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Genome-wide approaches to antidepressant treatment: working towards understanding and predicting response.

ABSTRACT: Antidepressants are among the most commonly prescribed drugs, and a range of medications are available. However, treatment response to a particular drug varies greatly between patients, with only 30% of patients responding well to the first treatment administered. Given evidence that antidepressant treatment response is a heritable trait, together with technological advances in genetic research, three recently published genome-wide investigations into antidepressant responses have examined the determinants of variability in treatment outcomes between depressed patients. Here, we review these studies within the context of wider research efforts to identify treatment response predictors. Some interesting genes have been implicated, but no variants have yet been robustly and reliably linked to response. This may suggest that genetic effect sizes are smaller than originally anticipated. Candidate gene approaches in these samples have lent support to the involvement of serotonergic, glutamatergic and stress-response systems in treatment response, although corroborative evidence from genome-wide analyses indicates these results should be interpreted cautiously. Closer examination of antidepressant response, considering it as a complex trait, has indicated that multiple genes of small effect are likely to be involved. Furthermore, there is some evidence that genetic influence on response to treatment may vary between patients with different symptom profiles or environmental exposures. This has implications for the translation of pharmacogenetic findings into clinical practice: genotypic information from multiple loci and data on non-genetic factors are likely to be needed to tailor antidepressant treatment to the individual patient.

SUBMITTER: Hodgson K

PROVIDER: S-EPMC3698532 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Genome-wide approaches to antidepressant treatment: working towards understanding and predicting response.

Hodgson Karen K Mufti Shaista Jeelani SJ Uher Rudolf R McGuffin Peter P

Genome medicine 20120627 6

Antidepressants are among the most commonly prescribed drugs, and a range of medications are available. However, treatment response to a particular drug varies greatly between patients, with only 30% of patients responding well to the first treatment administered. Given evidence that antidepressant treatment response is a heritable trait, together with technological advances in genetic research, three recently published genome-wide investigations into antidepressant responses have examined the d ...[more]

PMID: 22738351

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Project description:Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge. Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ?50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features. Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha1/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha2 being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 "concentration difficulty" scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha2 and frontopolar alpha1. Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, "biomarker"-based treatment approaches.

Project description:The "antidepressant efficacy" survey (AES) was deployed to > 50,000 23andMe, Inc. research participants to investigate the genetic basis of treatment-resistant depression (TRD) and non-treatment-resistant depression (NTRD). Genome-wide association studies (GWAS) were performed, including TRD vs. NTRD, selective serotonin reuptake inhibitor (SSRI) responders vs. non-responders, serotonin-norepinephrine reuptake inhibitor (SNRI) responders vs. non-responders, and norepinephrine-dopamine reuptake inhibitor responders vs. non-responders. Only the SSRI association reached the genome-wide significance threshold (p < 5 × 10-8): one genomic region in RNF219-AS1 (SNP rs4884091, p = 2.42 × 10-8, OR = 1.21); this association was also observed in the meta-analysis (13,130 responders vs. 6,610 non-responders) of AES and an earlier "antidepressant efficacy and side effects" survey (AESES) cohort. Meta-analysis for SNRI response phenotype derived from AES and AESES (4030 responders vs. 3049 non-responders) identified another genomic region (lead SNP rs4955665, p = 1.62 × 10-9, OR = 1.25) in an intronic region of MECOM passing the genome-wide significance threshold. Meta-analysis for the TRD phenotype (31,068 NTRD vs 5,714 TRD) identified one additional genomic region (lead SNP rs150245813, p = 8.07 × 10-9, OR = 0.80) in 10p11.1 passing the genome-wide significance threshold. A stronger association for rs150245813 was observed in current study (p = 7.35 × 10-7, OR = 0.79) than the previous study (p = 1.40 × 10-3, OR = 0.81), and for rs4955665, a stronger association in previous study (p = 1.21 × 10-6, OR = 1.27) than the current study (p = 2.64 × 10-4, OR = 1.21). In total, three novel loci associated with SSRI or SNRI (responders vs. non-responders), and NTRD vs TRD were identified; gene level association and gene set enrichment analyses implicate enrichment of genes involved in immune process.

Project description:We conducted a three-stage genome-wide association study (GWAS) of response to antidepressant drugs in an ethnically homogeneous sample of Korean patients in untreated episodes of nonpsychotic unipolar depression, mostly of mature onset. Strict quality control was maintained in case selection, diagnosis, verification of adherence and outcome assessments. Analyzed cases completed 6 weeks of treatment with adequate plasma drug concentrations. The overall successful completion rate was 85.5%. Four candidate single-nucleotide polymorphisms (SNPs) on three chromosomes were identified by genome-wide search in the discovery sample of 481 patients who received one of four allowed selective serotonin reuptake inhibitor (SSRI) antidepressant drugs (Stage 1). In a focused replication study of 230 SSRI-treated patients, two of these four SNP candidates were confirmed (Stage 2). Analysis of the Stage 1 and Stage 2 samples combined (n = 711) revealed GWAS significance (P = 1.60 × 10(-8)) for these two SNP candidates, which were in perfect linkage disequilibrium. These two significant SNPs were confirmed also in a focused cross-replication study of 159 patients treated with the non-SSRI antidepressant drug mirtazapine (Stage 3). Analysis of the Stage 1, Stage 2 and Stage 3 samples combined (n = 870) also revealed GWAS significance for these two SNPs, which was sustained after controlling for gender, age, number of previous episodes, age at onset and baseline severity (P = 3.57 × 10(-8)). For each SNP, the response rate decreased (odds ratio=0.31, 95% confidence interval: 0.20-0.47) as a function of the number of minor alleles (non-response alleles). The two SNPs significantly associated with antidepressant response are rs7785360 and rs12698828 of the AUTS2 gene, located on chromosome 7 in 7q11.22. This gene has multiple known linkages to human psychological functions and neurobehavioral disorders. Rigorous replication efforts in other ethnic populations are recommended.

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Genome-wide approaches to antidepressant treatment: working towards understanding and predicting response.

Publications

Genome-wide approaches to antidepressant treatment: working towards understanding and predicting response.

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