Neuropsychiatric disorders, such as depression, have not shown conclusive linkage or association study results. Today, an enormous portion of heritability for depression remains unexplained. Rather than reiterating studies attempting to identify genetic variants underlying depression pathophysiology, I propose a different approach of evaluating genetic variation in the safety and efficacy of the major class of antidepressants. I will identify patients with the quantitative and discrete phenotypes of serotonin-specific reuptake inhibitor (SSRI) response (as measured through the Patient's Health Questionnaire-9 pre- and post-SSRI treatment), and SSRI-associated serious side effects, such as abnormal bleeding and serotonin syndrome. After identification of these phenotypes from the electronic Medical Records and Genetic Epidemiology (eMERGE) consortium, I will perform a genome-wide association study (GWAS) on 50,109 subjects to identify candidate genes involved in SSRI pharmacology and then leverage overlapping exome sequence data to identify rare, potentially causative mutations. As many GWAS and rare variant studies suffer from a lack of functional validation (i.e., that the identified variant truly causes the phenotype), I will functionally valiate these identified rare mutations in the model organism Saccharomyces cerevisiae using a yeast growth-based assay as a direct measure of protein function. The P450 enzyme CYP3A4, which has been implicated in SSRI metabolism, will be used as a proof of concept for the yeast assay. Finally, I will then use this yeast growth assay to perform deep mutational scanning to create a pharmacogenomic map of all possible mutations and their effect on enzyme hydrolysis, thereby linking DNA sequence to protein function. This work will provide a unique resource for understanding SSRI pharmacology. Through completion of a GWAS on the pharmacogenomic phenotypes of SSRI response and separately, risk of SSRI-associated bleeding and serotonin syndrome, I will likely identify numerous candidate genes that may further inform on the metabolism, transport, and mechanism of action of SSRIs, thereby furthering basic science. Moreover, through functional validation and creation of a pharmacogenomic map with deep mutational scanning, I will create an invaluable resource for clinicians to interpret the likely efect of their patient's rare mutation. Through this project, I hope to further the effort to bring personalized, genomic medicine into clinical practice, to decrease the morbidity and mortality of depression, which is estimated by the Center for Disease Control to affect 1 in 10 adults in the U.S.
Depression is affects 1 in 10 adults in the United States and is one of the leading causes of disability. Depression is primarily treated with serotonin-specific reuptake inhibitors (SSRIs). Understanding how mutations in genes affect SSRI response and risk of side effects forwards the implementation of personalized, genomic medicine into evidence-based clinical practice. This integration of genomic information will likely greatly decrease the morbidity of depression in the United States and globally.
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