Understanding the Genotype-Phenotype Relationship (Relevant to areas: 1A, 2A, 2F, 3C)
Our understanding of the major genes associated with causal-mutations in the genetic epilepsies has vastly increased in the last two decades, there is still so much that is poorly understood about how genotypes correspond to phenotypic outcomes amongst patients with similar causal mutations. Researchers have begun to investigate the potential effects of genetic background and the roles of "modifier" genes that may impact the severity of a causal mutation, but much remains to be understood before these studies can truly make an impact on clinical outcomes. We need to be continually broadening our genetic tool set in terms of disease-models that can be used to investigate gene-gene and gene-environment interactions that modulate epilepsy and comorbid conditions. Investigations of such pre-clinical models should include assessment of interventions and therapies, as well as detection of biomarkers, that could translate to improved health outcomes for patients. Understanding how the immune system and metabolism interplay with epilepsy genes and the progression of epileptic disorders should also be incorporated into these studies to better understand what role these systems might play in the etiology of disease or to what extent any effects on these systems might be secondary to epileptic activity. The patient community is interested in expanded studies of non-pharmaceutical treatment approaches as well, which requires further assessment at the preclinical stages to better understand how these approaches act at the cellular and systems levels.
In addition to studies in pre-clinical disease models, we need to recognize the power of patient data to aid in the process of furthering our understanding of the genotype-phenotype correlation and the interplay of genetics with environmental factors and treatment approaches. The patient community carries the burden of frequent trial-and-error approaches to treatment options. If patient data were collected and compiled over time more consistently, analysis of outcomes following various treatments used in the normal progression of care could be stratified by genotype amongst other descriptive factors to predict optimal approaches for individualized treatment. This type of data has the power to significantly improve patient quality of life as well as our understanding of genetics, disease, and treatments more broadly. NINDS should be funding grants that aim to develop sound tools, run studies, and form collaborations that work towards this type of data collection and analysis.
Similarly, although also related to some additional benchmarks (4A, 4B, 4D, 4F), more research to understand the causes and treatments of comorbidities. Intellectual disability, behavioral issues, and mental health are particular areas where the patient community is particularly burdened, and yet research has not progressed to match the impact. There needs to be a better understanding of the contribution of genetics, seizures, and common medications to these comorbidities. Focus on treatments that go beyond seizure management and that focus on these comorbid conditions are desperately needed. Again, this should be prioritized in preclinical models and in retrospective and observational human studies to best tease apart these aspects and determine best approaches to treatment.