Genome-wide meta-analysis of ascertainment and symptom structures of major depression in case-enriched and community cohorts.

BACKGROUND: Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. METHODS: We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. RESULTS: The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms). CONCLUSION: The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

Genome-wide association study meta-analysis of suicide death and suicidal behavior.

Suicide is a worldwide health crisis. We aimed to identify genetic risk variants associated with suicide death and suicidal behavior. Meta-analysis for suicide death was performed using 3765 cases from Utah and matching 6572 controls of European ancestry. Meta-analysis for suicidal behavior using data across five cohorts (n = 8315 cases and 256,478 psychiatric or populational controls of European ancestry) was also performed. One locus in neuroligin 1 (NLGN1) passing the genome-wide significance threshold for suicide death was identified (top SNP rs73182688, with p = 5.48 × 10-8 before and p = 4.55 × 10-8 after mtCOJO analysis conditioning on MDD to remove genetic effects on suicide mediated by MDD). Conditioning on suicidal attempts did not significantly change the association strength (p = 6.02 × 10-8), suggesting suicide death specificity. NLGN1 encodes a member of a family of neuronal cell surface proteins. Members of this family act as splice site-specific ligands for beta-neurexins and may be involved in synaptogenesis. The NRXN-NLGN pathway was previously implicated in suicide, autism, and schizophrenia. We additionally identified ROBO2 and ZNF28 associations with suicidal behavior in the meta-analysis across five cohorts in gene-based association analysis using MAGMA. Lastly, we replicated two loci including variants near SOX5 and LOC101928519 associated with suicidal attempts identified in the ISGC and MVP meta-analysis using the independent FinnGen samples. Suicide death and suicidal behavior showed positive genetic correlations with depression, schizophrenia, pain, and suicidal attempt, and negative genetic correlation with educational attainment. These correlations remained significant after conditioning on depression, suggesting pleiotropic effects among these traits. Bidirectional generalized summary-data-based Mendelian randomization analysis suggests that genetic risk for the suicidal attempt and suicide death are both bi-directionally causal for MDD.

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes.

Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e-06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

Genome-wide association study of abnormal elevation of ALT in patients exposed to atabecestat.

BACKGROUND: Atabecestat, a potent brain penetrable BACE1 inhibitor that reduces CSF amyloid beta (Aß), was developed as an oral treatment for Alzheimer's disease (AD). Elevated liver enzyme adverse events were reported in three studies although only one case met Hy's law criteria to predict serious hepatotoxicity. METHOD: We performed a case-control genome-wide association study (GWAS) to identify genetic risk variants associated with liver enzyme elevation using 42 cases with alanine transaminase (ALT) above three times the upper limit of normal (ULN) and 141 controls below ULN. Additionally, we performed a GWAS using continuous maximal ALT/ULN (expressed as times the ULN) upon exposure to atabecestat as the outcome measure (n = 285). RESULTS: No variant passed the genome-wide significance threshold (p = 5 × 10- 8) in the case-control GWAS. We identified suggestive association signals in genes (NLRP1, SCIMP, and C1QBP) implicated in the inflammatory processes. Among the genes implicated by position mapping using variants suggestively associated (p < 1 × 10- 5) with ALT elevation case-control status, gene sets involved in innate immune response (adjusted p-value = 0.05) and regulation of cytokine production (adjusted p-value = 0.04) were enriched. One genomic region in the intronic region of GABRG3 passed the genome-wide significance threshold in the continuous max(ALT/ULN) GWAS, and this variant was nominally associated with ALT elevation case status (p = 0.009). CONCLUSION: The suggestive GWAS signals in the case-control GWAS analysis suggest the potential role of inflammation in atabecestat-induced liver enzyme elevation.

Neurexin 1 variants as risk factors for suicide death.

Suicide is a significant public health concern with complex etiology. Although the genetic component of suicide is well established, the scope of gene networks and biological mechanisms underlying suicide has yet to be defined. Previously, we reported genome-wide evidence that neurexin 1 (NRXN1), a key synapse organizing molecule, is associated with familial suicide risk. Here we present new evidence for two non-synonymous variants (rs78540316; P469S and rs199784139; H885Y) associated with increased familial risk of suicide death. We tested the impact of these variants on binding interactions with known partners and assessed functionality in a hemi-synapse formation assay. Although the formation of hemi-synapses was not altered with the P469S variant relative to wild-type, both variants increased binding to the postsynaptic binding partner, leucine-rich repeat transmembrane neuronal 2 (LRRTM2) in vitro. Our findings indicate that variants in NRXN1 and related synaptic genes warrant further study as risk factors for suicide death.

Extended familial risk of suicide death is associated with younger age at death and elevated polygenic risk of suicide.

Suicide accounts for >800,000 deaths annually worldwide; prevention is an urgent public health issue. Identification of risk factors remains challenging due to complexity and heterogeneity. The study of suicide deaths with increased extended familial risk provides an avenue to reduce etiological heterogeneity and explore traits associated with increased genetic liability. Using extensive genealogical records, we identified high-risk families where distant relatedness of suicides implicates genetic risk. We compared phenotypic and polygenic risk score (PRS) data between suicides in high-risk extended families (high familial risk (HFR), n = 1,634), suicides linked to genealogical data not in any high-risk families (low familial risk (LFR), n = 147), and suicides not linked to genealogical data with unknown familial risk (UFR, n = 1,865). HFR suicides were associated with lower age at death (mean = 39.34 years), more suicide attempts, and more PTSD and trauma diagnoses. For PRS tests, we included only suicides with >90% European ancestry and adjusted for residual ancestry effects. HFR suicides showed markedly higher PRS of suicide death (calculated using cross-validation), supporting specific elevation of genetic risk of suicide in this subgroup, and also showed increased PRS of PTSD, suicide attempt, and risk taking. LFR suicides were substantially older at death (mean = 49.10 years), had fewer psychiatric diagnoses of depression and pain, and significantly lower PRS of depression. Results suggest extended familiality and trauma/PTSD may provide specificity in identifying individuals at genetic risk for suicide death, especially among younger ages, and that LFR of suicide warrants further study regarding the contribution of demographic and medical risks.

