Imaging genetics: implications for research on variable antidepressant drug response

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Major depressive disorder (MDD), which is among the leading causes of disease bur-den, remains undertreated as well as under- recognized [1]. Despite the clear efficacy of modern anti depressants, response rates are still unsatisfy-ing [2–4] and successful treatment is accompanied by clear disadvantages, such as a delayed time of onset as well as a variety of minor side effects. Moreover, many patients only show a limited response or fail to respond at all and, unfortu-nately, a high risk of relapse persists even when remission is achieved [5,6].

Besides the well-studied effects of age, weight, comorbidity, gender, nutrition and coadministered drugs [7], individual genetic make-up is being increasingly appreciated as a profound factor which impacts on drug response [8,9]. This recent shift of attention is reflected in the growing number of publications over the past few years (Table 1) [10–12]. However, small effect sizes and a lack of replica-tions limit the clinical applicability of those studies [10–12]. Nevertheless, expectations of the capabili-ties of psycho pharmacogenetics to guide antide-pressant treatment remain high, even though the predictive utility of pharmacogenetic tests is still not ready for widespread clinical use [13,14].

Similar to pharmacogenetic studies, neuro-imaging studies investigating gene effects (imag-ing genetics) and drug response have become

tremendously popular over the last couple of years (Table 2). Those studies have highlighted the importance of brain circuitries of emotion processing, which are modulated by risk genes of depression as well as antidepressive drug effects (Figure 1) [15,16]. Since both the effects of antide-pressants and genes at risk of causing depression converge on almost identical neural substrates, it is tempting to amalgamate these findings with results of pharmacogenetic-association stud-ies in order to provide a better understanding of pharmacogenetic actions on a neurobiologi-cal level and to discuss possible implications for future research, as with the scope of this article. Publications have been selected by the authors from Pubmed according to the following inclu-sion criteria: the genetic variants chosen have been associated with depression and have been implicated in drug response or side effect rates. In addition, functional or anatomical neuro imaging studies have been performed in vivo, investigat-ing those genetic variants as well as antidepressive drug action in adult samples of healthy subjects or mood disorder patients. Although the section ‘Drug effects on regions of emotion processing’ contains several PET studies, the discussion of genetic impact only includes MRI studies, since MRI has become the standard technique used in imaging genetics.

Ulrich Rabl1, Christian Scharinger1, Markus Müller2 andLukas Pezawas†1

1Division of Biological Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18–20, A-1090 Vienna, Austria2Department of Clinical Pharmacology, Medical University of Vienna, Austria †Author for correspondence:Tel.: +43 140 400 3568 Fax: +43 140 400 3099 [email protected]

Genetic variation of SLC6A4, HTR1A, MAOA, COMT and BDNF has been associated with depression, variable antidepressant drug responses as well as impacts on brain regions of emotion processing that are modulated by antidepressants. Pharmacogenetic studies are using psychometric outcome measures of drug response and are hampered by small effect sizes that might be overcome by the use of intermediate endophenotypes of drug response, which are suggested by imaging studies. Such an approach will not only tighten the relationship between genes and drug response, but also yield new insights into the neurobiology of depression and individual drug responses. This article provides a comprehensive overview of pharmacogenetic, imaging genetics and drug response studies, utilizing imaging techniques within the context of antidepressive drug therapy.

Keywords: amygdala • anterior cingulate cortex • antidepressants • hippocampus • imaging genetics • major depressive disorder • MRI • orbitofrontal cortex • pharmacogenetics

Imaging genetics: implications for research on variable antidepressant drug responseExpert Rev. Clin. Pharmacol. 3(4), 471–489 (2010)

Expert Rev. Clin. Pharmacol. 3(4), (2010)472

Review Rabl, Scharinger, Müller & Pezawas

This article attempts to provide a comprehensive overview of the rapidly growing fields of imaging genetics and pharmaco-genetic studies. Owing to the large body of available literature and space limitations, we will only sporadically discuss the design of, or analytical differences between, studies. In order to facilitate the recognition of study limitations, we will emphasize important issues in the discussion of this article. Since this article aims to amalgamate available evidence regarding the impact of genetic variants and antidepressant drugs on brain systems of emo-tion processing that have been implicated in the neurobiology of depression, we have restricted the number of possible brain regions to those for which overwhelming evidence exists for play-ing a prominent role in antidepressant drug response as well as depression neurobiology [15–17].

Neuroimaging methods Since numerous neuroimaging studies are at the core of this arti-cle, this section aims to enhance readability by providing a brief description of the neuroimaging techniques mentioned in the course of this article.

Functional MRI (fMRI) is currently the most popular in vivo imaging technique and takes advantage of the magnetic proper-ties of hemoglobin [18]. In more detail, neural activity leads to increased oxygen extraction by neurons, which is accompanied by an increase of blood flow resulting in an increased fraction of oxygenated hemoglobin. Since oxygenated and deoxygenated hemoglobin have different magnetic susceptibilities, an increased blood oxygen level-dependent signal can be detected in areas of the brain that become active [18]. Activation in brain regions of

interest is typically induced by specific neuropsychological tasks or stimuli, which are known to be accompanied by neural activity within these specific neural networks, while baseline (resting-state) activations are recorded without the need for any specific task [19,20].

Another method capable of measuring neural activity is PET, a nuclear imaging technique, that utilizes the application of radioactive tracers in order to indirectly assess neural activity, such as regional cerebral blood flow or glucose metabolism as well as fractional receptor binding, reflecting regional receptor distribution [21].

Computational neuroanatomy provides the possibility to detect regional morphometric alterations with MRI. To date, several methods have been developed such as voxel-based morphometry, which allows for a voxel-wise comparison of local tissue volumes (specifically gray matter) in the spatially normalized anatomical images [22,23]. Furthermore, diffusion tensor imaging (DTI) utilizes differences in the directional mobility of water molecules (frac-tional anisotropy) in order to study the orientation and integrity of white matter tracts [24].

