Gene-environment interaction in psychiatry

Chapter 29

Gene-environment interaction in psychiatry Hans J€ orgen Grabe and Sandra Van der Auwera Department of Psychiatry and Psychotherapy, University Medicine of Greifswald, Greifswald, Germany

1 Introduction Adaptation is a driving force of the evolutionary process of the species, and optimal adaptation is the highest individual goal, ensuring optimal individual survival. Thus, the evolutionary process has created biological systems that are able to respond dynamically to environmental challenges. Such challenges are comprised of physical factors such as UV-radiation, caloric restriction, heat, and cold. As the evolutionary process brought complex social systems into being, these social systems became a determinant of survival itself (Brune & Brune-Cohrs, 2006; Emera, Yin, Reilly, Gockley, & Noonan, 2016). Thus, the regulation and the fine-tuning of social interactions became an evolutionary driver of brain systems and, for example, neuroendocrine functions. All biological systems and biochemical pathways that ensure the adaptability of mammalians and humans are coded by genes that carry common single nucleotides (SNP), rare variants, and other genetic variations such as insertions, deletions, or copy number variations. These genetic variations may contribute to the individual variability of the biological reactivity and response to the environmental challenge, and thus contribute to optimal or impaired individual adaptation. This individual variability gives rise to susceptibility or resilience when dealing with pathological conditions and disorders. The basic model of gene-environment interaction (GxE) assumes a different reactivity of the biological system under the condition “environmental challenge” (present/absent), dependent on a respective genotype (present/absent). Thus, in humans, the analysis of typical GxEs requires subjects who are exposed/nonexposed to an environmental challenge, and carrying the risk or the nonrisk allele. This is the conceptual point where GxE becomes accessible for future use in personalized psychiatry, as the two relevant factors determining the individual risk are measurable. In Fig. 1, the prototypic concept of GxE is depicted, resulting in four different groups with different risk of disease. Typically, the group of subjects carrying the genetic risk variant and having been exposed to stressful/traumatic events (group d) has the highest risk of disease, whereas the other groups (a–c) have markedly lower risks of disease. Generally it is assumed, however, that the etiology of mental disorders is multifactorial. Thus, any GxE is embedded within this multifactorial background. Although complex, the concept of genes—posing either risk or resilience toward the environmental exposure—is appealing as it captures the dynamic nature of individual adaptation. The clinical idea behind GxE in psychiatry is that the change of the reactivity of a biological system based on GxE “reaches” the behavioral level, or even the diagnostic level impacting the risk of disorder or disease. However, the differential reactivity of a biological system due to GxE might be better assessed by, for example, physiological, biochemical, or neuropsychological testing than by clinical categories of mental disorders. This concept of “endophenotypes” might constitute a relevant outcome in GxE research (Fig. 1). It is beyond the possibilities of this chapter to cover extensively the vast literature of GxE in various clinical conditions. However, we will give some insight into the challenges and highlights in GxE in the research of mental disorders.

2 Environmental conditions The analysis of GxE and its application in personalized psychiatry requires deep insight in the conceptualization and measurement of environmental factors (Grabe, Schulz, et al., 2012). It is evident that many environmental conditions act upon each individual throughout critical phases of mental and biological development in a very individual way. In contrast, the possibilities of assessing and analyzing these conditions are rather simple. We do not yet have any good insight Personalized Psychiatry. https://doi.org/10.1016/B978-0-12-813176-3.00029-8 Copyright © 2020 Elsevier Inc. All rights reserved.

