Cassava is the third-largest source of carbohydrates globally and plays a critical role in food security in developing regions, where the crop is a staple part of diets.
Synomics used its proprietary combinatorial analytics and prediction engine (adapted from a platform proven in human disease genetics) to analyse data from more than 5,000 Cassava samples to find potential routes for improving the crop’s resistance to Cassava Mosaic Disease (CMD).
The platform identifies novel genetic variants, and in particular the combinations of Single Nucleotide Polymorphisms (SNPs – pronounced ‘Snips’) that orchestrate the whole function of the gene that determines the traits of a plant. When statistically linked to a phenotype (e.g a plant’s height, leaf shape, disease resistance, etc), these SNPs become QTNs (Quantitative Trait Nucleotides), in many ways the ‘holy grail’ of genetics.
In this new piece of research, 67 unique QTNs were identified, narrowing down the search for scientists to develop new and more targeted ways of increasing disease resistance, either through selective breeding, gene-editing or new crop protection products and treatments.
Peter Kristensen, CEO of Synomics, says the business has developed the platform to enable agricultural scientists and producers to get an even better understanding of the crops that they grow: “Our platform provides insights they have not been previously able to liberate from the data they already hold or has already been published,” he explains.
“Synomics is in many respects the missing link between the huge amounts of raw data that farmers and scientists hold and a company’s own research and development team – interpreting the data and leading the team quickly to areas of immediate interest.
“Whilst current state-of-art practices are limited to looking at the impact of each individual SNP in isolation, scientists know that many traits are the result of SNPs (and genes) acting in complex combinations. Synomics is able to analyse and map these combinations, identifying previously disregarded SNPs as highly relevant.”
Synomics’ platform was able to identify novel genetic variants and produce biologically relevant results, for example identifying 285 and 136 unique candidate genes for CMD, not reported before in the literature, related to 1-month and 3-month disease cases, respectively. Furthermore, it was able to identify key biological mechanisms underlying CMD through the novel technique of using well-researched ortholog genes as replacements for poorly annotated species, such as cassava, to find enriched pathways.
Peter says these results show the value the Synomics’ platform has in discovering novel targets: “In this particular study, 67 QTNs were identified as either disease resistance or susceptibility loci for CMD and will pave the way for further functional validation and for potential use as gene introgression or editing targets.”
Identifying a small number of high-impact SNPs and QTNs is much more valuable to scientists than simply identifying a large number of SNPs and QTNs. By applying Synomics’ platform, scientists are enabled to target genes for intervention with more certainty and bring products/solutions to market more rapidly and at less expense.
“If we can find ways of treating the disease and increasing the yield, we can address one of the biggest food security issues in the world,” Peter concludes.