December, 2020

Target Discovery For Cassava Mosaic Disease

Study design

The study used public dataset from Cassavabase and Rabbit et al., 5,130 Cassava samples, 101,521 imputed SNPs, multi-year measurements taken – 2012-2015, across different locations in Africa. All datasets were filtered using the same options in PLINK with parameters chosen based on the data structure, resulting in a 30% reduction in SNPs. As SNPs were imputed in the raw data, there was no missing data for samples or SNPs. GWAS results were compared to the original paper using Manhattan plots which showed that the original findings were well maintained within our platform. Datasets were analysed using standard and inverted cases and controls to ensure all variants have either increasing or decreasing effects on CMD risks.

The Challenge

Whilst Cassava (Manihot esculenta) is one of the most important starchy root crops in the tropics, its genome biology is rarely studied using advanced tools for detecting phenotype impacting Single Nucleotide Polymorphisms (SNPs), genes and molecular pathways.

Synomics applied a combination of a proprietary technology platform and deep industry experience to identify predictive SNPs for Cassava Mosaic Disease (CMD) resistance. The aim was to understand whether our platform could identify novel insight that could improve disease resistance and help support food security in developing and under-developed parts of the world where the crop is a staple part of their diet.

Our Solution

Genotype and phenotype datasets were analysed in Synomics’ proprietary platform, identifying high-order combinations of SNPs (including epistatic interactions).  These combinations capture the non-linearity of biological effects and the impact they have on phenotypes much better than existing methods based on single features (such as GWAS). 

quantitative Trait Nucleotides (QTNs) linked to CMD
novel candidate genes
for 3-month CMD
novel candidate genes
for 1-month CMD

The Results

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 3-month and 1-month disease cases, respectively.  Furthermore, we were 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. These results show the value the Synomics’ platform has in discovering novel targets. The 67 QTNs identified as either disease resistance or susceptibility loci for CMD will pave the way for further functional validation and for potential use as gene introgression or editing targets.

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