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/lp/ou_press/the-genetics-of-treatment-resistant-depression-a-critical-review-and-sDeQy0lJbT
- Publisher
- Oxford University Press
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- © The Author(s) 2018. Published by Oxford University Press on behalf of CINP.
- ISSN
- 1461-1457
- eISSN
- 1469-5111
- DOI
- 10.1093/ijnp/pyy024
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Abstract
Background: One-third of depressed patients develop treatment-resistant depression with the related sequelae in terms of poor functionality and worse prognosis. Solid evidence suggests that genetic variants are potentially valid predictors of antidepressant efficacy and could be used to provide personalized treatments. Methods: The present review summarizes genetic findings of treatment-resistant depression including results from candidate gene studies and genome-wide association studies. The limitations of these approaches are discussed, and suggestions to improve the design of future studies are provided. Results: Most studies used the candidate gene approach, and few genes showed replicated associations with treatment- resistant depression and/or evidence obtained through complementary approaches (e.g., gene expression studies). These genes included GRIK4, BDNF, SLC6A4, and KCNK2, but confirmatory evidence in large cohorts was often lacking. Genome-wide association studies did not identify any genome-wide significant association at variant level, but pathways including genes modulating actin cytoskeleton, neural plasticity, and neurogenesis may be associated with treatment-resistant depression, in line with results obtained by genome-wide association studies of antidepressant response. The improvement of aggregated tests (e.g., polygenic risk scores), possibly using variant/gene prioritization criteria, the increase in the covering of genetic variants, and the incorporation of clinical-demographic predictors of treatment-resistant depression are proposed as possible strategies to improve future pharmacogenomic studies. Conclusions: Genetic biomarkers to identify patients with higher risk of treatment-resistant depression or to guide treatment in these patients are not available yet. Methodological improvements of future studies could lead to the identification of genetic biomarkers with clinical validity. Keywords: treatment-resistant depression, gene, pharmacogenomics, GWAS, antidepressant Received: January 5, 2018; Revised: March 2, 2018; Accepted: April 5, 2018 © The Author(s) 2018. Published by Oxford University Press on behalf of CINP. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, 93 provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/ijnp/article-abstract/22/2/93/4980962 by guest on 12 February 2019 Copyedited by: 94 | International Journal of Neuropsychopharmacology, 2019 MDD (Milaneschi et al., 2016). These findings together with the Introduction prognostic value of TRD suggest that TRD should be considered Major depressive disorder (MDD) is a primary health issue at as a separate phenotype in genetic studies. On the ground of both individual level and socio-economic level. In adolescents these observations, an increasing number of studies took into and young adults (aged between 15 and 39 years), depression is account not only antidepressant response/remission but also the third-leading cause of disability, while in middle-aged adults TRD or a measure of resistance stage (e.g., classes correspond - depression was reported to be the second cause of disability ing to the number/range of failed treatments). A relevant con - on a global scale (GBD 2015 Disease and Injury Incidence and tribution to the field has been provided by The European Group Prevalence Collaborators, 2016). The heavy burden of the disease for the Study of Resistant Depression, which has been working can be attributed both to the high lifetime prevalence (~13%) for over 15 years to study clinical and genetic variables associ - and to the insufficient response rates to antidepressant treat - ated with TRD, producing valuable data and anticipating a more ments. Complete symptom remission is achieved in approxi- recent spread of interest towards this phenotype (Schosser mately one-third of patients, while another approximately et al., 2012b). one-third develops treatment-resistant depression (TRD), but TRD is usually defined as nonremission after at least 2 ade - TRD estimates were up to 40% in other samples (Trivedi et al., quate antidepressant trials, but this standard definition may 2006; Souery et al., 2011). The high percentage of treatment not reflect the underlying pathophysiological mechanisms or a failure or incomplete remission is probably a consequence of reproducible set of genetic risk variants. The standard definition intrinsic biological and environmental heterogeneity among of TRD and the other available definitions have been formulated MDD patients (Gratten et al., 2014), suggesting that biomarkers on the basis of clinical observations (Souery et al., 2007). For of antidepressant response would be useful to guide treatment example, older definitions of TRD required nonremission to at at the individual level. least 2 adequate trials with antidepressants of different classes; Antidepressant response was demonstrated to have a rel - then the different class criterium was abolished after several evant genetic component by family studies and more recent studies demonstrated no significant difference in switching to approaches such as Genome-wide Complex Trait Analysis different types of treatments after the failure of the first treat - (Tansey et al., 2013). For this reason, genetic variants are con - ment trial (Rush et al., 2008; Souery et al., 2011). Several more sidered theoretically optimal biomarkers to provide personal - complex staging models have been developed, based on the ized antidepressant treatments and to reduce the proportion of duration of treatment, number, and type of treatments failed patients that develops TRD. (McIntyre et al., 2014). Available genetic studies generally used TRD may have a different genetic make-up compared with the standard definition of TRD, but recent literature underlines milder nonresponse cases, but previous pharmacogenetic stud - that MDD and TRD are probably heterogeneous entities under ies were mainly focused on a generic definition of response that the biological point of view, suggesting that classification sys - did not consider the number of failed antidepressants. A signifi- tems should include information about the specific pathoge - cant genetic heterogeneity was reported among different MDD netic mechanisms involved (Akil et al., 2018), a concept that has samples (Gratten et al., 2014), in line with the clinical observ - a been repeatedly underlined since the proposal of the Research tion that MDD is a heterogeneous entity. TRD patients were dem - Domain Criteria (Insel et al., 2010). onstrated to have some distinctive clinical features compared The present review provides an overview on the genetics of with non-TRD patients, such as higher symptom severity, more TRD, since the discussion on clinical relevance of this pheno - frequent suicidal risk, and comorbidity with anxiety (Souery type. Then, taking into account the pros and cons of previous et al., 2007; De Carlo et al., 2016; Kautzky et al., 2017). Despite studies, some possible strategies to improve future pharmaco - the fact that these features may depend also on environmental genetic studies are discussed to contribute to the advances of factors or non-genetic biological factors, other clinical subtypes this research field (Figure 1). of MDD probably have a genetic basis, such as atypical vs typical Figure 1. Representation of previous approaches used in the study of the genetics of TRD (treatment-resistant depression) and possible strategies to implement in future studies to improve power of identifying and replicating significant associations. Downloaded from https://academic.oup.com/ijnp/article-abstract/22/2/93/4980962 by guest on 12 February 2019 Copyedited by: Fabbri et al. | 95 synaptic plasticity were suggested as possible modulators of TRD. Overview of Previous Findings Brain derived neurotrophic factor (BDNF) has been extensively In the following 2 paragraphs, the results of genetic studies studied, since it plays a key role in promoting neuronal survival, investigating TRD are summarized T ( able 1). The most part of differentiation, and growth. Peripheral BDNF expression levels available studies used the candidate gene approach, that