Gene × environment interaction models in psychiatric genetics

Impact of gene-by-trauma interaction in MDD-related multimorbidity clusters

BACKGROUND

Major depressive disorder (MDD) is considerably heterogeneous in terms of comorbidities, which may hamper the disentanglement of its biological mechanism. In a previous study, we classified the lifetime trajectories of MDD-related multimorbidities into seven distinct clusters, each characterized by unique genetic and environmental risk-factor profiles. The current objective was to investigate genome-wide gene-by-environment (G × E) interactions with childhood trauma burden, within the context of these clusters.

METHODS

We analyzed 77,519 participants and 6,266,189 single-nucleotide polymorphisms (SNPs) of the UK Biobank database. Childhood trauma burden was assessed using the Childhood Trauma Screener (CTS). For each cluster, Plink 2.0 was used to calculate SNP × CTS interaction effects on the participants' cluster membership probabilities. We especially focused on the effects of 31 candidate genes and associated SNPs selected from previous G × E studies for childhood maltreatment's association with depression.

RESULTS

At SNP-level, only the high-multimorbidity Cluster 6 revealed a genome-wide significant SNP rs145772219. At gene-level, MPST and PRH2 were genome-wide significant for the low-multimorbidity Clusters 1 and 3, respectively. Regarding candidate SNPs for G × E interactions, individual SNP results could be replicated for specific clusters. The candidate genes CREB1, DBH, and MTHFR (Cluster 5) as well as TPH1 (Cluster 6) survived multiple testing correction.

LIMITATIONS

CTS is a short retrospective self-reported measurement. Clusters could be influenced by genetics of individual disorders.

CONCLUSIONS

The first G × E GWAS for MDD-related multimorbidity trajectories successfully replicated findings from previous G × E studies related to depression, and revealed risk clusters for the contribution of childhood trauma.

Deep phenotyping as a contribution to personalized depression therapy: the GEParD and DaCFail protocols

Depressive patients suffer from a complex of symptoms of varying intensity compromising their mood, emotions, self-concept, neurocognition, and somatic function. Due to a mosaic of aetiologies involved in developing depression, such as somatic, neurobiological, (epi-)genetic factors, or adverse life events, patients often experience recurrent depressive episodes. About 20-30% of these patients develop difficult-to-treat depression. Here, we describe the design of the GEParD (Genetics and Epigenetics of Pharmaco- and Psychotherapy in acute and recurrent Depression) cohort and the DaCFail (Depression-associated Cardiac Failure) case-control protocol. Both protocols intended to investigate the incremental utility of multimodal biomarkers including cardiovascular and (epi-)genetic markers, functional brain and heart imaging when evaluating the response to antidepressive therapy using comprehensive psychometry. From 2012 to 2020, 346 depressed patients (mean age 45 years) were recruited to the prospective, observational GEParD cohort protocol. Between 2016 and 2020, the DaCFail case-control protocol was initiated integrating four study subgroups to focus on heart-brain interactions and stress systems in patients > 50 years with depression and heart failure, respectively. For DaCFail, 120 depressed patients (mean age 60 years, group 1 + 2), of which 115 also completed GEParD, and 95 non-depressed controls (mean age 66 years) were recruited. The latter comprised 47 patients with heart failure (group 3) and 48 healthy subjects (group 4) of a population-based control group derived from the Characteristics and Course of Heart Failure Stages A-B and Determinants of Progression (STAAB) cohort study. Our hypothesis-driven, exploratory study design may serve as an exemplary roadmap for a standardized, reproducible investigation of personalized antidepressant therapy in an inpatient setting with focus on heart comorbidities in future multicentre studies.

Association of parental divorce, discord, and polygenic risk with children's alcohol initiation and lifetime risk for alcohol use disorder

BACKGROUND

Parental divorce and discord are associated with poorer alcohol-related outcomes for offspring. However, not all children exposed to these stressors develop alcohol problems. Our objective was to test gene-by-environment interaction effects whereby children's genetic risk for alcohol problems modifies the effects of parental divorce and discord to predict alcohol outcomes.

METHODS

The sample included European (EA; N = 5608, 47% male, Mage  ~ 36 years) and African (AA; N = 1714, 46% female, Mage  ~ 33 years) ancestry participants from the Collaborative Study on the Genetics of Alcoholism. Outcomes included age at initiation of regular drinking and lifetime DSM-5 alcohol use disorder (AUD). Predictors included parental divorce, parental relationship discord, and offspring alcohol problems polygenic risk scores (PRSALC ). Mixed effects Cox proportional hazard models were used to examine alcohol initiation and generalized linear mixed effects models were used to examine lifetime AUD. Tests of PRS moderation of the effects of parental divorce/relationship discord on alcohol outcomes were examined on multiplicative and additive scales.

RESULTS

Among EA participants, parental divorce, parental discord, and higher PRSALC were associated with earlier alcohol initiation and greater lifetime AUD risk. Among AA participants, parental divorce was associated with earlier alcohol initiation and discord was associated with earlier initiation and AUD. PRSALC was not associated with either. Parental divorce/discord and PRSALC interacted on an additive scale in the EA sample, but no interactions were found in AA participants.

