Clinical outcomes and genome-wide association for a brain methylation site in an antidepressant pharmacogenetics study in Mexican Americans

Abstract

Objective: The authors compared the effectiveness of fluoxetine and desipramine treatment in a prospective double-blind pharmacogenetics study in first-generation Mexican Americans and examined the role of whole-exome functional gene variations in the patients' antidepressant response.

Method: A total of 232 Mexican Americans who met DSM-IV criteria for major depressive disorder were randomly assigned to receive 8 weeks of double-blind treatment with desipramine (50-200 mg/day) or fluoxetine (10-40 mg/day) after a 1-week placebo lead-in period. Outcome measures included the Hamilton Depression Rating Scale (HAM-D), the Hamilton Anxiety Rating Scale, and the Beck Depression Inventory. At week 8, whole-exome genotyping data were obtained for 36 participants who remitted and 29 who did not respond to treatment.

Results: Compared with desipramine treatment, fluoxetine treatment was associated with a greater reduction in HAM-D score, higher response and remission rates, shorter time to response and remission, and lower incidences of anticholinergic and cardiovascular side effects. Pharmacogenetics analysis showed that exm-rs1321744 achieved exome-wide significance for treatment remission. This variant is located in a brain methylated DNA immunoprecipitation sequencing site, which suggests that it may be involved in epigenetic regulation of neuronal gene expression. This and two other common gene variants provided a highly accurate cross-validated predictive model for treatment remission of major depression (receiver operating characteristic integral=0.95).

Conclusions: Compared with desipramine, fluoxetine treatment showed a more rapid reduction of HAM-D score and a lower incidence of side effects in a population comprising primarily first-generation Mexican Americans with major depression. This study's pharmacogenetics approach strongly implicates the role of functional variants in antidepressant treatment response.

Trial registration: ClinicalTrials.gov NCT00265291.

Fig 1. Process used to filter out SNP Exomic Variants Genotyped with the Illumina® HumanExome BeadChip-12v1_A in a Mexican-American Cohort of Patients with Major Depressive Disorder (MDD) under Antidepressive Treatment to Evaluate Pharmacogenetic Response As Remitters (n=36) and Non-Responders (n=29)

From 247,876 SNPs 195,773 were discarded because they were either monoallelic, had more than two alleles, had a call rate lower than 90%, or their genotype proportions deviated from the expected ones as defined by the Hardy-Weinberg equilibrium theorem in both cases (Remitters) and controls (Non-responders), P<2 × 10⁻⁷. The remaining 52,103 SNPs were used for Exome-Wide Association Analysis.

Fig 2. Manhattan Plot to Evaluate Pharmacogenetic Response As Remitters (n=36) and Non-responders (n=29)

Genotype model (additive) after correcting stratification by principal component analysis (PCA) was used. A unique significant peak at chromosome 6 was found significant after correction for multiple comparisons by FDR. In the inset, the principal component analysis shows absence of stratification between remitters and non-responders (right box), and a Q-Q plot depicts the fitting of χ² against the χ² expected distribution (left box).

Fig 3. Results of Advance Recursive Partitioning (tree-based) Approach (ARPA): A

Reconstructed classificatory tree showing those variants involved in the process of branching. B. The ROC (Receiver Operating Characteristic) integral = 0.9454.

Fig 4. Patterns of Hamilton Depression (HAM-D) scores split the set of patients in two significantly different clusters associated to the exm-rs1321744 genotypes

Cluster 1 depicted by crosses is constituted by remitters and associated to the CC genotype. Cluster 2 in red is constituted by non-responders and associated with CT genotype. Treatment with either desipramine or fluoxetine did not produce any significant additional splitting of these two clusters.