The genetics community has a particularly important part to play in accelerating rare disease research and contributing to improving diagnosis and treatment. Innovations in sequencing technology and machine learning approaches have positively affected diagnostic success, but more coordinated efforts are needed to move towards effective therapies or even cures for these important, and sometimes overlooked, class of diseases.
Rare Disease Day was recently held on 28 February 2022, which aimed to raise awareness and promote advocacy for rare disease research. Globally, there are more than 300 million people living with rare diseases and there are no approved therapies for over 90% of these disorders. Because around 80% of rare diseases have a genetic basis, recent advances in genomic sequencing technologies and molecular gene therapies have enhanced diagnosis and expanded treatments. To ensure that these advances are benefitting as many patients as possible and doing so in an equitable manner, unified efforts that span different stakeholders across rare disease communities should be supported.
In this issue of Nature Genetics, Halley and colleagues present a Comment that calls for an integrated approach for rare disease research in the United States. The authors argue that rare diseases are an important public health issue that should be given commensurate attention for their collective effects on individual patients, disease communities and healthcare systems. As such, the approach to rare disease research needs to broader for maximum benefits to a greater number of patients. The authors call for integrated approaches to research infrastructure that would minimize barriers to making connections, whether biological, therapeutic or societal, within and between rare diseases.
The authors highlight that rare disease research is currently very siloed and often organized around single disorders. Although efforts such as the Rare Disease Clinical Research Network have taken a broader approach, overall, there is limited coordination across rare disease research networks. The single-disorder focus creates challenges for jointly combining efforts, sharing data, assessing outcomes and capturing knowledge that could be relevant across diseases. A more integrated structure with appropriate support for researchers to coordinate across rare diseases would minimize redundant efforts and increase efficiency, potentially accelerating development and the implementation of successful therapies.
Importantly, no recommendations intended to promote rare disease research can ignore equity; indeed, ensuring fair practices in funding and equitable benefits of research outcomes must be a central focus of any research initiatives into rare diseases. It is challenging to achieve greater parity across rare diseases within the current research infrastructure, as analyzing how outcomes vary within or across rare diseases in different populations or socioeconomic groups is not straightforward. A more integrated approach to rare disease research will enable the assessment of how various factors (such as income level, insurance status, or racism in health care) affect participation in rare disease research or access to its benefits.
Altogether, the authors advocate for moving towards a more coordinated approach to rare disease research that would enable analysis of the similarities and differences across diseases in terms of etiology, treatment and outcomes. Although this article is specifically focused on the United States, the authors also recognize existing international efforts, such as the Global Genes and Genetic Alliance and the International Rare Disease Research Consortium that are leading the way in facilitating coordinated research efforts and data sharing.
We are excited by new technical advances in rare disease genetics research that apply the latest technologies to improve diagnosis. As an example, also in this issue of Nature Genetics, Hsieh and colleagues report a tool that uses deep convolutional neural networks to aid in diagnosing ultra-rare disorders based on facial morphology. GestaltMatcher defines a Clinical Face Phenotype Space based on over 17,000 photographs of patients representing more than 1,100 rare disorders. An advantage of using this method is that patients who share the same genetic diagnosis can be matched, even in cases when the disorder is not part of the training set. This helps with the clinical diagnosis of both known and new phenotypes. The concept of matching patients with rare disease is also conveyed on our cover, with actual matches forming the shape of a human face.
Rare disease research encompasses passionate individuals who span different sectors of interest: clinicians, patients, genetic counselors, biologists, technicians, advocates, funders and educators. We hope that the common challenges facing rare disease research can be combatted through enhanced coordination and cooperation across research communities, with the goal of accelerating diagnosis, maximizing therapeutic benefits and reducing inefficiencies.
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Rare diseases, common challenges. Nat Genet 54, 215 (2022). https://doi.org/10.1038/s41588-022-01037-8
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DOI: https://doi.org/10.1038/s41588-022-01037-8
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