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Alcohol misuse is common in many societies worldwide and is associated with extensive morbidity and mortality, often leading to alcohol use disorders (AUD) and alcohol-related end-organ damage. The underlying mechanisms contributing to the development of AUD are largely unknown; however, growing evidence suggests that alcohol consumption is strongly associated with alterations in DNA methylation. Identification of alcohol-associated methylomic variation might provide novel insights into pathophysiology and novel treatment targets for AUD. Here we performed the largest single-cohort epigenome-wide association study (EWAS) of alcohol consumption to date (N = 8161) and cross-validated findings in AUD populations with relevant endophenotypes, as well as alcohol-related animal models. Results showed 2504 CpGs significantly associated with alcohol consumption (Bonferroni p value < 6.8 × 10−8) with the five leading probes located in SLC7A11 (p = 7.75 × 10−108), JDP2 (p = 1.44 × 10−56), GAS5 (p = 2.71 × 10−47), TRA2B (p = 3.54 × 10−42), and SLC43A1 (p = 1.18 × 10−40). Genes annotated to associated CpG sites are implicated in liver and brain function, the cellular response to alcohol and alcohol-associated diseases, including hypertension and Alzheimer’s disease. Two-sample Mendelian randomization confirmed the causal relationship of consumption on AUD risk (inverse variance weighted (IVW) p = 5.37 × 10−09). A methylation-based predictor of alcohol consumption was able to discriminate AUD cases in two independent cohorts (p = 6.32 × 10−38 and p = 5.41 × 10−14). The top EWAS probe cg06690548, located in the cystine/glutamate transporter SLC7A11, was replicated in an independent cohort of AUD and control participants (N = 615) and showed strong hypomethylation in AUD (p −17). Decreased CpG methylation at this probe was consistently associated with clinical measures including increased heavy drinking days (p −4), increased liver function enzymes (GGT (p = 1.03 × 10−21), ALT (p = 1.29 × 10−6), and AST (p = 1.97 × 10−8)) in individuals with AUD. Postmortem brain analyses documented increased SLC7A11 expression in the frontal cortex of individuals with AUD and animal models showed marked increased expression in liver, suggesting a mechanism by which alcohol leads to hypomethylation-induced overexpression of SLC7A11. Taken together, our EWAS discovery sample and subsequent validation of the top probe in AUD suggest a strong role of abnormal glutamate signaling mediated by methylomic variation in SLC7A11. Our data are intriguing given the prominent role of glutamate signaling in brain and liver and might provide an important target for therapeutic intervention.

A complex interplay of genetic and environmental risk factors influence global brain structural alterations associated with brain health and disease. Epigenome-wide association studies (EWAS) of global brain imaging phenotypes have the potential to reveal the mechanisms of brain health and disease and can lead to better predictive analytics through the development of risk scores.We perform an EWAS of global brain volumes in Generation Scotland using peripherally measured whole blood DNA methylation (DNAm) from two assessments, (i) at baseline recruitment, ~6 years prior to MRI assessment (N = 672) and (ii) concurrent with MRI assessment (N=565). Four CpGs at baseline were associated with global cerebral white matter, total grey matter, and whole-brain volume (Bonferroni p≤7.41×10-8, βrange = -1.46x10-6 to 9.59 × 10-7). These CpGs were annotated to genes implicated in brain-related traits, including psychiatric disorders, development, and ageing. We did not find significant associations in the meta-analysis of the EWAS of the two sets concurrent with imaging at the corrected level.These findings reveal global brain structural changes associated with DNAm measured ~6 years previously, indicating a potential role of early DNAm modifications in brain structure. Although concurrent DNAm was not associated with global brain structure, the nominally significant findings identified here present a rationale for future investigation of associations between DNA methylation and structural brain phenotypes in larger population-based samples.