is, they were found to be decreased in TRD patients compared with anti - genotyped relevant polymorphisms in genes having a known depressant-responsive patients, in line with the hypothesis of a link with antidepressant mechanisms of action (pharmaco - BDNF deficit in MDD and particularly in TRD (Hong et al., 2014). dynamics) or metabolism (pharmacokinetics). More recently The Valine66Methionine, or rs6265 polymorphism, was the most TRD was also investigated by some genome-wide association investigated BDNF variant, since the Met protein is less effi - studies (GWAS). GWAS represent the technological answer to ciently secreted, resulting in lower interaction with BDNF -tar the hypothesis that antidepressant response has a polygenic gets (Chen et al., 2004). A preclinical study (Liu et al., 2012) and a nature, that is, thousands of polymorphisms across the genome small clinical pilot study in a sample of mainly European origin are probably involved and many of them are supposedly outside (Laje et al., 2012) suggested that the presence of the Met allele genes or in genes with no known connection to antidepressant attenuates the antidepressant response to ketamine in TRD. response. GWAS arrays polymorphisms throughout the genome, A following study in a Chinese population did not confirm this concentrated in relevant regions, and they can provide signals in hypothesis and found that TRD patients showed dose-related previously unsuspected regions. efficacy of ketamine independently from rs6265 genotype (Su et al., 2017). In this last study, the lower prevalence of the Val/Val genotype in Chinese subjects compared with Caucasians may Candidate Genes have affected the power to detect a possible association with Genes more convincingly involved in TRD are related to glu - ketamine efficacy. Consistent with the lower functionality of the tamatergic and monoaminergic neurotransmission as well as Met protein, a small pilot study suggested that patients with synaptic plasticity, as suggested by the antidepressant efficacy TRD may show better response to repetitive transcranial ma -g of the N-methyl-D-aspartate (NMDA) receptor antagonist keta - netic stimulation (rTMS) when they carry the Val/Val genotype mine and electroconvulsive therapy (ECT) in TRD (Kellner et al., compared with the Met allele (Bocchio-Chiavetto et al., 2008). 2012; de Sousa et al., 2017). Other studies investigated the interaction between rs6265 and NMDA receptor (NMDAR) upregulation has been implicated polymorphisms in other genes in determining the risk of TRD. in TRD pathogenesis (Franklin et al., 2015) and ketamine works Interactions with polymorphisms in the NTRK2 gene (coding for by blocking this receptor. NMDAR is composed of a combina - BDNF receptor), PPP3CC gene, and serotonergic receptor genes tion of individual protein subunits, including one called GluN2B, (HTR1A and HTR2A) were reported to affect the risk of TRD, but coded by the GRIN2B gene, which is predominant in the human these results were not replicated and they should be considered cortex together with GluN2A. GluN2B-containing NMDARs cautiously (Anttila et al., 2007; Li et al., 2013; Kautzky et al., 2015). directly suppress mammalian target of rapamycin signaling and PPP3CC (protein phosphatase 3 catalytic subunit gamma) repress protein synthesis involved in excitatory synaptic trans - may have a role in the activation of a neuron-enriched phos - mission in cortical neurons (Wang et al., 2011). Ketamine rapidly phatase that regulates synaptic plasticity (Xia and Storm, 2005). activates the mammalian target of rapamycin pathway, leading It has been recently suggested as a candidate for TRD risk (Fabbri to increased synaptic signaling proteins and increased number et al., 2014), possibly through an interaction with BDNF and and function of new spine synapses (Li et al., 2010). A key role of HTR2A polymorphisms as reported above (Kautzky et al., 2015). GluN2B in mediating ketamine effects was demonstrated by the CREB1 (CAMP responsive element binding protein 1) codes for a observation that mice lacking GluN2B-containing receptors in transcription factor that is a downstream effector of BDNF, and their cortical neurons showed a reduced amount of depressive- its function can affect BDNF signaling pathway. Some polymor - like behavior, very similar to normal mice treated with ketamine phisms (rs2253206, rs7569963, rs4675690) in this gene were asso- (Miller et al., 2014). Consistent with these findings, the down - ciated with TRD (Serretti et al., 2011), despite that subsequent stream genetic variant rs1805502 in the GRIN2B gene has been studies did not genotype these variants and reported no asso - associated with TRD (Zhang et al., 2014). ciation with TRD for other CREB1 variants but a possible effect The long-term changes in synaptic strength induced by on symptom remission (Calati et al., 2013; Fabbri et al., 2017). ketamine are also dependent on NMDAR activation of AMPA/ Among other genes involved in the modulation of neur -o kainate glutamate receptors, since antagonists of these rece - p genesis and neuroplasticity, it is worth mentioning preliminary tors block the antidepressant-like effects of ketamine (Maeng results reported for growth-associated protein 43 and the Fas/ et al., 2008). The subunit 4 of the glutamate kainate receptor FasL system (Fabbri et al., 2015; Santos et al., 2015). growth- (coded by the GRIK4 gene) is expressed in the hippocampus associated protein 43 codes for a neuron-specific cytosolic pro- (Darstein et al., 2003), where it exerts a modulatory effect on tein involved in the development of neuronal growth cones and synaptic plasticity in conjunction with NMDARs (Lerma, 2006). axonal regeneration (Leu et al., 2010). The Fas/FasL system is one Variants within GRIK4 (rs11218030 and rs1954787) were asso - of the best-known death-receptor mediated cell signaling sys - ciated with TRD and the risk of developing psychotic symp - tems, and it may be relevant to neurogenesis and neuroplasti - toms during depressive episodes, a known clinical risk factor city (Santos et al., 2015). of TRD (Milanesi et al., 2015). The same polymorphisms were The monoaminergic theory, even though it only partially associated in a consistent direction with worse ECT response explains the pathogenesis of MDD, had a primary role in guid - in TRD patients (Minelli et al., 2016). According to these stud - ing genetic research in this field over the past few decades. ies, patients carrying the rs11218030 G allele or the rs1954787 Monoaminergic genes that were studied as possible predictors GG genotype had increased risk of TRD and lack of response of TRD included the serotonin transporter (SLC6A4), the norepi - to ECT. nephrine transporter (SLC6A2), serotonin receptors 2A and 1A The role of other genes coding for glutamate receptors was (HTR2A and HTR1A), and catechol-O-methyltransferase (COMT). unfortunately not investigated, while other genes involved in As previously reported, the effect of HTR2A and HTR1A on TRD Downloaded from https://academic.oup.com/ijnp/article-abstract/22/2/93/4980962 by guest on 12 February 2019 Copyedited by: 96 | International Journal of Neuropsychopharmacology, 2019 Table 1. Summary of Genetic Studies Investigating TRD Gene Polymorphisms Sample Size Main Findings Reference Glutamate ionotropic rs1805502 178 TRD, Increased risk of TRD in (Zhang et al., 2014) receptor NMDA type 612 non-TRD, rs1805502 G allele carriers. subunit 2B (GRIN2B) 779 HC Glutamate ionotropic rs11218030, rs1954787 380 TRD, 247 Increased risk of TRD and psychotic (Milanesi et al., 2015) receptor kainate type non-TRD symptoms during depressive subunit 4 (GRIK4) episodes in rs11218030 G allele and rs1954787 GG genotype. 100 TRD Increased risk of non-response (Minelli et al., 2016) after ECT in rs11218030 G allele and rs1954787 GG genotype. Brain derived neurotrophic rs6265 (Val66Met) 62 TRD Better response to ketamine in (Laje et al., 2012) factor (BDNF) G (Val) allele. 71 TRD No association with ketamine (Su et al., 2017) response. 36 TRD Better response to rTMS in (Bocchio-Chiavetto G (Val) allele. et al., 2008) BDNF, neurotrophic receptor rs6265 (BDNF), rs1387923, 644 non-TRD, rs1565445 T allele, rs1565445 TT (Li et al., 2013) tyrosine kinase 2 (NTRK2) rs2769605 and rs1565445 304 TRD genotype, rs1565445 and rs1387923 (NTRK2) T-T haplotype were associated with TRD. A genotypic combination at four loci in NTRK2 and BDNF (rs1387923-rs1565445- rs2769605-rs6265) was associated with TRD. CAMP responsive element rs2709376, rs2253206, 119 non-TRD, rs7569963 A allele, (Serretti et al., 2011) binding protein 1 (CREB1) rs7569963, rs7594560, 71 TRD rs2253206-rs7569963 A-A and rs4675690 rs7569963-rs4675690 A-C haplotypes were associated with TRD. Negative results for the other SNPs. s889895, rs6740584, 265 non-TRD, No association was found for (Calati et al., 2013) rs2551922, rs2254137 102 TRD TRD. rs889895 GG was associated with remission. 