Genome-wide pharmacogenomic analysis of response to treatment with antipsychotics

rs7968606 polymorphism of ANKS1B is associated with improvement in the PANSS general score of schizophrenia caused by amisulpride

A recent genome-wide pharmacogenomics study showed that the rs7968606 single-nucleotide polymorphism (SNP) of the ankyrin repeat and sterile alpha motif domain-containing protein 1B (ANKS1B) gene approached the threshold of statistical significance. The aim of this study was to determine the association between the rs7968606 SNP of ANKS1B and the treatment response to amisulpride in schizophrenia patients. In total, 154 participants were enrolled from six university hospitals in Korea. All the subjects were interviewed before and after 6 weeks of amisulpride treatment with the aid of the positive and negative syndrome scale and the clinical global impression-severity scale. Genotyping for the rs7968606 SNP of ANKS1B was performed in 101 subjects. Both the decrease (t = -2.067, p = 0.041) and improvement rate (t = -1.990, p = 0.049) in the positive and negative syndrome scale general score differed significantly between T-allele carriers and noncarriers of this polymorphism after 6 weeks of amisulpride treatment. To the best of our knowledge, this is the first genetic association study of the relationship between the rs7968606 SNP of ANKS1B and the response of schizophrenia patients to treatment with amisulpride. Future larger-scale studies involving more SNPs of ANKS1B will improve the understanding of the pharmacogenetics underlying the treatment responses to amisulpride.

Initial characterization of behavior and ketamine response in a mouse knockout of the post-synaptic effector gene Anks1b

The human ANKS1B gene encodes an activity-dependent effector of post-synaptic signaling. It was recently associated with neuropsychiatric phenotypes in genome-wide studies. While the biological function of ANKS1B has been partly elucidated, its role in behavior is poorly understood. Here, we breed and characterize a full knockout (KO) for murine Anks1b. We found that the homozygous KO genotype was partially lethal, showing significant deviation from expected segregation ratios at weaning. Behaviorally, KOs exhibited no difference in baseline acoustic startle response, but showed deficits in prepulse inhibition (PPI). KOs also exhibited locomotor hyperactivity and increased stereotypy at baseline. Administration of ketamine, a non-competitive NMDA-receptor antagonist, greatly exacerbated locomotor activity in the KOs at lower doses, but genotype groups were almost indistinguishable as dose increased. Stereotypy showed a complex response to ketamine in the KOs, with elevated stereotypy at lower doses and markedly less at high doses, compared to wild type. Our study is the first to probe the behavioral phenotypes associated with ablation of Anks1b. Deficits in PPI, locomotor hyperactivity, elevated stereotypy and altered response to NMDA receptor antagonism are murine behavioral outcomes with translational relevance for psychiatric disorders. These findings are also consistent with the role of Anks1b as an effector of glutamatergic signaling. As an intermediary between post-synaptic receptor stimulation and long-term changes to neuronal protein expression, further investigation of Anks1b is warranted.

Fine-mapping of antipsychotic response genome-wide association studies reveals novel regulatory mechanisms

Aim: Noncoding variation has demonstrated regulatory effects on disease treatment outcomes. This study investigated the potential functionality of previously implicated noncoding variants on schizophrenia treatment response. Materials & methods: Predicted regulatory potential of variation identified from antipsychotic response genome-wide association studies was determined. Prioritized variants were assessed for association(s) with treatment outcomes in a South African first episode schizophrenia cohort (n = 103). Results: Bioinformatic and association results implicated a relationship between regulatory variants, expression of MANBA, COL9A2 and NFKB1, and treatment response. Three SNPs were associated with poor outcomes (rs230493: p = 1.88 × 10-6; rs3774959: p = 1.75 × 10-5; and rs230504: p = 1.48 × 10-4). Conclusion: This study has thoroughly investigated previous GWAS to pinpoint variants that may play a causal role in poor schizophrenia treatment outcomes, and provides potential candidate genes for further study in the field of antipsychotic response.

Precision medicine for psychopharmacology: a general introduction

INTRODUCTION

Precision medicine is an emerging medical model that can provide accurate diagnoses and tailored therapeutic strategies for patients based on data pertaining to genes, microbiomes, environment, family history and lifestyle.

AREAS COVERED

Here, we provide basic information about precision medicine and newly introduced concepts, such as the precision medicine ecosystem and big data processing, and omics technologies including pharmacogenomics, pharamacometabolomics, pharmacoproteomics, pharmacoepigenomics, connectomics and exposomics. The authors review the current state of omics in psychiatry and the future direction of psychopharmacology as it moves towards precision medicine. Expert commentary: Advances in precision medicine have been facilitated by achievements in multiple fields, including large-scale biological databases, powerful methods for characterizing patients (such as genomics, proteomics, metabolomics, diverse cellular assays, and even social networks and mobile health technologies), and computer-based tools for analyzing large amounts of data.

Antipsychotic pharmacogenomics in first episode psychosis: a role for glutamate genes

Genetic factors may underlie beneficial and adverse responses to antipsychotic treatment. These relationships may be easier to identify among patients early in the course of disease who have limited exposure to antipsychotic drugs. We examined 86 first episode patients (schizophrenia, psychotic bipolar disorder and major depressive disorder with psychotic features) who had minimal to no prior antipsychotic exposure in a 6-week pharmacogenomic study of antipsychotic treatment response. Response was measured by change in Brief Psychiatric Rating Scale total score. Risperidone monotherapy was the primary antipsychotic treatment. Pharmacogenomic association studies were completed to (1) examine candidate single-nucleotide polymorphisms (SNPs) in genes known to be involved with glutamate signaling, and (2) conduct an exploratory genome-wide association study of symptom response to identify potential novel associations for future investigation. Two SNPs in GRM7 (rs2069062 and rs2014195) were significantly associated with antipsychotic response in candidate gene analysis, as were two SNPs in the human glutamate receptor delta 2 (GRID2) gene (rs9307122 and rs1875705) in genome-wide association analysis. Further examination of these findings with those from a separate risperidone-treated study sample demonstrated that top SNPs in both studies were overrepresented in glutamate genes and that there were similarities in neurodevelopmental gene categories associated with drug response from both study samples. These associations indicate a role for gene variants related to glutamate signaling and antipsychotic response with more broad association patterns indicating the potential importance of genes involved in neuronal development.

iCLIP identifies novel roles for SAFB1 in regulating RNA processing and neuronal function

Background

SAFB1 is a RNA binding protein implicated in the regulation of multiple cellular processes such as the regulation of transcription, stress response, DNA repair and RNA processing. To gain further insight into SAFB1 function we used iCLIP and mapped its interaction with RNA on a genome wide level.