The environment and events that we are exposed to in utero, during birth and in early childhood influence our future physical and mental health. The underlying mechanisms that lead to these outcomes are unclear, but long-term changes in epigenetic marks, such as DNA methylation, could act as a mediating factor or biomarker. DNA methylation data were assayed at 713 522 CpG sites from 9537 participants of the Generation Scotland: Scottish Family Health Study, a family-based cohort with extensive genetic, medical, family history and lifestyle information. Methylome-wide association studies of eight early life environment phenotypes and two adult mental health phenotypes (major depressive disorder and brief resilience scale) were conducted using DNA methylation data collected from adult whole blood samples. Two genes involved with different developmental pathways (PRICKLE2, Prickle Planar Cell Polarity Protein 2 and ABI1, Abl-Interactor-1) were annotated to CpG sites associated with preterm birth (P

BackgroundSchizophrenia is a heritable psychiatric disorder with a polygenic architecture. Genome-wide association studies (GWAS) have reported an increasing number of risk-associated variants and polygenic risk scores (PRS) now explain 17% of the variance in the disorder. There exists substantial heterogeneity in the effect of these variants and aggregating them based on biologically relevant functions may provide mechanistic insight into the disorder.MethodsUsing the largest schizophrenia GWAS to date, we calculated PRS based on 5 gene-sets previously found to contribute to the pathophysiology of schizophrenia: the postsynaptic density of excitatory synapses, postsynaptic membrane, dendritic spine, axon, and histone H3-K4 methylation gene-sets. We associated each PRS, along with respective whole-genome PRS (excluding single nucleotide polymorphisms in each gene-set), with neuroimaging (N>29,000; cortical, subcortical, and white matter microstructure) and clinical (N>119,000; psychotic-like experiences including conspiracies, communications, voices, visions, and distress) variables in healthy subjects in UK Biobank.ResultsA number of clinical and neuroimaging variables were significantly associated with the axon gene-set (psychotic-like communications: β=0.0916, pFDR=0.04, parahippocampal gyrus volume: β=0.0156, pFDR=0.03, FA thalamic radiations: β=-0.014, pFDR=0.036, FA posterior thalamic radiations: β=-0.016, pFDR=0.048), postsynaptic density gene-set (distress due to psychotic-like experiences: β=0.0588, pFDR=0.02, global surface area: β=-0.012, pFDR=0.034, and cingulate lobe surface area: β=-0.014, pFDR=0.04), and histone gene-set (entorhinal surface area: β=-0.016, pFDR=0.035). In the associations above, whole-genome PRS were significantly associated with psychotic-like communications (β=0.2218, pFDR =1.34×10−7), distress (β=0.1943, pFDR =7.28×10−16), and FA thalamic radiations (β=-0.0143, pFDR=0.036). Permutation analysis carried out for these associations revealed that they were not due to chance.ConclusionsOur results indicate that genetic variation in 3 gene-sets relevant to schizophrenia (axon, postsynaptic density, histone) may confer risk for the disorder through effects on a number of neuroimaging variables that have previously been implicated in schizophrenia. As neuroimaging associations were stronger for gene-set PRS than whole-genome PRS, findings here highlight that selection of biologically relevant variants may address the heterogeneity of the disorder by providing further mechanistic insight into schizophrenia.

Highlights • First study of both serum and DNAm CRP associations with depression/neuroimaging. • Serum CRP is associated with somatic symptoms and reduced entorhinal cortex thickness. • DNAm CRP is associated with widespread reductions in white matter integrity. • Evidence for central effects of peripheral inflammation from serum and DNAm markers., Inflammatory processes are implicated in the aetiology of Major Depressive Disorder (MDD); however, the relationship between peripheral inflammation, brain structure and depression remains unclear, partly due to complexities around the use of acute/phasic inflammatory biomarkers. Here, we report the first large-scale study of both serological and methylomic signatures of CRP (considered to represent acute and chronic measures of inflammation respectively) and their associations with depression status/symptoms, and structural neuroimaging phenotypes (T1 and diffusion MRI) in a large community-based sample (Generation Scotland; NMDD cases = 271, Ncontrols = 609). Serum CRP was associated with overall MDD severity, and specifically with current somatic symptoms- general interest (β = 0.145, PFDR = 6 × 10−4) and energy levels (β = 0.101, PFDR = 0.027), along with reduced entorhinal cortex thickness (β = −0.095, PFDR = 0.037). DNAm CRP was significantly associated with reduced global grey matter/cortical volume and widespread reductions in integrity of 16/24 white matter tracts (with greatest regional effects in the external and internal capsules, βFA= −0.12 to −0.14). In general, the methylation-based measures showed stronger associations with imaging metrics than serum-based CRP measures (βaverage = −0.15 versus βaverage = 0.01 respectively). These findings provide evidence for central effects of peripheral inflammation from both serological and epigenetic markers of inflammation, including in brain regions previously implicated in depression. This suggests that these imaging measures may be involved in the relationship between peripheral inflammation and somatic/depressive symptoms. Notably, greater effects on brain morphology were seen for methylation-based rather than serum-based measures of inflammation, indicating the importance of such measures for future studies.

