Shared Genetics and Couple-Associated Environment Are Major Contributors to the Risk of Both Clinical and Self-Declared Depression

Long-term risk of late-life depression in widowed elderly: a five-year follow-up study

BACKGROUND

Late-life depression (LLD) poses a significant health risk among the elderly, with widowhood as a prominent contributing factor. However, the mechanisms that render some widowed individuals susceptible to depression while others remain resilient remain poorly understood.

METHODS

In this five-year longitudinal study, we followed 203 cognitively healthy, widowed elderly individuals (mean age: 65.2 years, 100 women). The median follow-up time was 4.8 years. Brain structural networks were constructed via diffusion tensor imaging and analyzed using graph theory metrics. Logistic regression and Cox proportional hazards models were employed to assess the predictive role of structural network attributes in depression onset. Moderation models further examined the influence of psychosocial factors on depression risk.

RESULTS

During our follow-up, 22 participants developed LLD (mean age: 65.6 years, 12 women). Altered brain structural network properties, alongside key psychosocial factors, were observed in those at risk of developing depression prior to symptom emergence. Logistic and Cox regression models revealed that decreased rich-club connections, reduced nodal efficiency in the left hippocampus (HIP.L), and lower network modularity significantly predicted depression onset. Additionally, these network alterations correlated with greater depression severity at follow-up. Moderation analyses indicated that weekly exercise frequency and time spent with children notably mitigated the effects of network disruptions on depression severity.

CONCLUSIONS

Among cognitively healthy widowed elders, diminished rich-club connections, modularity, and HIP.L nodal efficiency are strong predictors of future depression risk. Furthermore, low physical activity and limited family interaction may amplify susceptibility within this high-risk group, suggesting that targeted early interventions could reduce depression risk in this vulnerable population.

CLINICAL TRIAL NUMBER

Not applicable.

Association between behavioral patterns and depression symptoms: dyadic interaction between couples

Background

Behavioral patterns are sometimes associated with depression symptoms; however, few studies have considered the intra-couple effects. This study examined the effect of a spouses' behavioral patterns on depression symptoms within themself and in their spouse.

Methods

A total of 61,118 childbearing age participants (30,559 husband-wife dyads) were surveyed. The depression symptoms were assessed using the nine-item Patient Health Questionnaire (PHQ-9). The behavioral patterns were identified by the latent class analysis. The effects of behavioral patterns on the couple's own depression symptoms (actor effect) and their partner's depression symptoms (partner effect) were analyzed using the Actor-Partner Interdependence Model (APIM).

Results

Three behavioral patterns were identified: low-risk group, moderate-risk group, and high-risk group. The high risk of these behavior patterns would be associated with a higher score on the PHQ-9; for both husbands and wives, their behavioral patterns were positively associated with PHQ-9 scores (βhusband = 0.53, P < 0.01; βwife = 0.58, P < 0.01). Wives' behavioral patterns were also positively associated with their husbands' PHQ-9 scores (β = 0.14, P < 0.01), but husbands' behavioral patterns were not associated with their wives' PHQ-9 scores.

Conclusions

Wives' depression symptoms were affected only by their own behavioral patterns, whereas husbands' depression symptoms were influenced by both their own and their spouses' behavioral patterns.

Interaction between COMT Val158 Met polymorphism and childhood trauma predicts risk for depression in men

Depression is a disabling illness with complex etiology. While the Catechol-O-Methyltransferase (COMT) gene, in particular the functional Val¹⁵⁸ Met polymorphism, has been related to depression, the mechanisms underlying this gene-disease association are not completely understood. Therefore, we explore the association of COMT Val¹⁵⁸ Met polymorphism with depression as well as its interaction with childhood trauma in 1,136 young adults from a population-based study carried out in the city of Pelotas-Brazil. The diagnosis was performed through the Mini International Neuropsychiatric Interview 5.0 (MINI 5.0), and trauma was assessed with the childhood trauma questionnaire (CTQ). Total DNA was extracted and genotyped by real-time PCR and the QTLbase dataset was queried to perform large-scale quantitative trait locus (QTL) analysis. Our research showed no direct association between the Val¹⁵⁸ Met polymorphism and the diagnosis of depression (women: χ2=0.10, d=1, p=0.751 and men: χ2=0.003, df=1 p=0.956). However, the Met-allele of the Val¹⁵⁸ Met polymorphism modified the effect of childhood trauma in men [OR=2.58 (95% CI:1.05-6.29); p=0.038] conferring risk for depression only on those who suffer from trauma. The conditional effect from moderation analysis showed that trauma impacts the risk of depression only in men carrying the Met-allele (Effect: 0.9490, Standard Error (SE): 0.2570; p=0.0002). QTLbase and dataset for Val¹⁵⁸ Met polymorphism were consistent for markers that influence chromatin accessibility transcription capacity including histone methylation and acetylation. The changes caused in gene regulation by childhood trauma exposure and polymorphism may serve as evidence of the mechanism whereby the interaction increases susceptibility to this disorder in men.

