Abstract:Depression has well-established influences from genetic and environmental risk factors. This has led to the diathesis-stress theory, which assumes a multiplicative gene-by-environment interaction (GxE) effect on risk. Recently, Colodro-Conde et al. empirically tested this theory, using the polygenic risk score for major depressive disorder (PRS, genes) and stressful life events (SLE, environment) effects on depressive symptoms, identifying significant GxE effects with an additive contribution to liability. We … Show more
“…To more comprehensively capture genome-by-trauma interactions within depression and neuroticism, the approach uses all genotyped variants to compute genetic similarity instead of individual SNVs or PGSs as in some previous studies. 16,19,27 We computed trauma exposure and genome-by-trauma interaction similarity to explore trait variance attributable to these effects by incorporating the genetic, trauma exposure, and interaction terms as random e f f e c t s a s o p p o s e d t o f i xe d e f f e c t s w it h i n l i n e a r models. 19,20,27 Moreover, we used all related individuals, with appropriate sensitivity analyses (limited to unrelated individuals only).…”
Section: Discussionmentioning
confidence: 99%
“…16,[19][20][21][22] Moreover, research using polygenic scores (PGSs)genetic measures that can be calculated for each individual by identifying, weighting, and summing genotyped risk variants found to be associated with depression 23,24 -have yielded inconsistent findings. Some studies have highlighted sex differences 25 and found significant interaction associations with MDD outcomes, 16,18,[25][26][27] whereas some replication attempts reported null findings. [28][29][30] An explanation for inconsistent findings may lie in the predictive accuracy and validity of PGSs.…”
ImportanceSelf-reported trauma exposure has consistently been found to be a risk factor for major depressive disorder (MDD), and several studies have reported interactions with genetic liability. To date, most studies have examined gene-environment interactions with trauma exposure using genome-wide variants (single-nucleotide variations [SNVs]) or polygenic scores, both typically capturing less than 3% of phenotypic risk variance.ObjectiveTo reexamine genome-by-trauma interaction associations using genetic measures using all available genotyped data and thus, maximizing accounted variance.Design, Setting, and ParticipantsThe UK Biobank study was conducted from April 2007 to May 1, 2016 (follow-up mental health questionnaire). The current study used available cross-sectional genomic and trauma exposure data from UK Biobank. Participants who completed the mental health questionnaire and had available genetic, trauma experience, depressive symptoms, and/or neuroticism information were included. Data were analyzed from April 1 to August 30, 2021.ExposuresTrauma and genome-by-trauma exposure interactions.Main Outcomes and MeasuresMeasures of self-reported depression, neuroticism, and trauma exposure with whole-genome SNV data are available from the UK Biobank study. Here, a mixed-model statistical approach using genetic, trauma exposure, and genome-by-trauma exposure interaction similarity matrices was used to explore sources of variation in depression and neuroticism.ResultsAnalyses were conducted on 148 129 participants (mean [SD] age, 56 [7] years) of which 76 995 were female (52.0%). The study approach estimated the heritability (SE) of MDD to be approximately 0.160 (0.016). Subtypes of self-reported trauma exposure (catastrophic, adult, childhood, and full trauma) accounted for a significant proportion of the variance of MDD, with heritability (SE) ranging from 0.056 (0.013) to 0.176 (0.025). The proportion of MDD risk variance accounted for by significant genome-by-trauma interaction revealed estimates (SD) ranging from 0.074 (0.006) to 0.201 (0.009). Results from sex-specific analyses found genome-by-trauma interaction variance estimates approximately 5-fold greater for MDD in male participants (0.441 [0.018]) than in female participants (0.086 [0.009]).Conclusions and RelevanceThis cross-sectional study used an approach combining all genome-wide SNV data when exploring genome-by-trauma interactions in individuals with MDD; findings suggest that such interactions were associated with depression manifestation. Genome-by-trauma interaction accounts for greater trait variance in male individuals, which points to potential differences in depression etiology between the sexes. The methodology used in this study can be extrapolated to other environmental factors to identify modifiable risk environments and at-risk groups to target with interventions.