Finally, this article also includes studies that have applied advanced MRI techniques such as functional or structural connectivity, which provide information on the inter-regional connection strengths of brain systems [25].

Drug effects on regions of emotion processingAmygdalaThe amygdala is considered to be a critical neural hub involved in both normal human behavior [26] as well as mental illness [27]. Increased amygdala activation has been related to fear condi-tioning and reaction [26,28] as well as to acute selective serotonin-reuptake inhibitor (SSRI) treatment [29–31], and is suggested to be an important endophenotype of acute MDD [17]. Moreover, specifically in children and adolescents, acute SSRI administration has been associated with a temporary increase of anxious [32,33] or even suicidal behavior [34]. Consequently, animal studies have reported an increase of fear following acute SSRI administration, but subsequent normalization under chronic treatment [35,36]. Similarly, studies in healthy humans reported decreases of fear [37] or decreases of amygdala reactivity in response to aversive stim-uli [38–40], and reduced basal glucose metabolism [41] under chronic SSRI treatment. Such an attenuation of amygdala reactivity is not unique to SSRIs, but has also been shown in response to other anxiolytic drugs such as benzodiazepines [42]. In MDD patients, fluoxetine [43], sertraline [44,45] and bupropion [46] have further been reported to normalize exaggerated amygdala activity under chronic treatment conditions, which appears to be tightly related to drug response.

Studies investigating the putative role of amygdala reactiv-ity as an imaging predictor of treatment response in mood and anxiety disorder samples are still limited. However, pretreatment amygdala reactivity has been found to positively correlate with fluoxetine treatment response [47] in pediatric anxiety disorder patients as well as long-term remission [48] and hospitalization [49] in MDD patients.

Table 1. Pharmacogenetic response studies.

Genes Comparison Nsj Nst Ws(%)

SLC6A4 S > L 411 4 5.0

S < L 2992 18 81.5

L = S 4443 8 13.5

HTR1A C > G 586 4 37.3

C < G 477 2 15.2

C = G 596 5 47.5

MAOA L > H 230† 1 17.0

L = H 376 3 83.0

COMT Met > Val 726 4 66.0

Met < Val 192 2 8.7

Met = Val 372 3 25.3

BDNF Met > Val 134 2 2.7

Met = Val 1397 7 97.3

Data is restricted to that from studies on antidepressants. Gene nomenclature corresponds to the Online Mendelian Inheritance in Man. †Females only.N

sj: Number of subjects; N

st: Number of studies; W

s: Support index weighted for

number of studies and number of subjects calculated according to [16].

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Anterior cingulate cortex

Orbitofrontal cortex

AMY–ACC

Amygdala

SLC6A4, COMT

MAOA, COMT

SLC6A4, MAOA

SLC6A4, MAOA, COMT Hippocampus

MAOA, COMT, BDNF

A C P

A C

C

A C P

A C PBOLD signal change in response to acute treatment

Regional effects associated with chronic treatment

Regional alterations predict remission

A

C

P

BOLD response

Volume

Functional connectivity

Imaging genetics: implications for research on variable antidepressant drug response

Anterior cingulate cortexThe anterior cingulate cortex (ACC) is an area of critical impor-tance for emotion processing comprising of the perigenual ACC, implicated in emotional and autonomic integration, and the subgenual ACC (sACC), which is engaged in the expres-sion of autonomic states [50]. With regard to MDD, increased metabolism [51] and decreased tissue volume [52] have been found in the sACC, a cortical region that exhibits high densities of serotonin transporter (5-HTT) and serotonin receptor 1A (5-HT

1A) [53,54].

Similar to findings for the amygdala, an increase in ACC activa-tion has been reported for acute [29] or subchronic administration of SSRIs [55]. An attenuation under chronic treatment in healthy subjects has also been observed [38], which is in accordance with studies demonstrating a decrease of ACC activation or metabolism under antidepressant treatment in patient samples [46,56–58].

Furthermore, ACC activation or metabolism is thought to be an encouraging imaging indicator of individual treatment response – ACC hypermetabolism at baseline has been found to indicate treatment responders [59], whereas successful treatment with fluoxetine [43,60], buproprion [46] and venlafaxine [61,62] has been accompanied by a decrease of ACC activity or metabolism. In addition, a greater gray matter volume and density as well as greater functional activation in the ACC have been associated with a rapid symptom decrease in patients undergoing fluoxetine treatment [63,64]. However, contradictory results exist [65].

Orbitofrontal cortexAnother key structure in emotion process-ing is the orbitofrontal cortex (OFC) [66], which receives inputs from the ventral striatum and amygdala [67]. It is implicated in reinforcement-guided decision-mak-ing, emotion and social behavior, sharing mant of the similarities with the ACC [67]. Similar to findings in the ACC, loss of tissue volume and metabolic increases in MDD patients have also been reported for the OFC [68].

In healthy participants, acute citalo-pram administration [31] as well as short-term citalopram or reboxetine treatment [69] have been associated with attenuated activation of the OFC. Chronic treatment in bupropion-treated MDD patients also resulted in decreased OFC activation [46].

With regard to response prediction in MDD patients, decreased OFC metabo-lism has been found to reciprocally corre-late with responses to venlafaxine [61] and paroxetine [70]. Similarly, decreased OFC activation in MDD patients undergoing chronic bupropion treatment was highly

correlated with symptom improvement [46]. Finally, OFC gray matter density has been implicated in the prediction of residual symptoms after fluoxetine treatment [64].