Assortative mating and within-spouse pair comparisons

Spousal comparisons have been proposed as a design that can both reduce confounding and estimate effects of the shared adulthood environment. However, assortative mating, the process by which individuals select phenotypically (dis)similar mates, could distort associations when comparing spouses. We evaluated the use of spousal comparisons, as in the within-spouse pair (WSP) model, for aetiological research such as genetic association studies. We demonstrated that the WSP model can reduce confounding but may be susceptible to collider bias arising from conditioning on assorted spouse pairs. Analyses using UK Biobank spouse pairs found that WSP genetic association estimates were smaller than estimates from random pairs for height, educational attainment, and BMI variants. Within-sibling pair estimates, robust to demographic and parental effects, were also smaller than random pair estimates for height and educational attainment, but not for BMI. WSP models, like other within-family models, may reduce confounding from demographic factors in genetic association estimates, and so could be useful for triangulating evidence across study designs to assess the robustness of findings. However, WSP estimates should be interpreted with caution due to potential collider bias.

The interaction between autophagy and neuroinflammation in major depressive disorder: From pathophysiology to therapeutic implications

The past decade has revealed neuroinflammation as an important mechanism of major depressive disorder (MDD). Nod-like receptors family pyrin domain containing 3 (NLRP3) inflammasome is the key regulator interleukin-1β (IL-1β) maturation, whose activation has been reported in MDD patients and various animal models. Function as a dominant driver of neuroinflammation, NLRP3 bridges the gap between immune activation with stress exposure, and further leads to subsequent occurrence of neuropsychiatric disorders such as MDD. Of note, autophagy is a tightly regulated cellular degradation pathway that removes damaged organelles and intracellular pathogens, and maintains cellular homeostasis from varying insults. Serving as a critical cellular monitoring system, normal functioned autophagy signaling prevents excessive NLRP3 inflammasome activation and subsequent release of IL-1 family cytokines. This review will describe the current understanding of how autophagy regulates NLRP3 inflammasome activity and discuss the implications of this regulation on the pathogenesis of MDD. The extensive crosstalk between autophagy pathway and NLRP3 inflammasome is further discussed, as it is critical for developing new therapeutic strategies for MDD aimed at modulating the neuroinflammatory responses.

Genetic stratification of depression by neuroticism: revisiting a diagnostic tradition

BACKGROUND

Major depressive disorder and neuroticism (Neu) share a large genetic basis. We sought to determine whether this shared basis could be decomposed to identify genetic factors that are specific to depression.

METHODS

We analysed summary statistics from genome-wide association studies (GWAS) of depression (from the Psychiatric Genomics Consortium, 23andMe and UK Biobank) and compared them with GWAS of Neu (from UK Biobank). First, we used a pairwise GWAS analysis to classify variants as associated with only depression, with only Neu or with both. Second, we estimated partial genetic correlations to test whether the depression's genetic link with other phenotypes was explained by shared overlap with Neu.

RESULTS

We found evidence that most genomic regions (25/37) associated with depression are likely to be shared with Neu. The overlapping common genetic variance of depression and Neu was genetically correlated primarily with psychiatric disorders. We found that the genetic contributions to depression, that were not shared with Neu, were positively correlated with metabolic phenotypes and cardiovascular disease, and negatively correlated with the personality trait conscientiousness. After removing shared genetic overlap with Neu, depression still had a specific association with schizophrenia, bipolar disorder, coronary artery disease and age of first birth. Independent of depression, Neu had specific genetic correlates in ulcerative colitis, pubertal growth, anorexia and education.

