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Review
. 2020 Sep 11;3(3):472-486.
doi: 10.1093/jamiaopen/ooaa030. eCollection 2020 Oct.

The case for open science: rare diseases

Affiliations
Review

The case for open science: rare diseases

Yaffa R Rubinstein et al. JAMIA Open. .

Abstract

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally.

Keywords: FAIR data; common data elements; data standards; ontology; open science; rare disease patients.

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Figures

Figure 1.
Figure 1.
Rare diseases. (A) RDs are individually rare but collectively impact ∼10% of the population. Here, RDs are represented in the classic aphorism, “When you hear hoofbeats, think of horses, not zebras”—in other words, look for the most common disease that matches the symptoms, not the rarest one. It was originally used by Theodore Woodward, professor at the University of Maryland School of Medicine in the 1940s. (B) Defining RDs requires carefully matching a patient’s spectrum of phenotypes with the phenotypic profile of candidate diseases, here represented by a single color-feature. Each zebra (patient) has a constellation of phenotypes that may match none, some (dashed lines), or all (solid lines) of the phenotypes of other zebras. The diagnosis of RDs often involves recognition of phenotypic patterns and is aided by computational phenotype analysis.
Figure 2.
Figure 2.
Phenopackets provide a mechanism for structured, de-identified, patient-level phenotype data sharing for computational use across the globe and in different information systems. Image credit: GA4GH Communications Team.

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