Hippocampus The hippocampus is thought to be another key region of emo-tion processing [71,72] and, along with amygdala hyperactivation, hippocampal volume loss is the best studied endophenotype of MDD [16,17,73,74]. Since the hippocampus is among the brain regions that are most vulnerable to the deleterious effects of chronic stress [75], such volume reductions have been related to stress exposure. This finding is supported by animal stress models showing a reversal of stress-induced hippocampal atrophy by antidepressants [76]. Furthermore, antidepressants are thought to directly impact on hippocampal function. In studies of healthy subjects, acute citalo-pram administration translated into increased hippocampal activa-tion [29], whereas chronic treatment was accompanied by a dimin-ished hippocampal response [38,40,55]. Similar findings have also been reported for chronically treated MDD patients [46,57,60,77,78].

With regard to drug response prediction, increased pre treatment hippocampal activation has been demonstrated to proceed clini-cal improvement and to normalize during treatment with bupro-prion [46] or fluoxetine [79]. Furthermore, lower pretreatment

Figure 1. Genetic variants that affect drug response as well as developmental and functional properties of brain regions of emotion processing that are modulated by antidepressive action. Gene nomenclature corresponds to the Online Mendelian Inheritance in Man. AMY–ACC: Amygdala–anterior cingulate cortex; BOLD: Blood oxygen level dependent; BDNF: Brain-derived neurotrophic factor; COMT: Catechol-O-methyltransferase.



hippocampal volumes and gray matter densities have been related to a reduced likelihood of remission [80–84] and an increased risk of residual symptoms in MDD patients [64]. Finally, hippo campal growth has been linked to antidepressant drug treatment in patients, probably reflecting neurotrophic effects [81].

Amygdala–ACC circuitry The limbic system is highly interconnected and complex, mak-ing research in the field of neuroimaging a challenge. In order to address this problem, it has been suggested to dissect this com-plex system into smaller neural circuitries [25]. The most studied subsystem today is the amygdala–ACC circuitry [85], which has

frequently been implicated in depressive illness [86,87] and comprises feed-forward projections from the amygdala to the sACC (unci-natus bundle), as well as feedback projections from the perigenual ACC/anterior midcingulate cortex back to the amygdala (cingulum bundle) [88].

A reduction of amygdala–ACC connectivity has been found in unmedicated MDD patients compared with healthy sub-jects [87]. Under chronic treatment with sertraline [45] and fluox-etine [89], amygdala–ACC connectivity has proven to normal-ize, likely reflecting neuroplastic remodeling of a dysfunctional neural circuitry [45]. It should be noted that the functional con-nectivity [88,90] and pathway strength [91] of this neural circuitry,

Table 2. Imaging genetics studies.

Genes Measure Comparison Amygdala ACC OFC Hippocampus

Nst Nsj Nst Nsj Nst Nsj Nst Nsj

SLC6A4 Function S > L 14 622 5 166 – – 1 48

S < L 1 54† 1 48 – – – –

L = S 1 29 – – – – – –

Volume S > L 1 37 – – – – – –

S < L 3 302 3 232 1 41 1 77

L = S 1 45 – – – – 3 129

HTR1A Function C > G 1 89 – – – – – –

C = G 1 54† – – – – – –

MAOA Function L > H 2 189 1 47† – – 2 189

L < H – – 2 177 2 177 – –

Volume L > H – – – – 2 101† – –

L < H 1 97 1 97 – – – –

L = H – – 1 59‡ 1 55† – –

COMT

Function Met > Val 3 129 2 94 1 27 4 268

Met < Val 1 74 1 27 2 40 – –

Met = Val 1 101 – – – – – –

Volume Met > Val 3 203 – – – – 4 353

Met < Val – – – – 1 53 – –

Met = Val – – – – – – 1 76

BDNF Function Met > Val 2 107 – – – – 2 58

Met < Val 1 29 1 70 1 29 2 95

Met = Val 4 153 – – – – – –

Volume Met < Val 1 87 1 42 – – 9 649

Met = Val 5 483 – – – – 2 167

Restricted to studies on brain function and morphology. Gene nomenclature corresponds to the Online Mendelian Inheritance in Man. †Females only.‡Males only.ACC: Anterior cingulate cortex; BDNF: Brain-derived neurotrophic factor; COMT: Catechol-O-methyltransferase; N

sj: Number of subjects; N

st: Number of studies;

OFC: Orbitofrontal cortex.


ReviewImaging genetics: implications for research on variable antidepressant drug response

as highlighted by fMRI and DTI studies, negatively correlate with trait anxiety, which is thought to be a vulnerable marker for depression.

Depression risk genes & drug responseFunctional polymorphisms of genes expressed in the brain, blood–brain barrier and liver have been linked to variable treat-ment responses to anti depressants [11,92]. Whereas genetic vari-ants associated with pharmacodynamic effects might also reflect disease-specific individual differences, polymorphisms affecting pharmacokinetics are agnostic against the variable neuro biology of depression. Pharmacokinetic genetic variability, such as in MDR1, affecting the penetration of drugs into brain tissue [11], has not been the focus of neuroimaging studies since it is unlikely to affect brain function or structure. Pharmacokinetic genetic variability in other genes, such as the cytochrome P450 (CYP450) genes involved in drug metabolism [11], may also impact on the brain since they are putatively involved in both drug and neurotransmitter metabo-lism [93]. By contrast, genes involved in pharmaco dynamic vari-ability have been investigated by both pharmacogenetic as well as imaging genetics studies and are probably implicated in the neuro biology of depression and variable treatment response, which will be the focus of this section of the article.

