NCT00088699 Strong evidence from family and twin studies demonstrates that major depressive disorder (MDD) is heritable, yet there has been limited progress in identifying the actual genes involved. A separate, perhaps overlapping set of genes is expected to play a role in individual variation in treatment response in MDD. By use of a large set of markers in many genes, we seek to characterize patients who differ in their response to standard antidepressant treatments. Past work led by an extramurally-funded fellow investigated why minority participants in clinical trials drop out of treatment and experience poorer treatment response than non-minorities. The research showed that race, ethnicity, genetic ancestry, and other factors affected Selective Serotonin Reuptake Inhibitor (SSRI) treatment response, but African ancestry remained a significant risk factor for poor response, even after other factors were taken into account. In the first years of this project, candidate genes studies implicated a few genes in treatment outcome and other genes in adverse effects. Future studies are needed to determine whether individuals who carry such genetic markers may benefit from closer monitoring or alternative treatments. We also participated in a meta-analysis of three genome-wide association studies of antidepressant outcome. Despite greater power of this combined sample to uncover association with common genetic markers, no genome-wide significant associations were found. We concluded that no common alleles of large effect on antidepressant outcome exist in these samples. In the past year, we re-examined this question in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) sample using newer analysis methods that improve upon older genotyping techniques initially used in this sample. In collaboration with Dr. Yin Yao and colleagues at NIMH, we carried out a new genome wide association study of antidepressant response in the STAR*D sample. We chose treatment response at study exit (after 2 to 12 weeks of citalopram treatment) as the main outcome, since our preliminary analyses showed this to be the most heritable phenotype. We found one SNP that was associated with this outcome at genome-wide significance, and identified some nearby genes. Ongoing work is aimed at replicating this finding in additional samples. We are now using new, high-throughput sequencing methods to test for rarer genetic variants that may exert larger effects, at least in patients with treatment resistant depression. Such variants may show larger effects, especially among patients with unusual treatment outcomes. Patients who respond to antidepressant treatment constitute a mixture of true responders, placebo responders, and those whose illness remits spontaneously. Patients who fail to respond to multiple treatments may be less heterogeneous, since placebo responders and spontaneous remitters are removed. From over 4,000 patients enrolled in the STAR*D study, we found that only 10% show treatment resistance that is not explained by non-adherence, comorbid substance use disorder, or other factors, and only 3% are highly treatment-resistant. Sequencing of the coding regions of the genome (exome) has now been completed on 150 treatment-resistant and 25 typically responsive patients. Samples were drawn from the STAR*D, the Univ. Michigan Depression Center, and patients enrolled in NIMH studies of novel antidepressants such as ketamine and scopolamine. Much of the exome sequencing was carried out at the NIH Intramural Sequencing Center with funds provided by the NIH Clinical Center Genomics Opportunity (CCGO) program. Of the 350,000 high-quality genetic variants that have been identified so far, about 100,000 are rare variants within the protein-coding regions of genes. We are now exploring various filtering strategies aimed at developing a list of genes that harbor deleterious variants in people with treatment-resistant depression. Pathway analysis of the implicated genes may point toward biological pathways important in the causes and treatment of major depression. In the coming year, we plan to expand the sample size in order to increase power and identify the genes most robustly associated with treatment resistance. We also plan to analyze a comparison sample of about 1000 individuals whose DNA also underwent exome sequencing as part of the CCGO study. These individuals will provide an important comparison sample that may reveal additional genes or pathways that could be good targets for development of novel therapeutics.
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