Possible association of CUX1 gene polymorphisms with antidepressant response in major depressive disorder

Genetic Landscape of Major Depressive Disorder: Assessment of Potential Diagnostic and Antidepressant Response Markers

BACKGROUND

The clinical heterogeneity in major depressive disorder (MDD), variable treatment response, and conflicting findings limit the ability of genomics toward the discovery of evidence-based diagnosis and treatment regimen. This study attempts to curate all genetic association findings to evaluate potential variants for clinical translation.

METHODS

We systematically reviewed all candidates and genome-wide association studies for both MDD susceptibility and antidepressant response, independently, using MEDLINE, particularly to identify replicated findings. These variants were evaluated for functional consequences using different in silico tools and further estimated their diagnostic predictability by calculating positive predictive values.

RESULTS

A total of 217 significantly associated studies comprising 1200 variants across 545 genes and 128 studies including 921 variants across 412 genes were included with MDD susceptibility and antidepressant response, respectively. Although the majority of associations were confirmed by a single study, we identified 31 and 18 replicated variants (in at least 2 studies) for MDD and antidepressant response. Functional annotation of these 31 variants predicted 20% coding variants as deleterious/damaging and 80.6% variants with regulatory effect. Similarly, the response-related 18 variants revealed 25% coding variant as damaging and 88.2% with substantial regulatory potential. Finally, we could calculate the diagnostic predictability of 19 and 5 variants whose positive predictive values ranges from 0.49 to 0.66 for MDD and 0.36 to 0.66 for response.

CONCLUSIONS

The replicated variants presented in our data are promising for disease diagnosis and improved response outcomes. Although these quantitative assessment measures are solely directive of available observational evidence, robust homogenous validation studies are required to strengthen these variants for molecular diagnostic application.

Shared peripheral blood biomarkers for Alzheimer’s disease, major depressive disorder, and type 2 diabetes and cognitive risk factor analysis

Background

Alzheimer's disease (AD), type 2 diabetes mellitus (T2DM), and Major Depressive Disorder (MDD) have a higher incidence rate in modern society. Although increasing evidence supports close associations between the three, the mechanisms underlying their interrelationships remain elucidated.

Objective

The primary purpose is to explore the shared pathogenesis and the potential peripheral blood biomarkers for AD, MDD, and T2DM.

Methods

We downloaded the microarray data of AD, MDD, and T2DM from the Gene Expression Omnibus database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis to identify differentially expressed genes. We took the intersection of differentially expressed genes to obtain co-DEGs. Then, we performed GO and KEGG enrichment analysis on the common genes in the AD, MDD, and T2DM-related modules. Next, we utilized the STRING database to find the hub genes in the protein-protein interaction network. ROC curves were constructed for co-DEGs to obtain the most diagnostic valuable genes and to make drug predictions against the target genes. Finally, we conducted a present condition survey to verify the correlation between T2DM, MDD and AD.

Results

Our findings indicated 127 diff co-DEGs, 19 upregulated co-DEGs, and 25 down-regulated co-DEGs. Functional enrichment analysis showed co-DEGs were mainly enriched in signaling pathways such as metabolic diseases and some neurodegeneration. Protein-protein interaction network construction identified hub genes in AD, MDD and T2DM shared genes. We identified seven hub genes of co-DEGs, namely, SMC4, CDC27, HNF1A, RHOD, CUX1, PDLIM5, and TTR. The current survey results suggest a correlation between T2DM, MDD and dementia. Moreover, logistic regression analysis showed that T2DM and depression increased the risk of dementia.

Conclusion

Our work identified common pathogenesis of AD, T2DM, and MDD. These shared pathways might provide novel ideas for further mechanistic studies and hub genes that may serve as novel therapeutic targets for diagnosing and treating.