Results

iCLIP analysis found SAFB1 binding was enriched, specifically in exons, ncRNAs, 3’ and 5’ untranslated regions. SAFB1 was found to recognise a purine-rich GAAGA motif with the highest frequency and it is therefore likely to bind core AGA, GAA, or AAG motifs. Confirmatory RT-PCR experiments showed that the expression of coding and non-coding genes with SAFB1 cross-link sites was altered by SAFB1 knockdown. For example, we found that the isoform-specific expression of neural cell adhesion molecule (NCAM1) and ASTN2 was influenced by SAFB1 and that the processing of miR-19a from the miR-17-92 cluster was regulated by SAFB1. These data suggest SAFB1 may influence alternative splicing and, using an NCAM1 minigene, we showed that SAFB1 knockdown altered the expression of two of the three NCAM1 alternative spliced isoforms. However, when the AGA, GAA, and AAG motifs were mutated, SAFB1 knockdown no longer mediated a decrease in the NCAM1 9–10 alternative spliced form. To further investigate the association of SAFB1 with splicing we used exon array analysis and found SAFB1 knockdown mediated the statistically significant up- and downregulation of alternative exons. Further analysis using RNAmotifs to investigate the frequency of association between the motif pairs (AGA followed by AGA, GAA or AAG) and alternative spliced exons found there was a highly significant correlation with downregulated exons. Together, our data suggest SAFB1 will play an important physiological role in the central nervous system regulating synaptic function. We found that SAFB1 regulates dendritic spine density in hippocampal neurons and hence provide empirical evidence supporting this conclusion.

Conclusions

iCLIP showed that SAFB1 has previously uncharacterised specific RNA binding properties that help coordinate the isoform-specific expression of coding and non-coding genes. These genes regulate splicing, axonal and synaptic function, and are associated with neuropsychiatric disease, suggesting that SAFB1 is an important regulator of key neuronal processes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12915-015-0220-7) contains supplementary material, which is available to authorized users.

Applicability of gene expression and systems biology to develop pharmacogenetic predictors; antipsychotic-induced extrapyramidal symptoms as an example

Pharmacogenetics has been driven by a candidate gene approach. The disadvantage of this approach is that is limited by our current understanding of the mechanisms by which drugs act. Gene expression could help to elucidate the molecular signatures of antipsychotic treatments searching for dysregulated molecular pathways and the relationships between gene products, especially protein-protein interactions. To embrace the complexity of drug response, machine learning methods could help to identify gene-gene interactions and develop pharmacogenetic predictors of drug response. The present review summarizes the applicability of the topics presented here (gene expression, network analysis and gene-gene interactions) in pharmacogenetics. In order to achieve this, we present an example of identifying genetic predictors of extrapyramidal symptoms induced by antipsychotic.

ANKS1B Gene Product AIDA-1 Controls Hippocampal Synaptic Transmission by Regulating GluN2B Subunit Localization

NMDA receptors (NMDARs) are key mediators of glutamatergic transmission and synaptic plasticity, and their dysregulation has been linked to diverse neuropsychiatric and neurodegenerative disorders. While normal NMDAR function requires regulated expression and trafficking of its different subunits, the molecular mechanisms underlying these processes are not fully understood. Here we report that the amyloid precursor protein intracellular domain associated-1 protein (AIDA-1), which associates with NMDARs and is encoded by ANKS1B, a gene recently linked to schizophrenia, regulates synaptic NMDAR subunit composition. Forebrain-specific AIDA-1 conditional knock-out (cKO) mice exhibit reduced GluN2B-mediated and increased GluN2A-mediated synaptic transmission, and biochemical analyses show AIDA-1 cKO mice have low GluN2B and high GluN2A protein levels at isolated hippocampal synaptic junctions compared with controls. These results are corroborated by immunocytochemical and electrophysiological analyses in primary neuronal cultures following acute lentiviral shRNA-mediated knockdown of AIDA-1. Moreover, hippocampal NMDAR-dependent but not metabotropic glutamate receptor-dependent plasticity is impaired in AIDA-1 cKO mice, further supporting a role for AIDA-1 in synaptic NMDAR function. We also demonstrate that AIDA-1 preferentially associates with GluN2B and with the adaptor protein Ca(2+)/calmodulin-dependent serine protein kinase and kinesin KIF17, which regulate the transport of GluN2B-containing NMDARs from the endoplasmic reticulum (ER) to synapses. Consistent with this function, GluN2B accumulates in ER-enriched fractions in AIDA-1 cKO mice. These findings suggest that AIDA-1 regulates NMDAR subunit composition at synapses by facilitating transport of GluN2B from the ER to synapses, which is critical for NMDAR plasticity. Our work provides an explanation for how AIDA-1 dysfunction might contribute to neuropsychiatric conditions, such as schizophrenia.

Association studies of genomic variants with treatment response to risperidone, clozapine, quetiapine and chlorpromazine in the Chinese Han population

Schizophrenia is a widespread mental disease with a prevalence of about 1% in the world population. Continuous long-term treatment is required to maintain social functioning and prevent symptom relapse of schizophrenia patients. However, there are considerable individual differences in response to the antipsychotic drugs. There is a pressing need to identify more drug-response-related markers. But most pharmacogenomics of schizophrenia have typically focused on a few candidate genes in small sample size. In this study, 995 subjects were selected for discovering the drug-response-related markers. A total of 77 single-nucleotide polymorphisms of 25 genes have been investigated for four commonly used antipsychotic drugs in China: risperidone, clozapine, quetiapine, and chlorpromazine. Significant associations with treatment response for several genes, such as CYP2D6, CYP2C19, COMT, ABCB1, DRD3 and HTR2C have been verified in our study. Also, we found several new candidate genes (TNIK, RELN, NOTCH4 and SLC6A2) and combinations (haplotype rs1544325-rs5993883-rs6269-rs4818 in COMT) that are associated with treatment response to the four drugs. Also, multivariate interactions analysis demonstrated the combination of rs6269 in COMT and rs3813929 in HTR2C may work as a predictor to improve the clinical antipsychotic response. So our study is of great significance to improve current knowledge on the pharmacogenomics of schizophrenia, thus promoting the implementation of personalized medicine in schizophrenia.The Pharmacogenomics Journal advance online publication, 18 August 2015; doi:10.1038/tpj.2015.61.