Variation in DNA methylation (DNAm) is associated with lifestyle factors such as smoking and body mass index (BMI) but there has been little research exploring its ability to identify individuals with major depressive disorder (MDD). Using penalised regression on genome-wide CpG methylation, we tested whether DNAm risk scores (MRS), trained on 1223 MDD cases and 1824 controls, could discriminate between cases (n = 363) and controls (n = 1417) in an independent sample, comparing their predictive accuracy to polygenic risk scores (PRS). The MRS explained 1.75% of the variance in MDD (β = 0.338, p = 1.17 × 10−7) and remained associated after adjustment for lifestyle factors (β = 0.219, p = 0.001, R2 = 0.68%). When modelled alongside PRS (β = 0.384, p = 4.69 × 10−9) the MRS remained associated with MDD (β = 0.327, p = 5.66 × 10−7). The MRS was also associated with incident cases of MDD who were well at recruitment but went on to develop MDD at a later assessment (β = 0.193, p = 0.016, R2 = 0.52%). Heritability analyses found additive genetic effects explained 22% of variance in the MRS, with a further 19% explained by pedigree-associated genetic effects and 16% by the shared couple environment. Smoking status was also strongly associated with MRS (β = 0.440, p ≤ 2 × 10−16). After removing smokers from the training set, the MRS strongly associated with BMI (β = 0.053, p = 0.021). We tested the association of MRS with 61 behavioural phenotypes and found that whilst PRS were associated with psychosocial and mental health phenotypes, MRS were more strongly associated with lifestyle and sociodemographic factors. DNAm-based risk scores of MDD significantly discriminated MDD cases from controls in an independent dataset and may represent an archive of exposures to lifestyle factors that are relevant to the prediction of MDD.

Antidepressants are an effective treatment for major depressive disorder (MDD), although individual response is unpredictable and highly variable. Whilst the mode of action of antidepressants is incompletely understood, many medications are associated with changes in DNA methylation in genes that are plausibly linked to their mechanisms. Studies of DNA methylation may therefore reveal the biological processes underpinning the efficacy and side effects of antidepressants. We performed a methylome-wide association study (MWAS) of self-reported antidepressant use accounting for lifestyle factors and MDD in Generation Scotland (GS:SFHS, N = 6428, EPIC array) and the Netherlands Twin Register (NTR, N = 2449, 450 K array) and ran a meta-analysis of antidepressant use across these two cohorts. We found ten CpG sites significantly associated with self-reported antidepressant use in GS:SFHS, with the top CpG located within a gene previously associated with mental health disorders, ATP6V1B2 (β = -0.055, pcorrected = 0.005). Other top loci were annotated to genes including CASP10, TMBIM1, MAPKAPK3, and HEBP2, which have previously been implicated in the innate immune response. Next, using penalised regression, we trained a methylation-based score of self-reported antidepressant use in a subset of 3799 GS:SFHS individuals that predicted antidepressant use in a second subset of GS:SFHS (N = 3360, β = 0.377, p = 3.12 × 10-11, R2 = 2.12%). In an MWAS analysis of prescribed selective serotonin reuptake inhibitors, we showed convergent findings with those based on self-report. In NTR, we did not find any CpGs significantly associated with antidepressant use. The meta-analysis identified the two CpGs of the ten above that were common to the two arrays used as being significantly associated with antidepressant use, although the effect was in the opposite direction for one of them. Antidepressants were associated with epigenetic alterations in loci previously associated with mental health disorders and the innate immune system. These changes predicted self-reported antidepressant use in a subset of GS:SFHS and identified processes that may be relevant to our mechanistic understanding of clinically relevant antidepressant drug actions and side effects., NIHR Senior Investigator Award