CONCLUSION

Our findings demonstrate that, while genetic risk factors for depression are largely shared with Neu, there are also non-Neu-related features of depression that may be useful for further patient or phenotypic stratification.

Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer

BACKGROUND

Smoking status, alcohol consumption and HPV infection (acquired through sexual activity) are the predominant risk factors for oropharyngeal cancer and are thought to alter the prognosis of the disease. Here, we conducted single-site and differentially methylated region (DMR) epigenome-wide association studies (EWAS) of these factors, in addition to ∼ 3-year survival, using Illumina Methylation EPIC DNA methylation profiles from whole blood in 409 individuals as part of the Head and Neck 5000 (HN5000) study. Overlapping sites between each factor and survival were then assessed using two-step Mendelian randomization to assess whether methylation at these positions causally affected survival.

RESULTS

Using the MethylationEPIC array in an OPC dataset, we found novel CpG associations with smoking, alcohol consumption and ~ 3-year survival. We found no CpG associations below our multiple testing threshold associated with HPV16 E6 serological response (used as a proxy for HPV infection). CpG site associations below our multiple-testing threshold (PBonferroni < 0.05) for both a prognostic factor and survival were observed at four gene regions: SPEG (smoking), GFI1 (smoking), PPT2 (smoking) and KHDC3L (alcohol consumption). Evidence for a causal effect of DNA methylation on survival was only observed in the SPEG gene region (HR per SD increase in methylation score 1.28, 95% CI 1.14 to 1.43, P 2.12 × 10-05).

CONCLUSIONS

Part of the effect of smoking on survival in those with oropharyngeal cancer may be mediated by methylation at the SPEG gene locus. Replication in data from independent datasets and data from HN5000 with longer follow-up times is needed to confirm these findings.

Generation Scotland participant survey on data collection

Background: Generation Scotland (GS) is a population and family-based study of genetic and environmental health determinants. Recruitment to the Scottish Family Health Study component of GS took place between 2006-2011. Participants were aged 18 or over and consented to genetic studies, linkage to health records and recontact. Several recontact exercises have been successfully conducted aimed at a) recruitment to embedded or partner studies and b) the collection of additional data. As the cohort matures in age, we were interested in surveying attitudes to potential new approaches to data collection and recruitment. Methods: A ten-question online survey was sent to those participants who provided an email address. Results: We report a high level of positive responses to encouraging relatives to participate, to remote data and sample collection and for research access to stored newborn dried blood spots. Conclusions: The majority of current and prospective GS participants are likely to respond positively to future requests for remote data and sample collection.

Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP

Antidepressants demonstrate modest response rates in the treatment of major depressive disorder (MDD). Despite previous genome-wide association studies (GWAS) of antidepressant treatment response, the underlying genetic factors are unknown. Using prescription data in a population and family-based cohort (Generation Scotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of (a) antidepressant treatment resistance and (b) stages of antidepressant resistance by inferring antidepressant switching as non-response to treatment. GWAS were conducted separately for antidepressant treatment resistance in GS:SFHS and the Genome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed (meta-analysis n = 4213, cases = 358). For stages of antidepressant resistance, a GWAS on GS:SFHS only was performed (n = 3452). Additionally, we conducted gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We did not identify any significant loci, genes or gene sets associated with antidepressant treatment resistance or stages of resistance. Significant positive genetic correlations of antidepressant treatment resistance and stages of resistance with neuroticism, psychological distress, schizotypy and mood disorder traits were identified. These findings suggest that larger sample sizes are needed to identify the genetic architecture of antidepressant treatment response, and that population-based observational studies may provide a tractable approach to achieving the necessary statistical power.

Generation Scotland participant survey on data collection

Background: Generation Scotland (GS) is a population and family-based study of genetic and environmental health determinants. Recruitment to the Scottish Family Health Study component of GS took place between 2006-2011. Participants were aged 18 or over and consented to genetic studies, linkage to health records and recontact. Several recontact exercises have been successfully conducted aimed at a) recruitment to embedded or partner studies and b) the collection of additional data. As the cohort matures in age, we were interested in surveying attitudes to potential new approaches to data collection and recruitment. Methods: A ten-question online survey was sent to those participants who provided an email address. Results: We report a high level of positive responses to encouraging relatives to participate, to remote data and sample collection and for research access to stored newborn dried blood spots. Conclusions: The majority of current and prospective GS participants are likely to respond positively to future requests for remote data and sample collection.