CONCLUSIONS

Children's genetic risk for alcohol problems modifies the impact of parental divorce/discord, consistent with an additive model of diathesis-stress interaction, with some differences across ancestry.

Unraveling the complex interplay between genes, environment, and climate in ALS

Various genetic and environmental risk factors have been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS). Despite this, the cause of most ALS cases remains obscure. In this review, we describe the current evidence implicating genetic and environmental factors in motor neuron degeneration. While the risk exerted by many environmental factors may appear small, their effect could be magnified by the presence of a genetic predisposition. We postulate that gene-environment interactions account for at least a portion of the unknown etiology in ALS. Climate underlies multiple environmental factors, some of which have been implied in ALS etiology, and the impact of global temperature increase on the gene-environment interactions should be carefully monitored. We describe the main concepts underlying such interactions. Although a lack of large cohorts with detailed genetic and environmental information hampers the search for gene-environment interactions, newer algorithms and machine learning approaches offer an opportunity to break this stalemate. Understanding how genetic and environmental factors interact to cause ALS may ultimately pave the way towards precision medicine becoming an integral part of ALS care.

Current Knowledge on Gene-Environment Interactions in Personality Disorders: an Update

PURPOSE OF REVIEW

We review the existing literature on gene-environment interactions (G×E) and epigenetic changes primarily in borderline personality disorder (BPD) but also in antisocial, schizotypal, and avoidant personality disorders.

RECENT FINDINGS

Research supports that susceptibility genes to BPD or its underlying traits may be expressed under certain environmental conditions such as physical or childhood sexual abuse. Epigenetic modifications of neurodevelopment- and stress-related genes are suggested to underlie the relationship between early life adversary and borderline personality disorder. Only limited studies have investigated the role of gene-environment interactions and epigenetic changes in the genesis of antisocial, schizotypal, and avoidant personality disorders. Considering the lack of pharmacological treatment for most personality disorders, the emerging evidence on the critical role of G×E and epigenetic changes in the genesis of personality disorders could help develop more biologically oriented therapeutic approaches. Future studies should explore the potential of this new therapeutic dimension.

Significance of risk polymorphisms for depression depends on stress exposure

Depression is a polygenic and multifactorial disorder where environmental effects exert a significant impact, yet most genetic studies do not consider the effect of stressors which may be one reason for the lack of replicable results in candidate gene studies, GWAS and between human studies and animal models. Relevance of functional polymorphisms in seven candidate genes previously implicated in animal and human studies on a depression-related phenotype given various recent stress exposure levels was assessed with Bayesian relevance analysis in 1682 subjects. This Bayesian analysis indicated a gene-environment interaction whose significance was also tested with a traditional multivariate analysis using general linear models. The investigated genetic factors were only relevant in the moderate and/or high stress exposure groups. Rank order of genes was GALR2 > BDNF > P2RX7 > HTR1A > SLC6A4 > CB1 > HTR2A, with strong relevance for the first four. Robust gene-gene-environment interaction was found between BDNF and HTR1A. Gene-environment interaction effect was confirmed, namely no main effect of genes, but a significant modulatory effect on environment-induced development of depression were found. Our data support the strong causative role of the environment modified by genetic factors, similar to animal models. Gene-environment interactions point to epigenetic factors associated with risk SNPs. Galanin-2 receptor, BDNF and X-type purin-7 receptor could be drug targets for new antidepressants.

Genetic variants in 5-HTTLPR, BDNF, HTR1A, COMT, and FKBP5 and risk for treated depression after cancer diagnosis

BACKGROUND

The role of gene-environment interactions in the pathogenesis of depression is unclear. Previous studies addressed vulnerability for depression after childhood adversity and stressful life events among carriers of numerous specific genetic variants; however, the importance of individual genetic variants, the environmental exposures with which they interact, and the magnitude of the risk conveyed by these interactions remain elusive.

METHODS

We included 7,320 people with a first primary cancer identified in the prospective Diet, Cancer and Health study in an exposed-only cohort study. The mean age of the individuals was 68 years (5th, 95th percentiles: 58, 78) at cancer diagnosis. Using Cox regression models and cumulative incidence plots, we analyzed the associations between genetic variants in 5-HTTLPR, BDNF, HTR1A, COMT, and FKBP5 and use of antidepressants as well as hospital contact for depression after diagnosis of cancer.

RESULTS

Overall, we observed no statistically significant associations, with nonsignificant hazard ratio estimates for use of antidepressants of 0.95-1.07.

CONCLUSIONS

This study of elderly people indicates that it is unlikely that the investigated genetic variants are clinically relevantly associated with depression after diagnosis of cancer. The mechanisms for gene-environment interactions in younger individuals are probably different, and we advise caution in extrapolating our results to early life stress. However, conclusion from the present study might be generalizable to elderly persons exposed to other stressful life events.

