Among the highest priorities in neuropsychiatry is relating the complex phenotypic expression to underlying genetic architecture. A major thrust of the NIH Blueprint and Roadmap initiatives is to prioritize underlying quantitative traits that are relevant across disorders and have better biological specificity than heterogeneous syndrome labels like schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder (ADHD). Yet executing genetic studies of quantitative traits is currently prohibitively expensive in both time and money. Here we provide a novel approach using high-throughput cognitive phenomics for iterative examination of quantitative cognitive traits in pursuit of genome-wide association targets. This application targets memory and response inhibition phenotypes that are critical components of symptom (e.g. impulsivity), neural systems (e.g. prefrontal cortex), and signaling pathway (e.g. dopamine) deficits that are prevalent across neuropsychiatric syndromes. Focusing on quantitative cognitive traits may improve the biological specificity of phenotypes allowing us to identify important risk genes.
The aim of this EUREKA application is to rigorously validate acquisition of a large Web-based cohort. This will provide: 1) a high-throughput framework for future GWAS applications, and 2) a sample of 7,000 with valid cognitive phenotypes and DNA able to demonstrate the utility of using quantitative cognitive traits for GWAS. Our preliminary data solidly demonstrates feasibility but rigorous validation of equivalency between measures across thousands of individuals is needed. The future of neuropsychiatric research must embrace high-throughput phenotyping to accumulate the necessary sample sizes needed for genetic study as well provide the flexibility to iteratively refine and rapidly improve the specificity of phenotypes. If successful, this approach would demonstrate the ability to rapidly collect well-powered studies of quantitative traits in a way not currently possible, providing the ability to iteratively refine and retest phenotypes quickly. This could have tremendous implications by drastically accelerating identification of novel risk genes for cognitive deficits that underlie many neuropsychiatric syndromes.
While we know that biological processes lead to increased risk for mental illnesses like attention deficit- hyperactivity disorder, bipolar disorder, and schizophrenia, their causes remain unknown, due in part to the complexity in the interaction between biological systems and behavior. While repeated study of behavioral processes in action is a good way to find these causes, carrying out these studies in enough people to identify genes currently requires many years of effort by both researchers and the public and is very costly. This research project validates a novel procedure capable of very quickly studying large numbers of healthy individuals using secure internet-based technology to pursue the genetic correlates of memory and impulse- control behaviors that go often awry in mental illness.