147 non-TRD, No association was found for (Fabbri et al., 2017) 73 TRD TRD. rs2254137 AA was associated with remission. Solute carrier family 5-HTTLPR 36 TRD Better response to rTMS in LL (Bocchio-Chiavetto 6 member 4 (SLC6A4 or genotype. et al., 2008) serotonin transporter) 5-HTTLPR, rs25531 310 TRD, L(A)L(A) homozygote haplotype (Bonvicini et al., 2010) 284 HC was more common in HC compared with TRD patients. SLC6A4, solute carrier 5-HTTLPR (SLC6A4), 119 TRD, 5-HTTLPR L/L in conjunction with (Kautto et al., 2015) family 6 member 2 rs2242446 (SLC6A2) 395 HC SLC6A2 rs2242446 TT was less (SLC6A2 or norepinephrine frequent in TRD patients compared transporter) with HC and in ECT non-responders compared with responders. Potassium two pore domain rs12031300, rs10779646, 264 non-TRD, rs2841616, rs2841608, rs12136349, (Perlis et al., 2008) channel subfamily rs17546779, rs12136349, 487 TRD rs10494996 were associated with K member 2 (KCNK2) rs2841616, rs7538655, TRD in the whole cohort and in rs2841608, rs7549184, Caucasian patients. rs10494996 Protein phosphatase rs7430, rs10108011, 276 non-TRD, rs7430 and rs10108011 (Fabbri et al., 2014) 3 catalytic subunit rs11780915, rs2249098 102 TRD were associated with TRD. gamma (PPP3CC) PPP3CC rs7430, rs10108011, 147 non-TRD, No association between genotypes (Fabbri et al., 2017) rs11780915, rs2249098 73 TRD and TRD. PPP3CC, BDNF, rs7430, rs10108011 (PPP3CC), 76 non-TRD, Using machine learning and (Kautzky et al., 2015) 5-hydroxytryptamine rs6265, rs11030101, 149 TRD clustering algorithms, a receptor 2A (HTR2A or rs11030104, rs12273363 combination of 3 SNPs (rs7430 in serotonin receptor 2A) (BDNF), rs643627, rs6313 PPP3CC, rs6265 in BDNF, rs6313 in (HTR2A) HTR2A) and the clinical feature melancholia showed the best predictive performance of TRD. Downloaded from https://academic.oup.com/ijnp/article-abstract/22/2/93/4980962 by guest on 12 February 2019 Copyedited by: Fabbri et al. | 97 Table 1. Continued Gene Polymorphisms Sample Size Main Findings Reference HTR2A rs643627, rs17288723, 276 non-TRD, No association between (Fabbri et al., 2014) rs6313 102 TRD these variants and TRD. Catechol-O-methyltransferase rs4680 (Val108/158Met) 100 TRD, 100 HC The alternative allele A (Met) was (Lin et al., 2015) (COMT) more frequent in TRD than in HC and it was associated with worse ECT response. 104 TRD The A (Met) allele was associated (Domschke et al., with worse ECT response 2010) particularly regarding the core symptoms of depression and sleep-related symptoms. 90 TRD No association between this (Malaguti et al., 2011) variant and TMS response. rs4680, rs174696 276 non-TRD, No association between these (Fabbri et al., 2014) 102 TRD variants and TRD. 5-hydroxytryptamine receptor rs6265 90 TRD CC genotype was associated with (Malaguti et al., 2011) 1A (HTR1A or serotonin higher symptom improvement receptor 1A) after treatment with TMS. HTR1A, BDNF rs6295 (HTR1A), 119 TRD, 392 HC The combination of rs6295 (Anttila et al., 2007) rs6265 (BDNF) (HTR1A) GG and rs6265 (BDNF) GA + AA genotypes was more frequent in TRD compared with HC. Poly(A) binding protein GWAS (CNVs) with 811 non-TRD, A modest enrichment of (O’Dushlaine et al., cytoplasmic 4 like pathway analysis 452 TRD duplications and a particular 2014) (PABPC4L) deletion spanning PABPC4L in TRD, but these findings were not significant after multiple- testing correction. Pathways regulating actin cytoskeleton were nominally associated with TRD. Calcium voltage-gated Pathway analysis 226 non-TRD, The Gene Ontology term (Fabbri et al., 2018) channel subunit alpha1 C in GWAS 394 TRD GO:0006942, including the (CACNA1C) (GO:0006942) CACNA1C gene, predicted the risk of TRD with a mean sensitivity of 0.83, specificity of 0.56, positive predictive value = 0.77, negative predictive value = 0.65 after cross-validation. / GWAS 7795 non-TRD, No genome-wide significant finding. (Li et al., 2016) 1311 TRD Abbreviations: CNV, copy number variations; ECT, electroconvulsive therapy; HC, healthy controls; rTMS r,epetitive transcranial magnetic stimulation. Only candidate genes investigated by at least 2 independent studies and/or with complementary evidence of association with TRD (e.g., gene expression studies, in vitro or in vivo models) were reported. The results of GWAS were also reported. For each gene the nonabbreviated name is reported correspondence to the first occurrence. risk was mainly studied in conjunction with variants in BDNF Bonvicini et al., 2010; Kautto et al., 2015), but confirmations in and PPP3CC genes, and results were unfortunately not repli - larger samples would provide higher confidence in these results. cated or negative. Thus, these genes are not further discussed A reciprocal regulation with the serotonin transporter involv - (Anttila et al., 2007; Malaguti et al., 2011; Fabbri et al., 2014; ing the 2-pore domain potassium channel TREK1 (KCNK2 gene) Kautzky et al., 2015). was recently found implicated in mood regulation. Genetic pol - y The insertion/deletion functional polymorphism (5-HTTLPR) morphisms in KCNK2 were found to predict TRD in a quite large of the SLC6A4 gene is a functional polymorphism which L (long) sample (Perlis et al., 2008) and TREK1 blockers have potential allele was associated with higher transcription of the gene and antidepressant effects (Ye et al., 2015). better antidepressant response compared with the S (short) Compared with the serotonin transporter, fewer data are allele in Caucasian samples (Porcelli et al., 2012). In line with available for the norepinephrine transporter (SLC6A2). TRD these findings, TRD patients carrying the SS genotype were patients carrying the C variant at SLC6A2 rs2242446 were found demonstrated to have smaller hippocampal volume compared to have smaller hippocampal volume compared with noncarri - with L carriers (Phillips et al., 2015), but the association between ers (Phillips et al., 2015). The same allele of this polymorphism 5-HTTLRP and TRD is controversial since only a few studies con - was observed more frequently in TRD patients vs healthy con - sidered this phenotype. Better response in TRD patients carry - trols (Kautto et al., 2015), but replications are lacking. ing the LL genotype or lower frequency of this genotype in TRD COMT is responsible for one of the major catabolic pathways vs healthy controls was reported (Bocchio-Chiavetto et al., 2008; of monoamines. Variants in this gene were associated with Downloaded from https://academic.oup.com/ijnp/article-abstract/22/2/93/4980962 by guest on 12 February 2019 Copyedited by: 98 | International Journal of Neuropsychopharmacology, 2019 increased risk of suicide in TRD (Schosser et al., 2012a), they while ~5000 are required for a binary trait with heritability of were observed more frequently in TRD compared with healthy 20% (Visscher et al., 2014). The heritability of TRD was estimated controls (Lin et al., 2015), and they may be associated with ECT to be ~17% (Li et al., 2016), despite other GWAS of antidepres - response in TRD (Domschke et al., 2010). On the other hand, sant response suggesting that the contribution of genes could negative results were also reported (Malaguti et al., 2011; Fabbri be higher (Tansey et al., 2013). On the basis of these consider- a et al., 2014). tions, previous GWAS had inadequate power except one that Other putative genetic modulators of TRD are involved in may have been limited by an unbalance between the number antidepressant pharmacokinetics, and they include genes of TRD and non-TRD patients (Li et al., 2016). Other relevant belonging to the cytochrome P450 (CYP450) superfamily and the limitations of previous GWAS have been the small effect size P-glycoprotein (P-gp) coded by the ABCB1 gene. of common risk loci associated with TRD and the relatively lim - Variants in genes coding for CYP450 enzymes may contrib - ited coverage of common human genetic variation, issues that ute to the risk of TRD despite there are no studies that inves - were demonstrated to significantly affect power independently tigated this association. Functional polymorphisms in CYP2D6 from heritability (Spencer et al., 2009). Possible alternative and CYP2C19 genes are the pharmacogenetic biomarkers with strategies to the recruitment of large