Evaluation of genetic association of neurodevelopment and neuroimmunological genes with antipsychotic treatment response in schizophrenia in Indian populations

Neurodevelopmental and neuroimmunological genes critically regulate antipsychotic treatment outcome. We report genetic associations of antipsychotic response in 742 schizophrenia patients from Indian populations of Indo‐European and Dravidian ancestry, segregated by disease severity. Meta‐analysis comparing the two populations identified CCL2 [rs4795893: OR (95% CI) = 1.79 (1.27–2.52), P = 7.62 × 10⁻⁴; rs4586: OR (95% CI) = 1.74 (1.24–2.43), P = 1.13 × 10⁻³] and GRIA4 [rs2513265: OR (95% CI) = 0.53 (0.36–0.78), P = 1.44 × 10⁻³] in low severity group; and, ADCY2 [rs1544938: OR (95% CI) = 0.36 (0.19–0.65), P = 7.68 × 10⁻⁴] and NRG1 [rs13250975, OR (95% CI) = 0.42 (0.23–0.79), P = 6.81 × 10⁻³; rs17716295, OR (95% CI) = 1.78 (1.15–2.75), P = 8.71 × 10⁻³] in high severity group, with incomplete response toward antipsychotics. To our knowledge, this is the first study to identify genetic polymorphisms associated with the efficacy of antipsychotic treatment of schizophrenia patients from two major India populations.

A double amino-acid change in the HLA-A peptide-binding groove is associated with response to psychotropic treatment in patients with schizophrenia

The choice of an efficient psychotropic treatment for patients with schizophrenia is a key issue to improve prognosis and quality of life and to decrease the related burden and costs. As for other complex disorders, response to drugs in schizophrenia is highly heterogeneous and the underlying molecular mechanisms of this diversity are still poorly understood. In a carefully followed-up cohort of schizophrenic patients prospectively treated with risperidone or olanzapine, we used a specially designed single-nucleotide polymorphism (SNP) array to perform a large-scale genomic analysis and identify genetic variants associated with response to psychotropic drugs. We found significant associations between response to treatment defined by the reduction in psychotic symptomatology 42 days after the beginning of treatment and SNPs located in the chromosome 6, which houses the human leukocyte antigen (HLA). After imputation of the conventional HLA class I and class II alleles, as well as the amino-acid variants, we observed a striking association between a better response to treatment and a double amino-acid variant at positions 62 and 66 of the peptide-binding groove of the HLA-A molecule. These results support the current notion that schizophrenia may have immune-inflammatory underpinnings and may contribute to pave the way for personalized treatments in schizophrenia.

Genome-Wide Meta-Analysis of Longitudinal Alcohol Consumption Across Youth and Early Adulthood

The public health burden of alcohol is unevenly distributed across the life course, with levels of use, abuse, and dependence increasing across adolescence and peaking in early adulthood. Here, we leverage this temporal patterning to search for common genetic variants predicting developmental trajectories of alcohol consumption. Comparable psychiatric evaluations measuring alcohol consumption were collected in three longitudinal community samples (N=2,126, obs=12,166). Consumption-repeated measurements spanning adolescence and early adulthood were analyzed using linear mixed models, estimating individual consumption trajectories, which were then tested for association with Illumina 660W-Quad genotype data (866,099 SNPs after imputation and QC). Association results were combined across samples using standard meta-analysis methods. Four meta-analysis associations satisfied our pre-determined genome-wide significance criterion (FDR<0.1) and six others met our 'suggestive' criterion (FDR<0.2). Genome-wide significant associations were highly biological plausible, including associations within GABA transporter 1, SLC6A1 (solute carrier family 6, member 1), and exonic hits in LOC100129340 (mitofusin-1-like). Pathway analyses elaborated single marker results, indicating significant enriched associations to intuitive biological mechanisms, including neurotransmission, xenobiotic pharmacodynamics, and nuclear hormone receptors (NHR). These findings underscore the value of combining longitudinal behavioral data and genome-wide genotype information in order to study developmental patterns and improve statistical power in genomic studies.

How can neuroimaging facilitate the diagnosis and stratification of patients with psychosis?

Early diagnosis and treatment of patients with psychosis are associated with improved outcome in terms of future functioning, symptoms and treatment response. Identifying neuroimaging biomarkers for illness onset and treatment response would lead to immediate clinical benefits. In this review we discuss if neuroimaging may be utilised to diagnose patients with psychosis, predict those who will develop the illness in those at high risk, and stratify patients. State-of-the-art developments in the field are critically examined including multicentre studies, longitudinal designs, multimodal imaging and machine learning as well as some of the challenges in utilising future neuroimaging biomarkers in clinical trials. As many of these developments are already being applied in neuroimaging studies of Alzheimer's disease, we discuss what lessons have been learned from this field and how they may be applied to research in psychosis.

The current and potential impact of genetics and genomics on neuropsychopharmacology

One justification for the major scientific and financial investments in genetic and genomic studies in medicine is their therapeutic potential, both for revealing novel targets for drugs which treat the disease process, as well as allowing for more effective and safe use of existing medications. This review considers the extent to which this promise has yet been realised within psychopharmacology, how things are likely to develop in the foreseeable future, and the key issues involved. It draws primarily on examples from schizophrenia and its treatments. One observation is that there is evidence for a range of genetic influences on different aspects of psychopharmacology in terms of discovery science, but far less evidence that meets the standards required before such discoveries impact upon clinical practice. One reason is that results reveal complex genetic influences that are hard to replicate and usually of very small effect. Similarly, the slow progress being made in revealing the genes that underlie the major psychiatric syndromes hampers attempts to apply the findings to identify novel drug targets. Nevertheless, there are some intriguing positive findings of various kinds, and clear potential for genetics and genomics to play an increasing and major role in psychiatric drug discovery.

Replication of SULT4A1-1 as a pharmacogenetic marker of olanzapine response and evidence of lower weight gain in the high response group

AIM

Antipsychotic efficacy biomarkers have the potential to improve outcomes in psychotic patients. This study examined the effect of SULT4A1-1 haplotype status (rs2285162 [A]-rs2285167 [G]) on olanzapine response.

PATIENTS & METHODS

We evaluated 87 olanzapine treated subjects from Phases 1, 1B and 2 of the CATIE trial for the impact of SULT4A1-1 status on change in Positive and Negative Syndrome Scale (PANSS) total score using two models of response. We also examined weight change.

RESULTS

SULT4A1-1-positive status correlated with superior olanzapine response in Phase 1 (p = 0.004 for model 1 and p = 0.001 for model 2) and Phases 1B/2 (p = 0.05 for model 1 and p = 0.007 for model 2). SULT4A1-1-positive subjects gained significantly less weight per month on olanzapine, 0.15 lbs, than did SULT4A1-1-negative subjects, 2.27 lbs (p = 0.04).

