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Genome-wide pharmacogenomic study of citalopram-induced side effects in STAR*D.

ABSTRACT: Affecting about 1 in 12 Americans annually, depression is a leading cause of the global disease burden. While a range of effective antidepressants are now available, failure and relapse rates remain substantial, with intolerable side effect burden the most commonly cited reason for discontinuation. Thus, understanding individual differences in susceptibility to antidepressant therapy side effects will be essential to optimize depression treatment. Here we perform genome-wide association studies (GWAS) to identify genetic variation influencing susceptibility to citalopram-induced side effects. The analysis sample consisted of 1762 depression patients, successfully genotyped for 421K single-nucleotide polymorphisms (SNPs), from the Sequenced Treatment Alternatives to Relieve Depression (STAR(*)D) study. Outcomes included five indicators of citalopram side effects: general side effect burden, overall tolerability, sexual side effects, dizziness and vision/hearing side effects. Two SNPs met our genome-wide significance criterion (q<0.1), ensuring that, on average, only 10% of significant findings are false discoveries. In total, 12 additional SNPs demonstrated suggestive associations (q<0.5). The top finding was rs17135437, an intronic SNP within EMID2, mediating the effects of citalopram on vision/hearing side effects (P=3.27 × 10(-8), q=0.026). The second genome-wide significant finding, representing a haplotype spanning ?30?kb and eight genotyped SNPs in a gene desert on chromosome 13, was associated with general side effect burden (P=3.22 × 10(-7), q=0.096). Suggestive findings were also found for SNPs at LAMA1, AOX2P, EGFLAM, FHIT and RTP2. Although our findings require replication and functional validation, this study demonstrates the potential of GWAS to discover genes and pathways that potentially mediate adverse effects of antidepressant medications.

SUBMITTER: Adkins DE

PROVIDER: S-EPMC3410623 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Genome-wide pharmacogenomic study of citalopram-induced side effects in STAR*D.

Adkins D E DE Clark S L SL Åberg K K Hettema J M JM Bukszár J J McClay J L JL Souza R P RP van den Oord E J C G EJ

Translational psychiatry 20120703

Affecting about 1 in 12 Americans annually, depression is a leading cause of the global disease burden. While a range of effective antidepressants are now available, failure and relapse rates remain substantial, with intolerable side effect burden the most commonly cited reason for discontinuation. Thus, understanding individual differences in susceptibility to antidepressant therapy side effects will be essential to optimize depression treatment. Here we perform genome-wide association studies ...[more]

PMID: 22760553

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Project description:ObjectiveFew data exist to help clinicians predict likelihood of treatment response in individual patients with major depressive disorder (MDD). Our aim was to identify subgroups of MDD patients with differential treatment outcomes based on presenting clinical characteristics. We also sought to quantify the likelihood of treatment success based on the degree of improvement and side effects after 2 and 4 weeks of selective serotonin reuptake inhibitor (SSRI) pharmacotherapy.MethodWe analyzed data from the first treatment phase of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial, in which subjects with a DSM-IV diagnosis of MDD were treated for 8-14 weeks with open-label citalopram. A receiver operating characteristic (ROC) analysis was conducted to determine homogenous subgroups with different rates of response and remission in depressive symptoms. Included predictor variables were initial clinical characteristics, initial improvement, and side effects after 2 and 4 weeks of SSRI treatment. The primary outcome measures were treatment response (defined as a greater than 50% reduction in 17-item Hamilton Depression Rating Scale [HDRS-17] score from baseline) and remission (defined as an HDRS-17 score ≤ 17).ResultsBaseline clinical characteristics were able to identify subgroups from a low likelihood of response of 18% (income < $10,000, comorbid generalized anxiety disorder, < 16 years of education; P < .01) to a high likelihood of response of 68% (income ≥ $40,000, no comorbid posttraumatic stress disorder; P < .01). Among baseline clinical characteristics, employment status (N = 2,477; χ²₁ = 78.1; P < .001) and income level (N = 2,512; χ²₁ = 77.7; P < .001) were the most informative in predicting treatment outcome. For the models at weeks 2 and 4, treatment success was best predicted by early symptom improvement.ConclusionsSocioeconomic data such as low income, education, and unemployment were most discriminative in predicting a poor response to citalopram, even with disparities in access to care accounted for. This finding implies that socioeconomic factors may be more useful predictors of medication response than traditional psychiatric diagnoses or past treatment history.Trial registrationClinicalTrials.gov identifier: NCT00021528.

Project description:ObjectiveThe goal of pharmacogenomics is the translation of genomic discoveries into individualized patient care. Recent advances in the means to survey human genetic variation are fundamentally transforming our understanding of the genetic basis of interindividual variation in therapeutic response. The goal of this study was to systematically evaluate high-throughput genotyping technologies for their ability to assay variation in pharmacogenetically important genes (pharmacogenes). These platforms are either being proposed for or are already being widely used for clinical implementation; therefore, knowledge of coverage of pharmacogenes on these platforms would serve to better evaluate current or proposed pharmacogenetic association studies.MethodAmong the genes included in our study are drug-metabolizing enzymes, transporters, receptors, and drug targets, of interest to the entire pharmacogenetic community. We considered absolute and linkage disequilibrium (LD)-informed coverage, minor allele frequency spectrum, and functional annotation for a Caucasian population. We also examined the effect of LD, effect size, and cohort size on the power to detect single nucleotide polymorphism associations.ResultsIn our analysis of 253 pharmacogenes, we found that no platform showed more than 85% coverage of these genes (after accounting for LD). Furthermore, the lack of coverage showed a marked increase at minor allele frequencies of less than 20%. Even after accounting for LD, only 30% of the missense polymorphisms (which are enriched for low-frequency alleles) were covered by HapMap, with still lower coverage on the other platforms.ConclusionWe have conducted the first systematic evaluation of the Axiom Genomic Database, Omni 2.5 M, and the Drug Metabolizing Enzymes and Transporters chip. This study is the first to utilize the 1000 Genomes Project to present a comprehensive evaluative framework. Our results provide a much-needed assessment of microarray-based genotyping and next-generation sequencing technologies' ability to survey fully the variation in genes of particular interest to the pharmacogenetics community. Our findings demonstrate the limitations of genome-wide methods and the challenges of implementing pharmacogenomic tests into the clinical context.

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Genome-wide pharmacogenomic study of citalopram-induced side effects in STAR*D.

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Genome-wide pharmacogenomic study of citalopram-induced side effects in STAR*D.

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