Rare apart, strong together: how Mondo brings interoperability to the rare disease community
A study conducted using data from Orphanet suggests that an estimated 300 million people, nearly 4% of the world’s population, are affected by a rare disease. However, because rare diseases are individually rare, for any given rare disease, both the patients and the relevant information are sparse and scattered. Information about the genetic underpinnings, treatments, and support networks is challenging to navigate and reconcile by patients, clinicians, and researchers alike.
Defining Disease: an ever evolving endeavor
There is no bright line in terms of what is considered a rare disease. Rare diseases exist on a spectrum of frequency globally and there is also geographic variation. For example, sickle cell disease is considered common in parts of sub-Saharan Africa, but it is considered rare in the United States (US). The US defines a rare disease as afflicting fewer than 200,000 people (from the 1983 Orphan Drug Act), whereas in the European Union, a disease is considered rare if it affects fewer than 1 in 2,000 people. Knowledge about common diseases has been greatly enhanced by studying related rare diseases and vice versa. This adds another dimension to why the Mondo Disease Ontology (Mondo) can be so useful: because it is comprehensive, it enables disease knowledge to flow across traditional boundaries, to the benefit of all.
Disease names and definitions change over time. The medical and research communities are constantly working to boost knowledge of disease mechanisms with improved patient outcomes in mind. As new information about diseases is discovered, it can cause a previous name or definition to be rendered inaccurate. When this happens, the name and corresponding definition of a disease may be updated. For example, MONDO:0004976, which previously had the name “Lou Gehrig’s disease”, is now “amyotrophic lateral sclerosis” (also known as ALS), with “Lou Gehrig’s disease” now maintained as an exact synonym of the term.
The lack of a single universally interoperable terminology of rare diseases has historically made aggregating and comparing all information about a specific rare disease challenging, which limits diagnostics, care management, and drug development. Recently, the rare disease community came together for an impromptu meeting after the ARPA-H Rare Disease AI/ML for Precision Integrated Diagnostics (RAPID) program Proposer’s day meeting was canceled. Interoperability across data, knowledge, and systems was recognized as a key challenge for the rare disease community — highlighting the great need for the community-developed Mondo Disease Ontology.
Rare Disease Day 2025
On Rare Disease Day, we celebrate the broad community working together to tackle the challenges of identifying and treating rare diseases. Mondo is grateful to all of the contributors who share their rare disease knowledge to help make us stronger together. With global contributions from rare disease authorities ClinGen, OMIM, Orphanet, GARD, MedGen, NORD, DBCLS, the power of this reconciliation is tremendous.
The Mondo knowledgebase is a community resource that provides interoperability across existing expert disease authorities, like those listed above. In addition, Mondo helps support knowledge sharing and improvement across all resources. Mondo now includes >15,000 disease classes considered rare by one or multiple rare disease authoritative source(s), which include disease entities, subtypes, and grouping classes. All rare disease classes in Mondo are fully attributed so that the information can be leveraged for multiple purposes, including but not limited to scientific and clinical research, patient information, public health, and policymaking.
Bringing interoperability to the rare disease community results in better diagnostic tools, better mechanism discovery, better treatment development, better patient engagement, and better collective understanding of rare diseases.
Today, we celebrate all of you, together! Together, we are not rare.
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Grant acknowledgement
This work is supported by the NHGRI Center of Excellence in Genome Sciences, RM1-HG010860.