Precision Medicine in Antidepressants Treatment

Precision medicine uses innovative approaches to improve disease prevention and treatment outcomes by taking into account people's genetic backgrounds, environments, and lifestyles. Treatment of depression is particularly challenging, given that 30-50% of patients do not respond adequately to antidepressants, while those who respond may experience unpleasant adverse drug reactions (ADRs) that decrease their quality of life and compliance. This chapter aims to present the available scientific data that focus on the impact of genetic variants on the efficacy and toxicity of antidepressants. We compiled data from candidate gene and genome-wide association studies that investigated associations between pharmacodynamic and pharmacokinetic genes and response to antidepressants regarding symptom improvement and ADRs. We also summarized the existing pharmacogenetic-based treatment guidelines for antidepressants, used to guide the selection of the right antidepressant and its dose based on the patient's genetic profile, aiming to achieve maximum efficacy and minimum toxicity. Finally, we reviewed the clinical implementation of pharmacogenomics studies focusing on patients on antidepressants. The available data demonstrate that precision medicine can increase the efficacy of antidepressants and reduce the occurrence of ADRs and ultimately improve patients' quality of life.

The association of ARRB1 polymorphisms with response to antidepressant treatment in depressed patients

Introduction: β-arrestin 1, a protein encoded by ARRB1 involved in receptor signaling, is a potential biomarker for the response to antidepressant drug (ATD) treatment in depression. We examined ARRB1 genetic variants for their association with response following ATD treatment in METADAP, a cohort of 6-month ATD-treated depressed patients. Methods: Patients (n = 388) were assessed at baseline (M0) and after 1 (M1), 3 (M3), and 6 months (M6) of treatment for Hamilton Depression Rating Scale (HDRS) changes, response, and remission. Whole-gene ARRB1 variants identified from high-throughput sequencing were separated by a minor allele frequency (MAF)≥5%. Frequent variants (i.e., MAF≥5%) annotated by RegulomeDB as likely affecting transcription factor binding were analyzed using mixed-effects models. Rare variants (i.e., MAF<5%) were analyzed using a variant set analysis. Results: The variant set analysis of rare variants was significant in explaining HDRS score changes (T = 878.9; p = 0.0033) and remission (T = -1974.1; p = 0.034). Rare variant counts were significant in explaining response (p = 0.016), remission (p = 0.022), and HDRS scores at M1 (p = 0.0021) and M3 (p=<0.001). rs553664 and rs536852 were significantly associated with the HDRS score (rs553664: p = 0.0055 | rs536852: p = 0.046) and remission (rs553664: p = 0.026 | rs536852: p = 0.012) through their interactions with time. At M6, significantly higher HDRS scores were observed in rs553664 AA homozygotes (13.98 ± 1.06) compared to AG heterozygotes (10.59 ± 0.86; p = 0.014) and in rs536852 GG homozygotes (14.88 ± 1.10) compared to AG heterozygotes (11.26 ± 0.95; p = 0.0061). Significantly lower remitter rates were observed in rs536852 GG homozygotes (8%, n = 56) compared to AG heterozygotes (42%, n = 105) at M6 (p = 0.0018). Conclusion: Our results suggest ARRB1 variants may influence the response to ATD treatment in depressed patients. Further analysis of functional ARRB1 variants and rare variant burden in other populations would help corroborate our exploratory analysis. β-arrestin 1 and genetic variants of ARRB1 may be useful clinical biomarkers for clinical improvement following ATD treatment in depressed individuals. Clinical Trial Registration: clinicaltrials.gov; identifier NCT00526383.