BackgroundDepression is a disabling and highly prevalent condition where genetic and epigenetic differences, such as DNA methylation (DNAm), contribute to prediction of disease liability.MethodWe investigated the association between polygenic risk scores (PRS) for depression and DNAm by conducting a methylome-wide association study (MWAS) in Generation Scotland (N=8,898, mean age=49.8 years) with replication in the Lothian Birth Cohorts of 1921 and 1936 and adults in Avon Longitudinal Study of Parents and Children (ALSPAC) (Ncombined=2,049, mean age=79.1, 69.6 and 47.2 years, respectively). We also conducted a replication MWAS in the ALSPAC children (N=423, mean age=17.1 years).ResultWide-spread associations were found between PRS constructed using genetic risk variants for depression and DNAm in cytosine-guanine dinucleotide (CpG) probes that localised to genes involved in immune responses and neural development (NCpG=599, pBonferroni−8). The effect sizes for the significant associations were highly correlated between the discovery and replication samples in adults (r=0.83) and in adolescents (r=0.76). Additional analysis on the methylome-wide associations was conducted for each lead genetic risk variant. Over 40% of the independent genetic risk variants showed associations with CpG probe DNAm located in both the same (cis) and distal probes (trans) to the genetic loci (pBonferroniFDRFDR ranged from 0.045 to 2.06×10−120; depression to DNAm: pFDR ranged from 0.046 to 2.1×10−23).ConclusionPolygenic risk scores for depression, especially those constructed from genome-wide significant genetic risk variants, showed epigenome-wide methylation association differences in the methylome associated with immune responses and brain development. We also found evidence from Mendelian randomisation evidence that DNAm may be causal to depression, as well as a causal consequence of depression.

Depression is assessed in various ways in research, with large population studies often relying on minimal phenotyping. Genetic results suggest clinical diagnoses and self-report measures of depression show some core similarities, but also important differences. It is not yet clear how neuroimaging associations depend on levels of phenotyping. We studied 39,300 UK Biobank imaging participants (20,701 female; aged 44.6 to 82.3 years, M = 64.1, SD = 7.5) with structural neuroimaging and lifetime depression data. Past depression phenotypes included a single-item self-report measure, an intermediate measure of 'probable' lifetime depression, derived from multiple questionnaire items relevant to a history of depression, and a retrospective clinical diagnosis according to DSM-IV criteria. We tested (i) associations between brain structural measures and each depression phenotype, and (ii) effects of phenotype on these associations. Depression-brain structure associations were small (β 0.1) for all phenotypes, but still significant after FDR correction for many regional metrics. Lifetime depression was consistently associated with reduced white matter integrity across phenotypes. Cortical thickness showed negative associations with Self-reported Depression in particular. Phenotype effects were small across most metrics, but significant for cortical thickness in most regions. We report consistent effects of lifetime depression in brain structural measures, including reduced integrity of thalamic radiations and association fibres. We also observed significant differences in associations with cortical thickness across depression phenotypes. Although these results did not relate to level of phenotyping as expected, effects of phenotype definition are still an important consideration for future depression research.