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FIG. 1 General model of gene-environment interaction. Subjects carrying the genetic risk variant and having been exposed to stressful/traumatic events (group d) have the highest risk of disease, whereas the other groups (a–c) have markedly lower risks of disease.

into how aversive and supportive influences balance each other and thereby affect possible GxE effects in the statistical models. Multiple environmental domains have been shown to be associated with mental disorders: urban upbringing, migration, stressful life events and early life stress, prenatal infections, obstetric complications, substance abuse or dependence of the parents, as well as the patient himself, and of course more subtle conditions such as parental bonding, quality of care, and learning copings strategies (Grech & van Os, 2017; van Os et al., 2014). Again, this list is not conclusive, but it illustrates the variety of influences that may act upon an individual. From clinical research, we do know that often, multiple negative events in a given period of time are needed to shift the individual from functional to dysfunctional, and from health to illness. Further evidence suggests that “double hits” may play a specific role for disease development—one developmentally early psychological or biological trauma, and a recent event that leads to the actual decompensation of mental functions (Giovanoli et al., 2013; Grabe, Schwahn, Mahler, Schulz, et al., 2012).

3

Candidate genes

The usual approach in GxE research is the analysis of a limited set of genes and polymorphisms that would currently also be the most feasible application for personalized psychiatry. Ideally, the selection of the candidate genes follows a plausible physiological model that connects the biochemical pathway to a broader biological domain, as the serotonin system or the HPA axis that connect biology to distinct human behaviors. In many published studies, several genes and polymorphisms have been analyzed without proper adjustments for multiple testing. Often, results are difficult to compare, as different SNPs from the same gene have been used in different studies. Except from a few candidate genes, it is still largely unclear how the genetic variants might lead to different biological responses in different environmental settings. Complex

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mechanisms of gene regulation such as DNA methylation, histone acetylation, and further regulatory processing such as noncoding RNAs might be involved (Braff & Tamminga, 2017). Adding these points to the uncertainties in assessment of complex environments and the diagnostic complexity in mental disorders, GxE analyses are definitely challenging. One has to keep these circumstances in mind when interpreting GxE studies.

4 Depressive disorders and anxiety The amount of GxE papers published in the field of depression and anxiety disorders is too large to give a substantial overview in this chapter. Some recent review papers might add further details (Gottschalk & Domschke, 2017a, 2017b; Sharma, Powers, Bradley, & Ressler, 2016). Here, we present three well studied candidate genes in greater detail, highlighting some of the controversies and related debates.

5 The insertion/deletion polymorphism of the serotonin transporter gene (5-HTTLPR) One prominent example that has frequently been studied is the insertion/deletion polymorphism of the serotonin transporter gene (5-HTTLPR). The serotonin system per se is involved in the regulation of multiple facets of human behavior such as mood, impulsivity, appetite, sleep, and sexuality. The 5-HTTLPR is thought to impact the transcription rate, thereby influencing the concentration of the serotonin transporter within the presynaptic membrane. In their landmark paper, Caspi et al. found an interaction between the number of life-events and the three genotypes (ss, sl, ll) of the 5HTTLPR, and the likelihood of increased self-reported depressive symptoms scores or the occurrence of depressive episodes. This interaction was present for adult stressful life-events, as well as for childhood maltreatment (Caspi et al., 2003). Our group could replicate the GxE properties of the 5-HTTLPR in the severity of psychosomatic complaints in the general population (Grabe et al., 2005; Grabe et al., 2011). Further, we could show that the combination of childhood and adult traumatization was important to reveal a risk increasing the interaction effect of the 5-HTTLPR s-allele on current depressive symptoms (Grabe, Schwahn, Mahler, Schulz, et al., 2012). However, in the past 15 years, many studies have been published on the role of 5-HTTLPR in GxE with mixed results. Two metaanalyses revealed overall negative results (Munafo, Durrant, Lewis, & Flint, 2009; Risch et al., 2009). Following the line of methodological criticism, especially on the metaanalysis performed by Risch et al., who only included 14 studies, Karg, Burmeister, Shedden, and Sen (2011)) performed a new metaanalysis including 54 studies. They found strong evidence that the 5-HTTLPR moderates the relationship between stress and depression, with the s-allele being associated with an increased risk of developing depression under stress. Karg et al. further reported that a strong interaction between the s-allele and childhood maltreatment and specific medical conditions was found, but almost never with adult stressful life events. When they restricted their analysis to the studies included in the previous metaanalyses from Munafo` and Risch, they found no evidence of interaction. Nevertheless, the difficulties in replicating GxE results with regard to the 5HTTLPR have stimulated further research activities. One huge international consortium led by Culverhouse et al. (2017) aimed at determining the magnitude of the GxE interaction of the 5-HTTLPR and the conditions under which it might be observed. A metaanalysis on 31 data sets comprised of 38,802 European ancestry subjects was performed. To reduce heterogeneity, a uniform data analysis script was used for all cohorts. Analyses targeted two stressors (narrow, broad) and two depression outcomes (current, lifetime). The findings did not support a general interaction using a multiplicative statistical model. From this paper, it follows that if interaction between stressful life-events and the 5-HTTLR do exist, this interaction seems to be present in subgroups, or under certain circumstances only. Taking these results, the initial expectation that the 5-HTTLPR could serve as a clinically useful biomarker has not proven true. However, from a psychobiological point of view, the 5-HTTLPR is still highly interesting. There are studies that associate the so-call risk allele (s) with behavioral beneficial traits, as with better risk assessment and better social recognition (Homberg & Lesch, 2011). In the study of Reinelt et al. (2015), subjects with low social support carrying at least one short allele reported significantly increased levels of “Sense of Coherence” and resilience, as well as less depressive symptoms than carriers of the l/l genotype. Other studies have found an association of the l/l-genotype with social anxiety disorders (Reinelt et al., 2014), and even against the symptoms of posttraumatic stress disorder (Grabe et al., 2009), concluding that the s-allele might be protective against psychopathological conditions that are frequently associated with depressive disorders. This contributes to a much more complex picture that the multiple environmental risks and protective factors modulate the interaction of the 5-HTTLPR.