CONCLUSION

This study provides a second replication of superior olanzapine response in SULT4A1-1-positive subjects compared with SULT4A1-1-negative subjects. SULT4A1-1-positive subjects treated with olanzapine also gained less weight than SULT4A1-1-negative subjects.

Pharmacogenetics of second-generation antipsychotics

This review considers pharmacogenetics of the so called 'second-generation' antipsychotics. Findings for polymorphisms replicating in more than one study are emphasized and compared and contrasted with larger-scale candidate gene studies and genome-wide association study analyses. Variants in three types of genes are discussed: pharmacokinetic genes associated with drug metabolism and disposition, pharmacodynamic genes encoding drug targets, and pharmacotypic genes impacting disease presentation and subtype. Among pharmacokinetic markers, CYP2D6 metabolizer phenotype has clear clinical significance, as it impacts dosing considerations for aripiprazole, iloperidone and risperidone, and variants of the ABCB1 gene hold promise as biomarkers for dosing for olanzapine and clozapine. Among pharmacodynamic variants, the TaqIA1 allele of the DRD2 gene, the DRD3 (Ser9Gly) polymorphism, and the HTR2C -759C/T polymorphism have emerged as potential biomarkers for response and/or side effects. However, large-scale candidate gene studies and genome-wide association studies indicate that pharmacotypic genes may ultimately prove to be the richest source of biomarkers for response and side effect profiles for second-generation antipsychotics.

The promise of psychiatric pharmacogenomics

Clinicians already face "personalized" medicine every day while experiencing the great variation in toxicities and drug efficacy among individual patients. Pharmacogenetics studies are the platform for discovering the DNA determinants of variability in drug response and tolerability. Research now focuses on the genome after its beginning with analyses of single genes. Therapeutic outcomes from several psychotropic drugs have been weakly linked to specific genetic variants without independent replication. Drug side effects show stronger associations to genetic variants, including human leukocyte antigen loci with carbamazepine-induced dermatologic outcome and MC4R with atypical antipsychotic weight gain. Clinical implementation has proven challenging, with barriers including a lack of replicable prospective evidence for clinical utility required for altering medical care. More recent studies show promising approaches for reducing these barriers to routine incorporation of pharmacogenetics data into clinical care.

Glucagon-like peptide 1 receptor (GLP1R) haplotypes correlate with altered response to multiple antipsychotics in the CATIE trial

Glucagon-like peptide 1 receptor (GLP1R) signaling has been shown to have antipsychotic properties in animal models and to impact glucose-dependent insulin release, satiety, memory, and learning in man. Previous work has shown that two coding mutations (rs6923761 and rs1042044) are associated with altered insulin release and cortisol levels. We identified four frequently occurring haplotypes in Caucasians, haplotype 1 through haplotype 4, spanning exons 4-7 and containing the two coding variants. We analyzed response to antipsychotics, defined as predicted change in PANSS-Total (dPANSS) at 18 months, in Caucasian subjects from the Clinical Antipsychotic Trial of Intervention Effectiveness treated with olanzapine (n=139), perphenazine (n=78), quetiapine (n=14), risperidone (n=143), and ziprasidone (n=90). Haplotype trend regression analysis revealed significant associations with dPANSS for olanzapine (best p=0.002), perphenazine (best p=0.01), quetiapine (best p=0.008), risperidone (best p=0.02), and ziprasidone (best p=0.007). We also evaluated genetic models for the two most common haplotypes. Haplotype 1 (uniquely including the rs1042044 [Leu(260)] allele) was associated with better response to olanzapine (p=0.002), and risperidone (p=0.006), and worse response to perphenazine (p=.03), and ziprasidone (p=0.003), with a recessive genetic model providing the best fit. Haplotype 2 (uniquely including the rs6923761 [Ser(168)] allele) was associated with better response to perphenazine (p=0.001) and worse response to olanzapine (p=.02), with a dominant genetic model providing the best fit. However, GLP1R haplotypes were not associated with antipsychotic-induced weight gain. These results link functional genetic variants in GLP1R to antipsychotic response.

Use of translational pharmacodynamic biomarkers in early-phase clinical studies for schizophrenia

Schizophrenia is a severe mental disorder characterized by cognitive deficits, and positive and negative symptoms. The development of effective pharmacological compounds for the treatment of schizophrenia has proven challenging and costly, with many compounds failing during clinical trials. Many failures occur due to disease heterogeneity and lack of predictive preclinical models and biomarkers that readily translate to humans during early characterization of novel antipsychotic compounds. Traditional early-phase trials consist of single- or multiple-dose designs aimed at determining the safety and tolerability of an investigational compound in healthy volunteers. However, by incorporating a translational approach employing methodologies derived from preclinical studies, such as EEG measures and imaging, into the traditional Phase I program, critical information regarding a compound's dose-response effects on pharmacodynamic biomarkers can be acquired. Furthermore, combined with the use of patients with stable schizophrenia in early-phase clinical trials, significant 'de-risking' and more confident 'go/no-go' decisions are possible.

Modeling heterogeneous patients with a clinical diagnosis of schizophrenia with induced pluripotent stem cells

Schizophrenia (SZ) is a devastating complex genetic mental condition that is heterogeneous in terms of clinical etiologies, symptoms, and outcomes. Despite decades of postmortem, neuroimaging, pharmacological, and genetic studies of patients, in addition to animal models, much of the biological mechanisms that underlie the pathology of SZ remain unknown. The ability to reprogram adult somatic cells into human induced pluripotent stem cells (hiPSCs) provides a new tool that supplies live human neurons for modeling complex genetic conditions such as SZ. The purpose of this review is to discuss the technical and clinical constraints currently limiting hiPSC-based studies. We posit that reducing the clinical heterogeneity of hiPSC-based studies, by selecting subjects with common clinical manifestations or rare genetic variants, will help our ability to draw meaningful insights from the necessarily small patient cohorts that can be studied at this time.

Genes and schizophrenia: from a Festschrift Seminar honoring William T. Carpenter Jr, MD

Recent data have begun to elucidate the genetic architecture of schizophrenia, as well as provide new insights into the relationships of specific genetic factors across diagnostic boundaries, with specific symptom domains, and in the prediction of antipsychotic treatment response. Not surprisingly, work conducted at the Maryland Psychiatric Research Center (MPRC), led by Dr William Carpenter, has helped to guide the thinking behind much of this work, as well as contributed valuable data toward these efforts. In this article, I will briefly summarize some of the major findings emerging from these lines of research and highlight the role of the Dr Carpenter and his colleagues at the MPRC in this area.