Selective Serotonin Reuptake Inhibitor Pharmaco-Omics: Mechanisms and Prediction

Selective serotonin reuptake inhibitors (SSRIs) are a standard of care for the pharmacotherapy of patients suffering from Major Depressive Disorder (MDD). However, only one-half to two-thirds of MDD patients respond to SSRI therapy. Recently, a "multiple omics" research strategy was applied to identify genetic differences between patients who did and did not respond to SSRI therapy. As a first step, plasma metabolites were assayed using samples from the 803 patients in the PGRN-AMPS SSRI MDD trial. The metabolomics data were then used to "inform" genomics by performing a genome-wide association study (GWAS) for plasma concentrations of the metabolite most highly associated with clinical response, serotonin (5-HT). Two genome-wide or near genome-wide significant single nucleotide polymorphism (SNP) signals were identified, one that mapped near the TSPAN5 gene and another across the ERICH3 gene, both genes that are highly expressed in the brain. Knocking down TSPAN5 and ERICH3 resulted in decreased 5-HT concentrations in neuroblastoma cell culture media and decreased expression of enzymes involved in 5-HT biosynthesis and metabolism. Functional genomic studies demonstrated that ERICH3 was involved in clathrin-mediated vesicle formation and TSPAN5 was an ethanol-responsive gene that may be a marker for response to acamprosate pharmacotherapy of alcohol use disorder (AUD), a neuropsychiatric disorder highly co-morbid with MDD. In parallel studies, kynurenine was the plasma metabolite most highly associated with MDD symptom severity and application of a metabolomics-informed pharmacogenomics approach identified DEFB1 and AHR as genes associated with variation in plasma kynurenine levels. Both genes also contributed to kynurenine-related inflammatory pathways. Finally, a multiply replicated predictive algorithm for SSRI clinical response with a balanced predictive accuracy of 76% (compared with 56% for clinical data alone) was developed by including the SNPs in TSPAN5, ERICH3, DEFB1 and AHR. In summary, application of a multiple omics research strategy that used metabolomics to inform genomics, followed by functional genomic studies, identified novel genes that influenced monoamine biology and made it possible to develop a predictive algorithm for SSRI clinical outcomes in MDD. A similar pharmaco-omic research strategy might be broadly applicable for the study of other neuropsychiatric diseases and their drug therapy.

ERICH3: vesicular association and antidepressant treatment response

Selective serotonin reuptake inhibitors (SSRIs) are standard of care for major depressive disorder (MDD) pharmacotherapy, but only approximately half of these patients remit on SSRI therapy. Our previous genome-wide association study identified a single-nucleotide polymorphism (SNP) signal across the glutamate-rich 3 (ERICH3) gene that was nearly genome-wide significantly associated with plasma serotonin (5-HT) concentrations, which were themselves associated with SSRI response for MDD patients enrolled in the Mayo Clinic PGRN-AMPS SSRI trial. In this study, we performed a meta-analysis which demonstrated that those SNPs were significantly associated with SSRI treatment outcomes in four independent MDD trials. However, the function of ERICH3 and molecular mechanism(s) by which it might be associated with plasma 5-HT concentrations and SSRI clinical response remained unclear. Therefore, we characterized the human ERICH3 gene functionally and identified ERICH3 mRNA transcripts and protein isoforms that are highly expressed in central nervous system cells. Coimmunoprecipitation identified a series of ERICH3 interacting proteins including clathrin heavy chain which are known to play a role in vesicular function. Immunofluorescence showed ERICH3 colocalization with 5-HT in vesicle-like structures, and ERICH3 knock-out dramatically decreased 5-HT staining in SK-N-SH cells as well as 5-HT concentrations in the culture media and cell lysates without changing the expression of 5-HT synthesizing or metabolizing enzymes. Finally, immunofluorescence also showed ERICH3 colocalization with dopamine in human iPSC-derived neurons. These results suggest that ERICH3 may play a significant role in vesicular function in serotonergic and other neuronal cell types, which might help explain its association with antidepressant treatment response.

Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants

Planning subsequent treatment strategies based on early responses rather than waiting for delayed antidepressant action can be helpful. We identified genetic markers for later non-remission in patients exhibiting poor early improvement using whole-exome sequencing data of depressive patients treated in a naturalistic manner. Among 1000 patients, early improvement at 2 weeks (reduction in Hamilton Depression Rating Scale [HAM-D] score ≥ 20%) and remission at 12 weeks (HAM-D score ≤ 7) were evaluated. Gene- and variant-level analyses were conducted to compare patients who did not exhibit early improvement and did not eventually achieve remission (n = 126) with those who exhibited early improvement and achieved remission (n = 385). Genes predicting final non-remission in patients who exhibited poor early improvement (COMT, PRNP, BRPF3, SLC25A40, and CGREF1 in males; PPFIBPI, LZTS3, MEPCE, MAP1A, and PFAS in females; ST3GAL5 in the total population) were determined. Among the significant genes, variants in the PRNP (rs1800014), COMT (rs6267), BRPF3 (rs200565609), and SLC25A40 genes (rs3213633) were identified. However, interpretations should be made cautiously, as complex pharmacotherapy involves various genes and pathways. Early detection of poor early improvement and final non-remission based on genetic risk would be helpful for decision-making in a clinical setting.

