Pharmacogenetics of antidepressant response: A polygenic approach

Leveraging DNA methylation to predict treatment response in major depressive disorder: A critical review

Major depressive disorder (MDD) is a debilitating and prevalent mental disorder with a high disease burden. Despite a wide array of different treatment options, many patients do not respond to initial treatment attempts. Selection of the most appropriate treatment remains a significant clinical challenge in psychiatry, highlighting the need for the development of biomarkers with predictive utility. Recently, the epigenetic modification DNA methylation (DNAm) has emerged to be of great interest as a potential predictor of MDD treatment outcomes. Here, we review efforts to date that seek to identify DNAm signatures associated with treatment response in individuals with MDD. Searches were conducted in the databases PubMed, Scopus, and Web of Science with the concepts and keywords MDD, DNAm, antidepressants, psychotherapy, cognitive behavior therapy, electroconvulsive therapy, transcranial magnetic stimulation, and brain stimulation therapies. We identified 32 studies implicating DNAm patterns associated with MDD treatment outcomes. The majority of studies (N = 25) are focused on selected target genes exploring treatment outcomes in pharmacological treatments (N = 22) with a few studies assessing treatment response to electroconvulsive therapy (N = 3). Additionally, there are few genome-scale efforts (N = 7) to characterize DNAm patterns associated with treatment outcomes. There is a relative dearth of studies investigating DNAm patterns in relation to psychotherapy, electroconvulsive therapy, or transcranial magnetic stimulation; importantly, most existing studies have limited sample sizes. Given the heterogeneity in both methods and results of studies to date, there is a need for additional studies before existing findings can inform clinical decisions.

Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities

Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.

Multimodal predictions of treatment outcome in major depression: A comparison of data-driven predictors with importance ratings by clinicians

BACKGROUND

Reliable prediction models of treatment outcome in Major Depressive Disorder (MDD) are currently lacking in clinical practice. Data-driven outcome definitions, combining data from multiple modalities and incorporating clinician expertise might improve predictions.

METHODS

We used unsupervised machine learning to identify treatment outcome classes in 1060 MDD inpatients. Subsequently, classification models were created on clinical and biological baseline information to predict treatment outcome classes and compared to the performance of two widely used classical outcome definitions. We also related the findings to results from an online survey that assessed which information clinicians use for outcome prognosis.

RESULTS

Three and four outcome classes were identified by unsupervised learning. However, data-driven outcome classes did not result in more accurate prediction models. The best prediction model was targeting treatment response in its standard definition and reached accuracies of 63.9 % in the test sample, and 59.5 % and 56.9 % in the validation samples. Top predictors included sociodemographic and clinical characteristics, while biological parameters did not improve prediction accuracies. Treatment history, personality factors, prior course of the disorder, and patient attitude towards treatment were ranked as most important indicators by clinicians.

LIMITATIONS

Missing data limited the power to identify biological predictors of treatment outcome from certain modalities.

CONCLUSIONS

So far, the inclusion of available biological measures in addition to psychometric and clinical information did not improve predictive value of the models, which was overall low. Optimized biomarkers, stratified predictions and the inclusion of clinical expertise may improve future prediction models.

Predicting treatment outcome in depression: an introduction into current concepts and challenges

Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.

Polygenic risk scores for predicting outcomes and treatment response in psychiatry: hope or hype?

Over the last years, the decreased costs and enhanced accessibility to large genome-wide association studies datasets have laid the foundations for the development of polygenic risk scores (PRSs). A PRS is calculated on the weighted sum of single nucleotide polymorphisms and measures the individual genetic predisposition to develop a certain phenotype. An increasing number of studies have attempted to utilize the PRSs for risk stratification and prognostic evaluation. The present narrative review aims to discuss the potential clinical utility of PRSs in predicting outcomes and treatment response in psychiatry. After summarizing the evidence on major mental disorders, we have discussed the advantages and limitations of currently available PRSs. Although PRSs represent stable trait features with a normal distribution in the general population and can be relatively easily calculated in terms of time and costs, their real-world applicability is reduced by several limitations, such as low predictive power and lack of population diversity. Even with the rapid expansion of the psychiatric genetic knowledge base, pure genetic prediction in clinical psychiatry appears to be out of reach in the near future. Therefore, combining genomic and exposomic vulnerabilities for mental disorders with a detailed clinical characterization is needed to personalize care.

Association Between Polygenic Risk Scores and Outcome of ECT

OBJECTIVE

Identifying biomarkers associated with response to electroconvulsive therapy (ECT) may aid clinical decisions. The authors examined whether greater polygenic liabilities for major depressive disorder, bipolar disorder, and schizophrenia are associated with improvement following ECT for a major depressive episode.

METHODS

Between 2013 and 2017, patients who had at least one treatment series recorded in the Swedish National Quality Register for ECT were invited to provide a blood sample for genotyping. The present study included 2,320 participants (median age, 51 years; 62.8% women) who had received an ECT series for a major depressive episode (77.1% unipolar depression), who had a registered treatment outcome, and whose polygenic risk scores (PRSs) could be calculated. Ordinal logistic regression was used to estimate the effect of PRS on Clinical Global Impressions improvement scale (CGI-I) score after each ECT series.

RESULTS

Greater PRS for major depressive disorder was significantly associated with less improvement on the CGI-I (odds ratio per standard deviation, 0.89, 95% CI=0.82, 0.96; R²=0.004), and greater PRS for bipolar disorder was associated with greater improvement on the CGI-I (odds ratio per standard deviation, 1.14, 95% CI=1.05, 1.23; R²=0.005) after ECT. PRS for schizophrenia was not associated with improvement. In an overlapping sample (N=1,207) with data on response and remission derived from the self-rated version of the Montgomery-Åsberg Depression Rating Scale, results were similar except that schizophrenia PRS was also associated with remission.

CONCLUSIONS

Improvement after ECT is associated with polygenic liability for major depressive disorder and bipolar disorder, providing evidence of a genetic component for ECT clinical response. These liabilities may be considered along with clinical predictors in future prediction models of ECT outcomes.

Ten challenges for clinical translation in psychiatric genetics

Genome-wide association studies have identified hundreds of robust genetic associations underlying psychiatric disorders and provided important biological insights into disease onset and progression. There is optimism that genetic findings will pave the way to precision psychiatry by facilitating the development of more effective treatments and the identification of groups of patients that these treatments should be targeted toward. However, there are several challenges that must be addressed before genetic findings can be translated into the clinic. In this Perspective, we highlight ten challenges for the field of psychiatric genetics, focused on the robust and generalizable detection of genetic risk factors, improved definition and assessment of psychopathology and achieving better clinical indicators. We discuss recent advancements in the field that will improve the explanatory and predictive power of genetic data and ultimately contribute to improving the management and treatment of patients with a psychiatric disorder.

A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study

The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG signatures may help predict antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a brain network in a large cohort (N=1,123), and discover it is sex-specifically (male patients, N=617) associated with polygenic risk score (PRS) of antidepressant response. Subsequently, we demonstrate in three independent datasets the utility of the network in predicting response to antidepressant medication (male, N=232) as well as repetitive transcranial magnetic stimulation (rTMS) and concurrent psychotherapy (male, N=95). This network significantly improves a treatment response prediction model with age and baseline severity data (area under the curve, AUC=0.623 for medicaton; AUC=0.719 for rTMS). A predictive model for MDD patients, aimed at increasing the likelihood of being a responder to antidepressants or rTMS and concurrent psychotherapy based on only this network, yields a positive predictive value (PPV) of 69% for medication and 77% for rTMS. Finally, blinded out-of-sample validation of the network as predictor for psychotherapy response in another independent dataset (male, N=50) results in a within-subsample response rate of 50% (improvement of 56%). Overall, the findings provide a first proof-of-concept of a combined genetic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders, and should encourage researchers to incorporate genetic information, such as PRS, in their search for clinically relevant neuroimaging biomarkers.