Pharmacogenetics of antipsychotics

OBJECTIVE

During the past decades, increasing efforts have been invested in studies to unravel the influence of genetic factors on antipsychotic (AP) dosage, treatment response, and occurrence of adverse effects. These studies aimed to improve clinical care by predicting outcome of treatment with APs and thus allowing for individualized treatment strategies. We highlight most important findings obtained through both candidate gene and genome-wide association studies, including pharmacokinetic and pharmacodynamic factors.

METHODS

We reviewed studies on pharmacogenetics of AP response and adverse effects published on PubMed until early 2012. Owing to the high number of published studies, we focused our review on findings that have been replicated in independent studies or are supported by meta-analyses.

RESULTS

Most robust findings were reported for associations between polymorphisms of the cytochrome P450 system, the dopamine and the serotonin transmitter systems, and dosage, treatment response, and adverse effects, such as AP-induced weight gain or tardive dyskinesia. These associations were either detected for specific medications or for classes of APs.

CONCLUSION

First promising and robust results show that pharmacogenetics bear promise for a widespread use in future clinical practice. This will likely be achieved by developing algorithms that will include many genetic variants. However, further investigation is warranted to replicate and validate previous findings, as well as to identify new genetic variants involved in AP response and for replication of existing findings.

Genome-wide association studies in pharmacogenomics: successes and lessons

OBJECTIVE

As genotyping technology has progressed, genome-wide association studies (GWAS) have matured into efficient and effective tools for mapping genes underlying human phenotypes.

METHODS

Recent studies have shown the utility of the GWAS approach for examining pharmacogenomic traits, including drug metabolism, efficacy, and toxicity.

RESULTS

Application of GWAS to pharmacogenomic outcomes presents unique challenges and opportunities.

CONCLUSION

In the current review, we discuss the potential promises and potential caveats of this approach specifically as it relates to pharmacogenomic studies. Concerns with study design, power and sample size, and analysis are reviewed. We further examine the features of successful pharmacogenomic GWAS, and describe consortia efforts that are likely to expand the reach of pharmacogenomic GWAS in the future.

The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration

Background

Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations.

Objective

To illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison.

Method

Data from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units.

Results

For 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their “Optimal” treatment versus those assigned to their “Non-optimal” treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17—1.01).

Conclusions

This approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments.

Pharmacogenetics of antipsychotic treatment in schizophrenia

Antipsychotics are the mainstay treatment for schizophrenia. There is large variability between individuals in their response to antipsychotics, both in efficacy and adverse effects of treatment. While the source of interindividual variability in antipsychotic response is not completely understood, genetics is a major contributing factor. The identification of pharmacogenetic markers that predict antipsychotic efficacy and adverse reactions is a growing area of research, and holds the potential to replace the current trial-and-error approach to treatment selection in schizophrenia with a personalized medicine approach.In this chapter, we provide an overview of the current state of pharmacogenetics in schizophrenia treatment. The most promising pharmacogenetic findings are presented for both antipsychotic response and commonly studied adverse reactions. The application of pharmacogenetics to schizophrenia treatment is discussed, with an emphasis on the clinical utility of pharmacogenetic testing and directions for future research.

Individualizing clozapine and risperidone treatment for schizophrenia patients

Schizophrenia is a severe disorder that significantly affects the quality of life and total functioning of patients and their caregivers. Clozapine is the first atypical antipsychotic with fewer adverse effects and established efficacy. As a rule of thumb, risperidone is one of the most reliable and effective antipsychotics for newly diagnosed and chronic schizophrenics. Pharmacogenetic studies have identified genomic variants of candidate genes that seem to be important in the way a patient responds to treatment. The recent progress made in pharmacogenomics will improve the quality of treatment, since drug doses will be tailored to the special needs of each patient. In this article, we review the available literature attempting to delineate the role of genomic variations in clozapine and risperidone response in schizophrenic patients of various ethnicities. We conclude that pharmacogenomics for these two drugs is still not ready for implementation in the clinic.

Horizons of psychiatric genetics and epigenetics: where are we and where are we heading?

Today multinational studies using genome-wide association scan (GWAS) for >1000,000 polymorphisms on >100,000 cases with major psychiatric diseases versus controls, combined with next-generation sequencing have found ~100 genetic polymorphisms associated with schizophrenia (SCZ), bipolar disorder (BD), autism, attention deficit and hyperactivity disorder (ADHD), etc. However, the effect size of each genetic mutation has been generally low (<1%), and altogether could portray a tiny fraction of these mental diseases. Furthermore, none of these polymorphisms was specific to disease phenotypes indicating that they are simply genetic risk factors rather than causal mutations. The lack of identification of the major gene(s) in huge genetic studies increased the tendency for reexamining the roles of environmental factors in psychiatric and other complex diseases. However, this time at cellular/molecular levels mediated by epigenetic mechanisms that are heritable, but reversible while interacting with the environment. Now, gene-specific or whole-genome epigenetic analyses have introduced hundreds of aberrant epigenetic marks in the blood or brain of individuals with psychiatric diseases that include aberrations in DNA methylation, histone modifications and microRNA expression. Interestingly, most of the current psychiatric drugs such as valproate, lithium, antidepressants, antipsychotics and even electroconvulsive therapy (ECT) modulate epigenetic codes. The existing data indicate that, the impacts of environment/nurture, including the uterine milieu and early-life events might be more significant than genetic/nature in most psychiatric diseases. The lack of significant results in large-scale genetic studies led to revise the bolded roles of genetics and now we are at the turning point of genomics for reconsidering environmental factors that through epigenetic mechanisms may impact the brain development/functions causing disease phenotypes.