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FKBP5 gene

Another prominent gene in GxE research is the FKBP5 gene. The FKBP5 gene is located on chromosome 6p21, and codes for FK506 binding protein 51 (FKBP5), a co-chaperone of hsp90, which regulates the glucocorticoid receptor (GR) sensitivity (Binder, 2009). FKBP5 is relevantly expressed in the brain (Gawlik et al., 2006). Functionally, cortisol induces the FKBP5 expression by activation of glucocorticoid-response-elements (Vermeer, Hendriks-Stegeman, van der Burg, van Buul-Offers, & Jansen, 2003). In turn, FKBP5 binding to the GR reduces the GR affinity for cortisol, and diminishes the amount of activated GR translocation to the cell nucleus (Wochnik et al., 2005). In a recent systematic review, (Wang, Shelton, & Dwivedi, 2018) the effects of the interaction between FKBP5 gene variants and early-life stress, and their associations with stress-related disorders such as major depression and PTSD were examined. Fourteen studies (15,109 participants) were included. Based on the literature, rs1360780, rs3800373, and rs9470080 SNPs were selected within the FKBP5 gene. Their metaanalysis showed that individuals carrying the T allele of rs1360780, C-allele of rs3800373, or T-allele of rs9470080, and having been exposed to early-life trauma, had higher risks for depression or PTSD. In a recent brain imaging study, we could demonstrate that large clusters of reduced gray matter (GM) volumes (left insula, superior and middle temporal gyrus, putamen, the bilateral hippocampus and olfactory cortex, right putamen, caudate nucleus, pallidum, amygdala) were present in abused TT-carriers (rs1360780) compared with abused non-TT carriers or (Grabe et al., 2016). Nevertheless, it is still unclear how informative FKBP5 genotypes will be for prevention, clinical use, and individualized treatment. Interestingly, the development of pharmacological compounds blocking FKBP5 action is underway.