The Potential of Polygenic Risk Scores to Predict Antidepressant Treatment Response in Major Depression: A Systematic Review

BACKGROUND

Understanding the genetic underpinnings of antidepressant treatment response in unipolar major depressive disorder (MDD) can be useful in identifying patients at risk for poor treatment response or treatment resistant depression. A polygenic risk score (PRS) is a useful tool to explore genetic liability of a complex trait such as antidepressant treatment response. Here, we review studies that use PRSs to examine genetic overlap between any trait and antidepressant treatment response in unipolar MDD.

METHODS

A systematic search of literature was conducted in PubMed, Embase, and PsycINFO. Our search included studies examining associations between PRSs of psychiatric as well as non-psychiatric traits and antidepressant treatment response in patients with unipolar MDD. A quality assessment of the included studies was performed.

RESULTS

In total, eleven articles were included which contained PRSs for 30 traits. Studies varied in sample size and endpoints used for antidepressant treatment response. Overall, PRSs for attention-deficit hyperactivity disorder, the personality trait openness, coronary artery disease, obesity, and stroke have been associated with antidepressant treatment response in patients with unipolar MDD.

LIMITATIONS

The endpoints used by included studies differed significantly, therefore it was not possible to perform a meta-analysis.

CONCLUSIONS

Associations between a PRS and antidepressant treatment response have been reported for a number of traits in patients with unipolar MDD. PRSs could be informative to predict antidepressant treatment response in this population, given advances in the field. Most importantly, there is a need for larger study cohorts and the use of standardized outcome measures.

A meta-analysis of polygenic risk scores for mood disorders, neuroticism, and schizophrenia in antidepressant response

About two-thirds of patients with major depressive disorder (MDD) fail to achieve symptom remission after the initial antidepressant treatment. Despite a role of genetic factors was proven, the specific underpinnings are not fully understood yet. Polygenic risk scores (PRSs), which summarise the additive effect of multiple risk variants across the genome, might provide insights into the underlying genetics. This study aims to investigate the possible association of PRSs for bipolar disorder, MDD, neuroticism, and schizophrenia (SCZ) with antidepressant non-response or non-remission in patients with MDD. PRSs were calculated at eight genome-wide P-thresholds based on publicly available summary statistics of the largest genome-wide association studies. Logistic regressions were performed between PRSs and non-response or non-remission in six European clinical samples, adjusting for age, sex, baseline symptom severity, recruitment sites, and population stratification. Results were meta-analysed across samples, including up to 3,637 individuals. Bonferroni correction was applied. In the meta-analysis, no result was significant after Bonferroni correction. The top result was found for MDD-PRS and non-remission (p = 0.004), with patients in the highest vs. lowest PRS quintile being more likely not to achieve remission (OR=1.5, 95% CI=1.11-1.98, p = 0.007). Nominal associations were also found between MDD-PRS and non-response (p = 0.013), as well as between SCZ-PRS and non-remission (p = 0.035). Although PRSs are still not able to predict non-response or non-remission, our results are in line with previous works; methodological improvements in PRSs calculation may improve their predictive performance and have a meaningful role in precision psychiatry.

What Do We Know About the Genetic Architecture of Psychopathology?

In the second half of the twentieth century, twin and family studies established beyond a reasonable doubt that all forms of psychopathology are substantially heritable and highly polygenic. These conclusions were simultaneously an important theoretical advance and a difficult methodological obstacle, as it became clear that heritability is universal and undifferentiated across forms of psychopathology, and the radical polygenicity of genetic effects limits the biological insight provided by genetically informed studies at the phenotypic level. The paradigm-shifting revolution brought on by the Human Genome Project has recapitulated the great methodological promise and the profound theoretical difficulties of the twin study era. We review these issues using the rubric of genetic architecture, which we define as a search for specific genetic insight that adds to the general conclusion that psychopathology is heritable and polygenic. Although significant problems remain, we see many promising avenues for progress.

Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Polygenic risk scores for neuropsychiatric, inflammatory, and cardio-metabolic traits highlight possible genetic overlap with suicide attempt and treatment-emergent suicidal ideation

Suicide is the second cause of death among youths. Genetics may contribute to suicidal phenotypes and their co-occurrence in other neuropsychiatric and medical conditions. Our study aimed to investigate the association of polygenic risk scores (PRSs) for 24 neuropsychiatric, inflammatory, and cardio-metabolic traits/diseases with suicide attempt (SA) or treatment-worsening/emergent suicidal ideation (TWESI). PRSs were computed based on summary statistics of genome-wide association studies. Regression analyses were performed between PRSs and SA or TWESI in four clinical cohorts. Results were then meta-analyzed across samples, including a total of 688 patients with SA (N_eff  = 2,258) and 214 with TWESI (N_eff  = 785). Stratified genetic covariance analyses were performed to investigate functionally cross-phenotype PRS associations. After Bonferroni correction, PRS for major depressive disorder (MDD) was associated with SA (OR = 1.24; 95% CI = 1.11-1.38; p = 1.73 × 10^-4 ). Nominal associations were shown between PRSs for coronary artery disease (CAD) (p = 4.6 × 10^-3 ), loneliness (p = .009), or chronic pain (p = .016) and SA, PRSs for MDD or CAD and TWESI (p = .043 and p = .032, respectively). Genetic covariance between MDD and SA was shown in 86 gene sets related to drugs having antisuicidal effects. A higher genetic liability for MDD may underlie a higher SA risk. Further, but milder, possible modulatory factors are genetic risk for loneliness and CAD.

Polygenic risk scores: An overview from bench to bedside for personalised medicine

Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field.

Genophenotypic Factors and Pharmacogenomics in Adverse Drug Reactions

Adverse drug reactions (ADRs) rank as one of the top 10 leading causes of death and illness in developed countries. ADRs show differential features depending upon genotype, age, sex, race, pathology, drug category, route of administration, and drug–drug interactions. Pharmacogenomics (PGx) provides the physician effective clues for optimizing drug efficacy and safety in major problems of health such as cardiovascular disease and associated disorders, cancer and brain disorders. Important aspects to be considered are also the impact of immunopharmacogenomics in cutaneous ADRs as well as the influence of genomic factors associated with COVID-19 and vaccination strategies. Major limitations for the routine use of PGx procedures for ADRs prevention are the lack of education and training in physicians and pharmacists, poor characterization of drug-related PGx, unspecific biomarkers of drug efficacy and toxicity, cost-effectiveness, administrative problems in health organizations, and insufficient regulation for the generalized use of PGx in the clinical setting. The implementation of PGx requires: (i) education of physicians and all other parties involved in the use and benefits of PGx; (ii) prospective studies to demonstrate the benefits of PGx genotyping; (iii) standardization of PGx procedures and development of clinical guidelines; (iv) NGS and microarrays to cover genes with high PGx potential; and (v) new regulations for PGx-related drug development and PGx drug labelling.

