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.
Dennis Lal, PhD, Genomic Medicine Institute, received a five-year, $3.3 million award from the National Institutes of Health to study the genetics of focal cortical dysplasia (FCD), one of the most common causes of drug-resistant epilepsy.
FCD refers to a specific area of the brain with abnormal brain cell organization and development that result in increased risk for seizures and disruption of brain function. There are several types of FCD, including FCD I, which usually affects the temporal lobe of the brain and causes seizures in adulthood, and FCD II, which can involve both the temporal and frontal lobes and is more often seen in children.
While genetic variants have been suggested as the underlying cause for FCD, Dr. Lal’s team and others have demonstrated that the majority of FCD I and FCD II patients lack any genetic abnormality when using currently available testing methods.
“Even more strikingly, only a single gene has been identified in FCD I,” said Dr. Lal. “One reason could be that all previous studies of FCD candidate genes were observational studies of small, poorly characterized cohorts without any controls.”
With this grant, Dr. Lal’s team will perform the most comprehensive genetic analysis of FCD using the largest FCD I and II cohort to date. By comparing well-characterized brain tissues and blood samples from patients with FCD I- or FCD II-related epilepsy to healthy participants, they will confirm proposed FCD-associated genes and identify novel FCD causal genes and variants.
“Successful completion of this project will improve our understanding of the genetic basis of FCD etiology, leading to the introduction of novel gene-based diagnostic strategies and targeted drugs that fully manage seizures while avoiding side effects typically associated with current therapeutic interventions,” said Dr. Lal.
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.