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Brain-derived neurotrophic factor (BDNF)

Another protein that has stimulated neurobiological research is the brain-derived neurotrophic factor (BDNF). The Met allele of the BDNF Val66Met polymorphisms was found to decrease the activity dependent secretion of BDNF by altering the intracellular trafficking. In line with this, the Val66Met was associated with lower hippocampal N-acetylaspartate levels, and with impaired episodic memory (Egan et al., 2003). Animal research showed that a reduction in BDNF expression in the dentate gyrus reduced neurogenesis, and impacted depression-like behaviors (Taliaz et al., 2011; Taliaz, Stall, Dar, & Zangen, 2010). Further, Adachi, Barrot, Autry, Theobald, and Monteggia (2008) showed that BDNF-dependent neurogenesis represents an important biological mechanism in response to antidepressant treatment. In support of the relevance of BDNF and the Val66Met polymorphism, evidence from a metaanalysis suggests a better response to antidepressant treatment in subjects carrying the Met allele of the BDNF Val66Met polymorphisms (Kato & Serretti, 2010). There are many lines of experimental evidence that support a tight functional interconnection between the serotonin system and BDNF, especially in neurogenesis and synaptic plasticity (Martinowich & Lu, 2008). Thus, it is reasonable to study gene x gene (GxG), as well as gene x gene x environment interactions (GxGxE) with regard to BDNF, the 5-HTTLPR and stressful life-events. At an early time in the study of GxE, a three-way interaction among BDNFVal66Met, the 5-HTTLPR, and childhood maltreatment was identified, predicting depression (Kaufman et al., 2006). Independent replication studies on this three-way interaction yielded inconsistent results: Kaufman et al. (2006) found significant three-way interactions with the highest scores of depression or distress in the Met allele and s/s genotype, whereas Wichers et al. found the highest distress in the Met allele and s/l genotype group (Wichers et al., 2008). Two larger studies did not replicate any three-way-interaction (Aguilera et al., 2009; Nederhof, Bouma, Oldehinkel, & Ormel, 2010). In more than 2000 individuals, Grabe, Schwahn, Mahler, Appel, et al. (2012) found support for a GxGxE interaction that relevantly impacts the role of the s/s genotype of the 5-HTTLPR in childhood abuse. Depending on the BDNF polymorphism (Val/Val versus Met allele), the s/s genotype showed either protective or risk properties with regard to depressive symptoms. Given the neurobiological importance of BDNF, numerous brain imaging studies have been performed aiming at elucidating genotype-dependent differences in brain structure. One recent metaanalysis evaluated the impact of rs6265 SNP on hippocampal volumes in neuropsychiatric patients with major depressive disorder, anxiety, bipolar disorder, or schizophrenia (Harrisberger et al., 2015). The metaanalysis was comprised of 18 independent clinical cohorts (n ¼ 1695 patients). For each BDNF genotype, the hippocampal volumes were significantly lower in neuropsychiatric patients than in healthy controls, but there was not a genotype-effect on hippocampal volumes. This might indicate that further factors such as GxG or GxGxE interactions are necessary to obtain structural brain differences in BDNF rs6265. It is noteworthy that BDNF also seems to play important roles in other tissues such as muscle, platelets, and heart muscle.

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8 Genome-wide challenges of GxE Most candidate gene approaches in GxE studies select single variants in specific genes belonging to plausible diseaserelated pathways. Although this approach seems sensible, there are many challenges. Comparing two studies is often difficult because of differences in sample size, assessments of environmental exposures, phenotype definition, or sample recruitment (Dunn et al., 2015; Mandelli & Serretti, 2013). Moreover, there are no common guidelines on how to set up the mathematical model to perform GxE analyses in MDD. Until now, only three GxE interaction analyses for depression have been performed on a genome-wide level. One was published by Dunn et al. (2016) in a sample of African American and Hispanic women where one genome-wide significant hit was found near CEP350, a centrosomal protein that has never been associated with a psychiatric phenotype before. Another study in a Japanese population (N ¼ 320) reported the genome-wide hit rs10510057 near RGS10 (Otowa et al., 2016), but given the small sample size, this result should be regarded with caution. In a recent metaanalysis in N ¼ 3944 Caucasian subjects, Van der Auwera et al. (2018) did not find any evidence for a genome-wide significant GxE hit, and no supporting evidence for the frequently studied candidate variants. Thus, it might be prudent to at least partially question some of the former candidate SNP results for MDD and harmonize the different models currently performed in GxE analyses for MDD to perform consistent analyses in samples large enough to identify robust GxE interaction signals.