Genetic contributions to alcohol use disorder treatment outcomes: a genome-wide pharmacogenomics study

Naltrexone can aid in reducing alcohol consumption, while acamprosate supports abstinence; however, not all patients with alcohol use disorder (AUD) benefit from these treatments. Here we present the first genome-wide association study of AUD treatment outcomes based on data from the COMBINE and PREDICT studies of acamprosate and naltrexone, and the Mayo Clinic CITA study of acamprosate. Primary analyses focused on treatment outcomes regardless of pharmacological intervention and were followed by drug-stratified analyses to identify treatment-specific pharmacogenomic predictors of acamprosate and naltrexone response. Treatment outcomes were defined as: (1) time until relapse to any drinking (TR) and (2) time until relapse to heavy drinking (THR; ≥ 5 drinks for men, ≥4 drinks for women in a day), during the first 3 months of treatment. Analyses were performed within each dataset, followed by meta-analysis across the studies (N = 1083 European ancestry participants). Single nucleotide polymorphisms (SNPs) in the BRE gene were associated with THR (min p = 1.6E-8) in the entire sample, while two intergenic SNPs were associated with medication-specific outcomes (naltrexone THR: rs12749274, p = 3.9E-8; acamprosate TR: rs77583603, p = 3.1E-9). The top association signal for TR (p = 7.7E-8) and second strongest signal in the THR (p = 6.1E-8) analysis of naltrexone-treated patients maps to PTPRD, a gene previously implicated in addiction phenotypes in human and animal studies. Leave-one-out polygenic risk score analyses showed significant associations with TR (p = 3.7E-4) and THR (p = 2.6E-4). This study provides the first evidence of a polygenic effect on AUD treatment response, and identifies genetic variants associated with potentially medication-specific effects on AUD treatment response.

The role of pharmacogenetics in the treatment of anxiety disorders and the future potential for targeted therapeutics

INTRODUCTION

Anxiety disorders (AD) are among the most common mental disorders worldwide. Pharmacotherapy, including benzodiazepines, selective serotonin reuptake inhibitors, serotonin-norepinephrine reuptake inhibitors, and tricyclic antidepressants is currently based on 'trial-and-error,' and is effective in a subset of patients or produces partial response only. Recent research proposes that treatment response and tolerability of the drugs are associated with genetic factors.

AREAS COVERED

In the present review, we provide information on pharmacogenetics (PGx) in AD, including pharmacokinetic and pharmacodynamic genes. Moreover, we discuss the future potential of PGx for personalized treatment.

EXPERT OPINION

In psychiatry, PGx testing is still in its infancy, especially in the treatment of AD. As of today, implementation in clinical routine is recommended only for CYP2D6 and CYP2C19, mainly in terms of safety of treatment and potentially of treatment outcome in general. However, the evidence for PGx testing addressing pharmacodynamics for specific AD is limited to date. Nevertheless, PGx may develop into a valuable and promising tool to improve therapy in AD, but there is a need for more research to fully exploit its possibilities. Future perspectives include research into single genes, polygenic risk scores, and pharmacoepigenetics to provide targeted therapy.

Gastrointestinal side effects associated with antidepressant treatments in patients with major depressive disorder: A systematic review and meta-analysis

Gastrointestinal side effects (SEs) are frequently observed in patients with major depressive disorder (MDD) while taking antidepressants and may lead to treatment discontinuation. The aim of this meta-analysis is to provide quantitative measures on short-term rates of gastrointestinal SEs in MDD patients treated with second-generation antidepressants. An electronic search of the literature was conducted by using MEDLINE, ISI Web of Science - Web of Science Core Collection, and Cochrane Library databases. Eligible studies had to focus on the use of at least one of 15 antidepressants commonly used in MDD (i.e., agomelatine, bupropion, citalopram, desvenlafaxine, duloxetine, escitalopram, fluoxetine, fluvoxamine, levomilnacipran, mirtazapine, paroxetine, reboxetine, sertraline, venlafaxine, and vortioxetine) and report data on treatment-emergent gastrointestinal SEs (i.e. nausea/vomiting, diarrhoea, constipation, abdominal pain, dyspepsia, anorexia, increased appetite and dry mouth) within 12 weeks of treatment. Overall, 304 studies were included in the meta-analyses. All the considered antidepressants showed higher rates of gastrointestinal SEs than placebo. Escitalopram and sertraline were shown to be the least tolerated antidepressants on the gastrointestinal tract, being associated with all the considered SEs with the exception of constipation and increased appetite, while mirtazapine was shown to be the antidepressant with fewer side effects on the gut, being only associated with increased appetite. In conclusion, commonly used antidepressants showed different profiles of gastrointestinal SEs, possibly related to their mechanisms of action. The specific tolerability profile of each compound should be considered by clinicians when prescribing antidepressants in order to improve adherence to treatment and increase positive outcomes in patients with MDD.

Higher polygenic risk scores for schizophrenia may be suggestive of treatment non-response in major depressive disorder

Up to 60% of patients with major depressive disorder (MDD) do not respond to the first treatment with antidepressants. Response to antidepressants is a polygenic trait, although its underpinning genetics has not been fully clarified. This study aimed to investigate if polygenic risk scores (PRSs) for major psychiatric disorders and trait neuroticism (NEU) were associated with non-response or resistance to antidepressants in MDD. PRSs for bipolar disorder, MDD, NEU, and schizophrenia (SCZ) were computed in 1,148 patients with MDD. Summary statistics from the largest meta-analyses of genome-wide association studies were used as base data. Patients were classified as responders, non-responders to one treatment, non-responders to two or more treatments (treatment-resistant depression or TRD). Regression analyses were adjusted for population stratification and recruitment sites. PRSs did not predict either non-response vs response or TRD vs response after Bonferroni correction. However, SCZ-PRS was nominally associated with non-response (p = 0.003). Patients in the highest SCZ-PRS quintile were more likely to be non-responders than those in the lowest quintile (OR = 2.23, 95% CI = 1.21-4.10, p = 0.02). Patients in the lowest SCZ-PRS quintile showed higher response rates when they did not receive augmentation with second-generation antipsychotics (SGAs), while those in the highest SCZ-PRS quintile had a poor response independently from the treatment strategy (p = 0.009). A higher genetic liability to SCZ may reduce treatment response in MDD, and patients with low SCZ-PRSs may show higher response rates without SGA augmentation. Multivariate approaches and methodological refinements will be necessary before clinical implementations of PRSs.

The promise of machine learning in predicting treatment outcomes in psychiatry

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Therapy response prediction in major depressive disorder: current and novel genomic markers influencing pharmacokinetics and pharmacodynamics

Major depressive disorder is connected with high rates of functional disability and mortality. About a third of the patients are at risk of therapy failure. Several pharmacogenetic markers especially located in CYP450 genes such as CYP2D6 or CYP2C19 are of relevance for therapy outcome prediction in major depressive disorder but a further optimization of predictive tools is warranted. The article summarizes the current knowledge on pharmacogenetic variants, therapy effects and side effects of important antidepressive therapeutics, and sheds light on new methodological approaches for therapy response estimation based on genetic markers with relevance for pharmacokinetics, pharmacodynamics and disease pathology identified in genome-wide association study analyses, highlighting polygenic risk score analysis as a tool for further optimization of individualized therapy outcome prediction.

Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response

Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 1257 with both valid genomic and outcome data). A confirmatory approach selected 11 SNPs previously reported to predict response to escitalopram in a sample different from the current study. A novel exploratory approach selected SNPs from across the genome using nested cross-validation with elastic net logistic regression with a predominantly lasso penalty (alpha = 0.99). SNPs from each approach were combined with baseline clinical predictors and treatment response outcomes were predicted using a stacked ensemble of gradient boosting decision trees. Using pre-treatment clinical and symptom predictors only, out-of-fold prediction of a novel treatment response definition based on STAR*D treatment guidelines was acceptable, AUC = .659, 95% CI [0.629, 0.689]. The inclusion of SNPs using confirmatory or exploratory selection methods did not improve the out-of-fold prediction of treatment response (AUCs were .662, 95% CI [0.632, 0.692] and .655, 95% CI [0.625, 0.685], respectively). A similar pattern of results were observed for the secondary outcomes of the presence or absence of distressing side effects regardless of treatment response and achieving remission or satisfactory partial response, assuming medication tolerance. In the current study, incorporating SNP variation into prognostic models did not enhance the prediction of citalopram response in the STAR*D sample.

Rare Functional Variants Associated with Antidepressant Remission in Mexican-Americans: Short title: Antidepressant remission and pharmacogenetics in Mexican-Americans

INTRODUCTION

Rare genetic functional variants can contribute to 30-40% of functional variability in genes relevant to drug action. Therefore, we investigated the role of rare functional variants in antidepressant response.

METHOD

Mexican-American individuals meeting the Diagnostic and Statistical Manual-IV criteria for major depressive disorder (MDD) participated in a prospective randomized, double-blind study with desipramine or fluoxetine. The rare variant analysis was performed using whole-exome genotyping data. Network and pathway analyses were carried out with the list of significant genes.

RESULTS

The Kernel-Based Adaptive Cluster method identified functional rare variants in 35 genes significantly associated with treatment remission (False discovery rate, FDR <0.01). Pathway analysis of these genes supports the involvement of the following gene ontology processes: olfactory/sensory transduction, regulation of response to cytokine stimulus, and meiotic cell cycleprocess.

LIMITATIONS

Our study did not have a placebo arm. We were not able to use antidepressant blood level as a covariate. Our study is based on a small sample size of only 65 Mexican-American individuals. Further studies using larger cohorts are warranted.

CONCLUSION

Our data identified several rare functional variants in antidepressant drug response in MDD patients. These have the potential to serve as genetic markers for predicting drug response.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00265291.

How to Utilize Clinical and Genetic Information for Personalized Treatment of Major Depressive Disorder: Step by Step Strategic Approach

Depression is the single largest contributor to non-fatal health loss and affects 322 million people globally. The clinical heterogeneity of this disorder shows biological correlates and it makes the personalization of antidepressant prescription an important pillar of treatment. There is increasing evidence of genetic overlap between depression, other psychiatric and non-psychiatric disorders, which varies across depression subtypes. Therefore, the first step of clinical evaluation should include a careful assessment of psychopathology and physical health, not limited to previously diagnosed disorders. In part of the patients indeed the pathogenesis of depression may be strictly linked to inflammatory and metabolic abnormalities, and the treatment should target these as much as the depressive symptoms themselves. When the evaluation of the symptom and drug tolerability profile, the concomitant biochemical abnormalities and physical conditions is not enough and at least one pharmacotherapy failed, the genotyping of variants in CYP2D6/CYP2C19 (cytochromes responsible for antidepressant metabolism) should be considered. Individuals with altered metabolism through one of these enzymes may benefit from some antidepressants rather than others or need dose adjustments. Finally, if available, the polygenic predisposition towards cardio-metabolic disorders can be integrated with non-genetic risk factors to tune the identification of patients who should avoid medications associated with this type of side effects. A sufficient knowledge of the polygenic risk of complex medical and psychiatric conditions is becoming relevant as this information can be obtained through direct-to-consumer genetic tests and in the future it may provided by national health care systems.

Serotonin Transporter Gene Promoter Hypomethylation as a Predictor of Antidepressant Treatment Response in Major Depression: A Replication Study

BACKGROUND

The serotonin transporter gene (SLC6A4; 5-HTT; SERT) is considered a prime candidate in pharmacogenetic research in major depressive disorder (MDD). Besides genetic variation, recent advances have spotlighted the involvement of epigenetic mechanisms such as DNA methylation in predicting antidepressant treatment response in "pharmaco-epigenetic" approaches. In MDD, lower SLC6A4 promoter methylation has been suggested to predict impaired response to serotonergic antidepressants. The present study sought to replicate and extend this finding in a large, independent sample of MDD patients.

METHODS

The sample comprised n = 236 Caucasian patients with MDD receiving antidepressant medication in a naturalistic treatment setting. Functional DNA methylation of 9 CpG sites located in the SLC6A4 promoter region was analyzed via direct sequencing of sodium bisulfite- treated DNA extracted from blood cells. Patients were assessed over the course of a 6-week in-patient treatment using the Hamilton Depression Scale (HAM-D).

RESULTS

Results confirm relative SLC6A4 hypomethylation to predict impaired antidepressant response both dimensionally and categorically (HAM-D reductions < 50%) and to furthermore be indicative of nonremission (HAM-D > 7). This also held true in a homogenous subgroup of patients continuously treated with selective serotonin reuptake inhibitors or serotonin/noradrenaline reuptake inhibitors (n = 110).

CONCLUSIONS

Impaired response to serotonergic antidepressants via SLC6A4 hypomethylation may be conveyed by increased gene expression and consequently decreased serotonin availability, which may counteract the effects of serotonergic antidepressants. The present results could in the future inform clinical decision-making towards a more personalized treatment of MDD.

Genome-wide association studies of antidepressant class response and treatment-resistant depression

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.

The influence of common polygenic risk and gene sets on social skills group training response in autism spectrum disorder

Social skills group training (SSGT) is a frequently used behavioral intervention in autism spectrum disorder (ASD), but the effects are moderate and heterogeneous. Here, we analyzed the effect of polygenic risk score (PRS) and common variants in gene sets on the intervention outcome. Participants from the largest randomized clinical trial of SSGT in ASD to date were selected (N = 188, 99 from SSGT, 89 from standard care) to calculate association between the outcomes in the SSGT trial and PRSs for ASD, attention-deficit hyperactivity disorder (ADHD), and educational attainment. In addition, specific gene sets were selected to evaluate their role on intervention outcomes. Among all participants in the trial, higher PRS for ADHD was associated with significant improvement in the outcome measure, the parental-rated Social Responsiveness Scale. The significant association was due to better outcomes in the standard care group for individuals with higher PRS for ADHD (post-intervention: β = −4.747, P = 0.0129; follow-up: β = −5.309, P = 0.0083). However, when contrasting the SSGT and standard care group, an inferior outcome in the SSGT group was associated with higher ADHD PRS at follow-up (β = 6.67, P = 0.016). Five gene sets within the synaptic category showed a nominal association with reduced response to interventions. We provide preliminary evidence that genetic liability calculated from common variants could influence the intervention outcomes. In the future, larger cohorts should be used to investigate how genetic contribution affects individual response to ASD interventions.

Polygenic Score for β-Blocker Survival Benefit in European Ancestry Patients With Reduced Ejection Fraction Heart Failure

BACKGROUND

β-Blockers (BBs) are mainstay therapy for heart failure with reduced ejection fraction. However, individual patient responses to BB vary, which may be partially due to genetic variation. The goal of this study was to derive and validate the first polygenic response predictor (PRP) for BB survival benefit in heart failure with reduced ejection fraction patients.