samples include the use the highest support in current clinical guidelines (Clinical of aggregated tests and genotype imputation using large and Pharmacogenetics Implementation Consortium, 2014) and in diverse reference panels as discussed more in detail elsewhere. antidepressant labeling according to regulatory agencies (FDA, A previous GWAS applied pathway analysis and machine learn- 2017). According to these recommendations, there is strong ing to investigate possible gene sets (pathways) implicated in or moderate evidence supporting specific actions (choice of the pathogenesis of TRD, with promising findings (Fabbri et al., antidepressant and dose) based on the presence of functional 2018). A gene set (GO:0006942) including the CACNA1C gene variants in these 2 CYP450 genes when treatment is a tricyclic showed a trend of association with TRD. Machine learning mod - antidepressant, sertraline, citalopram, escitalopram, fluvoxam - els showed that independent SNPs in this gene set predicted ine, or paroxetine. For example, the Clinical Pharmacogenetics TRD with a mean sensitivity of 0.83 and specificity of 0.56 after Implementation Consortium (CPIC) guidelines strongly recom - 10-fold cross validation repeated 100 times. CACNA1C encodes mend to avoid the prescription of paroxetine, amitriptyline, for the α-1C subunit of the L-type voltage-dependent calcium and nortriptyline in CYP2D6 ultrarapid metabolizers because of channel; it transiently increases calcium-mediated membrane increased risk of treatment failure (Clinical Pharmacogenetics depolarization and modulates intracellular signaling, gene tr - an Implementation Consortium, 2014). However the complex non - scription, and synaptic plasticity. This gene has been associated linear correlation between plasma level and clinical efficacy of with multiple psychiatric phenotypes, including schizophrenia, selective serotonin reuptake inhibitors complicates the picture bipolar disorder, and MDD, suggesting it plays a pleiotropic role (Florio et al., 2017). in psychiatric disorders (Cross-Disorder Group of the Psychiatric ABCB1 codes for an ATP-dependent efflux pump (P-gp), which Genomics Consortium, 2013). A number of other genes in the limits the uptake and accumulation of some lipophilic drugs, identified pathway (33 genes out of a total of 72 genes) were including a number of antidepressants, into the brain. Some linked to long-term potentiation, neural survival, neurogenesis, variants in this gene may impact on treatment response to anti - and neuroplasticity, but also to MDD and antidepressant efficacy depressants by affecting their transport across the brain-blood (Fabbri et al., 2018). Among these genes, some regulate glutamate barrier. In line with this hypothesis, ABCB1 gene expression was receptors involved in long-term potentiation, a form of persis - associated with TRD (Breitfeld et al., 2017), and polymorphisms tent strengthening of synapses (e.g., TRPM4, PIK3CG, SUMO1), or associated with P-gp increased activity may confer susceptibility calcium currents modulating the same process (e.g., CACNA1C, to TRD in patients treated with normal doses of antidepressants CAMK2D, FKBP1B, P2RX4, RYR2). Examples of genes previously targeted by P-gp (e.g., venlafaxine, paroxetine) (Rosenhagen and associated with antidepressant action include adrenergic rece -p Uhr, 2010). tors alpha 1A and 1B and CTNNA3, coding for a protein involved in cell-cell adhesion that may be relevant in antidepressant- induced hippocampal cell proliferation (Mostany et al., 2008; GWAS Fabbri et al., 2018). It is interesting to note that a previous GWAS identified a gene set involved in inorganic cation transmem - Three GWAS have investigated the genetics of TRD, and they brane transporter activity (GO:0022890) as a possible modulator used different approaches that do not allow a straightforward of antidepressant response in 2 samples (Cocchi et al., 2016). comparison of findings. One study was focused on copy num - This gene set includes CACNA1C and other calcium-channel ber variants (O’Dushlaine et al., 2014), another on common SNPs coding genes such as CACNB2; the latter gene was also identi - (single nucleotide polymorphisms) (Li et al., 2016), while the last fied as involved in the shared genetic susceptibility to several considered pathways (Fabbri et al., 2018). psychiatric traits including MDD (Cross-Disorder Group of the A modest enrichment of duplications was found in TRD, Psychiatric Genomics Consortium, 2013). and a particular deletion spanning the PABPC4L (poly(A) bind - ing protein cytoplasmic 4 like) gene was identified. PABPC4L is involved in the degradation of abnormal mRNA molecules, Strategies to Improve Future Studies but this association did not survive multiple-testing correction The previous paragraphs outline that pharmacogenetic research (O’Dushlaine et al., 2014). The analysis of common SNPs did not provide more encouraging results, since no genome-wide provided often nonreplicated results in the study of TRD and antidepressant response. Understanding the strengths and limi - significant or suggestive signal was identified (Li et al., 2016), suggesting that possible methodological limitations of these tations of previous studies is a key issue to facilitate advances in the field, including clinical applications. Both candidate gene GWAS should be considered. The most known one is inade - quate statistical power, given that a sample of ~2000 subjects studies and GWAS show pros and cons that should be taken into account. The candidate gene approach allows the detailed was estimated to provide adequate power to identify individual variants associated with a binary trait with heritability ~40%, study of variants in genes with higher pretest probability of Downloaded from https://academic.oup.com/ijnp/article-abstract/22/2/93/4980962 by guest on 12 February 2019 Copyedited by: Fabbri et al. | 99 association with TRD, but it is limited by previous knowledge rare variants. Several projects are planned to perform sequenc - (many signals may come from genes with unknown function or ing at the population level to promote the development of pr -e unknown link with antidepressant action) and by the number of cision medicine, that is, a new medical approach where each polymorphisms that can be studied. GWAS are suitable to study patient is considered a unique individual, with his/her own gen - polygenic traits such as TRD, but previous studies included only etic signature and other unique biomarkers. Some examples are a relatively small proportion of known variants in the human “deCODE genetics” in Iceland (deCODE, 2017) and “All of Us” in genome and they had mostly inadequate power to detect sig - the United States (NIH, 2017). nals with small effect size. For example, previous GWAS of anti - The use of genome sequencing is becoming not only feasi - depressant response included ~1 to 9 million common variants ble on a relatively large scale but also supported by the recent (GENDEP Investigators et al., 2013; Li et al., 2016), while ~40 mil - omnigenic hypothesis. According to this theory, the genetic lion common variants were identified in the human genome component of complex traits such as TRD is spread across most using whole-genome sequence data from a number of cohorts of the genome, including regions without an obvious connec - (McCarthy et al., 2016). Previous GWAS included only a fraction tion to the trait of interest (Boyle et al., 2017). For example, of known genetic variants, but distinguishing genuine small 71%– to 100% of 1-MB windows in the genome were estimated effects from false positives was a relevant issue because of the to contribute to heritability for schizophrenia (Loh et al., 2015). strict multiple-testing correction needed to have acceptable The effect size of each associated locus was calculated to be false positive risk. This issue can be addressed in different ways: approximately one-tenth the median effect size of genome- increase sample size or use analysis approaches that allow the wide significant variants, suggesting that the most part of the increase of power (e.g., aggregated approaches). Consortia such relevant signals is not captured using the traditional GWAS as the Psychiatric Genomics Consortium are working on the for - approach (Boyle et al., 2017). mer, but in the meantime analysis approaches aimed to maxi - mize power should also be developed and implemented. Aggregated Tests and Other Strategies to In the next 2 paragraphs, the advantages of improving the Improve Power coverage of genetic variants and other methodological