9 Schizophrenia and bipolar disorder Recent genome-wide association studies have been very fruitful, especially in highly powered metaanalysis of schizophrenia yielding new genetics signals, but also supporting pathways and mechanisms that have been proposed for decades, such as the dopaminergic system and inflammatory pathways (PGC, 2014). A recent review on GxE studies in schizophrenia and bipolar disorders (BD) identified 11 eligible studies from patients with BD, and 50 studies on schizophrenia spectrum phenotypes. These studies were grouped into five distinct domains in dependence of the environmental factor: (1) gene  cannabis interactions, (2) gene  stress and childhood trauma interactions, (3) gene  season of birth interactions, (4) gene  infectious factors interactions, and (5) gene  obstetric complications interactions (Misiak et al., 2017). Most of the studies investigating the interactions between cannabis use and genetic factors focused on genetic variants affecting the dopaminergic system. One important dopamine-inactivating enzyme is coded by the catechol-Omethyltransferase (COMT) gene. Carriers of the functional 158Val allele with cannabis use in their adolescence were found to have an increased risk of schizophrenia (Caspi et al., 2005). In line with this finding, other studies described higher risk for psychotic symptoms (Ermis et al., 2015; Henquet et al., 2006; Nieman et al., 2016), and earlier age of onset (Estrada et al., 2011). Despite these encouraging studies, other studies have not supported the interaction between the COMT 158Val/Met polymorphism and cannabis use on the risk of psychosis (Kantrowitz et al., 2009; van Winkel, 2011; Zammit et al., 2007), or risk of subclinical psychotic experiences (Zammit, Lewis, Dalman, & Allebeck, 2010) and age of psychosis onset (De Sousa et al., 2013). The DRD2 rs1076560 T allele had a threefold increase in psychosis risk compared with GG homozygotes in cannabisusing, first-episode psychosis patients compared with controls (Colizzi et al., 2015). Investigating the effects of the rs1360780 polymorphism of the FKBP5 gene in first-episode psychosis patients and healthy controls (Ajnakina et al., 2014) did not find significant interactions between genetic variation in the FKBP5 gene and cannabis use on psychosis. Genetic variations in the gene of the brain-derived neurotrophic factor (BDNF) were associated with earlier age of psychosis onset in female BDNF 66Met allele carriers (Decoster et al., 2011). However, this effect-modification of the BDNF 66Val/Met polymorphism was not seen in males. Many studies investigating gene  stress interactions in psychotic disorders found positive results. Positive interactions were reported for the BDNF 66 Val/Met polymorphism and parameters of increased risk of psychosis or higher symptom load (Alemany et al., 2016; de Castro-Catala et al., 2016). Additionally, an additive effect of the BDNF 66Met allele and a history of childhood trauma on reduced levels of BDNF mRNA in whole blood was reported. Met carriers who reported high levels of childhood trauma also had significantly reduced hippocampal subfield volumes (Aas et al., 2014). Four studies have found positive effects for an interaction of genetic polymorphisms within the FKBP5 gene and childhood trauma on psychosis (Ajnakina et al., 2014; Collip et al., 2013; Cristobal-Narvaez et al., 2016; Green et al., 2015). For example, Collip et al. (2013) found a significant interaction between the rs9296158 and rs4713916 polymorphisms of the FKBP5 gene and childhood trauma on psychotic symptoms and cortisol levels in a twin sample that was replicated for the rs4713916 polymorphism in siblings, and for rs9296158 in patients. Ajnakina et al. (2014) reported a significant interaction between the FKBP5 rs1360780 polymorphism and parental separation on psychosis risk. In a newer