METHODS

Derivation and validation analyses were performed in n=1436 total HF patients of European descent and with ejection fraction <50%. The PRP was derived in a random subset of the Henry Ford Heart Failure Pharmacogenomic Registry (n=248) and then validated in a meta-analysis of the remaining patients from Henry Ford Heart Failure Pharmacogenomic Registry (n=247), the TIME-CHF (Trial of Intensified Versus Standard Medical Therapy in Elderly Patients With Congestive Heart Failure; n=431), and HF-ACTION trial (Heart Failure: a Controlled Trial Investigating Outcomes of Exercise Training; n=510). The PRP was constructed from a genome-wide analysis of BB×genotype interaction predicting time to all-cause mortality, adjusted for Meta-Analysis Global Group in Chronic Heart Failure score, genotype, level of BB exposure, and BB propensity score.

RESULTS

Five-fold cross-validation summaries out to 1000 single-nucleotide polymorphisms identified optimal prediction with a 44 single-nucleotide polymorphism score and cutoff at the 30th percentile. In validation testing (n=1188), greater BB exposure was associated with reduced all-cause mortality in patients with low PRP score (n=251; hazard ratio, 0.19 [95% CI, 0.04-0.51]; P=0.0075) but not high PRP score (n=937; hazard ratio, 0.84 [95% CI, 0.53-1.3]; P=0.448)-a difference that was statistically significant (P interaction, 0.0235). Results were consistent regardless of atrial fibrillation, ejection fraction (≤40% versus 41%-50%), or when examining cardiovascular death.

CONCLUSIONS

Among patients of European ancestry with heart failure with reduced ejection fraction, a PRP distinguished patients who derived substantial survival benefit from BB exposure from a larger group that did not. Additional work is needed to prospectively test clinical utility and to develop PRPs for other population groups and other medications.

Pharmacogenetics in psychiatric care, a call for uptake of available applications

In this narrative, we evaluate the role of pharmacogenetics in psychiatry from a pragmatic clinical perspective and address current barriers of clinical implementation of pharmacogenetics. Pharmacogenetics has been successfully implemented to improve drug therapy in several clinical areas, but not psychiatry. Yet, psychotropics account for more than one-third of the drugs for which pharmacogenetic guidelines are available and drug therapy in mental disorders is suboptimal with insufficient effectiveness and frequent adverse events. The limited application of pharmacogenetics in psychiatry is influenced by several factors; e.g. the complexity of psychotropic drug metabolism, possibly impeding the clinical understanding of the benefits of pharmacogenetics. Also, recommendations for most psychotropics classify pharmacogenetic testing only as (potentially) beneficial, not as essential, possibly because life-threatening adverse events are often not involved in these drug-gene interactions. Implementing pharmacogenetics in psychiatry could improve the current practice of time-consuming switching of therapies causing undue delays associated with worse outcomes. We expect pharmacogenetics in psychiatry to expedite with panel-based genotyping, including clinically relevant variants, which will address the complex enzymatic metabolism of psychotropic drugs. Until then, we stress that available pharmacogenetic testing should be seen as an integrated companion, not a competitor, in current clinical psychiatric care.

Genetics and major depressive disorder: clinical implications for disease risk, prognosis and treatment

In the post-genomic era, genetics has led to limited clinical applications in the diagnosis and treatment of major depressive disorder (MDD). Variants in genes coding for cytochrome enzymes are included in guidelines for assisting in antidepressant choice and dosing, but there are no recommendations involving genes responsible for antidepressant pharmacodynamics and no consensus applications for guiding diagnosis or prognosis. However, genetics has contributed to a better understanding of MDD pathogenesis and the mechanisms of antidepressant action, also thanks to recent methodological innovations that overcome the challenges posed by the polygenic architecture of these traits. Polygenic risk scores can be used to estimate the risk of disease at the individual level, which may have clinical relevance in cases with extremely high scores (e.g. top 1%). Genetic studies have also shed light on a wide genetic overlap between MDD and other psychiatric disorders. The relationships between genes/pathways associated with MDD and known drug targets are a promising tool for drug repurposing and identification of new pharmacological targets. Increase in power thanks to larger samples and methods integrating genetic data with gene expression, the integration of common variants and rare variants, are expected to advance our knowledge and assist in personalized psychiatry.

Optimising Seniors' Metabolism of Medications and Avoiding Adverse Drug Events Using Data on How Metabolism by Their P450 Enzymes Varies with Ancestry and Drug-Drug and Drug-Drug-Gene Interactions

Many individuals ≥65 have multiple illnesses and polypharmacy. Primary care physicians prescribe >70% of their medications and renew specialists’ prescriptions. Seventy-five percent of all medications are metabolised by P450 cytochrome enzymes. This article provides unique detailed tables how to avoid adverse drug events and optimise prescribing based on two key databases. DrugBank is a detailed database of 13,000 medications and both the P450 and other complex pathways that metabolise them. The Flockhart Tables are detailed lists of the P450 enzymes and also include all the medications which inhibit or induce metabolism by P450 cytochrome enzymes, which can result in undertreatment, overtreatment, or potentially toxic levels. Humans have used medications for a few decades and these enzymes have not been subject to evolutionary pressure. Thus, there is enormous variation in enzymatic functioning and by ancestry. Differences for ancestry groups in genetic metabolism based on a worldwide meta-analysis are discussed and this article provides advice how to prescribe for individuals of different ancestry. Prescribing advice from two key organisations, the Dutch Pharmacogenetics Working Group and the Clinical Pharmacogenetics Implementation Consortium is summarised. Currently, detailed pharmacogenomic advice is only available in some specialist clinics in major hospitals. However, this article provides detailed pharmacogenomic advice for primary care and other physicians and also physicians working in rural and remote areas worldwide. Physicians could quickly search the tables for the medications they intend to prescribe.

Systematic review and meta-analysis of the moderating effect of rs1799971 in OPRM1, the mu-opioid receptor gene, on response to naltrexone treatment of alcohol use disorder

BACKGROUND AND AIMS

There is wide inter-individual variability in response to the treatment of alcohol use disorder (AUD) with the opioid receptor antagonist naltrexone. To identify patients who may be most responsive to naltrexone treatment, studies have examined the moderating effect of rs1799971, a single nucleotide polymorphism (SNP) that encodes a non-synonymous substitution (Asn40Asp) in the mu-opioid receptor gene, OPRM1. The aims of this study were to: (1) conduct a systematic review of randomized clinical trials (RCTs); (2) assess the bias of the available studies and gauge publication bias; and (3) meta-analyze the interaction effect of the Asn40Asp SNP on the response to naltrexone treatment.

METHODS

We searched for placebo-controlled RCTs that examined the effect of Asn40Asp on the response to naltrexone treatment of heavy drinking or AUD. We tested the hypothesis that the minor (Asp40) allele was associated with a greater reduction in five alcohol consumption measures (relapse to heavy drinking, abstinence, percentage of heavy drinking days, percentage of days abstinent and drinks per day) in naltrexone-treated participants by meta-analyzing the interaction effects using a random effects model.

RESULTS

Seven RCTs met the study criteria. Overall, risk of bias was low and we observed no evidence of publication bias. Of the five alcohol consumption outcomes considered, there was a nominally significant moderating effect of the Asn40Asp SNP only on drinks per day (d = -0.18, P = 0.02). However, the effect was not significant when multiple comparisons were taken into account.