strate - High throughput data (GWAS and sequencing data) provide gies to improve power are discussed (Figure 1). the valuable opportunity to implement aggregated approaches to study polygenic/omnigenic traits such as antidepressant Methods to Improve the Coverage of Genetic response. Polymorphisms do no act as single units, but they Variants: Imputation and Sequencing interact among each other, within the same gene, and also In 2001 the first draft of the human genomic sequence was across different genes. In other words, the effect of a single vari - available thanks to the Human Genome Project (International ant could be nullified or modified by the concomitant presence Human Genome Sequencing Consortium, 2004). The Human of other variants, and the cumulative effect of a number of pol - y Genome Project was the world’s largest collaborative biolo - gi morphisms is expected to possibly alter the function of a gene cal project; it was a $3.8 billion investment and launched the or gene set (pathway). For this reason, in pathway analysis the genomic revolution. After 2001, 3 different generations of DNA unit of analysis is a set of genes functionally connected among sequencing technologies can be identified that provide an output each other (e.g., part of the same chemical or cellular process orders of magnitude higher than the first sequencing technique or involved in protein-protein interactions; Li et al., 2017). The and dramatically reduce cost per base (Gut, 2013). According to identification of pathways associated with TRD can be helpful the US National Human Genome Research Institute, the cost for not only to develop polygenic biomarkers but also to contribute sequencing one human genome dropped from $100,000,000 in to the clarification of the pathogenetic mechanisms involved 2001 to $1000 in 2015, and cost dropping exceeded the Moore’s in TRD. Several GWAS applied pathway analysis to the study Law (which describes a long-term trend in the computer har-d of antidepressant response and remission. They reported that ware industry that involves the doubling of “compute power” pathways involved in neuroplasticity, neurogenesis, and inflam- every 2 years) around 2008, meaning that more than excellent mation/immune response probably contribute to antidepres - technological improvement was achieved (Human Genome sant response/remission (Fabbri et al., 2016). To the best of our Research Institute, 2017). knowledge, only 2 studies implemented pathway analysis in the The use of sequencing is becoming widespread in genetic study of TRD, using rare variants (O’Dushlaine et al., 2014) or research thanks to the described technological improvements and common variants (Fabbri et al., 2018). The former study found drop of costs. The growth of publicly available genome sequenc - a possible role of pathways regulating actin cytoskeleton that ing datasets allowed the development of new and more compr -e did not survive multiple-testing correction (O’Dushlaine et al., hensive reference panels for genotype imputation, a method used 2014). Several studies demonstrated the importance of actin to improve the coverage of common genetic variants in GWAS. In cytoskeleton in dendritic spine morphology, synaptic plasticity, the last 2 to 3 years, the reference databases made available by the and psychiatric disorders such as depression, providing a pos - Haplotype Reference Consortium (HRC) and by the Trans-Omics sible biological rationale (Piubelli et al., 2011; Grintsevich, 2017). for Precision Medicine (TOPMed) Program represent the larg - The second study was already mentioned; it found that a gene est and most diverse whole genome sequence datasets available ontology gene set (GO:0006942) including the CACNA1C gene (McCarthy et al., 2016; NIH, 2018). The use of these reference data - predicted TRD risk with a mean sensitivity of 0.83, specificity sets for imputation is expected to provide the coverage of the most of 0.56, positive predictive value = 0.77, nd predictive value = 0.65 part of human common genetic variation in an accurate manner after 10-fold cross validation repeated 100 times (Fabbri et al., and to increase the percent of explained heritability in a given sam - 2018). Thirty-three genes of a total 72 genes in this gene set were ple size, or in other words improve power (McCarthy et al., 2016). previously associated with long-term potentiation, neural sur - Imputation can provide very good coverage of common gen - vival, neurogenesis, and neuroplasticity, but also MDD and anti - etic variants (at least in samples of European and Asian ances - depressant efficacy. One of the main limitations of this study try), but sequencing is needed to investigate the contribution of was the lack of result validation in an independent sample. Downloaded from https://academic.oup.com/ijnp/article-abstract/22/2/93/4980962 by guest on 12 February 2019 Copyedited by: 100 | International Journal of Neuropsychopharmacology, 2019 Pathways associated with TRD can also be used to prioritize relative small sample size but also to the poor implementation polygenic risk scores, which are calculated as the sum of alleles of analysis approaches able to maximize power. For example, associated with the trait of interest, weighted by effect sizes, results obtained by gene set (pathway) analysis showed higher for polymorphisms with P values less than predefined thresh - similarity across different GWAS and higher biological ration - olds. Previous GWAS applied this approach to the prediction ale than signals at variant level, since they pointed towards the of antidepressant response with unsatisfying results (GENDEP involvement of pathways mediating neural plasticity, neurogen - Investigators et al., 2013; García-González et al., 2017). A possible esis, and inflammation (Fabbri et al., 2016). Pathways possibly explanation is the lack of statistical power and insufficient co -v associated with TRD are comparable, because of the importance erage of variants, which could be partly addressed by prioritiz - of actin cytoskeleton in dendritic spine morphology/synaptic ing variants with higher pretest probability of exerting an effect plasticity and the role of GO:0006942 genes in neural plasticity/ on TRD, such as variants in pathways previous associated with neurogenesis (O’Dushlaine et al., 2014; Fabbri et al., 2018). These this trait or antidepressant response. Prioritization can be per - findings support the hypothesis that antidepressant response formed by assigning incremental weights to variants based on and TRD are polygenic traits, and the methodological impro- ve the results of previous GWAS but also functional considerations. ment of aggregated tests should be pursued to disentangle the The incorporation of variant functional annotation including whole contribution of genetic variants to these traits. On the enrichment for expression quantitative trait loci, methylation other hand, these results should not be interpreted as the proof quantitative trait, cis-regulatory elements (CREs), and pleiotropy that antidepressant action depends on a few pathways and across different traits was reported to improve the prediction of these could be sufficient to explain the genetics of this trait, but complex traits (Shi et al., 2016). these pathways probably have a higher weight in determining The integration of different types of -omics data (e.g., TRD. This idea leads to the hypothesis that not all genes should genomics, transcriptomics, and proteomics) with molecular, be considered equally in GWAS, but information about gene and behavioral, imaging, environmental, and clinical data is also a polymorphism function should be incorporated in genetic stud - possible approach to increase power and replication of findings. ies to assign specific weights and prioritize variants/genes. The This approach is the key feature of the TOPMed program, which inclusion of this information in aggregated tests such as pol - y answers to many of the objectives of the 2016 strategic vision genic risk scores could allow an increase in prediction and abil - released by the US NIH (NIH, 2018). For example, the incorpo - ity to replicate findings. ration of clinical information in genetic studies should not be Another limitation of previous GWAS was the covering of overlooked, and clinical risk factors for TRD should not be con - a limited proportion of human common genetic variants (not sidered pertinent to clinicians only. A number of clinical and more than ~7–9 millions vs the ~40 millions of common vari - socio-demographic factors were consistently associated with