Insulin resistance: Genetic associations with depression and cognition in population based cohorts

Insulin resistance, broadly defined as the reduced ability of insulin to exert its biological action, has been associated with depression and cognitive dysfunction in observational studies. However, it is unclear whether these associations are causal and whether they might be underpinned by other shared factors. To address this knowledge gap, we capitalized on the stability of genetic biomarkers through the lifetime, and on their unidirectional relationship with depression and cognition. Specifically, we determined the association between quantitative measures of cognitive function and depression and genetic instruments of insulin resistance traits in two large-scale population samples, the Generation Scotland: Scottish Family Health Study (GS: SFHS; N = 19,994) and in the UK Biobank (N = 331,374). In the GS:SFHS, the polygenic risk score (PRS) for fasting insulin was associated with verbal intelligence and depression while the PRS for the homeostasis model assessment of insulin resistance was associated with verbal intelligence. Despite this overlap in genetic architecture, Mendelian randomization analyses in the GS:SFHS and in the UK Biobank samples did not yield evidence for causal associations from insulin resistance traits to either depression or cognition. These findings may be due to weak genetic instruments, limited cognitive measures and insufficient power but they may also indicate the need to identify other biological mechanisms that may mediate the relationship from insulin resistance to depression and cognition.

Uncovering the Genetic Architecture of Major Depression

There have been several recent studies addressing the genetic architecture of depression. This review serves to take stock of what is known now about the genetics of depression, how it has increased our knowledge and understanding of its mechanisms, and how the information and knowledge can be leveraged to improve the care of people affected. We identify four priorities for how the field of MD genetics research may move forward in future years, namely by increasing the sample sizes available for genome-wide association studies (GWASs), greater inclusion of diverse ancestries and low-income countries, the closer integration of psychiatric genetics with electronic medical records, and the development of the neuroscience toolkit for polygenic disorders.

Parent of origin genetic effects on methylation in humans are common and influence complex trait variation

Parent-of-origin effects (POE) exist when there is differential expression of alleles inherited from the two parents. A genome-wide scan for POE on DNA methylation at 639,238 CpGs in 5,101 individuals identifies 733 independent methylation CpGs potentially influenced by POE at a false discovery rate ≤ 0.05 of which 331 had not previously been identified. Cis and trans methylation quantitative trait loci (mQTL) regulate methylation variation through POE at 54% (399/733) of the identified POE-influenced CpGs. The combined results provide strong evidence for previously unidentified POE-influenced CpGs at 171 independent loci. Methylation variation at 14 of the POE-influenced CpGs is associated with multiple metabolic traits. A phenome-wide association analysis using the POE mQTL SNPs identifies a previously unidentified imprinted locus associated with waist circumference. These results provide a high resolution population-level map for POE on DNA methylation sites, their local and distant regulators and potential consequences for complex traits.

Genetic and environmental determinants of stressful life events and their overlap with depression and neuroticism

Background: Stressful life events (SLEs) and neuroticism are risk factors for major depressive disorder (MDD). However, SLEs and neuroticism are heritable and genetic risk for SLEs is associated with risk for MDD. We sought to investigate the genetic and environmental contributions to SLEs in a family-based sample, and quantify genetic overlap with MDD and neuroticism. Methods: A subset of Generation Scotland: the Scottish Family Health Study (GS), consisting of 9618 individuals with information on MDD, past 6 month SLEs, neuroticism and genome-wide genotype data was used in the present study. We estimated the heritability of SLEs using GCTA software. The environmental contribution to SLEs was assessed by modelling familial, couple and sibling components. Using polygenic risk scores (PRS) and LD score regression (LDSC) we analysed the genetic overlap between MDD, neuroticism and SLEs. Results: Past 6-month life events were positively associated with lifetime MDD status (β=0.21, r ²=1.1%, p=2.5 x 10 ^-25) and neuroticism (β =0.13, r ²=1.9%, p=1.04 x 10 ^-37) at the phenotypic level.  Common SNPs explained 8% of the phenotypic variance in personal life events (those directly affecting the individual) (S.E.=0.03, p= 9 x 10 ^-4). A significant effect of couple environment was detected accounting for 13% (S.E.=0.03, p=0.016) of the phenotypic variation in SLEs. PRS analyses found that reporting more SLEs was associated with a higher polygenic risk for MDD (β =0.05, r ²=0.3%, p=3 x 10 ^-5), but not a higher polygenic risk for neuroticism. LDSC showed a significant genetic correlation between SLEs and both MDD (r _G=0.33, S.E.=0.08 ) and neuroticism (r _G=0.15, S.E.=0.07). Conclusions: These findings suggest that SLEs should not be regarded solely as environmental risk factors for MDD as they are partially heritable and this heritability is shared with risk for MDD and neuroticism. Further work is needed to determine the causal direction and source of these associations.