“…To more comprehensively capture genome-by-trauma interactions within depression and neuroticism, the approach uses all genotyped variants to compute genetic similarity instead of individual SNVs or PGSs as in some previous studies. 16,19,27 We computed trauma exposure and genome-by-trauma interaction similarity to explore trait variance attributable to these effects by incorporating the genetic, trauma exposure, and interaction terms as random e f f e c t s a s o p p o s e d t o f i xe d e f f e c t s w it h i n l i n e a r models. 19,20,27 Moreover, we used all related individuals, with appropriate sensitivity analyses (limited to unrelated individuals only).…”
Section: Discussionmentioning
confidence: 99%
“…16,[19][20][21][22] Moreover, research using polygenic scores (PGSs)genetic measures that can be calculated for each individual by identifying, weighting, and summing genotyped risk variants found to be associated with depression 23,24 -have yielded inconsistent findings. Some studies have highlighted sex differences 25 and found significant interaction associations with MDD outcomes, 16,18,[25][26][27] whereas some replication attempts reported null findings. [28][29][30] An explanation for inconsistent findings may lie in the predictive accuracy and validity of PGSs.…”
ImportanceSelf-reported trauma exposure has consistently been found to be a risk factor for major depressive disorder (MDD), and several studies have reported interactions with genetic liability. To date, most studies have examined gene-environment interactions with trauma exposure using genome-wide variants (single-nucleotide variations [SNVs]) or polygenic scores, both typically capturing less than 3% of phenotypic risk variance.ObjectiveTo reexamine genome-by-trauma interaction associations using genetic measures using all available genotyped data and thus, maximizing accounted variance.Design, Setting, and ParticipantsThe UK Biobank study was conducted from April 2007 to May 1, 2016 (follow-up mental health questionnaire). The current study used available cross-sectional genomic and trauma exposure data from UK Biobank. Participants who completed the mental health questionnaire and had available genetic, trauma experience, depressive symptoms, and/or neuroticism information were included. Data were analyzed from April 1 to August 30, 2021.ExposuresTrauma and genome-by-trauma exposure interactions.Main Outcomes and MeasuresMeasures of self-reported depression, neuroticism, and trauma exposure with whole-genome SNV data are available from the UK Biobank study. Here, a mixed-model statistical approach using genetic, trauma exposure, and genome-by-trauma exposure interaction similarity matrices was used to explore sources of variation in depression and neuroticism.ResultsAnalyses were conducted on 148 129 participants (mean [SD] age, 56 [7] years) of which 76 995 were female (52.0%). The study approach estimated the heritability (SE) of MDD to be approximately 0.160 (0.016). Subtypes of self-reported trauma exposure (catastrophic, adult, childhood, and full trauma) accounted for a significant proportion of the variance of MDD, with heritability (SE) ranging from 0.056 (0.013) to 0.176 (0.025). The proportion of MDD risk variance accounted for by significant genome-by-trauma interaction revealed estimates (SD) ranging from 0.074 (0.006) to 0.201 (0.009). Results from sex-specific analyses found genome-by-trauma interaction variance estimates approximately 5-fold greater for MDD in male participants (0.441 [0.018]) than in female participants (0.086 [0.009]).Conclusions and RelevanceThis cross-sectional study used an approach combining all genome-wide SNV data when exploring genome-by-trauma interactions in individuals with MDD; findings suggest that such interactions were associated with depression manifestation. Genome-by-trauma interaction accounts for greater trait variance in male individuals, which points to potential differences in depression etiology between the sexes. The methodology used in this study can be extrapolated to other environmental factors to identify modifiable risk environments and at-risk groups to target with interventions.