CONCLUSIONS

From the evidence to date, it remains unclear whether rs1799971, the OPRM1 Asn40Asp single nucleotide polymorphism, predicts naltrexone treatment response in individuals with alcohol use disorder or heavy drinking.

Developing and testing high‐efficacy patient subgroups within a clinical trial using risk scores

There is the potential for high-dimensional information about patients collected in clinical trials (such as genomic, imaging, and data from wearable technologies) to be informative for the efficacy of a new treatment in situations where only a subset of patients benefits from the treatment. The adaptive signature design (ASD) method has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using genetic data. The method requires selection of three tuning parameters which may be highly computationally expensive. We propose a variation to the ASD method, the cross-validated risk scores (CVRS) design method, that does not require selection of any tuning parameters. The method is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure.We assess the properties of CVRS against the originally proposed cross-validated ASD using simulation data and a real psychiatry trial. CVRS, as assessed for various sample sizes and response rates, has a substantial reduction in the computational time required. In many simulation scenarios, there is a substantial improvement in the ability to correctly identify the sensitive group and the power of the design to detect a treatment effect in the sensitive group.We illustrate the application of the CVRS method on the psychiatry trial.

Polygenic risk scores: from research tools to clinical instruments

Genome-wide association studies have shown unequivocally that common complex disorders have a polygenic genetic architecture and have enabled researchers to identify genetic variants associated with diseases. These variants can be combined into a polygenic risk score that captures part of an individual's susceptibility to diseases. Polygenic risk scores have been widely applied in research studies, confirming the association between the scores and disease status, but their clinical utility has yet to be established. Polygenic risk scores may be used to estimate an individual's lifetime genetic risk of disease, but the current discriminative ability is low in the general population. Clinical implementation of polygenic risk score (PRS) may be useful in cohorts where there is a higher prior probability of disease, for example, in early stages of diseases to assist in diagnosis or to inform treatment choices. Important considerations are the weaker evidence base in application to non-European ancestry and the challenges in translating an individual's PRS from a percentile of a normal distribution to a lifetime disease risk. In this review, we consider how PRS may be informative at different points in the disease trajectory giving examples of progress in the field and discussing obstacles that need to be addressed before clinical implementation.

Classical Human Leukocyte Antigen Alleles and C4 Haplotypes Are Not Significantly Associated With Depression

BACKGROUND

The prevalence of depression is higher in individuals with autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Shared genetic etiology is a plausible explanation for the overlap, and in this study we tested whether genetic variation in the major histocompatibility complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression.

METHODS

We fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 human leukocyte antigen (HLA) alleles and 4 complement component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium Major Depressive Disorder Working Group and the UK Biobank. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants, applying both a region-wide significance threshold (3.9 × 10-6) and a candidate threshold (1.6 × 10-4).

RESULTS

No HLA alleles or C4 haplotypes were associated with depression at the region-wide threshold. HLA-B*08:01 was associated with modest protection for depression at the candidate threshold for testing in HLA genes in the meta-analysis (odds ratio = 0.98, 95% confidence interval = 0.97-0.99).

CONCLUSIONS

We found no evidence that an increased risk for depression was conferred by HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia. These results suggest that any HLA or C4 variants associated with depression either are rare or have very modest effect sizes.

Genetics of Treatment Outcomes in Major Depressive Disorder: Present and Future

Pharmacogenetic testing is a useful and increasingly widespread tool to assist in antidepressant prescription. More than ten antidepressants (including tricyclics, selective serotonin reuptake inhibitors and venlafaxine) have already genetic biomarkers of response/side effects in clinical guidelines and drug labels. These are represented by functional genetic variants in genes coding for cytochrome enzymes (CYP2D6 and CYP2C19). Depending on the predicted metabolic activity, guidelines provide recommendations on drug choice and dosing. Despite not conclusive, the current evidence suggests that testing can be useful in patients who did not respond or tolerate at least one previous pharmacotherapy. However, the current recommendations are based on pharmacokinetic genes only (CYP450 enzymes), while pharmacodynamic genes (modulating antidepressant mechanisms of action in the brain) are still being studied because of their greater complexity. This may be captured by polygenic risk scores, which reflect the cumulative contribution of many genetic variants to a trait, and they may provide future clinical applications of pharmacogenetics. A more extensive use of genotyping in clinical practice may lead to improvement in treatment outcomes thanks to personalized treatments, but possible ethical issues and disparities should be taken into account and prevented.

Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP

Antidepressants demonstrate modest response rates in the treatment of major depressive disorder (MDD). Despite previous genome-wide association studies (GWAS) of antidepressant treatment response, the underlying genetic factors are unknown. Using prescription data in a population and family-based cohort (Generation Scotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of (a) antidepressant treatment resistance and (b) stages of antidepressant resistance by inferring antidepressant switching as non-response to treatment. GWAS were conducted separately for antidepressant treatment resistance in GS:SFHS and the Genome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed (meta-analysis n = 4213, cases = 358). For stages of antidepressant resistance, a GWAS on GS:SFHS only was performed (n = 3452). Additionally, we conducted gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We did not identify any significant loci, genes or gene sets associated with antidepressant treatment resistance or stages of resistance. Significant positive genetic correlations of antidepressant treatment resistance and stages of resistance with neuroticism, psychological distress, schizotypy and mood disorder traits were identified. These findings suggest that larger sample sizes are needed to identify the genetic architecture of antidepressant treatment response, and that population-based observational studies may provide a tractable approach to achieving the necessary statistical power.

What are the chances for personalised treatment with antidepressants? Detection of patient-by-treatment interaction with a variance ratio meta-analysis

OBJECTIVES

To investigate if the treatment effect of antidepressants in patients with depression substantially varies in each patient (patient-by-treatment interaction or treatment heterogeneity), a necessary but largely unexplored prerequisite of personalised antidepressant treatment.

DESIGN

Meta-analytic variance comparison of treatment outcome between drug arms and placebo arms of clinical trials, based on the assumption that patient-by-treatment interaction should lead to larger variances in drug arms than placebo arms. To put the results into context, we run simple simulations, assuming different definitions and rates of those who respond especially well to antidepressants.

DATA SOURCES

163 randomised, placebo-controlled trials (51 396 patients) with complete results for pre-post differences, selected from a recently published systematic review.

ANALYSIS

Variance ratios (VRs) and coefficients of variance ratios (CVRs) of individual trials were meta-analytically combined. The analysis was repeated for classes of antidepressants and specific antidepressants.

RESULTS

VRs (VR=1.01, CI 0.99 to 1.02) and CVRs (CVR=0.82, CI 0.80 to 0.84) of the antidepressant-treatment arms were comparable or smaller than in placebo arms. Similar results were observed for classes of antidepressants and for specific antidepressants. Our simulation analysis confirmed that equal VRs can only be obtained if they are not more than a few patients who respond slightly above average.

CONCLUSIONS

The lack of increased treatment-outcome variance in the antidepressants versus placebo groups in randomised controlled trials indicates that no or only very small subgroups of patients respond particularly well to antidepressants. Thus, the scope for personalised treatment with antidepressants seems to be limited.