Genetic and environmental determinants of stressful life events and their overlap with depression and neuroticism

Background: Stressful life events (SLEs) and neuroticism are risk factors for major depressive disorder (MDD). However, SLEs and neuroticism are heritable and genetic risk for SLEs is associated with risk for MDD. We sought to investigate the genetic and environmental contributions to SLEs in a family-based sample, and quantify genetic overlap with MDD and neuroticism.

Methods: A subset of Generation Scotland: the Scottish Family Health Study (GS), consisting of 9618 individuals with information on MDD, past 6 month SLEs, neuroticism and genome-wide genotype data was used in the present study. We estimated the heritability of SLEs using GCTA software. The environmental contribution to SLEs was assessed by modelling familial, couple and sibling components. Using polygenic risk scores (PRS) and LD score regression (LDSC) we analysed the genetic overlap between MDD, neuroticism and SLEs.

Results: Past 6-month life events were positively associated with lifetime MDD status (β=0.21, r ²=1.1%, p=2.5 x 10 ^-25) and neuroticism (β =0.13, r ²=1.9%, p=1.04 x 10 ^-37) at the phenotypic level.  Common SNPs explained 8% of the phenotypic variance in personal life events (those directly affecting the individual) (S.E.=0.03, p= 9 x 10 ^-4). A significant effect of couple environment was detected accounting for 13% (S.E.=0.03, p=0.016) of the phenotypic variation in SLEs. PRS analyses found that reporting more SLEs was associated with a higher polygenic risk for MDD (β =0.05, r ²=0.3%, p=3 x 10 ^-5), but not a higher polygenic risk for neuroticism. LDSC showed a significant genetic correlation between SLEs and both MDD (r _G=0.33, S.E.=0.08 ) and neuroticism (r _G=0.15, S.E.=0.07).

Conclusions: These findings suggest that SLEs should not be regarded solely as environmental risk factors for MDD as they are partially heritable and this heritability is shared with risk for MDD and neuroticism. Further work is needed to determine the causal direction and source of these associations.

Association of Whole-Genome and NETRIN1 Signaling Pathway-Derived Polygenic Risk Scores for Major Depressive Disorder and White Matter Microstructure in the UK Biobank

Background

Major depressive disorder is a clinically heterogeneous psychiatric disorder with a polygenic architecture. Genome-wide association studies have identified a number of risk-associated variants across the genome and have reported growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide important routes for identification of disease mechanisms by focusing on a specific process, excluding heterogeneous risk-associated variation in other pathways. Here, we sought to investigate whether major depressive disorder polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRSs) and the whole genome, excluding NETRIN1 pathway genes (genomic-PRSs), were associated with white matter microstructure.

Methods

We used two diffusion tensor imaging measures, fractional anisotropy (FA) and mean diffusivity (MD), in the most up-to-date UK Biobank neuroimaging data release (FA: n = 6401; MD: n = 6390).

Results

We found significantly lower FA in the superior longitudinal fasciculus (β = −.035, p_corrected = .029) and significantly higher MD in a global measure of thalamic radiations (β = .029, p_corrected = .021), as well as higher MD in the superior (β = .034, p_corrected = .039) and inferior (β = .029, p_corrected = .043) longitudinal fasciculus and in the anterior (β = .025, p_corrected = .046) and superior (β = .027, p_corrected = .043) thalamic radiation associated with NETRIN1-PRS. Genomic-PRS was also associated with lower FA and higher MD in several tracts.

Conclusions

Our findings indicate that variation in the NETRIN1 signaling pathway may confer risk for major depressive disorder through effects on a number of white matter tracts.

