Major Depressive Disorder is a common and disabling psychiatric illness, usually treated with a selective serotonin reuptake inhibitor antidepressant. Studies to date suggest that there are associations between antidepressant response and genes in both neurotransmitter regulatory pathways and antidepressant metabolism pathways. Based on these observations, this application is designed to identify genetic determinants for antidepressant response in a clinical sample of unprecedented size, treated with a single antidepressant, whose treatment response has been carefully ascertained. The ultimate goal is to elucidate genetic determinants of response to antidepressants as an important prerequisite to understanding the mechanism of antidepressant action and development of novel therapeutic agents for depression. Our specific hypothesis is that important phenotypes involving response to citalopram are in part mediated by detectable genetic factors. We propose a large-scale genetic association study on a collection of DNA's (about 1,400) obtained during the STAR*D (Sequenced Treatment Alternatives to Relieve Depression) protocol, a large multi-site treatment study involving about 4,000 persons with DSM-IV Major Depressive Disorder. We propose to: 1) genotype this sample for association between response phenotypes and variants in 20 antidepressant response candidate genes based on prior biological or genetic evidence, and 2) perform a whole genome association study between response phenotypes and about 100,000 gene-based DNA variants. Secondary specific aims are to: 1) sequence genes positively associated with response phenotypes identified using candidate gene or whole genome approaches to identify potential response-related alleles, and 2) develop refined phenotypes and novel hypotheses to test for association to treatment response outcomes. Power calculations suggest meaningful differences between phenotypic groupings can be detected. Detecting any association between DNA variations and antidepressant response could ultimately have a significant clinical impact if a genotype that accounts for a substantial portion of variance in response or tolerability of these medications is identified. These findings could provide steps toward our ability to define clinically useful genetic predictors of pharmacological treatment and apply them to patient populations.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH072802-03
Application #
7218627
Study Section
Special Emphasis Panel (ZMH1-CRB-B (06))
Program Officer
Lehner, Thomas
Project Start
2005-04-11
Project End
2009-03-31
Budget Start
2007-05-03
Budget End
2008-03-31
Support Year
3
Fiscal Year
2007
Total Cost
$700,344
Indirect Cost
Name
University of California San Francisco
Department
Psychiatry
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
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Corfitsen, Henrik Thyge; Drago, Antonio (2017) Insight gained from genome-wide interaction and enrichment analysis on weight gain during citalopram treatment. Neurosci Lett 637:38-43
Palmer, Rohan H C; Beevers, Christopher G; McGeary, John E et al. (2017) A Preliminary Study of Genetic Variation in the Dopaminergic and Serotonergic Systems and Genome-wide Additive Genetic Effects on Depression Severity and Treatment Response. Clin Psychol Sci 5:158-165
Power, Robert A; Tansey, Katherine E; Buttenschøn, Henriette Nørmølle et al. (2017) Genome-wide Association for Major Depression Through Age at Onset Stratification: Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Biol Psychiatry 81:325-335
Peyrot, W J; Lee, S H; Milaneschi, Y et al. (2015) The association between lower educational attainment and depression owing to shared genetic effects? Results in ~25,000 subjects. Mol Psychiatry 20:735-43
Power, Robert A; Keller, Matthew C; Ripke, Stephan et al. (2014) A recessive genetic model and runs of homozygosity in major depressive disorder. Am J Med Genet B Neuropsychiatr Genet 165B:157-66
O'Dushlaine, Colm; Ripke, Stephan; Ruderfer, Douglas M et al. (2014) Rare copy number variation in treatment-resistant major depressive disorder. Biol Psychiatry 76:536-41
Tansey, Katherine E; Guipponi, Michel; Hu, Xiaolan et al. (2013) Contribution of common genetic variants to antidepressant response. Biol Psychiatry 73:679-82
GENDEP Investigators; MARS Investigators; STAR*D Investigators (2013) Common genetic variation and antidepressant efficacy in major depressive disorder: a meta-analysis of three genome-wide pharmacogenetic studies. Am J Psychiatry 170:207-17
Hunter, Aimee M; Leuchter, Andrew F; Power, Robert A et al. (2013) A genome-wide association study of a sustained pattern of antidepressant response. J Psychiatr Res 47:1157-65

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