BackgroundDepression is assessed in many different ways, with large population studies often relying on minimal phenotyping approaches. Genetic results suggest that more formal clinical diagnoses and simpler self-report measures of depression show some core similarities, but also important differences. It is not yet clear whether this is also the case for neuroimaging measures.MethodsWe studied 39,300 UK Biobank imaging participants (20,701 female; aged 44.6 to 82.3 years, M = 64.1, SD = 7.5) with structural neuroimaging (T1 and DTI) and depression data. Depression phenotypes included a minmal single-item self-report measure, an intermediate symptom-based measure of ‘probable’ depression, and a more clinically robust measure based on DSM-IV criteria. We tested i) associations between brain structural measures and each depression phenotype, and ii) the effects of depression phenotype on these associations.ResultsSmall depression-brain structure associations (β < 0.1) were significant after FDR correction for many global and regional metrics for all three phenotypes. The most consistent imaging associations across depression phenotypes were for measures of white matter integrity. There were small but significant effects of phenotype definition primarily for cortical thickness, which showed stronger negative associations with Self-reported Depression than the symptom-based measures.ConclusionSimilar to previous genetic studies, we found some consistent associations indicating a core component of depression across phenotypes, and some additional associations that were phenotype-specific. Although these specific results did not relate to depth of phenotyping as expected, effects of phenotype definition are still an important consideration for future depression research.

Antidepressants are an effective treatment for major depressive disorder (MDD), although individual response is unpredictable and highly variable. Whilst the mode of action of antidepressants is incompletely understood, many medications are associated with changes in DNA methylation in genes that are plausibly linked to their mechanisms. Studies of DNA methylation may therefore reveal the biological processes underpinning the efficacy and side effects of antidepressants. We performed a methylome-wide association study (MWAS) of self-reported antidepressant use accounting for lifestyle factors and MDD in Generation Scotland (GS:SFHS, N = 6428, EPIC array) and the Netherlands Twin Register (NTR, N = 2449, 450 K array) and ran a meta-analysis of antidepressant use across these two cohorts. We found ten CpG sites significantly associated with self-reported antidepressant use in GS:SFHS, with the top CpG located within a gene previously associated with mental health disorders, ATP6V1B2 (β = -0.055, p

BackgroundInflammatory processes are implicated in the aetiology of Major Depressive Disorder (MDD); however, the relationship between peripheral inflammation, brain structure and depression remains unclear. This study investigates associations between depression, structural neuroimaging measures and two measures of inflammation: serum-based C-reactive protein (CRP) and a methylation-based score for CRP (DNAm CRP) in a large community-based sample.MethodsSerum CRP and DNAm CRP were assessed for participants in Generation Scotland (NMDD cases = 240, Ncontrols = 558) alongside structural brain scans (T1 and diffusion MRI). Linear regression was used to investigate associations between (i) both CRP measures and depressive symptoms, (ii) both CRP measures and structural imaging variables and (iii) inflammation x MDD interaction effects (for both CRP measures) with imaging measures.ResultsIncreased serum CRP was significantly associated with overall MDD symptom severity and specifically with somatic symptoms-general interest (β= 0.145, PFDR = 6×10-4) and energy levels (β= 0.101, PFDR = 0.027) and also reduced entorhinal cortex thickness (β= – 0.095, PFDR = 0.037). DNAm CRP was significantly associated with reduced global grey matter/cortical volume and reduced integrity of 16 white matter tracts and showed larger effect sizes (βaverage = −0.15) compared to serum CRP across all measures (βaverage = 0.01).ConclusionsAcute measures of CRP were related to current depression symptoms, specifically somatic symptoms, whereas methylation signatures of inflammation demonstrated greater differences in global and regional brain structure. This study highlights the utility of combining serological and methylation markers to study chronic inflammation effects on the brain in psychiatric disorders.

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches., Previous case‐control classification studies of depression have reported promising accuracies up to 80% and above, but investigated relatively small samples with clinically‐ascertained cases. In the current study we explore multiple classification approaches with brain structural and white matter integrity measures, and achieve up to 75% predictive classification accuracy in a small sample with clinical interview‐based diagnoses (N 700 cases, UK Biobank cohort), and also in larger samples with remitted MDD (STRADL) and lifetime‐experienced MDD (UK Biobank). These results indicate that high predictive case‐control classification accuracies may not immediately translate to larger samples with broader diagnostic criteria for depression.

