Spotlight on Translational Genetics: Integrating “-Omics” Data to Connect Genetics to Gout
Translational genetics at Variant Bio can be broadly defined as the integration of genomics with other molecular data types — such as proteomics, metabolomics, and transcriptomics, among others, which we can refer to as “-omics” — in order to better understand disease mechanisms and contribute to the translation of genetics results to R&D efforts. As with everything we do at Variant Bio, the long-term goal is to use our research to develop therapies that can improve people’s lives around the world.
When we find a genetic variant that is significantly associated with a given disease, it’s often not easy to tell exactly how the two are associated. Integrating other biological data into our analysis — like levels of gene expression or metabolite levels — can help us connect the dots between a genetic variant and a disease state.
Applying translational genetics to a project in French Polynesia
In a study on gout in French Polynesia, the translational genetics team at Variant Bio has worked to integrate two different types of “-omics” data into results from genetics analyses. The first additional data type is metabolomics, which tells us the levels of different metabolites present in the blood. Using this data, we can find metabolites that are associated with certain phenotypes like gout. We can also find genetic variants that are associated with changing levels of metabolites.
The second type of -omics data integrated into the analysis is transcriptomics, or gene expression levels. We can perform analogous analyses with transcriptomic data as with metabolomics data: connecting gene expression levels to the presence or absence of gout and gout-related traits, and finding variants that are significantly associated with expression levels of a certain gene.
We use all of these results to help us link a specific genetic variant or gene to gout-related traits. If we have metabolomics and transcriptomics results supporting that connection, we can be more confident that it is biologically meaningful.
The integration of these other data types helps us narrow down a large number of findings into a short list of good candidates for further study. This short list is passed to our drug discovery team, and together we make an even more targeted list of genes that may have potential therapeutic benefit in gout.
Through this process, the translational genetics team helps find biologically meaningful associations between genetic variants and disease and thus contributes to the prioritization of potential targets with the best chances of becoming an impactful drug.