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study, however, an interacting effect of variants within the FKBP5 gene and childhood trauma could be identified in subclinical depression and anxiety, but not for schizotypic or psychotic-like experiences in 808 young adults (de Castro-Catala et al., 2017). One study on the interaction of childhood trauma and the forkhead box protein 2 (FOXP2) gene in predicting a lifetime history of auditory hallucinations found positive results (McCarthy-Jones et al., 2014). Other studies reported on the interactions between recent or daily life stressors and the COMT gene with inconsistent results for either the COMT 158 Met/Met genotype (Peerbooms et al., 2012; van Winkel et al., 2008), or the Val-allele being associated with higher risk of psychotic symptoms (Simons et al., 2009; Stefanis et al., 2013). As seasonality of birth is associated with the risk of schizophrenia, possibly through infectious/immunological processes, the presence of the HLA-DR1 allele was tested for interaction with seasonality of birth schizophrenia. Narita et al. (2000) reported that the HLA-DR1 allele was associated with increased incidence of winter birth in patients with schizophrenia, which was not replicated in another study (Tochigi et al., 2002). Other studies yielded negative or preliminary positive results for tryptophan hydroxylase 1 (TPH1), the dopamine D4 receptor (DRD4) 7-repeat allele polymorphism, and 5-HTTLPR L/S polymorphism (Chotai, Serretti, Lattuada, Lorenzi, & Lilli, 2003), and for the C677T polymorphism of the methylenetetrahydrofolate reductase (MTHFR) gene, which codes for an essential enzyme in the folate mediated methylation transfer reactions (Muntjewerff, Ophoff, Buizer-Voskamp, Strengman, & den Heijer, 2011). With respect to BD, an association between the HLA-G 14 bp ins/del polymorphism and seasonality of birth was found (Debnath et al., 2013). As infectious diseases may represent a risk factor for the development of schizophrenia, interactions with genetic variants have been tested. Genetic variants of the glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B) gene and maternal herpes simplex virus type 2 (HSV-2) seropositivity yielded a significant interaction on schizophrenia risk in the offspring (Demontis et al., 2011). Shirts et al. (2007) found that variations in the MHC Class I Polypeptide-Related Sequence B (MICB) gene may interact with cytomegalovirus (CMV) and herpes simplex virus type 1 (HSV-1) seropositivity, impacting the risk of schizophrenia. Dickerson et al. (2006) revealed that the COMT 158Val/Val genotype and HSV-1 seropositivity impacted independently on global cognitive performance. Moreover, patients carrying the risk genotype (Val/Val) and being HSV-1 positive had an 85% higher risk of low global cognitive performance. Seropositivity for Toxoplasma gondii revealed and significant interaction with TLR2 gene polymorphism (rs3804099) for the risk of BD (Oliveira et al., 2016). The toll-like receptor (TLR) family plays a fundamental role in pathogen recognition and activation of innate immunity. Obstetric complications have long been identified as risk-increasing factors for major psychiatric disorders. A few studies so far have investigated putative interactions between genetic factors and obstetric complications for the risk of schizophrenia or BD. Nicodemus et al. (2008) investigated a set of 13 candidate genes in a family-based study of 116 trios. Twenty-nine probands had at least one serious obstetric complication. Four genes (serine-threonine protein kinase (AKT1), BDNF, dystrobrevin (DTNBP1) and glutamate metabotropic receptor 3 (GRM3) showed significant evidence for gene-byenvironment interaction. Ursini et al. (2016) found that methylation at the BDNF rs6265 Val allele in peripheral blood of healthy subjects is associated with hypoxia-related early life events and intermediate phenotypes for schizophrenia. Haukvik et al. (2010) investigated interaction effects of severe hypoxia-related OCs and variation in four hypoxia-regulated schizophrenia susceptibility genes (BDNF, DTNBP1, GRM3, and NRG1) on hippocampal volume in schizophrenia patients and healthy controls. The effects of severe OCs on hippocampal volume were associated with rs13242038 of the GRM3 gene (one out of 32 SNPs studied), but the interaction effect was not specific for schizophrenia.