Serotonin transporter geneSerotonin-reuptake inhibitors are the first-line treatment for MDD [94] and target 5-HTT, which is expressed by the serotonin transporter gene (SLC6A4). A variable number of tandem repeats (VNTR) polymorphism (5-HTTLPR) within the promotor region of SLC6A4 resulting in variable 5-HTT expression [95], has been the primary target of research over the last couple of years since it has been associated with anxiety-related temperamental traits as well as depression in the context of environmental adversity [96,97]. Approximately two thirds of Caucasians are carriers of the short (S) allele of 5-HTTLPR, whereas allele frequencies in other ethnicities differ [98]. The S allele of 5-HTTLPR is associated with a twofold decreased expression and transport activity in human cell lines in vitro [96]. Although 5-HTT concentrations in the synaptic cleft cannot be measured directly, it is assumed that the S allele results in higher extracellular concentrations of serotonin (5-HT) in the synaptic cleft [99]. Several PET studies measuring 5-HTT binding have reported increased serotonin transporter binding in individu-als homozygous for the long (L) allele [100–102]. However, others have been unable to show the effects of 5-HTTLPR on 5-HTT binding in vivo [103–106] or postmortem [107]. Such inconsistencies have been explained by time-specific effects such as seasonality [108] and advances in tracer development [109], as well as the neurodevel-opmental effects of serotonin [53,110,111] underpinned by an animal study demonstrating that transient inhibition of 5-HTT during early development results in anxious behavior in adults [112].

Since blockade of 5-HTT is an important mode of action of anti-depressants, 5-HTTLPR has also been investigated with regard to drug response. Available evidence from pharmacogenetic studies in Caucasians who were homozygous for the S allele suggests lower

remission rates of MDD under SSRI treatment [113]. Similarly, the majority of available studies reported that the S allele was associated with lower response rates to antidepressant drugs [10]. In detail, studies reported a better or faster treatment outcome in those with the L/L genotype undergoing fluvoxamine [114–118], paroxetine [115–117,119–122], fluoxetine [123–126], citalopram [127], escitalopram [128], sertraline [129] and antidepressant treatment in general [130,131]. However, studies in Asian samples suggest the opposite [132–135], which might be caused by differences in genetic background as well as study lengths. In addition, several negative studies exist for milnacipran [136], sertraline [137], fluoxetine [138], citalopram [139,140], escitalopram [141] and antidepressant treatment in general [142,143].

In addition to treatment response, pharmacogenetic studies also investigate the effects on treatment tolerability – the L allele has been reported to be associated with a lower side effect rate and to be a possible predictor of treatment tolerance [10] for drugs such as fluoxetine [144] and citalopram [139], and antidepressant drug treat-ment in general [131,145,146]. Similar to studies on drug response, conflicting data exist, for example, some studies indicate fewer side effects in S-allele carriers undergoing mirtazapine treatment [121] and other studies report a lack of association between 5-HTTLPR and observed side effect frequencies [117,147–149].

The majority of studies have consistently demonstrated an S allele attenuated treatment response; however, several negative studies exist, which is not suprising, since effect sizes are probably to be smaller than originally believed. Today, it is estimated that 5-HTTLPR only accounts for a 2–3% explained variance in treat-ment response [113]. This may be explained, at least partly, by less studied genetic variability in SLC6A4 [150] such as a single nucleotide polymorphism (A>G substitution, rs25531) within the L allele of 5-HTTLPR [101,141,151] and a VNTR in the second intron [152,153]. Moreover, other promising genetic locations [154] and contributing factors [155] affecting antidepressive drug binding to 5-HTT have been found in vitro. However, as of yet none of those preclinical findings have been transferred to humans.

By contrast, results of fMRI studies investigating the neural impact of 5-HTTLPR on amygdala reactivity in healthy subjects are highly consistent [156,157]. They suggest that S-allele carriers show an exaggerated amygdala reactivity in the presence of fearful or negatively valenced stimuli [49,88,156,158–168] as well as under base-line conditions [161,166] in comparison with those with a L/L geno-type. Similar results have been obtained in a limited number of studies in MDD patients [49,162,169].

With regard to ACC function, increases in ACC basal metabo-lism [170] as well as blood flow [166,171] in healthy subjects have been associated with the S allele. Accordingly, S-allele-driven increases have been reported by activation studies [49,162,167,172,173]. Only one study reported opposing results [174]. Structural MRI studies in healthy subjects revealed S-allele-moderated gray matter volume loss in the ACC, or sACC [88,160,175], which is similar to a primate study with orthologous 5-HTTLPR (rh5-HTTLPR) [176]. Patient studies have been inconclusive with authors reporting S-allele-driven increases [49,177] or decreases of ACC activation [174], and results are lacking [162,175].



Finally, 5-HTTLPR effects have also been demonstrated on a brain systems level, demonstrating that the S allele reduces structural and functional connectivity between the amygdala and sACC [88]. These measurements suggest a relative uncoupling of those struc-tures resulting in a disinhibition of this feedback loop and hence, an increased amygdala activity [88]. This finding is in accordance with other fMRI [162,165,169,173] and DTI studies [178] of patients or healthy subjects reporting S-allele effects on amygdala–ACC coupling.

Serotonin receptor 1A geneThe G allele of C(-1019)G (rs6295), a single nucleotide polymor-phism within the promotor region of the 5-HT

1A gene (HTR1A),

can be found in 50% of Caucasians and 21% of Asians [148] and has been associated with impaired transcriptional repression mecha-nisms leading to increased 5-HT

1A density [179]. It is assumed to

be a risk allele for MDD, since it has been reported to be over-represented in MDD patients and increases of post synaptic 5-HT

1A

signaling have been implicated in the mode of action of several anti-depressant treatments [180,181]. Similar to SLC6A4, it has been sug-gested that HTR1A specifically impacts early brain development, since transient gene knockout in newborn mice leads to anxiety-like behavior, whereas its absence during adult life has no effect [182]. Furthermore, it has been found that the anxiety phenotype of HTR1A-knockout mice can be moderated by early environmental factors, suggesting gene–environment interactions [183].

With regard to drug response studies, homozygous G(-1019) allele carriers have been reported to show a diminished response to flibanserin [184], which has failed to show antidepressive effects, as well as fluoxetine [126,148] and several other anti depressants compared with subjects with the C/C genotype [185]. By con-trast, other authors reported the G allele to be associated with favorable response rates in patients undergoing fluvoxamine, par-oxetine or milnacipran treatment [186] along with several other antidepressants [187].