Towards precision medicine in generalized anxiety disorder: Review of genetics and pharmaco(epi)genetics

Generalized anxiety disorder (GAD) is a prevalent and chronic mental disorder that elicits widespread functional impairment. Given the high degree of non-response/partial response among patients with GAD to available pharmacological treatments, there is a strong need for novel approaches that can optimize outcomes, and lead to medications that are safer and more effective. Although investigations have identified interesting targets predicting treatment response through pharmacogenetics (PGx), pharmaco-epigenetics, and neuroimaging methods, these studies are often solitary, not replicated, and carry several limitations. This review provides an overview of the current status of GAD genetics and PGx and presents potential strategies to improve treatment response by combining better phenotyping with PGx and improved analytical methods. These strategies carry the dual benefit of delivering data on biomarkers of treatment response as well as pointing to disease mechanisms through the biology of the markers associated with response. Overall, these efforts can serve to identify clinical, genetic, and epigenetic factors that can be incorporated into a pharmaco(epi)genetic test that may ultimately improve treatment response and reduce the socioeconomic burden of GAD.

Pharmacogenetics and Depression: A Critical Perspective

Depression leads the higher personal and socio-economical burden within psychiatric disorders. Despite the fact that over 40 antidepressants (ADs) are available, suboptimal response still poses a major challenge and is thought to be partially a result of genetic variation. Pharmacogenetics studies the effects of genetic variants on treatment outcomes with the aim of providing tailored treatments, thereby maximizing efficacy and tolerability. After two decades of pharmacogenetic research, variants in genes coding for the cytochromes involved in ADs metabolism (CYP2D6 and CYP2C19) are now considered biomarkers with sufficient scientific support for clinical application, despite the lack of conclusive cost/effectiveness evidence. The effect of variants in genes modulating ADs mechanisms of action (pharmacodynamics) is still controversial, because of the much higher complexity of ADs pharmacodynamics compared to ADs metabolism. Considerable progress has been made since the era of candidate gene studies: the genomic revolution has made possible to assess genetic variance on an unprecedented scale, throughout the whole genome, and to analyze the cumulative effect of different variants. The results have revealed key information on the biological mechanisms mediating ADs effect and identified hypothetical new pharmacological targets. They also paved the way for future availability of polygenic pharmacogenetic panels to predict treatment outcome, which are expected to explain much higher variance in ADs response compared to CYP2D6 and CYP2C19 only. As the demand and availability of AD pharmacogenetic testing is projected to increase, it is important for clinicians to keep abreast of this evolving area to facilitate informed discussions with their patients.

Towards High-Throughput Chemobehavioural Phenomics in Neuropsychiatric Drug Discovery

Identifying novel marine-derived neuroactive chemicals with therapeutic potential is difficult due to inherent complexities of the central nervous system (CNS), our limited understanding of the molecular foundations of neuro-psychiatric conditions, as well as the limited applications of effective high-throughput screening models that recapitulate functionalities of the intact CNS. Furthermore, nearly all neuro-modulating chemicals exhibit poorly characterized pleiotropic activities often referred to as polypharmacology. The latter renders conventional target-based in vitro screening approaches very difficult to accomplish. In this context, chemobehavioural phenotyping using innovative small organism models such as planarians and zebrafish represent powerful and highly integrative approaches to study the impact of new chemicals on central and peripheral nervous systems. In contrast to in vitro bioassays aimed predominantly at identification of chemicals acting on single targets, phenotypic chemobehavioural analysis allows for complex multi-target interactions to occur in combination with studies of polypharmacological effects of chemicals in a context of functional and intact milieu of the whole organism. In this review, we will outline recent advances in high-throughput chemobehavioural phenotyping and provide a future outlook on how those innovative methods can be utilized for rapidly screening and characterizing marine-derived compounds with prospective applications in neuropharmacology and psychosomatic medicine.

Pharmacogenetics of Antidepressants: from Genetic Findings to Predictive Strategies

The constantly growing contribution of depressive disorders to the global disease statistics calls for a growth of treatment effectiveness and optimization. Antidepressants are the most frequently prescribed medicines for depressive disorders. However, development of a standardized pharmacotherapeutic approach is burdened by the genomic heterogeneity, lack of reliable predictive biomarkers and variability of the medicines metabolism aggravated by multiple side effects of antidepressants. According to modern assessments up to 20 % of the genes expressed in our brain are involved in the pathogenesis of depression. Large-scale genetic and genomic research has found a number of potentially prognostic genes. It has also been proven that the effectiveness and tolerability of antidepressants directly depend on the variable activity of the enzymes that metabolize medicines. Almost all modern antidepressants are metabolized by the cytochrome P450 family enzymes. The most promising direction of research today is the GWAS (Genome-Wide Association Study) method that is aimed to link genomic variations with phenotypical manifestations. In this type of research genomes of depressive patients with different phenotypes are compared to the genomes of the control group containing same age, sex and other parameters healthy people. Notably, regardless of the large cohorts of patients analyzed, none of the GWA studies conducted so far can reliably reproduce the results of other analogous studies. The explicit heterogeneity of the genes associated with the depression pathogenesis and their pleiotropic effects are strongly influenced by environmental factors. This may explain the difficulty of obtaining clear and reproducible results. However, despite any negative circumstances, the active multidirectional research conducted today, raises the hope of clinicians and their patients to get a whole number of schedules how to achieve remission faster and with guaranteed results

Machine learning and big data in psychiatry: toward clinical applications

Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cultural, and experiential factors. This makes them exquisitely complex phenomena that depend on and interact with a large number of variables. Computational psychiatry provides two ways of approaching this complexity. Theory-driven computational approaches employ mechanistic models to make explicit hypotheses at multiple levels of analysis. Data-driven machine-learning approaches can make predictions from high-dimensional data and are generally agnostic as to the underlying mechanisms. Here, we review recent advances in the use of big data and machine-learning approaches toward the aim of alleviating the suffering that arises from psychiatric disorders.

Do polygenic risk and stressful life events predict pharmacological treatment response in obsessive compulsive disorder? A gene-environment interaction approach

The rate of response to pharmacological treatment in Obsessive-compulsive disorder (OCD) oscillates between 40 and 70%. Genetic and environmental factors have been associated with treatment response in OCD. This study analyzes the predictive ability of a polygenic risk score (PRS) built from OCD-risk variants, for treatment response in OCD, and the modulation role of stressful life events (SLEs) at the onset of the disorder. PRSs were calculated for a sample of 103 patients. Yale-Brown Obsessive Compulsive Scale (YBOCS) scores were obtained before and after a 12-week treatment. Regression analyses were performed to analyze the influence of the PRS and SLEs at onset on treatment response. PRS did not predict treatment response. The best predictive model for post-treatment YBOCS (post YBOCS) included basal YBOCS and age. PRS appeared as a predictor for basal and post YBOCS. SLEs at onset were not a predictor for treatment response when included in the regression model. No evidence for PRS predictive ability for treatment response was found. The best predictor for treatment response was age, agreeing with previous literature specific for SRI treatment. Suggestions are made on the possible role of neuroplasticity as a mediator on this association. PRS significantly predicted OCD severity independent on pharmacological treatment. SLE at onset modulation role was not evidenced. Further research is needed to elucidate the genetic and environmental bases of treatment response in OCD.

