An Open-Access Platform for Translating Diabetes and Cardiometabolic Disease Genetics Into Accessible Knowledge

Most associations from genome-wide association studies (GWAS) result from as-yet-unknown alterations of molecular or cellular function; the causal variants and effector genes responsible for them, and the tissues and pathways through which they act, remain largely unknown. Thousands of associated loci have now been identified for each common disease and its related traits. In order to translate GWAS data into biological knowledge, they must be integrated with functional genomic annotations reflecting tissue-specific regulation and with the results of bioinformatic methods that predict the functional effects of associations. However, these data types are typically spread across disparate resources, and working with them requires bioinformatic expertise. To make these results accessible and understandable to the broader diabetes and cardiometabolic disease research communities, we have developed the open-access Common Metabolic Diseases Knowledge Portal (CMDKP; cmdkp.org), which brings together a robust software and data storage platform with a streamlined and intuitive user interface for four disease areas: diabetes (both types 1 and 2); cardiovascular disease; cerebrovascular disease; and sleep and circadian disorders. The CMDKP enables researchers to access and explore a comprehensive matrix of genetic, genomic, and computational results. It includes 3 classes of genomic data: 1) GWAS summary statistics from the most current and authoritative datasets available, as identified by disease-area experts; 2) functional genomic annotations, such as chromatin accessibility, that reflect the tissue-specific regulatory potential of genomic regions; and 3) the results of bioinformatic methods applied to these aggregated data (for example, overlap-aware meta-analysis to determine “bottom-line” p-values, the GREGOR method for determining tissue-specific enrichment of genetic associations, the MAGMA method for generating gene-level association scores, and more). All of these data types are integrated and accessible via interactive tools that allow researchers to explore and evaluate the data in order to identify candidate disease effector genes for further research. The CMDKP provides researchers with the data and tools necessary to translate genetic associations and functional annotations into knowledge about disease mechanisms and potential therapeutic targets.