Some of this conf licting evidence might be explained by gene–gene interactions (epistasis) between C(-1019)G HTR1A and 5-HTTLPR in SLC6A4 with respect to citalopram treatment [188], or between C(-1019)G HTR1A and Val66Met BNDF [189], which has been linked to treatment resistance.

However, evidence for the pharmacogenetic effects of C(-1019)G HTR1A is very limited since negative studies exist for fluoxetine [184,190], fluvoxamine [191] and SSRIs in general [192,193], and a recent meta-analysis was not able to detect an overall impact on treatment efficacy [10].

Similarly discouraging are findings of imaging genetics stud-ies reporting a G allele-associated amygdala activation decrease in healthy subjects [194], whereas another study in patients by Dannlowski et al found the opposite [169]. Furthermore, a replication study failed in a group of healthy Asian patients [195].

Monoamine oxidase A geneThe X-chromosome-linked MAOA gene encodes monoamine oxidase A, a key enzyme in the degradation of 5-HT, which is expressed in the outer mitochondrial membrane of monoaminer-gic neurons. The promoter region of the MAOA gene comprises

a functional VNTR polymorphism resulting in a highly active MAOA-H and less active MAOA-L allele depending on gene expression differences [196]. Since MAOA inhibitors are highly effective drugs in MDD treatment, associations of the MAOA VNTR with antidepressant drug response have been investigated. However, few studies are available so far and the majority of stud-ies lack results. Accordingly, no asssociations of MAOA VNTR genotype with drug response to fluvoxamine [197], paroxetine [197], moclobemide [198] and fluvoxamine [199] have been found. To our knowledge, only a single study reported a favorable treatment response to fluoxetine in females who were homozygous for the MAO-L allele compared with MAO-H allele carriers [200].

With regard to imaging genetics studies, genetic variation in MAOA has repeatedly been associated with functional and structural alterations in depression-related brain circuitries. Functional studies reported increases of brain activation in the amygdala and hippocampus [201,202], as well as decreases in the OFC and ACC [172,202] of healthy MAOA-L allele carriers. With regard to brain structure, increases of OFC [202,203] and decreases of ACC volume [202] have been found in healthy MAOA-L car-riers. However, negative findings exist with regard to amygdala and ACC volume in an underpowered sample [203]. Moreover, in a non-Caucasian and gender-restricted sample, a lack of impact on OFC and inverse effects on ACC function have been reported [201].

Finally, on a brain systems level, the effects of MAOA on the amygdala–ACC circuitry have been reported in healthy subjects demonstrating MAOA-L allele-induced increases in functional connectivity that correlate positively with harm avoidance and negatively with reward dependence [204]. In addition, a study in MDD patients demonstrated decreases of amygdala–ACC cou-pling, which have been linked to a more severe clinical course of depression, in carriers of the MAO-H allele [205].

Catechol-O-methyltransferase geneCatechol-O-methyltransferase (COMT) is a monoamine-degrading, extracellularly located enzyme encoded by the COMT gene [206], which profoundly impacts on dopamine catabolism in the prefrontal cortex, a region in which dopamine transport-ers are rare [207]. The higher active and phylogenetically ancestral Val allele of Val158Met COMT (rs4680) has been demonstrated to increase cognitive flexibility [208]. Moreover, it also has been reported to impact on the human reward center, which is involved in anhedonia, a key symptom of depression, as well as euphoria, which can be induced by dopaminergic drug abuse [209].

Pharmacogenetic studies of Val158Met COMT are contra-dictory. Some authors found a diminished treatment response in homozygous Val allele carriers [210] for paroxetine [211], milnacip-ran [212] and fluoxetine [213]. However, opposing effects have been found for mirtazapine [214] and citalopram [215], as well as lack-ing effects for paroxetine [214,215] and duloxetine [216]. In addition, a reduced risk of antidepressant-induced weight gain has been reported for Val158Met heterozygotes [217].

By contrast, results of imaging genetics studies on COMT por-tray a more distinct picture. The Met allele has been associated with increased amygdala volume [218–220] and activation [167,221–222].



So far, only one study has been unable to confirm COMT impact on amygdala activation [223], whereas another reported opposing effects, particularly in females [224]. Analogous to findings in the amygdala, increased activation has also been detected in the ACC of healthy Met allele carriers [167,221,225].

With regard to the findings in OFC, the majority of studies have demonstrated a Met allele-associated decrease of activation [225–227] and volume [219] in healthy controls. Similar decreased activa-tion [167,222,223,228] and volume [218–220,229] have been reported for the hippocampus. Only a single study has been unable to detect COMT volume effects in a healthy control group [230], which is probably related to ethnicity [230].

Finally, on a brain system level, reduced functional coupling of the amygdala–OFC circuitry has been linked to Val allele carriers [223].

Brain-derived neurotrophic factor gene The neurotrophin brain-derived neurotrophic factor (BDNF) is a key protein involved in long-term potentiation and memory encoding. Its precursor (pro-BDNF) binds to the p75 neuro-trophin receptor (p75NTR), eliciting apoptosis, whereas its mature form predominantly activates the tropomyosin-related kinase B receptor, promoting neurotrophic and antiapoptotic effects [231].

Over the last few years, BDNF has been implicated in various neurologic and psychiatric phenotypes including mood disor-ders [232]. BDNF signaling interacts with the monoaminergic as well as the excitatory and inhibitory neurotransmitter system [233], which are all supposed to contribute to the complex neurobiology of depression. Further support stems from animal studies linking stress exposure and glucocorticoid administration to reduced hip-pocampal BDNF mRNA expression, as well as from studies show-ing antidepressant efficacy of intracerebral BDNF infusions [234].