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Future relevance of GxE interaction

From the examples described herein, it becomes clear that methodological challenges for the development of diagnostic and therapeutic algorithms for personalized psychiatry are enormous, and the replication of associations is of utmost importance, but often difficult to achieve. Given the heterogeneity of samples, genetic backgrounds, stressors, and phenotypes studied, it is not even clear if nonreplicated GxE effects were truly false positives, or if they indeed occurred in the initial cohort. Nevertheless, independent replications still provide the best empirical support for GxE interactions that generalize to different samples and settings. GxE interactions for personalized psychiatry in prevention, treatment decision, or risk assessment would require reproducible interaction effects that would allow for sensitivity, specificity, and positive/negative predictive value estimations. In one example for the FKBP5 rs1360780 SNP in the Study of Health in Pomerania (SHIP), we have performed such a promising estimation (Grabe & Schwahn, 2011). Given the large effect sizes demonstrated by Appel et al. (2011),

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rs1360780 TT-risk allele carriers were included in prediction models for depression in individuals exposed to childhood abuse. At a given prevalence rate of MDD, for example, 30%, the risk prediction (positive predictive value) by genotype and abuse status was about 70%. However, positive and negative predictive values were still far too low to recommend individual risk prediction via this approach. A further refinement of this model was possible via the inclusion of the status “high or low resilience.” Considering the inclusion criteria “low resilience,” the prevalence rate of MDD increased up to 35% in the selected subjects. Thus, the positive predictive value of the screen-positive subjects (TT-carriers and child abuse) increased to 80%. Although promising, these effects were not replicated in another independent sample. However, the search for variants with more robust associations is ongoing. Currently, genome-wide approaches are underway to take advantage of the unbiased inclusion of genetic variants for the identification of GxE interactions. Like in a conventional GWAS, this approach gives rise to the expectation to discover new, yet unknown biological pathways that interact with environmental factors. One has to keep in mind that the sample size needed to yield genome-wide significant associations will be enormous, for example, in the range of at least 50–100 K subjects for disorders such as major depression. In psychiatry, the distance between the biological mechanisms and underpinnings and the clinical phenotypes are supposed to be large. This justifies the idea of “endophenotypes” that are much more closely related to biology than the clinical phenotypes themselves. Especially for the field of GxE, this approach could be very informative for personalized psychiatry. Endophenotypes could be derived from, for example, cognitive measures (Burrows & Hannan, 2016), analyses of structural brain imaging data (Grabe et al., 2016), and from functional brain imaging studies (Braff & Tamminga, 2017) (see Fig. 1). Animal studies that allow precise experimental modulation of environmental stressors are of the utmost importance to gain mechanistic insights on how GxE interactions biologically might alter epigenetic regulations, miRNA dynamics, gene expression, and ultimately the structure and function of neural networks (Braun et al., 2017). Also “secondhits” models that capture the temporal dynamics of trauma and biological long-term response will increase our understanding of the nature of mental diseases substantially (Lesse, Rether, Groger, Braun, & Bock, 2017). Thus, GxE research will most likely not contribute to personalized diagnostics and treatments in the near future, but the mechanistic understanding of the development of mental diseases that emerge from this research approach will significantly influence our conceptual understanding of mental diseases. However, in the longer perspective, findings of GxE research might be implemented in new strategies in personalized psychiatry. For example, it is conceivable that in the future researchers may develop “posttrauma-medications” that will prevent negative biological adaptations after traumatic events (Kao et al., 2016), to develop medications that will stabilize biological processes under conditions of stress, or to develop stress prevention strategies in subjects at higher biological risk of maladaptation.

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