DNA methylation from germline cells in veterans with PTSD

In this study we investigated genome-wide sperm DNA methylation patterns in trauma-exposed Vietnam veterans. At the genome-wide level, we identified 3 CpG sites associated with PTSD in sperm including two intergenic and one CpG within the CCDC88C gene. Of those associated with PTSD in sperm at a nominal level, 1868 CpGs were also associated with PTSD in peripheral blood (5.6% overlap) including the RORA, CRHR1 and DOCK2 genes that have been previously implicated in PTSD. A total of 10 CpG sites were significantly associated with a reported history of a diagnosed mental health condition in children and reached genome-wide significance. CpGs associated with a history of a reported mental health condition in children were also enriched (90% of tested genes) for genes previously reported to be resistant to demethylation, making them strong candidates for transgenerational inheritance. In conclusion, our findings identify a unique sperm-specific DNA methylation pattern that is associated with PTSD.

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.

Unravelling the genetic basis of schizophrenia and bipolar disorder with GWAS: A systematic review

OBJECTIVES

To systematically review findings of GWAS in schizophrenia (SZ) and in bipolar disorder (BD); and to interpret findings, with a focus on identifying independent replications.

METHOD

PubMed search, selection and review of all independent GWAS in SZ or BD, published since March 2011, i.e. studies using non-overlapping samples within each article, between articles, and with those of the previous review (Li et al., 2012).

RESULTS

From the 22 GWAS included in this review, the genetic associations surviving standard GWAS-significance were for genetic markers in the regions of ACSL3/KCNE4, ADCY2, AMBRA1, ANK3, BRP44, DTL, FBLN1, HHAT, INTS7, LOC392301, LOC645434/NMBR, LOC729457, LRRFIP1, LSM1, MDM1, MHC, MIR2113/POU3F2, NDST3, NKAPL, ODZ4, PGBD1, RENBP, TRANK1, TSPAN18, TWIST2, UGT1A1/HJURP, WHSC1L1/FGFR1 and ZKSCAN4. All genes implicated across both reviews are discussed in terms of their function and implication in neuropsychiatry.

CONCLUSION

Taking all GWAS to date into account, AMBRA1, ANK3, ARNTL, CDH13, EFHD1 (albeit with different alleles), MHC, PLXNA2 and UGT1A1 have been implicated in either disorder in at least two reportedly non-overlapping samples. Additionally, evidence for a SZ/BD common genetic basis is most strongly supported by the implication of ANK3, NDST3, and PLXNA2.

Neonatal amygdalae and hippocampi are influenced by genotype and prenatal environment, and reflected in the neonatal DNA methylome

The amygdala and hippocampus undergo rapid development in early life. The relative contribution of genetic and environmental factors to the establishment of their developmental trajectories has yet to be examined. We performed imaging on neonates and examined how the observed variation in volume and microstructure of the amygdala and hippocampus varied by genotype, and compared with prenatal maternal mental health and socioeconomic status. Gene × Environment models outcompeted models containing genotype or environment only to best explain the majority of measures but some, especially of the amygdaloid microstructure, were best explained by genotype only. Models including DNA methylation measured in the neonate umbilical cords outcompeted the Gene and Gene × Environment models for the majority of amygdaloid measures and minority of hippocampal measures. This study identified brain region-specific gene networks associated with individual differences in fetal brain development. In particular, genetic and epigenetic variation within CUX1 was highlighted.