ants so far identified; McCarthy et al., 2016). Genotype imputa - TRD by several studies, for example older age, chronic depres - tion using large and diverse reference panels (e.g., Haplotype sion, moderate-severe suicidal ideation, high level of anxiety Reference Consortium and TOPMed) is expected to allow the symptoms or comorbidity with anxiety disorder, lower educa - inclusion of more and more common variants as improvements tion, being single, or divorced (Perlis, 2013; De Carlo et al., 2016; in the method and growth of reference panels proceed, but rare Kautzky et al., 2017). As discussed in the Introduction, some of variants are largely not considered in genome-wide arrays. For these risk factors (e.g., severity, suicidal ideation, anxiety comor - this reason and for the evidence suggesting that complex traits bidity) may have a genetic base that overlaps with the genetics may have a genetic component spread in small signals across of TRD, but others are independent (socio-demographic factors) the most part of the genome (Boyle et al., 2017), the collection of or probably independent (e.g., duration of the depressive epi- sequencing data would be helpful to perform polygenic anal -y sode) from the effect of genetic variants. The lack of consider -a sis able to capture the complexity of TRD. No study has applied tion of the latter group’s influence on TRD may bias the results whole genome sequencing to the study of TRD so far, leaving of pharmacogenetic studies and be responsible for false nega - this area unexplored, but national projects such as “All of Us” in tive or false positive findings. the United States may allow this kind of study on large popula - tion samples (NIH, 2017). Finally, clinical-demographic factors associated with TRD Discussion should not be overlooked in genetic studies. Some clinical fea - Few studies have investigated the genetics of TRD compared with tures that are commonly observed in TRD, such as high suicidal overall antidepressant efficacy and results were often obtained risk and anxiety comorbidities, may have a genetic basis that by candidate gene studies in relatively small samples. The most overlaps with TRD genetics and in this case they would repr-e robust results in terms of replication, evidence using comple - sent the features of an endophenotype of MDD. Other known mentary approaches (e.g., gene expression, neuroimaging), and clinical and socio-demographic predictors of TRD risk have no biological rationale are variants in GRIK4, BDNF, SLC6A4, and genetic basis (e.g., poor education, old age), and the noninclu - KCNK2 genes. Functional variants in CYP450 genes may hypo - sion of these modulators in pharmacogenetic studies may lead thetically play a role, but no study specifically investigated this to biases in results. question. Only 3 GWAS studied the genetics of TRD (O’Dushlaine Currently, there are no recommended genetic biomark - et al., 2014; Li et al., 2016; Fabbri et al., 2018), and no genome- ers to predict the risk of TRD or to guide treatment choice in wide significant signal was identified at single variant level. TRD patients. Clinical guidelines such as CPIC guidelines rec - The lack of genome-wide significant polymorphisms should ommend that CYP2D6 and CYP2C19 functional genotypes are be interpreted in light of similar results obtained by GWAS of taken into account for some antidepressant treatments (Clinical antidepressant response. The few significant hits identified by Pharmacogenetics Implementation Consortium, 2014). We sug - these studies were inconsistent across independent samples gest that genetic testing of polymorphisms in these 2 genes (Fabbri et al., 2016), supporting the hypothesis that some major may be helpful in some patients with TRD treated with drugs limitations affected GWAS. As discussed previously, lack of metabolized by these cytochromes to exclude a major metabolic power was among these limitations and this was both due to alteration. Monitoring drug plasma levels may also be helpful. Downloaded from https://academic.oup.com/ijnp/article-abstract/22/2/93/4980962 by guest on 12 February 2019 Copyedited by: Fabbri et al. | 101 On the other hand, a number of variants in genes involved in depression. Prog Neuropsychopharmacol Biol Psychiatry antidepressant pharmacodynamics are also involved in deter - 34:934–939. mining antidepressant action; thus, focusing on CYP450 genes is Boyle EA, Li YI, Pritchard JK (2017) An expanded view of complex not expected to be sufficient in most cases. The implementation traits: from polygenic to omnigenic. Cell 169:1177–1186. of future studies that include the improvements suggested by Breitfeld J, Scholl C, Steffens M, Laje G, Stingl JC (2017) Gene this review (Figure 1) may provide more valid genetic biomark - expression and proliferation biomarkers for antidepressant ers of TRD, probably sets of hundreds or thousands of polymor - treatment resistance. Transl Psychiatry 7:e1061. phisms selected to maximize the sensitivity and specificity of Calati R, Crisafulli C, Balestri M, Serretti A, Spina E, Calabrò M, the prediction test. The availability of this kind of clinical appli - Sidoti A, Albani D, Massat I, Höfer P, Amital D, Juven-Wetzler cation would help in guiding treatment choice and dramatically A, Kasper S, Zohar J, Souery D, Montgomery S, Mendlewicz J reduce the individual and socio-economic burden resulting (2013) Evaluation of the role of MAPK1 and CREB1 polymor - from poor antidepressant response in MDD. phisms on treatment resistance, response and remission in mood disorder patients. Prog Neuropsychopharmacol Biol Psychiatry 44:271–278. Acknowledgments Chen ZY, Patel PD, Sant G, Meng CX, Teng KK, Hempstead BL, Lee FS (2004) Variant brain-derived neurotrophic factor None. (BDNF) (met66) alters the intracellular trafficking and acti - v ity-dependent secretion of wild-type BDNF in neurosecretory Statement of Interest cells and cortical neurons. J Neurosci 24:4401–4411. Clinical Pharmacogenetics Implementation Consortium (2014) Dr Souery has received grant/research support from CPIC guidelines. Available at: https://cpicpgx.org/guidelines/. GlaxoSmithKline and Lundbeck and has served as a consultant Retrieved 27 Nov 2017. or on advisory boards for AstraZeneca, Bristol-Myers Squibb, Eli Cocchi E, Fabbri C, Han C, Lee SJ, Patkar AA, Masand PS, Pae CU, Lilly, Janssen, and Lundbeck. Dr Montgomery has been a con - Serretti A (2016) Genome-wide association study of anti - sultant or served on advisory boards for AstraZeneca, Bionevia, depressant response: involvement of the inorganic cat - Bristol Myers Squibb, Forest, GlaxoSmithKline, Grunenthal, ion transmembrane transporter activity pathway. BMC Intellect Pharma, Johnson & Johnson, Lilly, Lundbeck, Merck, Psychiatry 16:106. Merz, M’s Science, Neurim, Otsuka, Pierre Fabre, Pfizer, Cross-Disorder Group of the Psychiatric Genomics Consortium Pharmaneuroboost, Richter, Roche, Sanofi, Sepracor, Servier, (2013) Identification of risk loci with shared effects on five Shire, Synosis, Takeda, Theracos, Targacept, Transcept, UBC, major psychiatric disorders: a genome-wide analysis. Lancet Xytis, and Wyeth. Dr Kasper received grants/research support, 381:1371–1379. consulting fees, and/or honoraria within the last 3 years from Darstein M, Petralia RS, Swanson GT, Wenthold RJ, Heinemann Angelini, AOP Orphan Pharmaceuticals AG, AstraZeneca, Eli SF (2003) Distribution of kainate receptor subunits at hip - Lilly, Janssen, KRKA-Pharma, Lundbeck, Neuraxpharm, Pfizer, pocampal mossy fiber synapses. J Neurosci 23:8013–8019. Pierre Fabre, Schwabe, and Servier. Dr Zohar has received grant/ De Carlo V, Calati R, Serretti A (2016) Socio-demographic and research support from Lundbeck, Servier, Brainsway, and Pfizer; clinical predictors of non-response/non-remission in treat - has served as a consultant or on advisory boards for Servier, ment resistant depressed patients: a systematic review. Pfizer, Abbott, Lilly, Actelion, AstraZeneca, and Roche; and has Psychiatry Res 240:421–430. served on speakers’ bureaus for Lundbeck, Roch, Lilly, Servier, deCODE (2017) DeCODE genetics. Available at: https://www. Pfizer, and Abbott. Dr Mendlewicz is a member of the Board of decode.com/research/. Retrieved 27 Nov 2017. the Lundbeck International Neuroscience Foundation and of the de Sousa RT, Loch AA, Carvalho AF , Brunoni AR, Haddad MR, Henter advisory board of Servier. Dr Serretti is or has been consultant/ ID, Zarate CA, Machado-Vieira R (2017) Genetic studies on speaker for Abbott, Abbvie, Angelini, Astra Zeneca, Clinical Data, the tripartite glutamate synapse in