BackgroundExpression quantitative trait loci (eQTL) are genetic variants associated with gene expression. Using genome-wide genotype data, it is now possible to impute gene expression using eQTL mapping efforts. This approach can be used to analyse previously unexplored relationships between gene expression and heritable in vivo measures of human brain structural connectivity.MethodsUsing large-scale eQTL mapping studies, we computed 6,457 gene expression scores (eQTL scores) using genome-wide genotype data in UK Biobank, where each score represents a genetic proxy measure of gene expression. These scores were then tested for associations with two diffusion tensor imaging measures, fractional anisotropy (NFA=14,518) and mean diffusivity (NMD=14,485), representing white matter structural integrity.ResultsWe found FDR-corrected significant associations between 8 eQTL scores and structural connectivity phenotypes, including global and regional measures (βabsolute FA=0.0339-0.0453; MD=0.0308-0.0381) and individual tracts (βabsolute FA=0.0320-0.0561; MD=0.0295-0.0480). The loci within these eQTL scores have been reported to regulate expression of genes involved in various brain-related processes and disorders, such as neurite outgrowth and Parkinson’s disease (DCAKD, SLC35A4, SEC14L4, SRA1, NMT1, CPNE1, PLEKHM1, UBE3C).DiscussionOur findings indicate that eQTL scores are associated with measures of in vivo brain connectivity and provide novel information not previously found by conventional genome-wide association studies. Although the role of expression of these genes regarding white matter microstructural integrity is not yet clear, these findings suggest it may be possible, in future, to map potential trait- and disease-associated eQTL to in vivo brain connectivity and better understand the mechanisms of psychiatric disorders and brain traits, and their associated imaging findings.

Background: Depression is the leading cause of disability worldwide with >50% of cases emerging before the age of 25yrs. Large-scale neuroimaging studies in depression implicate robust structural brain differences in the disorder. However most studies have been conducted in adults and therefore, the temporal origins of depression-related imaging features remain unknown. This has important implications for understanding aetiology and informing timings of potential intervention. Methods: Here, we examine associations between brain structure (cortical metrics and white matter microstructural integrity) and depression ratings (from caregiver and child), in a large sample of early adolescents from the Adolescent Brain and Cognitive Development (ABCD) Study (N=9981, 9-11-year olds). Findings: We report significantly decreased global cortical and white matter metrics, and regionally in frontal, limbic and temporal areas in adolescent depression (Cohen’s d = -0.018 to -0.041, β = -0.019 to -0.057). Further, we report consistently stronger imaging associations for caregiver-reported compared to child-reported depression ratings. Divergences between reports (caregiver vs child) were found to significantly relate to negative socio-environmental factors (e.g., family conflict, absolute β=0.048 to 0.169). Interpretation: Depression ratings in early adolescence were associated with similar imaging findings to those seen in adult depression samples, suggesting neuroanatomical abnormalities may be present early in the disease course, arguing for the importance of early intervention. Associations between socio-environmental factors and reporter discrepancy warrant further consideration, both in the wider context of the assessment of adolescent psychopathology, and in relation to their role in aetiology. Funding: The study is funded by Wellcome Trust Strategic Award “Stratifying Resilience and Depression Longitudinally” (STRADL) (Reference 104036/Z/14/Z) and MRC Mental Health Data Pathfinder Award (Reference MC_PC_17209). HCW is supported by a JMAS SIM fellowship from the Royal College of Physicians of Edinburgh and by an ESAT College Fellowship from the University of Edinburgh. AMM receives the Wellcome Trust Strategic Award (Reference 104036/Z/14/Z). Declaration of Interests: AMM has received research funding from The Sackler Trust, Eli Lilly and Janssen. No other conflicts of interest declared by other authors. Ethics Approval Statement: The study was approved by the National Institute of Mental Health Data Archive, United States (NIMH). Data was accessed through the NDA data base (http://nda.nih.gov/abcd, Federal-Wide Assurance: FWA00018101).

