Project Summary/Abstract Anxiety disorders cause significant distress on a personal level, and cost > $40,000,000,000 annually in the United States alone. They are common, with lifetime prevalence estimated at about 28.8%. These disorders are frequently co-morbid with other psychiatric disorders such as major depression (MDD) and post-traumatic stress disorder (PTSD). These disorders may share commonalities in the underlying genetic architecture that influences the risk for disease. More than 90% of patients with generalized anxiety disorder (GAD) present with additional comorbid psychiatric diagnoses. PTSD, sometimes considered an anxiety disorder, has been shown in a 20- year longitudinal cohort of veterans to present more commonly as comorbid with anxiety and/or depression than as a lone diagnosis. Up to 90% of individuals with PTSD may present with comorbid anxiety disorders. We propose to study the underlying genetic architecture of anxiety disorders and how they overlap, interact with and influence other co-morbid disorders. We will perform genome-wide association studies in the Million Veteran Program sample (~200,000 informative individuals and growing), UK Biobank (~100,000 individuals), and All of Us (~36,000 informative individuals and growing) using anxiety traits to investigate the genetic architecture of anxiety disorders. We will perform sex and ancestry stratified as well as joint-analyses to search for sex or ancestry specific variation. Sex-chromosome-wide association studies will be conducted in the same samples. The sex chromosomes (and mitochondrial DNA) are understudied in genetics, with most studies focused on the autosomes. This is despite evidence for a role for the polyamine system in depression, stress disorders and suicide and the gene for the rate-limiting enzyme in polyamine catabolism, “spermidine/spermine N1-acetyltransferase 1” (SAT1) being found the X-chromosome. It is critical to include the sex-chromosomes for a complete picture of the genetic architecture of disease. Finally, we will seek to use functional genomics, polygenic risk scoring, and causal inference to assay the functional importance of lead findings, genetic overlap and liability to related traits, and potential for clinical translation of findings.