Genotypic variation in the SV2C gene impacts response to atypical antipsychotics the CATIE study

Pharmacogenetic (PGx) predictors of response would improve outcomes in antipsychotic treatment. Based on both biological rationale and prior evidence of an impact on Parkinson's disease, we conducted an association study for 106 SNPs in the synaptic vesicle protein 2C (SV2C) gene using genetic and treatment response data from the Clinical Trial of Antipsychotic Intervention Effectiveness (CATIE). We examined response to the atypical antipsychotics for Caucasian subjects in the blinded phases, Phases 1A, 1B, and 2, of CATIE with sample sizes as follows: olanzapine (N=134), quetiapine (N=124), risperidone (N=134), and ziprasidone (N=74). Response was defined as change in the Positive and Negative Syndrome Scale (PANSS) score using a mixed model repeat measures approach. Subjects homozygous for the T allele of rs11960832 displayed significantly worse response to olanzapine treatment, the only finding with study-wide significance (p=2.94×10(-5); false discovery rate=2.18×10(-2)). These subjects also displayed worse response to quetiapine with nominal significance (p=4.56×10(-2)). While no other SNP achieved study-wide significance, one SNP (rs10214163) influencing Parkinson's disease displayed nominally significant association with olanzapine and quetiapine response, while the second such SNP (rs30196) showed a statistical trend toward correlating with olanzapine and quetiapine response. Furthermore, both coding SNPs examined (rs31244 and rs2270927) displayed nominally significant correlations with treatment response: one for olanzapine (rs227092), and one for quetiapine (rs31244). The fact that multiple SNPs in SV2C may impact response to atypical antipsychotics suggests that further evaluation of SNPs in this gene as PGx predictors of antipsychotic response is warranted.

Strengths and weaknesses of pharmacogenetic studies of antipsychotic drugs: the potential value of the PEPs study

The successful application of pharmacogenetics in routine clinical practice is still a long way from becoming a reality. In order to favor the transfer of pharmacogenetic results to clinical practice, especially in psychiatry, these studies must be optimized. This article reviews the strengths and weaknesses that characterize pharmacogenetic studies in psychiatry and condition their implementation in clinical practice. We also include recommendations for improving the design of pharmacogenetic studies, which may convert their limitations into strengths and facilitate the implementation of their results into clinical practice. Finally, we discuss the potential value of naturalistic, prospective, multicenter and coordinated projects such as the 'Phenotype-genotype and environmental interaction. Application of a predictive model in first psychotic episodes' (known as the PEPs study, from the Spanish abbreviation) in pharmacogenetic studies.

AKAP13, CACNA1, GRIK4 and GRIA1 genetic variations may be associated with haloperidol efficacy during acute treatment

We previously investigated a sample of psychotic patients acutely ill and acutely treated with haloperidol in the search for genetic predictors of response at PANSS scores during the first month of treatment. In the present work we extend the analysis to a wider panel of genetic variations including SNPs harbored by genes whose products are involved in molecular pathways consistent with the latest results of genome-wide association studies (GWAS) of antipsychotic efficacy. 96 Patients were investigated. The results were replicated in an independent sample of bipolar manic patients treated with antipsychotics (n tot=470, the sample was retrieved from the STEP-BD). Outcomes were the PANSS variation through time in the first sample, and changes of mania symptomatology at any two consecutive observations in the public available STEP-BD replication sample. A list of variations harbored by AKAP13, CACNA1, GRIK4 and GRIA1 were found to be significantly associated with outcome in both samples (different set of variations for each sample). Results did not survived multiple testing in the original sample but were replicated in both samples. This finding stresses the relevance of the glutamatergic system and regulatory molecular cascades in antipsychotic response. Nonetheless, the level of significance and the indirect and incomplete replication mandate cautiousness and further replication.

Genome-wide association study of patient-rated and clinician-rated global impression of severity during antipsychotic treatment

OBJECTIVE

To examine the unique and congruent findings between multiple raters in a genome-wide association study (GWAS) in the context of understanding individual differences in treatment response during antipsychotic therapy for schizophrenia.

MATERIALS AND METHODS

We performed GWAS to search for genetic variation affecting treatment response. The analysis sample included 738 patients with schizophrenia, successfully genotyped for ∼492k single nucleotide polymorphisms (SNPs) from the Clinical Antipsychotic Trial of Intervention Effectiveness. Outcomes included both clinician and patient report of illness severity on global impression scales, the clinical global impression severity scale and patient global impression, respectively. Our criterion for genome-wide significance was a prespecified threshold ensuring that, on average, only 10% of the significant findings are false discoveries.

RESULTS

Thirteen SNPs reached genome-wide significance. The top findings indicated three SNPs in PDE4D, 5q12.1 (P=4.2×10, 1.6×10, 1.8×10), mediating the effects of quetiapine on patient-reported severity and an additional three SNPs in TJP1, 15q13.1 (P=2.25×10, 4.86×10, 4.91×10), mediating the effects of risperidone on patient-reported severity. For clinician-reported severity, two SNPs in PPA2, 4q24 (P=3.68×10, 5.05×10), were found to reach genome-wide significance.

CONCLUSION

We found evidence of both a novel and a consistent association when examining the results from the patient and clinician ratings, suggesting that different raters may capture unique facets of schizophrenia. Although our findings require replication and functional validation, this study shows the potential of GWAS to discover genes that potentially mediate treatment response of antipsychotic medication.

Personalized medicine in psychiatry: problems and promises

The central theme of personalized medicine is the premise that an individual's unique physiologic characteristics play a significant role in both disease vulnerability and in response to specific therapies. The major goals of personalized medicine are therefore to predict an individual's susceptibility to developing an illness, achieve accurate diagnosis, and optimize the most efficient and favorable response to treatment. The goal of achieving personalized medicine in psychiatry is a laudable one, because its attainment should be associated with a marked reduction in morbidity and mortality. In this review, we summarize an illustrative selection of studies that are laying the foundation towards personalizing medicine in major depressive disorder, bipolar disorder, and schizophrenia. In addition, we present emerging applications that are likely to advance personalized medicine in psychiatry, with an emphasis on novel biomarkers and neuroimaging.

Pharmacogenomics can improve antipsychotic treatment in schizophrenia

Schizophrenia is a widespread mental disease with a prevalence of about 1% in the world population, and heritability of up to 80%. Drug therapy is an important approach to treating the disease. However, the curative effect of antipsychotic is far from satisfactory in terms of tolerability and side effects. Many studies have indicated that about 30% of the patients exhibit little or no improvements associated with antipsychotics. The response of individual patients who are given the same dose of the same drug varies considerably. In addition, antipsychotic drugs are often accompanied by adverse drug reactions (ADRs), which can cause considerable financial loss in addition to the obvious societal harm. So, it is strongly recommended that personalized medicine should be implemented both to improve drug efficacy and to minimize adverse events and toxicity. There is therefore a need for pharmacogenomic studies into the factors affecting response of schizophrenia patients to antipsychotic drugs to provide informed guidance for clinicians. Individual differences in drug response is due to a combination of many complex factors including ADEM (absorption, distribution, metabolism, excretion) process, transporting, binding with receptor and intracellular signal transduction. Pharmacogenetic and pharmacogenomic studies have successfully identified genetic variants that contribute to this interindividual variability in antipsychotics response. In addition, epigenetic factors such as methylation of DNA and regulation by miRNA have also been reported to play an important role in the complex interactions between the multiple genes and environmental factors which influence individual drug response phenotypes in patients. In this review, we will focus on the latest research on polymorphisms of candidate genes that code for drug metabolic enzymes (CYP2D6, CYP1A2, CYP3A4, etc.), drug transporters (mainly ABCB1) and neurotransmitter receptors (dopamine receptors and serotonin receptors, etc.). We also discuss the genome-wide pharmacogenomic study of schizophrenia and review the current state of knowledge on epigenetics and potential clinical applications.