ObjectiveDNA methylation (DNAm) is associated with environmental risk factors for major depressive disorder (MDD) but has not yet been tested for its ability to discriminate individuals with MDD from unaffected individuals.MethodsUsing penalized regression based on genome-wide CpG methylation, we trained a DNAm risk score of MDD (DNAm-RS) in 1,223 cases and 1,824 controls and tested in a second independent sample of 363 prevalent cases and 1,417 controls. Using DNA from 1,607 unaffected individuals, we tested whether DNAm-RS could discriminate the 190 incident cases of lifetime MDD from the 1,417 individuals who remained unaffected at follow-up.ResultsA weighted linear combination of 196 CpG sites were derived from the training sample to form a DNAm-RS. The DNAm-RS explained 1.75% of the variance in MDD risk in an independent case-control sample and significantly predicted future incident episodes of MDD at follow up (R2=0.52%). DNAm-RS and MDD polygenic risk scores together additively explained 3.99% of the variance in prevalent MDD. The DNAm-RS was also significantly associated with lifestyle factors associated with MDD, including smoking status (β=0.440, p=−16) and alcohol use (β=0.092, p=9.85×10−5). The DNAm-RS remained significantly associated with MDD after adjustment for these environmental factors (independent association: β=0.338, p=1.17×10−7 association post-adjustment: β=0.081, p=0.0006).ConclusionsA novel risk score of MDD based on DNAm data significantly discriminated MDD cases from controls in an independent dataset, and controls who would subsequently develop MDD from those who remained unaffected. DNAm-RS captured the effects of exposure to key lifestyle risk factors for MDD, revealing a potential role in risk stratification.

Major depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

BackgroundMajor Depressive Disorder (MDD) 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 growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide an important route 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 MDD polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRS) and the whole genome excluding NETRIN1 pathway genes (genomic-PRS) were associated with white matter integrity.MethodsWe 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 = 6,401; MD: N = 6,390).ResultsWe found significantly lower FA in the superior longitudinal fasciculus (β = -0.035, pcorrected = 0.029) and significantly higher MD in a global measure of thalamic radiations (β = 0.029, pcorrected = 0.021), as well as higher MD in the superior (β = 0.034, pcorrected = 0.039) and inferior (β = 0.029, pcorrected = 0.043) longitudinal fasciculus and in the anterior (β = 0.025, pcorrected = 0.046) and superior (β = 0.027, pcorrected = 0.043) thalamic radiation associated with NETRIN1-PRS. Genomic-PRS was also associated with lower FA and higher MD in several tracts.ConclusionsOur findings indicate that variation in the NETRIN1 signaling pathway may confer risk for MDD through effects on thalamic radiation white matter microstructure.

Depression is a polygenic trait that causes extensive periods of disability and increases the risk of suicide, a leading cause of death in young people. Previous genetic studies have identified a number of common risk variants which have increased in number in line with increasing sample sizes. We conducted a genome-wide association study (GWAS) in the largest single population-based cohort to date, UK Biobank. This allowed us to estimate the effects of ≈ 8 million genetic variants in 320,000 people for three depression phenotypes: broad depression, probable major depressive disorder (MDD), and International Classification of Diseases (ICD, version 9 or 10)-coded MDD. Each phenotype was found to be significantly genetically correlated with the results from a previous independent study of clinically defined MDD. We identified 14 independent loci that were significantly associated (P < 5 × 10−8) with broad depression, two independent variants for probable MDD, and one independent variant for ICD-coded MDD. Gene-based analysis of our GWAS results with MAGMA revealed 46 regions significantly associated (P < 2.77 × 10−6) with broad depression, two significant regions for probable MDD and one significant region for ICD-coded MDD. Gene region-based analysis of our GWAS results with MAGMA revealed 59 regions significantly associated (P < 6.02 × 10−6) with broad depression, of which 27 were also detected by gene-based analysis. Variants for broad depression were enriched in pathways for excitatory neurotransmission, mechanosensory behavior, postsynapse, neuron spine and dendrite. This study provides a number of novel genetic risk variants that can be leveraged to elucidate the mechanisms of MDD and low mood.