These observations have led to the so-called neurotrophin hypothesis of depression, suggesting a loss of BDNF effects to be the underlying cause of depression due to introducing dysfunc-tional anatomical mood circuitries [235]. However, contradictory results have been obtained from animal models of depression, where BDNF mRNA is increased within the reward system and local BDNF perfusion promotes depressive-like behavior [236]. Therefore, the exact role of BDNF is currently far from being understood and complex molecular mechanisms, such as tran-scription through multiple promoters, trafficking, cleavage and secretion, have been suggested to contribute to the observed inconsistencies [237]. In addition, BDNF expression seems to be subjected to profound epigenetic control [238].

The BDNF gene contains several polymorphic sites [239]; how-ever, most attention has been paid to Val66Met BDNF (rs6265), which has been shown to impair intracellular trafficking and the activity-dependent secretion of BDNF [240].

In overall meta-analyses, no significant association between Val66Met and MDD has been found [241–243], although, taking gender into account, one study reported a significant association of the Met allele with MDD in males [242]. However, with regard to personality traits, the Met allele has been associated with lower neuroticism scores [244].

Whereas the distinct role of BDNF in mood disorder etiology remains unclear, its involvement in antidepressant action has been more consistently demonstrated in a variety of studies [236], for example, it has been observed that heterozygous BDNF-knockout mice are unresponsive to antidepressants [245]. Consequently, sev-eral pharmacogenetic studies addressing BDNF have been pub-lished. Effects of BDNF polymorphisms on treatment response have been studied for antidepressive drug therapy [239,246–252] in MDD as well as lithium treatment in bipolar disorder [253]. A favorable response to antidepressive treatment has been reported for Met allele carriers of Val66Met BNDF in Asian samples, including drugs such as citalopram [251], fluvoxamine and mil-nacipran [247]. Opposing results have been found in Caucasians, reporting higher response rates in Val allele carriers undergoing citalopram treatment, but this was not replicated within a large-scale study [250]. Furthermore, a lack of results have been reported for antidepressant treatments in general [246,248], and specifically for nortriptyline [249], escitalopram [249] and despiramine [239]. Finally, an improved response to fluoxetine in heterozygous patients (Val/Met) compared with the homozygous groups has been reported [252]. However, recent meta-analyses [10,254] found evidence for increased response rates in Met allele carriers, spe-cifically in Asian populations [247,251,252,254]. Counterintuitively, preclinical evidence obtained from genetically engineered mice suggested the Met allele to be unresponsive to fluoxetine [255].

With regard to brain structure, healthy carriers of the Met allele of Val66Met BDNF have been overwhelmingly asso-ciated with smaller hippocampal volume [256–263]. However, Jessen & Koolschijn failed to detect volumetric effects [264,265] and a co-dependence on neuroticism has been reported [266]. Functional studies are as yet less conclusive, associating the Val allele with increased hippocampal activation in healthy subjects [267,268], in contrast to others favoring the Met allele [240,259]. With respect to patient studies, reduced hippocampal volume has also been reported for Met carriers independent of clinical diagnosis [256,261], although one author was not able to replicate those findings [264]. Further support stems from animal studies confirming the assumption of BDNF-mediated effects on the hippocampus: BDNF has been demonstrated to be crucial for hippocampal functioning and integrity [269,270]. Moreover, selec-tive loss of BDNF in the dentate gyrus has been demonstrated to attenuate antidepressant efficacy [271]. Less evidence exists for BDNF-related effects on the amygdala. Met allele-associated amygdalar volume reduction has recently been reported [272]

although earlier studies have been unable to show such an effect [256–259,273]. Similarly, most studies failed to detect the effects of Val66Met on amyg dala function [240,259,267,268]. However, a single study found increased activity in Val homo zygotes [274], whereas studies two reported the Met allele to be associated with increased amygdala response [275,276]. The latter observation, however, is vaguely supported by animal studies in which Met homozygous mice exhibited increased anxiety [255] and impaired extinc-tion learning [275] – features tightly associated with amygdala functioning. Similarly, limited evidence is available for BDNF effects on the ACC, where reduced volume and function in Met



carriers have been reported in two studies [257,275]. Finally, one study reported an increase in OFC activation in Val homozygous individuals [274].

DiscussionSince neuroimaging and pharmacogenetic studies have both merits and demerits, we will discuss some major issues in the course of this section. More indepth information can be found elsewhere [20,92,277].

When tools for gene identification, such as linkage studies, became available, these methods were enthusiastically adopted for psychiatric research [278]. However, in contrast to Mendelian disorders, results have been discouraging, which is understandable in the face of obvious differences in the disorders’ genetic het-erogeneity, risk gene effect size and the mode of inheritance [279]. Association studies comparing the prevalence of candidate gene markers derived from a priori hypotheses in affected and unaf-fected individuals have been considered to be a more powerful approach [280] and, within a short period of time, have also been utilized in clinical pharmacology within pharmacogenetic asso-ciation studies of variable drug response [92]. However, although meta-analyses indicate that such studies are capable of risk variant identification [281], a lack of replication appears to be more the rule rather than the exception, since effect sizes of such risk variants are very small [282]. One typically applied exit strategy is to dra-matically increase sample sizes. As compared with initial studies including not more than 100 participants [114], recent trials such as the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study enrolled up to 2000 patients [283] in order to com-pensate for small effect sizes. Given the neuro biological complexity of psychiatric disorders, the small effect sizes typically found in association studies are no surprise. The same argument can be applied to antidepressive treatment response, which is also highly complex, and the gene–gene as well as gene–environment inter-actions impacting on it are currently poorly understood [284,285]. However, association studies are agnostic to the biological under-pinnings of such complex traits and, therefore, are only capable of a crude risk variant identification.

In order to overcome the missing link between genes and behav-ioral traits, the concept of intermediate endophenotypes has been introduced [286,287]. Intermediate endophenotypes are quantita-tive traits that are thought to more directly index the biology of a complex behavioral phenotype [287]. This framework attempts to bypass the uncertainty of psychiatric diagnoses using more objective measures and promises to inform more deeply regarding the underlying biological processes [287].