Scientific challenges and implementation barriers to translation of pharmacogenomics in clinical practice

The mapping of the human genome and subsequent advancements in genetic technology had provided clinicians and scientists an understanding of the genetic basis of altered drug pharmacokinetics and pharmacodynamics, as well as some examples of applying genomic data in clinical practice. This has raised the public expectation that predicting patients' responses to drug therapy is now possible in every therapeutic area, and personalized drug therapy would come sooner than later. However, debate continues among most stakeholders involved in drug development and clinical decision-making on whether pharmacogenomic biomarkers should be used in patient assessment, as well as when and in whom to use the biomarker-based diagnostic tests. Currently, most would agree that achieving the goal of personalized therapy remains years, if not decades, away. Realistic application of genomic findings and technologies in clinical practice and drug development require addressing multiple logistics and challenges that go beyond discovery of gene variants and/or completion of prospective controlled clinical trials. The goal of personalized medicine can only be achieved when all stakeholders in the field work together, with willingness to accept occasional paradigm change in their current approach.

Genotype-based ancestral background consistently predicts efficacy and side effects across treatments in CATIE and STAR*D

Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient’s unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (n = 765) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 1892). Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (p<0.01, both samples). Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient’s unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions.

Genome-wide association analysis of age-at-onset in Alzheimer's disease

The risk of Alzheimer's disease (AD) is strongly determined by genetic factors and recent genome-wide association studies (GWAS) have identified several genes for the disease risk. In addition to the disease risk, age-at-onset (AAO) of AD has also strong genetic component with an estimated heritability of 42%. Identification of AAO genes may help to understand the biological mechanisms that regulate the onset of the disease. Here we report the first GWAS focused on identifying genes for the AAO of AD. We performed a genome-wide meta-analysis on three samples comprising a total of 2222 AD cases. A total of ~2.5 million directly genotyped or imputed single-nucleotide polymorphisms (SNPs) were analyzed in relation to AAO of AD. As expected, the most significant associations were observed in the apolipoprotein E (APOE) region on chromosome 19 where several SNPs surpassed the conservative genome-wide significant threshold (P<5E-08). The most significant SNP outside the APOE region was located in the DCHS2 gene on chromosome 4q31.3 (rs1466662; P=4.95E-07). There were 19 additional significant SNPs in this region at P<1E-04 and the DCHS2 gene is expressed in the cerebral cortex and thus is a potential candidate for affecting AAO in AD. These findings need to be confirmed in additional well-powered samples.

Pharmacogenomics discovery and implementation in genome-wide association studies era

Clinical response to therapeutic treatments often varies among individual patients, ranging from beneficial effect to even fatal adverse reaction. Pharmacogenomics holds the promise of personalized medicine through elucidating genetic determinants responsible for pharmacological outcomes (e.g., cytotoxicities to anticancer drugs) and therefore guide the prescription decision prior to drug treatment. Besides traditional candidate gene-based approaches, technical advances have begun to allow application of whole-genome approaches to pharmacogenomic discovery. In particular, comprehensive understanding of human genetic variation provides the basis for applying GWAS (genome-wide association studies) in pharmacogenomic research to identify genomic loci associated with pharmacological phenotypes (e.g., individual dose requirement for warfarin). We therefore briefly reviewed the background for pharmacogenetic/pharmacogenomic research with statins and warfarin as examples for the GWAS discovery and their clinical implementation. In conclusion, with some challenges, whole-genome approaches such as GWAS have allowed unprecedented progress in identifying genetic variants associated with pharmacological phenotypes, as well as provided foundation for the next wave of pharmacogenomic discovery utilizing sequencing-based approaches. Furthermore, investigation of the complex interactions among genetic and epigenetic factors on the whole-genome scale will become the post-GWAS research focus for pharmacologic complex traits.

Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction

We have used a translational convergent functional genomics (CFG) approach to identify and prioritize genes involved in schizophrenia, by gene-level integration of genome-wide association study data with other genetic and gene expression studies in humans and animal models. Using this polyevidence scoring and pathway analyses, we identify top genes (DISC1, TCF4, MBP, MOBP, NCAM1, NRCAM, NDUFV2, RAB18, as well as ADCYAP1, BDNF, CNR1, COMT, DRD2, DTNBP1, GAD1, GRIA1, GRIN2B, HTR2A, NRG1, RELN, SNAP-25, TNIK), brain development, myelination, cell adhesion, glutamate receptor signaling, G-protein-coupled receptor signaling and cAMP-mediated signaling as key to pathophysiology and as targets for therapeutic intervention. Overall, the data are consistent with a model of disrupted connectivity in schizophrenia, resulting from the effects of neurodevelopmental environmental stress on a background of genetic vulnerability. In addition, we show how the top candidate genes identified by CFG can be used to generate a genetic risk prediction score (GRPS) to aid schizophrenia diagnostics, with predictive ability in independent cohorts. The GRPS also differentiates classic age of onset schizophrenia from early onset and late-onset disease. We also show, in three independent cohorts, two European American and one African American, increasing overlap, reproducibility and consistency of findings from single-nucleotide polymorphisms to genes, then genes prioritized by CFG, and ultimately at the level of biological pathways and mechanisms. Finally, we compared our top candidate genes for schizophrenia from this analysis with top candidate genes for bipolar disorder and anxiety disorders from previous CFG analyses conducted by us, as well as findings from the fields of autism and Alzheimer. Overall, our work maps the genomic and biological landscape for schizophrenia, providing leads towards a better understanding of illness, diagnostics and therapeutics. It also reveals the significant genetic overlap with other major psychiatric disorder domains, suggesting the need for improved nosology.

Targeted pharmacogenetic analysis of antipsychotic response in the CATIE study

AIM

This study evaluated the impact of 6789 SNPs on treatment response to antipsychotics in Caucasian patients from the CATIE study.