the pathophysiology and Boheringer, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, therapeutics of mood disorders. Neuropsychopharmacology Innovapharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, 42:787–800. Polifarma, Sanofi, and Servier. The other authors declare no Domschke K, Zavorotnyy M, Diemer J, Nitsche S, Hohoff C, Baune potential conflict of interest. BT, Deckert J, Arolt V, Zwanzger P (2010) COMT val158met influ- ence on electroconvulsive therapy response in major depres- References sion. Am J Med Genet B Neuropsychiatr Genet 153B:286–290. Fabbri C, Marsano A, Albani D, Chierchia A, Calati R, Drago A, Akil H, Gordon J, Hen R, Javitch J, Mayberg H, McEwen B, Meaney MJ, Nestler EJ (2018) Treatment resistant depression: a multi- Crisafulli C, Calabrò M, Kasper S, Lanzenberger R, Zohar J, Juven-Wetzler A, Souery D, Montgomery S, Mendlewicz J, scale, systems biology approach. Neurosci Biobehav Rev 84:272–288. Serretti A (2014) PPP3CC gene: a putative modulator of anti - depressant response through the B-cell receptor signaling Anttila S, Huuhka K, Huuhka M, Rontu R, Hurme M, Leinonen E, Lehtimäki T (2007) Interaction between 5-HT1A and BDNF pathway. Pharmacogenomics J 14:463–472. Fabbri C, Crisafulli C, Gurwitz D, Stingl J, Calati R, Albani D, Forloni genotypes increases the risk of treatment-resistant depres - sion. J Neural Transm (Vienna) 114:1065–1068. G, Calabrò M, Martines R, Kasper S, Zohar J, Juven-Wetzler A, Souery D, Montgomery S, Mendlewicz J, Girolamo GD, Serretti Bocchio-Chiavetto L, Miniussi C, Zanardini R, Gazzoli A, Bignotti S, Specchia C, Gennarelli M (2008) 5-HTTLPR and BDNF val - A (2015) Neuronal cell adhesion genes and antidepressant response in three independent samples. Pharmacogenomics 66met polymorphisms and response to rtms treatment in drug resistant depression. Neurosci Lett 437:130–134. J 15:538–548. Fabbri C, Crisafulli C, Calabrò M, Spina E, Serretti A (2016) Bonvicini C, Minelli A, Scassellati C, Bortolomasi M, Segala M, Sartori R, Giacopuzzi M, Gennarelli M (2010) Serotonin Progress and prospects in pharmacogenetics of antidepres- sant drugs. Expert Opin Drug Metab Toxicol 12:1157–1168. transporter gene polymorphisms and treatment-resistant Downloaded from https://academic.oup.com/ijnp/article-abstract/22/2/93/4980962 by guest on 12 February 2019 Copyedited by: 102 | International Journal of Neuropsychopharmacology, 2019 Fabbri C, Crisafulli C, Calati R, Albani D, Forloni G, Calabrò M, The combined effect of genetic polymorphisms and clinical Martines R, Kasper S, Zohar J, Juven-Wetzler A, Souery D, parameters on treatment outcome in treatment-resistant Montgomery S, Mendlewicz J, Serretti A (2017) Neuroplasticity depression. Eur Neuropsychopharmacol 25:441–453. and second messenger pathways in antidepressant efficacy: Kautzky A, Baldinger-Melich P, Kranz GS, Vanicek T, Souery D, pharmacogenetic results from a prospective trial investigat - Montgomery S, Mendlewicz J, Zohar J, Serretti A, Lanzenberger ing treatment resistance. Eur Arch Psychiatry Clin Neurosci R, Kasper S (2017) A new prediction model for evaluating 267:723–735. treatment-resistant depression. J Clin Psychiatry 78:215–222. Fabbri C, Corponi F, Albani D, Raimondi I, Forloni G, Schruers Kellner CH, Greenberg RM, Murrough JW, Bryson EO, Briggs MC, K, Kasper S, Kautzky A, Zohar J, Souery D, Montgomery S, Pasculli RM (2012) ECT in treatment-resistant depression. Am Cristalli CP, Mantovani V, Mendlewicz J, Serretti A (2018) J Psychiatry 169:1238–1244. Pleiotropic genes in psychiatry: calcium channels and the Laje G, Lally N, Mathews D, Brutsche N, Chemerinski A, Akula stress-related FKBP5 gene in antidepressant resistance. Prog N, Kelmendi B, Simen A, McMahon FJ, Sanacora G, Zarate C Neuropsychopharmacol Biol Psychiatry 81:203–210. Jr (2012) Brain-derived neurotrophic factor val66met pol-y FDA (2017) Table of pharmacogenomic biomarkers in morphism and antidepressant efficacy of ketamine in drug labeling. Available at: https://www.fda.gov/Drugs/ depressed patients. Biol Psychiatry 72:e27–e28. ScienceResearch/ucm572698.htm. Retrived 27 Nov 2017. Lerma J (2006) Kainate receptor physiology. Curr Opin Pharmacol Florio V, Porcelli S, Saria A, Serretti A, Conca A (2017) 6:89–97. Escitalopram plasma levels and antidepressant response. Eur Leu B, Koch E, Schmidt JT (2010) GAP43 phosphorylation is Neuropsychopharmacol 27:940–944. critical for growth and branching of retinotectal arbors in Franklin M, Hlavacova N, Babic S, Pokusa M, Bermudez I, Jezova D zebrafish. Dev Neurobiol 70:897–911. (2015) Aldosterone signals the onset of depressive behaviour Li N, Lee B, Liu RJ, Banasr M, Dwyer JM, Iwata M, Li XY, Aghajanian in a female rat model of depression along with SSRI treat - G, Duman RS (2010) Mtor-dependent synapse formation ment resistance. Neuroendocrinology 102:274–287. underlies the rapid antidepressant effects of NMDA antago - García-González J, et al. (2017) Pharmacogenetics of anti - nists. Science 329:959–964. depressant response: a polygenic approach. Prog Li QS, Tian C, Seabrook GR, Drevets WC, Narayan VA (2016) Neuropsychopharmacol Biol Psychiatry 75:128–134. Analysis of 23andme antidepressant efficacy survey data: GBD 2015 Disease and Injury Incidence and Prevalence implication of circadian rhythm and neuroplasticity in Collaborators (2016) Global, regional, and national incidence, bupropion response. Transl Psychiatry 6:e889. prevalence, and years lived with disability for 310 diseases Li T, Wernersson R, Hansen RB, Horn H, Mercer J, Slodkowicz and injuries, 1990–2015: a systematic analysis for the global G, Workman CT, Rigina O, Rapacki K, Stærfeldt HH, Brunak burden of disease study 2015. Lancet 388:1545–1602. S, Jensen TS, Lage K (2017) A scored human protein-protein GENDEP Investigators, MARS Investigators, STAR*D Investigators interaction network to catalyze genomic interpretation. Nat (2013) Common genetic variation and antidepressant efficacy Methods 14:61–64. in major depressive disorder: a meta-analysis of three genome- Li Z, Zhang Y, Wang Z, Chen J, Fan J, Guan Y, Zhang C, Yuan C, Hong wide pharmacogenetic studies. Am J Psychiatry 170:207–217. W, Wang Y, Wu Z, Huang J, Hu Y, Cao L, Yi Z, Cui D, Yu S, Fang Gratten J, Wray NR, Keller MC, Visscher PM (2014) Large-scale Y (2013) The role of BDNF, NTRK2 gene and their interaction genomics unveils the genetic architecture of psychiatric dis - in development of treatment-resistant depression: data orders. Nat Neurosci 17:782–790. from multicenter, prospective, longitudinal clinic practice . Grintsevich EE (2017) Remodeling of actin filaments by drebrin J Psychiatr Res 47:8–14. A and its implications. Adv Exp Med Biol 1006:61–82. Lin Z, He H, Zhang C, Wang Z, Jiang M, Li Q, Lan X, Zhang M, Gut IG (2013) New sequencing technologies. Clin Transl Oncol Huang X (2015) Influence of val108/158met COMT gene poly - 15:879–881. morphism on the efficacy of modified electroconvulsive ther - Hong W, Fan J, Yuan C, Zhang C, Hu Y, Peng D, Wang Y, Huang apy in patients with treatment resistant depression. Cell J, Li Z, Yu S, Liu X, Wu Z, Chen J, Yi Z, Xu L, Fang Y (2014) Biochem Biophys 71:1387–1393. Significantly decreased mrna levels of BDNF and MEK1 genes Liu RJ, Lee FS, Li XY, Bambico F, Duman RS, Aghajanian GK (2012) in treatment-resistant depression. Neuroreport 25:753–755. Brain-derived neurotrophic factor val66met allele impairs Human Genome Research Institute (2017) Sequencing cost data. basal and ketamine-stimulated synaptogenesis in prefrontal Available at: https://www.genome.gov/sequencingcostsdata/. cortex. Biol Psychiatry 71:996–1005. Retrieved 27 Nov 2017. Loh PR, Bhatia G, Gusev A, Finucane HK, Bulik-Sullivan BK, Pollack Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, SJ, de Candia TR, Lee SH, Wray NR, Kendler KS, O’Donovan MC, Sanislow C, Wang P (2010) Research domain criteria (rdoc): Neale BM, Patterson N, Price AL, Schizophrenia Working Group toward a new classification framework for research on men- of Psychiatric Genomics Consortium (2015) Contrasting genetic tal disorders. Am J Psychiatry 167:748–751. architectures of schizophrenia and other complex diseases using International Human Genome Sequencing Consortium (2004) fast variance-components analysis. Nat Genet 47:1385–1392. Finishing the euchromatic sequence of the human genome. Maeng S, Zarate CA Jr, Du J, Schloesser RJ, McCammon J, Chen Nature 431:931–945. G, Manji HK (2008) Cellular mechanisms underlying the anti - Kautto M, Kampman O, Mononen N, Lehtimäki T, Haraldsson depressant effects of ketamine: role of alpha-amino-3-h-y S, Koivisto PA, Leinonen E (2015) Serotonin transporter droxy-5-methylisoxazole-4-propionic acid receptors. Biol (5-HTTLPR) and norepinephrine transporter (NET) gene poly- Psychiatry 63:349–352. morphisms: susceptibility and treatment response of elec - Malaguti A, Rossini D, Lucca A, Magri L, Lorenzi C, Pirovano troconvulsive therapy in treatment resistant depression. A, Colombo C, Smeraldi E, Zanardi R (2011) Role of COMT, Neurosci Lett 590:116–120. 