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. 2020 Jan 8;48(D1):D845-D855.
doi: 10.1093/nar/gkz1021.

The DisGeNET knowledge platform for disease genomics: 2019 update

Janet Piñero  1 , Juan Manuel Ramírez-Anguita  1 , Josep Saüch-Pitarch  1 , Francesco Ronzano  1 , Emilio Centeno  1 , Ferran Sanz  1 , Laura I Furlong  1
Affiliations

The DisGeNET knowledge platform for disease genomics: 2019 update

Janet Piñero et al. Nucleic Acids Res. .

Abstract

One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.

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Figures

Figure 1.
Figure 1.
The DisGeNET platform. (A) Simplified DisGeNET database schema. (B) Tools to access DisGeNET data.
Figure 2.
Figure 2.
Data sources and data types in DisGeNET. For Gene–Disease Associations (GDAs) the data sources are classified as Curated, Animal models, Inferred and Literature. For Variant-Disease Associations (VDAs) the data sources are classified as Curated and Literature. The data sources in white are developed in-house, while the others are third-party resources.
Figure 3.
Figure 3.
Disease-Disease associations in DisGeNET. (A) Two diseases are connected if they share at least one gene or one variant in the GDA or the VDA dataset, respectively. A Jaccard Index with its associated P-value are provided for each association to rank and filter the Disease-Disease association results. For more details see http://www.disgenet.org/dbinfo. (B) The Disease-Disease association network of Type 2 Diabetes Mellitus (T2D, CUI: C0011860). The network shows the diseases associated to T2D through common variants from DisGeNET curated databases with a P-value ≤ 0.05.
Figure 4.
Figure 4.
Distribution of most severe consequence types in DisGeNET variants. Consequence types are obtained from the Variant Effect Predictor (ENSEMBL).
Figure 5.
Figure 5.
Distribution of number of associated diseases per gene (panel A) and variant (panel B) in the DisGeNET Curated subset and in the whole database (ALL). Note that genes or variants associated to a single UMLS concept have a DSI equal to one, and a DPI close to zero, while genes or variants associated to several UMLS concepts have higher DPI, and lower DSI.
Figure 6.
Figure 6.
DisGeNET provides comprehensive information on genes and variants for rare diseases. (A) Highest scoring genes associated to Duchenne Muscular Dystrophy in DisGeNET. In blue background, the genes annotated by CURATED resources. (B) DisGeNET annotates 350 DMD variants to Duchenne muscular dystrophy, being most of them stop-gained variants. (C) The DMD gene is associated to a large number of diseases and phenotypes belonging to different disease classes. (D) Pathways associated with Duchenne Muscular Dystrophy obtained by a federated query interrogating DisGeNET and WikiPathways.
Figure 7.
Figure 7.
Analysis of GWAs results with DisGeNET. (A) 61 out of 143 variants identified by a recent GWAs of Type 2 Diabetes Mellitus (T2D) (29) are reported in DisGeNET as associated to cardiometabolic diseases and traits, and 47 variants are annotated to T2D. (B) Top-scoring variants in DisGeNET from those found in the study (29). DisGeNET provides additional information such as the consequence type of the variant according to VEP, allele frequencies from the gnomAD database, DisGeNET score, number of supporting publications with linkouts to MEDLINE, to name a few attributes. (C) Network of diseases and phenotypes associated with variant rs7903146 annotated by curated databases, created with the DisGeNET Cytoscape App. Examples of text excerpts extracted by text mining from publications supporting the association are shown.
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