. One of the main issues in viticulture is the identification of cultivars. Many of the over 6000 different types of grape cultivars look similar in colour or shape but may have very different taste profiles and require different growing conditions, while some have the same name but are genetically different (homonym) or having different names but are genetically identical (synonyms). Genetic tests based on the use of simple single repeat (SSR), or short tandem repeats (STR) markers have been developed to determine the genetic identity of different grapevine cultivars. However, the markers used in existing tests were identified more than 2 decades ago without optimization, and with the service limited to a few places around the world imposing many hurdles for international users. This research aims to develop a new grapevine genetic test by selecting the best STR markers in taking advantage of recently available rich grapevine genomic resources. Using a computational genomics approach, a total of 13 top performing STR markers were selected based on their discrimination power for 304 grapevine cultivars. A Polymerase Chain Reaction (PCR) based test was designed to group these 13 STR markers into 5 multiplex PCR groups for assaying using the QIAGEN QIAxcel Advanced System™ for its speedy and cost-efficient DNA fragment analysis. As a way of evaluating the performance of the designed test, in silico genotyping was performed for 304 grapevine cultivars and 37 Chardonnay clones based on available whole genome sequencing data. The results showed that the test was able to distinguish all these grape cultivars and Chardonnay clones, and furthermore, the number of STR markers used in the test can be reduced to a minimum of 6 for distinguishing these cultivars and clones. Genotype-based phylogeny analysis of these cultivars and clones showed meaningful clustering patterns matching their known or assumed relationships, indicating the validity of the test. In conclusion, despite not being able to perform evaluations of the STR markers in the laboratory, the preliminary in silico results demonstrate the high efficiency of the computation genomic approach in finding top performing STR markers and predicts an excellent performance of the designed grapevine genetic test