Principal Investigator: Dr Brad Verhulst
Department: Texas A&M University, College StationTags: 57923, Complex Traits, GWAS, Imaging, mental health, neural network, substance use
Mental health problems pose a significant threat to individuals health and well-being, reducing the quality of family interactions and placing a large burden on societies around the world. Genetic and neurological factors play a large role in a variety of mental health problems, but the specific ways that genes or neurological signals culminate in behavioral or psychological problems remains vague. In order to develop more effective treatments for a variety of mental health traits, we must develop a more complete understanding of the causes of mental health problems that are situated within the network of environmental factors that contribute to the disorders. Technological advances in high-throughput genotyping and imaging enable us to systematically measure genetic variants and body structures with an extremely high level of precision. This technology holds the potential to illuminate the underlying mechanisms that culminate in mental health problems. The massive amount of genetic and imaging data that is produced by these technologies poses great analytical challenges. Thus, it is necessary to integrate the genetic, neurological and phenotypic information into a single analytical frame to address these challenges. In this project, we will develop advanced statistical methods for high-dimensional genetic and imaging data analysis that can model the multi-faceted nature of mental health more accurately, and account for the complex relationships that we know exist between genetic, imaging and disease outcome data. We will apply these methods to the UK Biobank genetic, imaging and disease outcome data to identify genetic variants and imaging measures that convey risk for mental health problems. Due to the nature of statistical methods development we anticipate that this project will take up to three years. The results of this project will yield novel genetic and imaging biomarkers for risk of mental health problems, which will enhance our understanding of the etiology of mental health, help identify individuals at risk for developing problems, and result in more effective treatments.