However, this concept has been criticized as well, arguing that the biological basis of some endophenotypes might be less com-plicated than the phenotype itself [288]. Nevertheless, although this might be the case for biologically complex traits such as Wisconsin Card Sorting Task performance, an endophenotype used in schizophrenia research [288], meta-analyses of imaging genetics studies reveal a different picture [157,289]. For instance, in contrast to the 5-HTTLPR polymorphism accounting for only 2–3% of the estimated variance of treatment response [113], it has

been shown to account for up to 10% of the variance in amygdala activation [157]. Evidence from single studies promise even stronger effect sizes for connectivity measurements [25,88,204,223].

Owing to their obvious advantages, neuroimaging techniques have been widely accepted in the study of mental disorders and gained tremendous popularity, partially because they provide seem-ingly simplistic explanations of complex behavioral phenotypes [18]. However, despite this superficial simplicity, neuro imaging methods suffer from important limitations and are potentially error-prone, since they are fairly sophisticated and the measured signals are rather small [277]. Briefly, the following limitations have to be considered: the underlying mechanism of the blood oxygen level-dependent signal used in fMRI is still not fully understood and it only rep-resents a surrogate measure for neuronal mass activity [277]. Low task-related signals are compromised by large levels of non-task-related noise caused by thermal noise, system variability in scanner hardware and physiological variability or head motion requiring sophisticated preprocessing procedures [290]. Another important concern is how to deal with multiple comparisons, especially in voxel-wise fMRI analy ses, but empirical evidence suggests that cur-rently used methods are overly strict [291]. Furthermore, there is no genuine experimental design that fits for all fMRI studies, and a design that is not properly adjusted to the hypothesis may lead to the wrong conclusions being drawn, since irrelevant brain activation can be elicited. However, several well-estalished tasks are available today and have been sufficiently used in MDD, such as a paradigm employing fearful faces [164]. Small effect sizes of genetic variants require a power analysis in order to choose the appropriate sample size for pharmacogenetic as well as for imaging genetics studies. While the appropriate sample size for a given genetic variant still needs to be determined for pharmacogenetic imaging studies, it has been estimated that for imaging genetic studies investigating SLC6A4, the minimal sample size is 70 subjects [157], which is widely acknowledged by recent studies. It should be noted that this number is significantly smaller than the number of subjects needed for asso-ciation studies, which supports the intermediate endophenotype concept. Another important issue is the impact of demographic data, such as age and gender data, as well as clinical specifiers, which can profoundly affect neuroimaging results. This underlines the neces-sity for thoughtfully collected samples and statistical tests correcting for confounding variables in order to control neural effects between genotype and age [272,273], disease course [292,293] or treatment [294].

In summary, neuroimaging techniques require several caveats, since they are fairly sophisticated. Nevertheless, when properly applied, they offer intruiging insights into the neurobiology of mental disorders.

Conclusion & future directionsGenetic variants in SLC6A4, HTR1A, MAOA, COMT and BDNF have been linked to depression as well as antidepressant drug response and impact on brain regions of emotion processing that are modulated by antidepressants.

Both depression and antidepressant drug response are geneti-cally complex and probably result from interactions of multiple genetic variants (epistasis) with small effect sizes, and they are



further affected by gene–environment interactions [285]. Hence, single genetic variants only exhibit subtle biological effects and do not encode for behavioral phenotypes [287]. Similarly, effect sizes of association and pharmacogenetic studies have been, at most, mod-est or even undetectable [113,287,295]. Behavioral measures of depres-sion or drug response add further inaccuracy to the highly variable neurobiology of depression. Recently, further criticism has been raised that the use of symptom-based anti depressive response scales (e.g., Hamilton Depression Rating Scale) as clinical end points does not sufficently account for the multi dimensional and widely subjective nature of depression symptoms [296]. Such problems may be, at least, partially overcome by studies assessing intermediate endophenotypes that more directly impact the neurobiological underpinnings of symptom-derived psychiatric diagnoses [287]. Several intermediate phenotypes have been demonstrated to be closely related to MDD with most evidence available for amygdala hyper-reactivity and hippocampal volume reduction [16,17]. In addi-tion, other regions of emotion processing, particulary the ACC and OFC, appear to be further valid intermediate phenotypes, since they have been found to be structurally and functionally affected in acute depression; connectivity measurements encom-passing the ACC–amygdala circuitry may be even more powerful [16,17]. Imaging genetics studies highlight that such intermediate endophenotypes are under heavy genetic control. Moreover, phar-macological fMRI and pharmacological MRI studies have iden-tified intermediate endophenotypes of antidepressant treatment response, which are intriguingly similar to those of depression. Since both depression risk genes and variable treatment response have been linked to several identical neural targets, it is tempting to propose that genetic variants impact on antidepressant treat-ment response via neurocircuitries that have been identified as intermediate endophenotypes of depression. Such an approach indicates the notion that individually varying drug effects can-not simply be explained by mechanistic pharmacogenetic models of variable protein expression, but implicate more complex proc-esses. Among those are likely to be developmental genetic effects, which have been demonstrated by structural imaging studies (e.g., smaller sACC volume and amygdala–ACC connectivity in S-allele carriers of 5-HTTLPR) and probably result in altered individual responsiveness [49,88,160,162,167,172,173,175]. Hence, lower response rates in S-allele carriers might be, at least partly, explained by develop-mental deficits of the amygdala–ACC circuitry [88,162,165,169,173,178], which has proven to be regulated by SSRI treatment [45,89]. While there is overwhelming evidence of an important role during brain development for SLC6A4 [53] and BDNF [297], there is also some evidence for the remaining genes, which have been the focus of this article [53,112,204,223].