Systems Approach to Identify Common Genes and Pathways Associated with Response to Selective Serotonin Reuptake Inhibitors and Major Depression Risk

Despite numerous studies on major depressive disorder (MDD) susceptibility, the precise underlying molecular mechanism has not been elucidated which restricts the development of etiology-based disease-modifying drug. Major depressive disorder treatment is still symptomatic and is the leading cause of (~30%) failure of the current antidepressant therapy. Here we comprehended the probable genes and pathways commonly associated with antidepressant response and MDD. A systematic review was conducted, and candidate genes/pathways associated with antidepressant response and MDD were identified using an integrative genetics approach. Initially, single nucleotide polymorphisms (SNPs)/genes found to be significantly associated with antidepressant response were systematically reviewed and retrieved from the candidate studies and genome-wide association studies (GWAS). Also, significant variations concerning MDD susceptibility were extracted from GWAS only. We found 245 (Set A) and 800 (Set B) significantly associated genes with antidepressant response and MDD, respectively. Further, gene set enrichment analysis revealed the top five co-occurring molecular pathways (p ≤ 0.05) among the two sets of genes: Cushing syndrome, Axon guidance, cAMP signaling pathway, Insulin secretion, and Glutamatergic synapse, wherein all show a very close relation to synaptic plasticity. Integrative analyses of candidate gene and genome-wide association studies would enable us to investigate the putative targets for the development of disease etiology-based antidepressant that might be more promising than current ones.

The MAKE Biomarker Discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) Study: Design and Methodology

OBJECTIVE

Depression is associated with a major disease burden, and many individuals suffer from depressive symptoms due to an insufficient response to ostensibly adequate antidepressant treatment. Therefore, it is important to identify reliable treatment response predictors for use in developing personalized treatment strategies.

METHODS

The MAKE Biomarker discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) study was performed to identify predictors of antidepressant response using a 2-year naturalistic prospective design. Participants in the MAKE BETTER study were consecutively recruited from patients who visited the Psychiatry Department of Chonnam National University Hospital, Gwangju, South Korea for treatment of a depressive disorder. Data on demographic and clinical characteristics, genetic markers measured by whole-exome sequencing, and blood markers were obtained. The types and doses of antidepressants were determined based on the clinical judgment of the psychiatrist, and the treatment outcomes (e.g., depressive and other psychiatric symptoms and issues related to safety) were assessed.

RESULTS

We will be able to use the data collected in this study to develop a treatment-response prediction index composed of biomarkers.

CONCLUSION

The MAKE BETTER study will provide an empirical basis for a personalized medicine approach to depression by enabling the prediction of antidepressant treatment response according the characteristics of each patient. It will thereby support evidence-based decision-making that decreases the use of a trial-and-error approach to the treatment of depressive disorders.

Pharmacogenomics in Asia: a systematic review on current trends and novel discoveries

While early pharmacogenomic studies have primarily been carried out in Western populations, there has been a notable increase in the number of Asian studies over the past decade. We systematically reviewed all pharmacogenomic studies conducted in Asia published before 2016 to highlight trends and identify research gaps in Asia. We observed that pharmacogenomic research in Asia was dominated by larger developed countries, notably Japan and Korea, and mainly driven by local researchers. Studies were focused on drugs acting on the CNS, chemotherapeutics and anticoagulants. Significantly, several novel pharmacogenomic associations have emerged from Asian studies. These developments are highly encouraging for the strength of regional scientific and clinical community and propound the importance of discovery studies in different populations.

Pharmacogenetics of antidepressant response: A polygenic approach

BACKGROUND

Major depressive disorder (MDD) has a high personal and socio-economic burden and >60% of patients fail to achieve remission with the first antidepressant. The biological mechanisms behind antidepressant response are only partially known but genetic factors play a relevant role. A combined predictor across genetic variants may be useful to investigate this complex trait.

METHODS

Polygenic risk scores (PRS) were used to estimate multi-allelic contribution to: 1) antidepressant efficacy; 2) its overlap with MDD and schizophrenia. We constructed PRS and tested whether these predicted symptom improvement or remission from the GENDEP study (n=736) to the STAR*D study (n=1409) and vice-versa, including the whole sample or only patients treated with escitalopram or citalopram. Using summary statistics from Psychiatric Genomics Consortium for MDD and schizophrenia, we tested whether PRS from these disorders predicted symptom improvement in GENDEP, STAR*D, and five further studies (n=3756).