MATERIALS & METHODS

An Illumina (CA, USA) BeadChip was designed that targeted genes potentially impacting disease risk, disease presentation or antipsychotic response. SNPs tagged regions of linkage disequilibrium or functional variants not detectable using previous genotypes for CATIE. Change in Positive and Negative Syndrome scale total score was modeled using a mixed model repeated measures method that assumed a 30-day lag period. Genetic association analysis was performed using linear regression.

RESULTS

Association analysis identified 20 SNPs with p-values of ≤5 × 10(-4). Many of these are in genes previously implicated in schizophrenia and other neuropsychiatric diseases.

CONCLUSION

The targeted approach identified SNPs possibly influencing response to antipsychotic drugs in Caucasian patients suffering from schizophrenia. The findings support a biological link between disease risk and presentation and antipsychotic response.

Pharmacogenetics in psychiatry: translating research into clinical practice

Pharmacogenetic/pharmacogenomic (PGx) approaches to psychopharmacology aim to identify clinically meaningful predictors of drug efficacy and/or side-effect burden. To date, however, PGx studies in psychiatry have not yielded compelling results, and clinical utilization of PGx testing in psychiatry is extremely limited. In this review, the authors provide a brief overview on the status of PGx studies in psychiatry, review the commercialization process for PGx tests and then discuss methodological considerations that may enhance the potential for clinically applicable PGx tests in psychiatry. The authors focus on design considerations that include increased ascertainment of subjects in the earliest phases of illness, discuss the advantages of drug-induced adverse events as phenotypes for examination and emphasize the importance of maximizing adherence to treatment in pharmacogenetic studies. Finally, the authors discuss unique aspects of pharmacogenetic studies that may distinguish them from studies of other complex traits. Taken together, these data provide insights into the design and methodological considerations that may enhance the potential for clinical utility of PGx studies.

Institutional Profile: The Center for Biomarker Research and Personalized Medicine at Virginia Commonwealth University: advancing psychiatric drug treatment

The Center for Biomarker Research and Personalized Medicine is a small, focused and technology-driven organization, sited within the School of Pharmacy on the Medical College of Virginia Campus of Virginia Commonwealth University. The Center was established in 2006, with a mission to improve understanding and treatment of psychiatric disease by employing the latest advances in molecular biology, informatics and statistics. We take the philosophy that large-scale, exploratory studies are crucial to achieve our aims because strong biological associations have been historically absent for psychiatric disorders. Our work follows two main streams: the first being disease biomarker research, such as discovering genes contributing risk for schizophrenia or depression. The second stream is the discovery of biomarkers for therapeutic drug response, where our genome-wide association studies of antipsychotic and antidepressant response have yielded multiple new leads. With the recent success of large-scale biological investigations of psychiatric disorders, we are very optimistic about the future. By engaging cutting-edge technologies such as next-generation DNA sequencing, coupled with biological data integration, we may further probe the biological underpinnings of psychiatric disorders and response to drug treatment.

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

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

The influence of five monoamine genes on trajectories of depressive symptoms across adolescence and young adulthood

The influence of five monoamine candidate genes on depressive symptom trajectories in adolescence and young adulthood were examined in the Add Health genetic sample. Results indicated that, for all respondents, carriers of the dopamine receptor D4 5-repeat allele were characterized by distinct depressive symptom trajectories across adolescence and early adulthood. Similarly, for males, individuals with the monoamine oxidase A 3.5-repeat allele exhibited unique depressive symptom trajectories. Specifically, the trajectories of those with the dopamine receptor D4 5-repeat allele were characterized by rising levels in the transition to adulthood, while their peers were experiencing a normative drop in depressive symptom frequency. Conversely, males with the monoamine oxidase A 3.5-repeat allele were shown to experience increased distress in late adolescence. An empirical method for examining a wide array of allelic combinations was employed, and false discovery rate methods were used to control the risk of false positives due to multiple testing. Special attention was given to thoroughly interrogate the robustness of the putative genetic effects. These results demonstrate the value of combining dynamic developmental perspectives with statistical genetic methods to optimize the search for genetic influences on psychopathology across the life course.

Pharmacogenomic study of side-effects for antidepressant treatment options in STAR*D

BACKGROUND

Understanding individual differences in susceptibility to antidepressant therapy side-effects is essential to optimize the treatment of depression.

METHOD

We performed genome-wide association studies (GWAS) to search for genetic variation affecting the susceptibility to side-effects. The analysis sample consisted of 1439 depression patients, successfully genotyped for 421K single nucleotide polymorphisms (SNPs), from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. Outcomes included four indicators of side-effects: general side-effect burden, sexual side-effects, dizziness and vision/hearing-related side-effects. Our criterion for genome-wide significance was a prespecified threshold ensuring that, on average, only 10% of the significant findings are false discoveries.

RESULTS

Thirty-four SNPs satisfied this criterion. The top finding indicated that 10 SNPs in SACM1L mediated the effects of bupropion on sexual side-effects (p = 4.98 × 10(-7), q = 0.023). Suggestive findings were also found for SNPs in MAGI2, DTWD1, WDFY4 and CHL1.

CONCLUSIONS

Although our findings require replication and functional validation, this study demonstrates the potential of GWAS to discover genes and pathways that could mediate adverse effects of antidepressant medication.

Integrative genomics strategies to elucidate the complexity of drug response

Pharmacogenomic investigation from both genome-wide association studies and experiments focused on candidate loci involved in drug mechanism and metabolism has yielded a substantial and increasing list of robust genetic effects on drug therapy in humans. At the same time, reasonably comprehensive molecular data such as gene expression, proteomic and metabolomic data are now available for collections of hundreds to thousands of individuals. If these data are structured in a statistically robust and computationally tractable way, such as a network model, they can aid in the analysis of new pharmacogenomics studies by suggesting novel hypotheses for the regulation of genes involved in drug metabolism and response. Similarly, hypotheses taken from these same models can direct genome-wide association studies by focusing the genome-wide association studies analysis on a number of specific hypotheses informed by the relationships customarily seen between a gene's expression or protein activity and genetic variation at a particular locus. Network models based on other sorts of systematic biological data such as cell-based surveys of drug effect on gene expression and mining of literature and electronic medical records for associations between clinical and molecular phenotypes also promise similar utility. Although surely primitive in comparison with what will be developed, these model-based approaches to leveraging the increasing volume of data generated in the course of patient care and medical research nevertheless suggest a huge opportunity to improve our understanding of biological systems involved in pharmacogenomics and apply them to questions of medical relevance.