What genome-wide association studies reveal about the association between intelligence and mental health

Intelligence, as measured by standardised tests of cognitive function, such as IQ-type tests, is predictive of psychiatric diagnosis and psychological wellbeing. Using genome-wide association study (GWAS) data, a measure of the shared genetic effect across traits, can be quantified; because this can be done across samples, the confounding effects of psychiatric diagnosis do not influence the magnitude of these relationships. It is now known that there are genetic effects that act across intelligence and psychiatric diagnoses, which provide a partial explanation for the phenotypic link between intelligence and mental health. Potential causal effects between intelligence and mental health have been identified, and the regions of the genome responsible for some of these cross trait associations have begun to be characterised.

The Gene Encoding Protocadherin 9 (PCDH9), a Novel Risk Factor for Major Depressive Disorder

Genomic analyses have identified only a handful of robust risk loci for major depressive disorder (MDD). In addition to the published genome-wide significant genes, it is believed that there are undiscovered 'treasures' underlying the current MDD genome-wide association studies (GWASs) and gene expression data sets, and digging into these data will allow better understanding of the illness and development of new therapeutic approaches. For this purpose, we performed a meta-analytic study combining three MDD GWAS data sets (23andMe, CONVERGE, and PGC), and then conducted independent replications of significant loci in two additional samples. The genome-wide significant variants then underwent explorative analyses on MDD-related phenotypes, cognitive function alterations, and gene expression in brains. In the discovery meta-analysis, a previously unidentified single-nucleotide polymorphism (SNP) rs9540720 in the PCDH9 gene was genome-wide significantly associated with MDD (p=1.69 × 10^-8 in a total of 89 610 cases and 246 603 controls), and the association was further strengthened when additional replication samples were included (p=1.20 × 10^-8 in a total of 136 115 cases and 355 275 controls). The risk SNP was also associated with multiple MDD-related phenotypes and cognitive function impairment in diverse samples. Intriguingly, the risk allele of rs9540720 predicted lower PCDH9 expression, consistent with the diagnostic analysis results that PCDH9 mRNA expression levels in the brain and peripheral blood tissues were reduced in MDD patients compared with healthy controls. These convergent lines of evidence suggest that PCDH9 is likely a novel risk gene for MDD. Our study highlights the necessity and importance of excavating the public data sets to explore risk genes for MDD, and this approach is also applicable to other complex diseases.

Prenatal stress and genetic risk: How prenatal stress interacts with genetics to alter risk for psychiatric illness

Risk for neuropsychiatric disorders is complex and includes an individual's internal genetic endowment and their environmental experiences and exposures. Embryonic development captures a particularly complex period, in which genetic and environmental factors can interact to contribute to risk. These environmental factors are incorporated differently into the embryonic brain than postnatal one. Here, we comprehensively review the human and animal model literature for studies that assess the interaction between genetic risks and one particular environmental exposure with strong and complex associations with neuropsychiatric outcomes-prenatal maternal stress. Gene-environment interaction has been demonstrated for stress occurring during childhood, adolescence, and adulthood. Additional work demonstrates that prenatal stress risk may be similarly complex. Animal model studies have begun to address some underlying mechanisms, including particular maternal or fetal genetic susceptibilities that interact with stress exposure and those that do not. More specifically, the genetic underpinnings of serotonin and dopamine signaling and stress physiology mechanisms have been shown to be particularly relevant to social, attentional, and internalizing behavioral changes, while other genetic factors have not, including some growth factor and hormone-related genes. Interactions have reflected both the diathesis-stress and differential susceptibility models. Maternal genetic factors have received less attention than those in offspring, but strongly modulate impacts of prenatal stress. Priorities for future research are investigating maternal response to distinct forms of stress and developing whole-genome methods to examine the contributions of genetic variants of both mothers and offspring, particularly including genes involved in neurodevelopment. This is a burgeoning field of research that will ultimately contribute not only to a broad understanding of psychiatric pathophysiology but also to efforts for personalized medicine.

Genetic and environmental contributions to psychological resilience and coping

Background: Twin studies indicate that genetic and environmental factors contribute to both psychological resilience and coping style, but estimates of their relative molecular and shared environmental contributions are limited. The degree of overlap in the genetic architectures of these traits is also unclear.