Correcting systematic inflation in genetic association tests that consider interaction effects: application to a genome-wide association study of posttraumatic stress disorder

IMPORTANCE

Genetic association studies of psychiatric outcomes often consider interactions with environmental exposures and, in particular, apply tests that jointly consider gene and gene-environment interaction effects for analysis. Using a genome-wide association study (GWAS) of posttraumatic stress disorder (PTSD), we report that heteroscedasticity (defined as variability in outcome that differs by the value of the environmental exposure) can invalidate traditional joint tests of gene and gene-environment interaction.

OBJECTIVES

To identify the cause of bias in traditional joint tests of gene and gene-environment interaction in a PTSD GWAS and determine whether proposed robust joint tests are insensitive to this problem.

DESIGN, SETTING, AND PARTICIPANTS

The PTSD GWAS data set consisted of 3359 individuals (978 men and 2381 women) from the Grady Trauma Project (GTP), a cohort study from Atlanta, Georgia. The GTP performed genome-wide genotyping of participants and collected environmental exposures using the Childhood Trauma Questionnaire and Trauma Experiences Inventory.

MAIN OUTCOMES AND MEASURES

We performed joint interaction testing of the Beck Depression Inventory and modified PTSD Symptom Scale in the GTP GWAS. We assessed systematic bias in our interaction analyses using quantile-quantile plots and genome-wide inflation factors.

RESULTS

Application of the traditional joint interaction test to the GTP GWAS yielded systematic inflation across different outcomes and environmental exposures (inflation-factor estimates ranging from 1.07 to 1.21), whereas application of the robust joint test to the same data set yielded no such inflation (inflation-factor estimates ranging from 1.01 to 1.02). Simulated data further revealed that the robust joint test is valid in different heteroscedasticity models, whereas the traditional joint test is invalid. The robust joint test also has power similar to the traditional joint test when heteroscedasticity is not an issue.

CONCLUSIONS AND RELEVANCE

We believe the robust joint test should be used in candidate-gene studies and GWASs of psychiatric outcomes that consider environmental interactions. To make the procedure useful for applied investigators, we created a software tool that can be called from the popular PLINK package for analysis.

Genomewide interaction and enrichment analysis on antidepressant response

BACKGROUND

Genomewide association studies (GWASs) on antidepressant efficacy have yielded modest results. A possible reason is that response is influenced by other factors, which possibly interact with genetic variation. We used a GWAS model to predict antidepressant response, by including predictors previously known to affect response, such as quality of life (QoL). We also evaluated the association between genes, previously implicated in gene-environment (G × E) interactions, and response using an enrichment analysis.

METHOD

We examined a sample of 1426 depressed patients from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial: 774 responders, 652 non-responders and 418,865 single nucleotide polymorphisms (SNPs) were analysed. First, in a GWAS model, we investigated whether genetic variations interact with patients' levels of QoL to predict response, after controlling for demographic characteristics, severity and population stratification. Second, we conducted an enrichment analysis exploring whether candidate genes that have emerged from prior G × E interaction studies on depression are associated with treatment response.

RESULTS

The GWAS model, with QoL as a moderator, yielded one SNP (rs520210) associated with response in the NEDD4L gene (p = 3.64 × 10⁻⁸). In the Caucasian sample only, we observed a drop in significance for this SNP. The enrichment analysis showed that SNPs within serotonergic genes contained more significant markers that predicted response, compared with a random set of genes in the genome.

CONCLUSIONS

Our findings point to possible target genes, which are proposed for further independent replication. Our enrichment analysis provides further support, in a genomewide context, of the role of serotonergic genes in influencing antidepressant response.

Linking genetic variants of the mineralocorticoid receptor and negative memory bias: interaction with prior life adversity

Substantial research has been conducted investigating the association between life adversity and genetic vulnerability for depression, but clear mechanistic links are rarely identified and investigation often focused on single genetic variants. Complex phenotypes like depression, however, are likely determined by multiple variants in interaction with environmental factors. As variations in the mineralocorticoid receptor gene (NR3C2) have been related to a higher risk for depression, we investigated whether NR3C2 variance is related to negative memory bias, an established endophenotype for depression, in healthy participants. Furthermore, we explored the influence of life adversity on this association. We used a set-based analysis to simultaneously test all measured variation in NR3C2 for an association with negative memory bias in 483 participants and an interaction with life adversity. To further specify this interaction, we split the sample into low and high live adversity groups and repeated the analyses in both groups separately. NR3C2 variance was associated with negative memory bias, especially in the high life adversity group. Additionally, we identified a functional polymorphism (rs5534) related to negative memory bias and demonstrating a gene×life adversity interaction. Variations in NR3C2 are associated with negative memory bias and this relationship appears to be influenced by life adversity. As negative memory bias is implicated in the susceptibility to depression, our findings provide mechanistic support for the notion that variations in NR3C2 - which could compromise the proper function of this receptor - are a risk factor for the development of mood disorders.