RESULTS

No significant prediction of antidepressant efficacy was obtained from PRS in GENDEP/STAR*D but this analysis might have been underpowered. There was no evidence of overlap in the genetics of antidepressant response with either MDD or schizophrenia, either in individual studies or a meta-analysis. Stratifying by antidepressant did not alter the results.

DISCUSSION

We identified no significant predictive effect using PRS between pharmacogenetic studies. The genetic liability to MDD or schizophrenia did not predict response to antidepressants, suggesting differences between the genetic component of depression and treatment response. Larger or more homogeneous studies will be necessary to obtain a polygenic predictor of antidepressant response.

Genome-wide association study of antidepressant response: involvement of the inorganic cation transmembrane transporter activity pathway

BACKGROUND

Genome-wide association studies (GWAS) represent the current frontier in pharmacogenomics. Thousands of subjects of Caucasian ancestry have been included in previous GWAS investigating antidepressant response. GWAS focused on this phenotype are lacking in Asian populations.

METHODS

A sample of 109 major depressive disorder (MDD) patients of Korean origin in antidepressant treatment was collected. Phenotypes were response and remission according to the Hamilton Rating Scale for Depression (HRSD). Genome-wide genotyping was performed using the Illumina Human Omni2.5-8 platform. The same phenotypes were used in the STAR*D level 1 (n = 1677) for independent replication. In order to corroborate findings and increase the comparability between the two datasets, three levels of analysis (SNPs, genes and pathways) were carried out. Bonferroni correction, permutations, and replication across samples were used to reduce the risk of false positives.

RESULTS

Among the genes replicated across the two samples (permutated p < 0.05 in both of them), CTNNA3 appeared promising. The inorganic cation transmembrane transporter activity pathway (GO:0022890) was associated with antidepressant response in both samples (p = 2.9e-5 and p = 0.001 in the Korean and STAR*D samples, respectively) and this pathway included CACNA1A, CACNA1C, and CACNB2 genes.

CONCLUSIONS

The present study supported the involvement of genes coding for subunits of L-type voltage-gated calcium channel in antidepressant efficacy across different ethnicities but replication of findings is required before any definitive statement.

A genome-wide association study of antidepressant response in Koreans

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

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.

Antidepressant pharmacogenetics

PURPOSE OF REVIEW

This article reviews recent literature published over the period March 2012-August 2013 on antidepressant pharmacogenetics, with a focus on clinical translation and methodological challenges.

RECENT FINDINGS

Recently, various polymorphisms associated with differential antidepressant efficacy, tolerability, and safety have emerged in association studies, but mixed findings, limited effect sizes, and poor control of confounders have prevented findings translating to practice. Although promising steps have been made, empirically robust clinically translatable pharmacogenetic tests are not yet established. The complex neurobiology of major depressive disorder (MDD) together with the evolving understanding of genetic processes present research challenges for clinical translation.

SUMMARY

Early reports of clinical utility are published. The current evidence base for antidepressant pharmacogenetics is, however, not yet empirically robust enough to inform routine prescribing guidelines. Over the coming years, genetically guided versus unguided trials will help determine if antidepressant pharmacogenetics merits more widespread application.

Polymorphisms in neuropsychiatric and neuroinflammatory disorders and the role of next generation sequencing in early diagnosis and treatment

A number of polymorphisms have been implicated in different neuropsychiatric and neurological disorders. Polymorphisms in neurological disorders with a central immune component are well described, mainly due to their role in increasing neurodegeneration. For example, the role of polymorphisms in Alzheimer's disease in accumulation of amyloid plaques is now well established. In contrast, polymorphisms resulting in or affecting psychiatric disorders are less well studied and frequently are not replicated by meta-analysis. Furthermore, even if a significant association has been confirmed, the role of the identified polymorphism in causing and/or augmenting the disorder is often difficult to rationalize. Here, we review polymorphisms found associated with different neuroinflammatory and neuropsychiatric disorders and discuss the role of next generation sequencing in early diagnosis and treatment and as a tool in studying their functional consequences.