Methods: Using data from a large population- and family-based cohort Generation Scotland (N = 8,734), we estimated the genetic and shared environmental variance components for resilience, task-, emotion-, and avoidance-oriented coping style in a linear mixed model (LMM). Bivariate LMM analyses were used to estimate the genetic correlations between these traits. Resilience and coping style were measured using the Brief Resilience Scale and Coping Inventory for Stressful Situations, respectively.

Results: The greatest proportion of the phenotypic variance in resilience remained unexplained, although significant contributions from common genetic variants and family-shared environment were found. Both task- and avoidance-oriented coping had significant contributions from common genetic variants, sibling- and couple-shared environments, variance in emotion-oriented coping was attributable to common genetic variants, family- and couple-shared environments. The estimated correlation between resilience and emotion-oriented coping was high for both common-variant-associated genetic effects (r _G = -0.79, se = 0.19), and for the additional genetic effects from the pedigree (r _K = -0.94, se = 0.30). Genetic correlations between resilience and task- and avoidance-oriented coping did not meet statistical significance.

Conclusions: Both genetics and shared environmental effects were major contributing factors to coping style, whilst the variance in resilience remains largely unexplained. Strong genetic overlap between resilience and emotion-oriented coping suggests a relationship whereby genetic factors that increase negative emotionality also lead to decreased resilience. We suggest that genome-wide family-based studies of resilience and coping may help to elucidate tractable methodologies to identify genetic architectures and modifiable environmental risk factors to protect against psychiatric illness, although further work with larger sample sizes is needed.

Genome-wide meta-analyses of stratified depression in Generation Scotland and UK Biobank

Few replicable genetic associations for Major Depressive Disorder (MDD) have been identified. Recent studies of MDD have identified common risk variants by using a broader phenotype definition in very large samples, or by reducing phenotypic and ancestral heterogeneity. We sought to ascertain whether it is more informative to maximize the sample size using data from all available cases and controls, or to use a sex or recurrent stratified subset of affected individuals. To test this, we compared heritability estimates, genetic correlation with other traits, variance explained by MDD polygenic score, and variants identified by genome-wide meta-analysis for broad and narrow MDD classifications in two large British cohorts - Generation Scotland and UK Biobank. Genome-wide meta-analysis of MDD in males yielded one genome-wide significant locus on 3p22.3, with three genes in this region (CRTAP, GLB1, and TMPPE) demonstrating a significant association in gene-based tests. Meta-analyzed MDD, recurrent MDD and female MDD yielded equivalent heritability estimates, showed no detectable difference in association with polygenic scores, and were each genetically correlated with six health-correlated traits (neuroticism, depressive symptoms, subjective well-being, MDD, a cross-disorder phenotype and Bipolar Disorder). Whilst stratified GWAS analysis revealed a genome-wide significant locus for male MDD, the lack of independent replication, and the consistent pattern of results in other MDD classifications suggests that phenotypic stratification using recurrence or sex in currently available sample sizes is currently weakly justified. Based upon existing studies and our findings, the strategy of maximizing sample sizes is likely to provide the greater gain.

A continuum of genetic liability for minor and major depression

The recent success of a large genome-wide association (GWA) study-analysing 130 620 major depression cases and 347 620 controls-in identifying the first single-nucleotide polymorphism (SNP) loci robustly associated with major depression in Europeans confirms that immense sample sizes are required to identify risk loci for depression. Given the phenotypic similarity between major depressive disorder (MDD) and the less severe minor depressive disorder (MiDD), we hypothesised that broadening the case definition to include MiDD may be an efficient approach to increase sample sizes in GWA studies of depression. By analysing two large twin pair cohorts, we show that minor depression and major depression lie on a single genetic continuum, with major depression being more severe but not aetiologically distinct from minor depression. Furthermore, we estimate heritabilities of 37% for minor depression, 46% for major depression and 48% for minor or major depression in a cohort of older adults (aged 50-92). However, the heritability of minor or major depression was estimated at 40% in a cohort of younger adults (aged 23-38). Moreover, two robust major depression-risk SNPs nominally associated with major depression in our Australian GWA data set produced more significant evidence for association with minor or major depression. Hence, broadening the case phenotype in GWA studies to include subthreshold definitions, such as MiDD, should facilitate the identification of additional genetic risk loci for depression.