The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

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.