Identifying such candidate effectors is the goal of the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D), established in 2014. AMP T2D brought together stakeholders from government, academia, and industry in order to speed up translation of genetic data into insights about disease mechanisms and drug targets. The generation, aggregation, and analysis of unprecedented amounts of data in this collaborative effort has spurred efforts to develop methods for the systematic integration of data (see for example Fernandez-Tajes et al., 2019).
Now, by prioritizing and integrating multiple sources of evidence, Mahajan and McCarthy have classified genes according to the likelihood that they are involved in development of T2D. The sources of evidence that they consider include genetic association data; functional genomic data such as eQTLs and chromatin conformation; mutant phenotype evidence from model organisms and knockdown screens in human cells; and other evidence gathered from the literature. The heuristic is described in detail in downloadable documentation.
Today's release of the T2DKP includes an interactive table that displays these classifications and allows you to view and explore all of the evidence underlying them.
|Section of the Predicted T2D effector gene table. Columns are sortable, and columns containing combined evidence expand to show the individual evidence types comprising that classification.|
When viewing this list, several caveats should be remembered. These are predictions only, and the strength of the predictions varies considerably among genes in the list. Also, any heuristic has limits, especially those developed in the absence of a clear "gold-standard" set, as this one was. Still, we hope that this list will be a valuable resource that can help suggest or support experimental directions for T2D researchers. We welcome feedback on the heuristic and the interface. Over the next year we plan to develop software to facilitate the generation and updating of these results.
Today's release of the T2DKP also includes 8 new datasets:
- BioBank Japan GWAS (an overall set plus sex-stratified sets) bring to the T2DKP genetic associations for a wide range of phenotypes from over 190,000 individuals of East Asian ancestry. Phenotypes in these sets include many clinical measures as well as disease status for T2D, atrial fibrillation, and open-angle glaucoma.
- Singapore Chinese Eye Study (SCES) GWAS, Singapore Malay Eye Study (SiMES) GWAS, and Singapore Indian Eye Study (SINDI) GWAS provide T2D associations for individuals of East Asian and South Asian ancestry.
- Singapore Living Biobank GWAS datasets include associations with anthropometric and lipid traits for Chinese and Malay populations.
All of these datasets are described fully on the T2DKP Data page.
Another new feature of today's release is that a link to standalone versions of our custom association analysis tools, the Genetic Association Interactive Tool (GAIT) and the Custom burden test, is now available on the Analysis Modules page. Both of these tools securely access individual-level data to compute on-the-fly genetic associations using custom parameters. GAIT, for single-variant association analysis, was previously only accessible on Variant pages; the Custom burden test for computing the disease burden across a gene was previously accessible only on the High-impact Variants tab of Gene pages.
Finally, today's release includes a new instructional video that leads you through the features of the T2DKP Variant page. The video is listed on, and linked from, the T2DKP Resources page.
Check out our latest newsletter for more details about these and other recent additions to the T2DKP.