Whereas risk variants are indistinguishable in their contri-bution to clinical treatment response, they seem to affect char-acteristic patterns of brain regions on a brain systems level. Following this assumption, the variable antidepressant drug response related to SLC6A4 might be mediated over the amy-gdala [49,88,156,158–169], sACC [49,88,160,162,167,172,173,175] and amy-gdala–sACC circuitry [88,162,165,169,173,178], whereas MAOA affects a broader variety of brain systems such as the OFC [172,202–203],

ACC [202], amygdala [201,202], hippocampus [195,202] and amyg-dala–ACC circuitry [204,205]. Similar to MAOA, COMT affects the OFC [219,225–227], ACC [167,221,225], amygdala [167,218–222], hippo-campus [167,218–220,222,223,228,229] and amygdala–OFC circuitry [223]. While genetic variation in HTR1A has not been demonstrated to impact significantly on drug response or emotion processing in humans, genetic variability in BDNF has been clearly proven to affect the hippocampus [240,256–263,267,268].

Following this chain of arguments, we propose that the applica-tion of imaging endophenotypes within the framework of variable antidepressant treatment response might provide several advantages: first, studies investigating the impact of different genotypes on neu-ral correlates of antidepressant drug action might reveal information regarding subtypes of antidepressant treatment response that oth-erwise would be indiscriminable in clinical behavioral assessments and, therefore, are capable of bridging the gap between varying genetic make-up and variable drug response rates. This would prob-ably be an important step forward towards personalized treatment strategies, since genetic testing on its own provides too little infor-mation to be translated from bench to bedside (e.g., 5-HTTLPR accounting for only 2–3% of the variance of treatment response)[113]. Accordingly, it has been suggested that personalized treatment can only be achieved by a combination of different strategies, such as imaging, genetics, proteomics or neuroendocrinology [14].

Growing numbers of studies have begun to address more com-plex models, such as haplotypes, epistasis and gene–environment interactions [16]. An imaging genetics approach might be specifi-cally suitable for the investigation of more complex mechanisms including gene–gene or gene–environment interactions as well as haplotypes, since imaging endophenotypes may dramatically decrease the required sample size. Several recent publications underline the feasability of such an approach [161,167,169,229,298–301].

Recently, the focus of pharmacogenetic studies has moved from a likely biased candidate gene approach based on a priori hypoth-eses to an ‘agnostic’ genome-wide association (GWA) approach. New and unexpected candidate genes associated with drug response [302,303] and depression [172,304–307] have been reported. Interestingly, such studies only partly support the findings of can-didate gene studies and suggest that multiple loci of small effect sizes may determine treatment response [302,303]. Although several open questions remain regarding the analysis of genome-wide association studies (GWAS), such as the appropriate correction for multiple comparisons, they will likely stimulate research in the field of variable treatment responses [308]. Imaging genetics studies have been demonstrated to be a valuable tool to further evaluate risk variant candidates derived from GWAS [309,310]. Moreover, the first voxel-wise GWAS are on the horizon [311].

Variable antidepressant treatment response is genetically deter-mined; however, multiple loci of small effect sizes hamper the applicability of genetic testing for personalized treatment regimes. Therefore, more translational-oriented research is urgently needed to disentangle the underlying neurobiological mechanisms. Among other techniques, such as genetics, proteomics and animal models, neuroimaging will likely provide a powerful armamentarium of tools towards the goal of personalized treatment regimes.



Expert commentaryGenetic variation implicated in the pharmacodynamic action of antidepressants is frequently associated with variable drug response. However, since drug response is exclusively evaluated by psychometric scales, low associations with gene candidates have been found. The use of intermediate endophenotypes for drug response research, which are already available in the field of neuroimaging, will likely tighten the relationship between these genotypes and drug response. It will further facilitate the biological understanding of a complex behavioral trait such as antidepressant drug response.

Five-year viewAlthough the neurobiological understanding of depression is still in its infancy, there is reason to believe that growing insights from human neuroimaging studies will doubtlessly yield a more thor-ough understanding of the relationship between variable antide-pressive treatment response and underlying differences in brain architecture and function. Several intermediate endophenotypes of depression derived from neuroimaging studies are already avail-able today and are ready to serve as biological indicators in the context of drug response studies, which might eliminate problems

inherent in the current psychometric assessment of drug response. Psychopharmacogenetic effect sizes of single disease-related genes are small and, therefore, of limited clinical use. This could be over-come by the use of more sophisticated psychopharmocogentic mod-els appreciating the complex neuro biology of depression and drug response such as gene–gene or gene–environment interactions, epi-genetics and imprinting, some of which have already demonstrated neurobiological relevance in imaging genetics studies.

AcknowledgementsThis article is part of the educational efforts of the Special Research Project SFB-35 (Project No. F3514-B11 and F3506-B11), funded by the Austrian Science Fund (FWF).

Financial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.

Key issues

• Shortcomings of antidepressant treatment such as unsatisfying drug response, undesirable side effects and high recurrence rates have been promoting the hunt for personalized treatment regimes.

• Genetics is appreciated as an important factor that impacts on drug response; however, the genes implicated in the pharmacodynamics are still controversial.

• Effect sizes of genetic associations with clinical measures such as drug response are small and can be overcome by the use of intermediate endophenotypes.

• Pharmacological imaging studies have identified the neural targets of drug response.

• Imaging genetics studies have identified the neural targets of genes that are related to pharmacodynamic action.

• Imaging genetics and pharmacological imaging studies suggest that simple mechanistic pharmacogenetic models cannot be translated on a brain systems level in a simplistic fashion.

• A comprehensive perspective on drug effects obtained from interdisciplinary research including imaging genetics will probably stimulate the development of personalized treatment regimes.

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https://www.researchgate.net/publication/270051573_Meta-analysis_of_genetic_association_studies_supports_a_role_for_common_variants_in_common_disease_risk?el=1_x_8&enrichId=rgreq-a1a08631-df92-4808-8f7a-2973a849a53f&enrichSource=Y292ZXJQYWdlOzUxODI2OTMzO0FTOjIxMzQ5OTg2NDE5NTA3N0AxNDI3OTEzNzQyODQy






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Imaging genetics: implications for research on variable antidepressant drug response