5-HT(1A), and SERT genetic polymorphisms on antidepres- Kautzky A, Baldinger P, Souery D, Montgomery S, Mendlewicz sant response to transcranial magnetic stimulation. Depress J, Zohar J, Serretti A, Lanzenberger R, Kasper S (2015) Anxiety 28:568–573. Downloaded from https://academic.oup.com/ijnp/article-abstract/22/2/93/4980962 by guest on 12 February 2019 Copyedited by: Fabbri et al. | 103 McCarthy S, et al., Haplotype Reference Consortium (2016) A ref - second-step antidepressant medication monotherapies: pre - erence panel of 64,976 haplotypes for genotype imputation. dictive value of clinical, demographic, or first-step treatment Nat Genet 48:1279–1283. features. Arch Gen Psychiatry 65:870–880. McIntyre RS, Filteau MJ, Martin L, Patry S, Carvalho A, Cha DS, Santos M, Carvalho S, Lima L, Mota-Pereira J, Pimentel P, Maia D, Barakat M, Miguelez M (2014) Treatment-resistant depres - Correia D, Gomes S, Cruz A, Medeiros R (2015) FAS -670A>G sion: definitions, review of the evidence, and algorithmic genetic polymorphism is associated with treatment resistant approach. J Affect Disord 156:1–7. depression. J Affect Disord 185:164–169. Milaneschi Y, Lamers F, Peyrot WJ, Abdellaoui A, Willemsen G, Schosser A, Calati R, Serretti A, Massat I, Kocabas NA, Hottenga JJ, Jansen R, Mbarek H, Dehghan A, Lu C, Boomsma Papageorgiou K, Linotte S, Mendlewicz J, Souery D, Zohar J, DI, Penninx BW, CHARGE inflammation working group (2016) Juven-Wetzler A, Montgomery S, Kasper S (2012a) The impact Polygenic dissection of major depression clinical heterogen - of COMT gene polymorphisms on suicidality in treatment eity. Mol Psychiatry 21:516–522. resistant major depressive disorder–a European multicenter Milanesi E, Bonvicini C, Congiu C, Bortolomasi M, Gainelli G, study. Eur Neuropsychopharmacol 22:259–266. Gennarelli M, Minelli A (2015) The role of GRIK4 gene in treat - Schosser A, Serretti A, Souery D, Mendlewicz J, Zohar J, ment-resistant depression. Genet Res (Camb) 97:e14. Montgomery S, Kasper S (2012b) European group for the Miller OH, Yang L, Wang CC, Hargroder EA, Zhang Y, Delpire E, study of resistant depression (GSRD)–where have we Hall BJ (2014) Glun2b-containing NMDA receptors regulate gone so far: review of clinical and genetic findings. Eur depression-like behavior and are critical for the rapid anti - Neuropsychopharmacol 22:453–468. depressant actions of ketamine. Elife 3:e03581. Serretti A, Chiesa A, Calati R, Massat I, Linotte S, Kasper S, Minelli A, Congiu C, Ventriglia M, Bortolomasi M, Bonvicini C, Lecrubier Y, Antonijevic I, Forray C, Snyder L, Bollen J, Abate M, Sartori R, Gainelli G, Gennarelli M (2016) Influence Zohar J, De Ronchi D, Souery D, Mendlewicz J (2011) A pre- of GRIK4 genetic variants on the electroconvulsive therapy liminary investigation of the influence of CREB1 gene on response. Neurosci Lett 626:94–98. treatment resistance in major depression. J Affect Disord Mostany R, Valdizán EM, Pazos A (2008) A role for nuclear beta- 128:56–63. catenin in SNRI antidepressant-induced hippocampal cell Shi J, et al. (2016) Winner’s curse correction and variable thresh - proliferation. Neuropharmacology 55:18–26. olding improve performance of polygenic risk modeling National Institutes of Health (2017) All of us. Available at: https:// based on genome-wide association study summary-level allofus.nih.gov/. data. Plos Genet 12:e1006493. National Institutes of Health (2018) Trans-Omics for Precision Souery D, Oswald P, Massat I, Bailer U, Bollen J, Demyttenaere Medicine (TOPMed) Program. Available at: https://www.nhlbi. K, Kasper S, Lecrubier Y, Montgomery S, Serretti A, Zohar J, nih.gov/science/trans-omics-precision-medicine-topmed- Mendlewicz J, Group for the Study of Resistant Depression program. Accessed 27 Feb 2018. (2007) Clinical factors associated with treatment resistance O’Dushlaine C, Ripke S, Ruderfer DM, Hamilton SP, Fava M, in major depressive disorder: results from a European multi - Iosifescu DV, Kohane IS, Churchill SE, Castro VM, Clements center study. J Clin Psychiatry 68:1062–1070. CC, Blumenthal SR, Murphy SN, Smoller JW, Perlis RH (2014) Souery D, Serretti A, Calati R, Oswald P, Massat I, Konstantinidis Rare copy number variation in treatment-resistant major A, Linotte S, Bollen J, Demyttenaere K, Kasper S, Lecrubier Y, depressive disorder. Biol Psychiatry 76:536–541. Montgomery S, Zohar J, Mendlewicz J (2011) Switching anti - Perlis RH (2013) A clinical risk stratification tool for predict - depressant class does not improve response or remission ing treatment resistance in major depressive disorder. Biol in treatment-resistant depression. J Clin Psychopharmacol Psychiatry 74:7–14. 31:512–516. Perlis RH, Moorjani P, Fagerness J, Purcell S, Trivedi MH, Fava M, Spencer CC, Su Z, Donnelly P, Marchini J (2009) Designing Rush AJ, Smoller JW (2008) Pharmacogenetic analysis of genes genome-wide association studies: sample size, power, implicated in rodent models of antidepressant response: imputation, and the choice of genotyping chip. Plos Genet association of TREK1 and treatment resistance in the STAR(*) 5:e1000477. D study. Neuropsychopharmacology 33:2810–2819. Su TP, Chen MH, Li CT, Lin WC, Hong CJ, Gueorguieva R, Tu PC, Phillips JL, Batten LA, Tremblay P, Aldosary F, Du L, Blier P (2015) Bai YM, Cheng CM, Krystal JH (2017) Dose-related effects Impact of monoamine-related gene polymorphisms on hip- of adjunctive ketamine in Taiwanese patients with treat - pocampal volume in treatment-resistant depression. Acta ment-resistant depression. Neuropsychopharmacology Neuropsychiatr 27:353–361. 42:2482–2492. Piubelli C, Gruber S, El Khoury A, Mathé AA, Domenici E, Tansey KE, Guipponi M, Hu X, Domenici E, Lewis G, Malafosse Carboni L (2011) Nortriptyline influences protein pathways A, Wendland JR, Lewis CM, McGuffin P, Uher R (2013) involved in carbohydrate metabolism and actin-related pr - o Contribution of common genetic variants to antidepressant cesses in a rat gene-environment model of depression. Eur response. Biol Psychiatry 73:679–682. Neuropsychopharmacol 21:545–562. Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden Porcelli S, Fabbri C, Serretti A (2012) Meta-analysis of ser -o D, Ritz L, Norquist G, Howland RH, Lebowitz B, McGrath PJ, tonin transporter gene promoter polymorphism Shores-Wilson K, Biggs MM, Balasubramani GK, Fava M, (5-HTTLPR) association with antidepressant efficacy. Eur STAR*D Study Team (2006) Evaluation of outcomes with cit - Neuropsychopharmacol 22:239–258. alopram for depression using measurement-based care in Rosenhagen MC, Uhr M (2010) Single nucleotide polymorph - STAR*D: implications for clinical practice. Am J Psychiatry ism in the drug transporter gene ABCB1 in treatment-resist - 163:28–40. ant depression: clinical practice. J Clin Psychopharmacol Visscher PM, Hemani G, Vinkhuyzen AA, Chen GB, Lee SH, Wray 30:209–211. NR, Goddard ME, Yang J (2014) Statistical power to detect gen - Rush AJ, Wisniewski SR, Warden D, Luther JF, Davis LL, Fava etic (co)variance of complex traits using SNP data in unr -e M, Nierenberg AA, Trivedi MH (2008) Selecting among lated samples. Plos Genet 10:e1004269. Downloaded from https://academic.oup.com/ijnp/article-abstract/22/2/93/4980962 by guest on 12 February 2019 Copyedited by: 104 | International Journal of Neuropsychopharmacology, 2019 Wang CC, Held RG, Chang SC, Yang L, Delpire E, Ghosh A, Hall BJ response synergizing with 5-HT1A receptor signaling. Eur (2011) A critical role for glun2b-containing NMDA receptors in Neuropsychopharmacol 25:2426–2436. cortical development and function. Neuron 72:789–805. Zhang C, Li Z, Wu Z, Chen J, Wang Z, Peng D, Hong W, Yuan C, Wang Xia Z, Storm DR (2005) The role of calmodulin as a signal integr - a Z, Yu S, Xu Y, Xu L, Xiao Z, Fang Y (2014) A study of N-methyl- tor for synaptic plasticity. Nat Rev Neurosci 6:267–276. D-aspartate receptor gene (GRIN2B) variants as predictors of Ye D, Li Y, Zhang X, Guo F, Geng L, Zhang Q, Zhang Z (2015) treatment-resistant major depression. Psychopharmacology TREK1 channel blockade induces an antidepressant-like (Berl) 231:685–693.
Journal
International Journal of Neuropsychopharmacology – Oxford University Press
Published: Feb 1, 2019