Genetics of response to cognitive behavior therapy in adults with major depression: a preliminary report

Major depressive disorder is heritable and a leading cause of disability. Cognitive behavior therapy is an effective treatment for major depression. By quantifying genetic risk scores based on common genetic variants, the aim of this report was to explore the utility of psychiatric and cognitive trait genetic risk scores, for predicting the response of 894 adults with major depressive disorder to cognitive behavior therapy. The participants were recruited in a psychiatric setting, and the primary outcome score was measured using the Montgomery Åsberg Depression Rating Scale-Self Rated. Single-nucleotide polymorphism genotyping arrays were used to calculate the genomic risk scores based on large genetic studies of six phenotypes: major depressive disorder, bipolar disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, intelligence, and educational attainment. Linear mixed-effect models were used to test the relationships between the six genetic risk scores and cognitive behavior therapy outcome. Our analyses yielded one significant interaction effect (B = 0.09, p < 0.001): the autism spectrum disorder genetic risk score correlated with Montgomery Åsberg Depression Rating Scale-Self Rated changes during treatment, and the higher the autism spectrum disorder genetic load, the less the depressive symptoms decreased over time. The genetic risk scores for the other psychiatric and cognitive traits were not related to depressive symptom severity or change over time. Our preliminary results indicated, as expected, that the genomics of the response of patients with major depression to cognitive behavior therapy were complex and that future efforts should aim to maximize sample size and limit subject heterogeneity in order to gain a better understanding of the use of genetic risk factors to predict treatment outcome.

Exploratory genome-wide association analysis of response to ketamine and a polygenic analysis of response to scopolamine in depression

Growing evidence suggests that the glutamatergic modulator ketamine has rapid antidepressant effects in treatment-resistant depressed subjects. The anticholinergic agent scopolamine has also shown promise as a rapid-acting antidepressant. This study applied genome-wide markers to investigate the role of genetic variants in predicting acute antidepressant response to both agents. The ketamine-treated sample included 157 unrelated European subjects with major depressive disorder (MDD) or bipolar disorder (BD). The scopolamine-treated sample comprised 37 unrelated European subjects diagnosed with either MDD or BD who had a current Major Depressive Episode (MDE), and had failed at least two adequate treatment trials for depression. Change in Montgomery-Asberg Depression Rating Scale (MADRS) or the 17-item Hamilton Depression Rating Scale (HAM-D) scale scores at day 1 (24 h post-treatment) was considered the primary outcome. Here, we conduct pilot genome-wide association study (GWAS) analyses to identify potential markers of ketamine response and dissociative side effects. Polygenic risk score analysis of SNPs ranked by the strength of their association with ketamine response was then calculated in order to assess whether common genetic markers from the ketamine study could predict response to scopolamine. Findings require replication in larger samples in light of low power of analyses of these small samples. Neverthless, these data provide a promising illustration of our future potential to identify genetic variants underlying rapid treatment response in mood disorders and may ultimately guide individual patient treatment selection in the future.

Polygenic risk scores for major depressive disorder and neuroticism as predictors of antidepressant response: Meta-analysis of three treatment cohorts

There are currently no reliable approaches for correctly identifying which patients with major depressive disorder (MDD) will respond well to antidepressant therapy. However, recent genetic advances suggest that Polygenic Risk Scores (PRS) could allow MDD patients to be stratified for antidepressant response. We used PRS for MDD and PRS for neuroticism as putative predictors of antidepressant response within three treatment cohorts: The Genome-based Therapeutic Drugs for Depression (GENDEP) cohort, and 2 sub-cohorts from the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study PRGN-AMPS (total patient number = 760). Results across cohorts were combined via meta-analysis within a random effects model. Overall, PRS for MDD and neuroticism did not significantly predict antidepressant response but there was a consistent direction of effect, whereby greater genetic loading for both MDD (best MDD result, p < 5*10-5 MDD-PRS at 4 weeks, β = -0.019, S.E = 0.008, p = 0.01) and neuroticism (best neuroticism result, p < 0.1 neuroticism-PRS at 8 weeks, β = -0.017, S.E = 0.008, p = 0.03) were associated with less favourable response. We conclude that the PRS approach may offer some promise for treatment stratification in MDD and should now be assessed within larger clinical cohorts.

The use of polygenic risk scores to identify phenotypes associated with genetic risk of bipolar disorder and depression: A systematic review

BACKGROUND

Identifying the phenotypic manifestations of increased genetic liability for depression (MDD) and bipolar disorder (BD) can enhance understanding of their aetiology. The polygenic risk score (PRS) derived using data from genome-wide-association-studies can be used to explore how genetic risk is manifest in different samples.

AIMS

In this systematic review, we review studies that examine associations between the MDD and BD polygenic risk scores and phenotypic outcomes.

METHODS

Following PRISMA guidelines, we searched EMBASE, Medline and PsycINFO (from August 2009 - 14th March 2016) and references of included studies. Study inclusion was based on predetermined criteria and data were extracted independently and in duplicate.

RESULTS

Twenty-five studies were included. Overall, both polygenic risk scores were associated with other psychiatric disorders (not the discovery sample disorder) such as depression, schizophrenia and bipolar disorder, greater symptom severity of depression, membership of a creative profession and greater educational attainment. Both depression and bipolar polygenic risk scores explained small amounts of variance in most phenotypes (< 2%).

LIMITATIONS

Many studies did not report standardised effect sizes. This prevented us from conducting a meta-analysis.

CONCLUSIONS

Polygenic risk scores for BD and MDD are associated with a range of phenotypes and outcomes. However, they only explain a small amount of the variation in these phenotypes. Larger discovery and adequately powered target samples are required to increase power of the PRS approach. This could elucidate how genetic risk for bipolar disorder and depression is manifest and contribute meaningfully to stratified medicine.

Clinical application of antidepressant pharmacogenetics: Considerations for the design of future studies

A frustrating inertia has affected the development of clinical applications of antidepressant pharmacogenetics and personalized treatments of depression are still lacking 20 years after the first findings. Candidate gene studies provided replicated findings for some polymorphisms, but each of them shows at best a small effect on antidepressant efficacy and the cumulative effect of different polymorphisms is unclear. Further, no candidate was immune by at least some negative studies. These considerations give rise to some concerns about the clinical benefits of currently available pharmacogenetic tests since they are based on the results of candidate gene studies. Clinical guidelines in fact suggest that only polymorphisms that alter cytochrome 2D6 or 2C19 enzymatic activity probably provide useful clinical indications, while variants in genes involved in antidepressant pharmacodynamics have no recommended clinical applications. The present review discusses possible strategies to facilitate the identification of genetic biomarkers with clinical usefulness in guiding antidepressant treatments. These include analysis methods for the study of the polygenic/omnigenic nature of antidepressant response, the prioritization of polymorphisms on the basis of functional considerations, the incorporation of clinical-demographic predictors in pharmacogenetic studies (e.g. mixed polygenic and clinical risk scores), the application of methodological improvements to the design of future studies in order to maximize the comparability of results and improve power.

Polygenic Risk Scores in Clinical Psychology: Bridging Genomic Risk to Individual Differences

Genomewide association studies (GWASs) across psychiatric phenotypes have shown that common genetic variants generally confer risk with small effect sizes (odds ratio < 1.1) that additively contribute to polygenic risk. Summary statistics derived from large discovery GWASs can be used to generate polygenic risk scores (PRS) in independent, target data sets to examine correlates of polygenic disorder liability (e.g., does genetic liability to schizophrenia predict cognition?). The intuitive appeal and generalizability of PRS have led to their widespread use and new insights into mechanisms of polygenic liability. However, when currently applied across traits they account for small amounts of variance (<3%), are relatively uninformative for clinical treatment, and, in isolation, provide no insight into molecular mechanisms. Larger GWASs are needed to increase the precision of PRS, and novel approaches integrating various data sources (e.g., multitrait analysis of GWASs) may improve the utility of current PRS.