“…Interaction between polygenic risk of MDD and recent SLE are reported to increase liability to depressive symptoms 4,16 ; validating the implementation of genome-wide approaches to study GxE in depression. Most GxE studies for MDD have been conducted on candidate genes, or using polygenic approaches to a wide range of environmental risk factors, with some contradictory findings 28-32 .…”
Section: Introductionmentioning
confidence: 86%
“…There is strong evidence for the role of stressful life events (SLE) as risk factor and trigger for depression 8-12 . Genetic control of sensitivity to stress may vary between individuals, resulting in individual differences in the depressogenic effects of SLE, i.e., genotype-by-environment interaction (GxE) 4,13-16 . Significant evidence of GxE has been reported for common respiratory diseases and some forms of cancer 17-22 , and GxE studies have identified genetic risk variants not found by genome-wide association studies (GWAS) 23-27 .…”
Stress is associated with poorer physical and mental health. To improve our understanding of this link, we performed genome-wide association studies (GWAS) of depressive symptoms and genome-wide by environment interaction studies (GWEIS) of depressive symptoms and stressful life events (SLE) in two UK population cohorts (Generation Scotland and UK Biobank). No SNP was individually significant in either GWAS, but gene-based tests identified six genes associated with depressive symptoms in UK Biobank (DCC, ACSS3, DRD2, STAG1, FOXP2 and KYNU; p < 2.77×10-6). Two SNPs with genome-wide significant GxE effects were identified by GWEIS in Generation Scotland: rs12789145 (53kb downstream PIWIL4; p = 4.95×10-9; total SLE) and rs17070072 (intronic to ZCCHC2; p = 1.46×10-8; dependent SLE). A third locus upstream CYLC2 (rs12000047 and rs12005200, p < 2.00×10-8; dependent SLE) when the joint effect of the SNP main and GxE effects was considered. GWEIS gene-based tests identified: MTNR1B with GxE effect with dependent SLE in Generation Scotland; and PHF2 with the joint effect in UK Biobank (p < 2.77×10-6). Polygenic risk scores (PRS) analyses incorporating GxE effects improved the prediction of depressive symptom scores, when using weights derived from either the UK Biobank GWAS of depressive symptoms (p = 0.01) or the PGC GWAS of major depressive disorder (p = 5.91×10-3). Using an independent sample, PRS derived using GWEIS GxE effects provided evidence of shared aetiologies between depressive symptoms and schizotypal personality, heart disease and COPD. Further such studies are required and may result in improved treatments for depression and other stress-related conditions.
“…Replication of gene-trait associations identified in GWAS and their functional analyses continues to be critical. GWEIS (genome-wide by environment interaction study) brings G-E interaction analyses into GWAS, but GWEISs are uncommon (23). One challenge is to develop models that will enable multilayered measurements within environments to be incorporated into GWEIS designs.…”
A now substantial body of science implicates a dynamic interplay between genetic and environmental variation in the development of individual differences in behavior and health. Such outcomes are affected by molecular, often epigenetic, processes involving gene–environment (G–E) interplay that can influence gene expression. Early environments with exposures to poverty, chronic adversities, and acutely stressful events have been linked to maladaptive development and compromised health and behavior. Genetic differences can impart either enhanced or blunted susceptibility to the effects of such pathogenic environments. However, largely missing from present discourse regarding G–E interplay is the role of time, a “third factor” guiding the emergence of complex developmental endpoints across different scales of time. Trajectories of development increasingly appear best accounted for by a complex, dynamic interchange among the highly linked elements of genes, contexts, and time at multiple scales, including neurobiological (minutes to milliseconds), genomic (hours to minutes), developmental (years and months), and evolutionary (centuries and millennia) time. This special issue of PNAS thus explores time and timing among G–E transactions: The importance of timing and timescales in plasticity and critical periods of brain development; epigenetics and the molecular underpinnings of biologically embedded experience; the encoding of experience across time and biological levels of organization; and gene-regulatory networks in behavior and development and their linkages to neuronal networks. Taken together, the collection of papers offers perspectives on how G–E interplay operates contingently within and against a backdrop of time and timescales.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.