Some of the first epidemiologic insights into brain disorders caused by de novo variants
A Cleveland Clinic-led research team has estimated for the first time the frequency of more than 100 rare and severe neurodevelopmental disorders, as detailed in a new study published in Brain.
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Accurate estimates of disease burden — previously unavailable for nearly all the disorders addressed in the study — are critically important to clinicians, researchers, patient advocacy groups, biopharmaceutical companies and policymakers as they make strategic decisions around care and investment priorities.
The researchers, led by Dennis Lal, PhD, of Cleveland Clinic’s Genomic Medicine Institute and Epilepsy Center, developed a method combining genetic data and statistical modeling to predict the incidence of 101 monogenic neurodevelopmental disorders. These rare disorders, severe subtypes of common conditions such as epilepsy and autism, are caused by known de novo variants (DNVs), or noninherited genetic mutations.
“Currently, it is difficult to accurately track the burden of DNV-related disorders, in part because they are rare and often present differently from one patient to another,” says Dr. Lal. “But having an accurate understanding of how many people are affected by a given disease is an important factor to consider when allocating resources to treat or prevent it. The provided incidence estimates will prove important to professionals across all portions of the research and healthcare continuum.”
The team’s model, which is based on mutation rate, predicted the global incidence of DNV-associated brain disorders as 329 cases per every 100,000 births. Of the 101 disorders studied, the two with the highest incidence estimates were Charcot-Marie-Tooth disease (19.2 cases per 100,000 births) and Weidemann-Steiner syndrome (11.5 cases per 100,000 births).
The researchers also used the model to predict the incidences of more than 3,000 additional monogenic disorders suspected to be caused by DNVs. Their model was found to be well-calibrated based on variant reporting data from public databases, diagnostic outcomes from gene panel testing, and epidemiological incidence estimates in the scientific and clinical literature — which suggests that the team’s estimates are reliable.
“Our model currently can calculate incidence estimates only for DNV-associated disorders, in large part because the field of genomics knows the most about these variants,” notes Dr. Lal. “As our understanding of other types of variants grows, we are excited to expand our incidence model to match.”
The study was funded in part by a training award from the National Institute of General Medical Sciences and the Dravet Syndrome Foundation. Javier López-Rivera, a doctoral student in Dr. Lal’s lab, was first author on the study.
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