Dr. Lal adjusted and used a statistical model based on mutation rate to predict the annual number of new cases of over 100 rare monogenetic neurodevelopmental disorders caused by de novo variants, offering previously unavailable estimates for disease burden.
A Cleveland Clinic-led team of researchers has estimated for the first time the frequency of more than 100 rare and severe neurodevelopmental disorders, according to a new study published in Brain. Accurate estimates of disease burden—which were previously unavailable for nearly all of the disorders on this list—are critically important for clinicians, researchers, patient advocacy groups, pharmaceutical companies and policymakers as they make strategic decisions around care and investment priorities.
The research team, led by Dennis Lal, PhD, Genomic Medicine Institute, adjusted a statistical method to predict the incidence (number of new cases) of 101 monogenic neurodevelopmental disorders caused by known de novo variants (DNVs)—random, non-inherited genetic mutations. Monogenic disorders are caused by the activity of a single gene.
The team’s adjusted 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 incidences include Charcot-Marie-Tooth disease (19.2 cases: 100,000 births) and Weidemann-Steiner syndrome (10.8 cases: 100,000 births).
“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,” said 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. This prediction model will prove important to professionals across all levels of the research and healthcare continuum.”
The researchers also used the model to predict the incidences of over 3,000 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 found in scientific and clinical literature, suggesting the team’s estimates are reliable.
“Our model can currently only calculate incidence estimates for DNV-associated disorders, in large part because the field of genomics knows the most about these variants. As our understanding of other types of variants grows, we are excited to expand our incidence model to match,” noted Dr. Lal.
This study was funded in part by a training award from the National Institute of General Medical Sciences (part of the National Institutes of Health) and the Dravet Syndrome Foundation. Javier Lopez-Rivera, a doctoral student in Dr. Lal’s lab, was first author on the study. Dr. Lal is jointly appointed in the Cleveland Clinic Epilepsy Center.
Dr. Lal’s team conducted the first big data characterization of missense variants from 1,300 disease-associated genes to identify features associated with pathogenic and benign variants.
Dr. Lal and colleagues have developed a statistical model that integrates genetic and clinical data to calculate the probability of developing Dravet syndrome.
Dr. Lal’s team will perform the most comprehensive genetic analysis of focal cortical dysplasia (FCD) to confirm proposed FCD-associated genes and identify novel FCD causal genes and variants.