Developing a Pathway from Genetic Locus to Gene for Complex Traits in Rodents
An open question in neurobehavioral genetics involves both the identification and experimental validation of genomic regions and candidate genes associated with behavioral variation. Our lab is interested in developing a quick and efficient way to progress from genetic mapping to gene identification in rodent models of complex disease, especially genes involved in anxiety and depression. We fine-map anxiety-related QTLs (quantitative trait loci) by taking advantage of the genetic and behavioral variation between different mouse strains and identify QTLs containing a testable number of candidate genes for genetic engineering experiments. Using CRISPR/Cas9, we then make recessive knockouts of the candidate genes and test the behavior of these animals through a QTL interaction test to determine whether any of these predicted genes are involved in our anxiety phenotypes.
Identifying how genetic variation affects neural circuit function
Though hundreds of genomic loci have been found to affect complex behaviors and neuropsychiatric disorders in humans, a clear biological mechanism linking genetic variation to changes in molecules, cells, and circuits underlying behavioral variation has been difficult to establish. By using the available tools in rodent model systems, we can begin to identify the relationship between genetics and higher-order organization in the brain. Through circuit manipulations, imaging, and tracing, we are interested in 1) establishing the functional variation in cells and circuits of key brain regions in different mouse strains, and 2) genetically mapping circuit functions as a means to identify the mechanisms and precise locations in the brain where genetic variants are acting.
Using voice to diagnose depression
The deployment of objective assessments of psychiatric phenomenology would transform the ability to diagnose, treat and prevent major depressive disorder (MDD). MDD is very common, affecting almost one in ten people and recently recognized to be the world’s leading cause of disability. Yet currently only about half of those suffering MDD are detected and offered treatmen. One of the main obstacles preventing effective use of existing therapies is the difficulty of diagnosing MDD. Diagnosis is still made on the basis of a clinical interview and mental status examination, a method with relatively low reliability; screening instruments are hampered by poor specificity and sensitivity and no reliable biomarkers exist. Further complicating the problem, MDD remains a syndromal diagnosis, leaving open the possibility that it consists of a number of different conditions, each with different outcomes and treatment response.
A possible source of information for improving diagnosis, and recognizing subtypes, is characterization of MDD by a person’s voice. A cardinal symptom of depression is psychomotor retardation which is associated with changes in brain function, and vocal features, supporting the hypothesis that neurophysiological changes associated with mood are reflected in speech. However, despite considerable advances over more than 60 years, analysis of vocal features has not so far been clinically useful.
This project combines genomics with voice data and apples novel computational approaches to achieve automated recognition of MDD (thereby potentially alleviating MDD’s worst consequences, including suicide). We are using voice recordings acquired during a genetic association study of MDD to show that voice features are heritable and are genetically correlated with MDD. We are developing analyses that predict MDD from voice, and we use combined genetic and vocal features to identify MDD subtypes