Genome-wide significant regions in 43 Utah high-risk families implicate multiple genes involved in risk for completed suicide.

Suicide is the 10th leading cause of death in the United States. Although environment has undeniable impact, evidence suggests that genetic factors play a significant role in completed suicide. We linked a resource of ~ 4500 DNA samples from completed suicides obtained from the Utah Medical Examiner to genealogical records and medical records data available on over eight million individuals. This linking has resulted in the identification of high-risk extended families (7-9 generations) with significant familial risk of completed suicide. Familial aggregation across distant relatives minimizes effects of shared environment, provides more genetically homogeneous risk groups, and magnifies genetic risks through familial repetition. We analyzed Illumina PsychArray genotypes from suicide cases in 43 high-risk families, identifying 30 distinct shared genomic segments with genome-wide evidence (p = 2.02E-07-1.30E-18) of segregation with completed suicide. The 207 genes implicated by the shared regions provide a focused set of genes for further study; 18 have been previously associated with suicide risk. Although PsychArray variants do not represent exhaustive variation within the 207 genes, we investigated these for specific segregation within the high-risk families, and for association of variants with predicted functional impact in ~ 1300 additional Utah suicides unrelated to the discovery families. None of the limited PsychArray variants explained the high-risk family segregation; sequencing of these regions will be needed to discover segregating risk variants, which may be rarer or regulatory. However, additional association tests yielded four significant PsychArray variants (SP110, rs181058279; AGBL2, rs76215382; SUCLA2, rs121908538; APH1B, rs745918508), raising the likelihood that these genes confer risk of completed suicide.

Exploring the genetic overlap of suicide-related behaviors and substance use disorders.

Suicide-related behaviors are heterogeneous and transdiagnostic, and may demonstrate varying levels of genetic overlap with different substance use disorders (SUDs). We used linkage disequilibrium score regression, genomic structural equation models, and Mendelian randomization to examine the genetic relationships between several SUDs and suicide-related behaviors. Our analyses incorporated summary statistics from the largest genome-wide association studies (GWAS) of problematic alcohol use, the Fagerström test for nicotine dependence, cannabis use disorder, and opioid use disorder (Ns ranging from 46,213-435,563) and GWAS of ever self-harmed, suicide attempt, and suicide death (Ns ranging from 18,223-117,733). We also accounted for genetic liability to depression (N = 500,199) and risk tolerance (N = 315,894). Suicide-related behaviors were significantly genetically correlated with each other and each SUD, but there was little evidence of causal relationships between the traits. Simultaneously correlating a common SUD factor with each specific suicide indicator while controlling for depression and risk tolerance revealed significant, positive genetic correlations between the SUD factor and suicide-related behaviors (rg  = 0.26-0.45, SE = 0.08-0.09). In the model, depression's association with suicide death (ß = 0.42, SE = 0.06) was weaker compared to ever-self harmed and suicide attempt (ß = 0.58, SE = 0.05 and ß = 0.50, SE = 0.06, respectively). We identify a general level of genetic overlap between SUDs and suicide-related behaviors, which is independent of depression and risk tolerance. Additionally, our findings suggest that genetic and behavioral contributions to suicide death may somewhat differ from nonlethal suicide-related behaviors.

Rare protein-coding variants implicate genes involved in risk of suicide death.

Identification of genetic factors leading to increased risk of suicide death is critical to combat rising suicide rates, however, only a fraction of the genetic variation influencing risk has been accounted for. To address this limitation, we conducted the first comprehensive analysis of rare genetic variation in suicide death leveraging the largest suicide death biobank, the Utah Suicide Genetic Risk Study (USGRS). We conducted a single-variant association analysis of rare (minor allele frequency <1%) putatively functional single-nucleotide polymorphisms (SNPs) present on the Illumina PsychArray genotyping array in 2,672 USGRS suicide deaths of non-Finnish European (NFE) ancestry and 51,583 NFE controls from the Genome Aggregation Database. Secondary analyses used an independent control sample of 21,324 NFE controls from the Psychiatric Genomics Consortium. Five novel, high-impact, rare SNPs were identified with significant associations with suicide death (SNAPC1, rs75418419; TNKS1BP1, rs143883793; ADGRF5, rs149197213; PER1, rs145053802; and ESS2, rs62223875). 119 suicide decedents carried these high-impact SNPs. Both PER1 and SNAPC1 have other supporting gene-level evidence of suicide risk, and psychiatric associations exist for PER1 (bipolar disorder, schizophrenia), and for TNKS1BP1 and ESS2 (schizophrenia). Three of the genes (PER1, TNKS1BP1, and ADGRF5), together with additional genes implicated by genome-wide association studies on suicidal behavior, showed significant enrichment in immune system, homeostatic and signal transduction processes. No specific diagnostic phenotypes were associated with the subset of suicide deaths with the identified rare variants. These findings suggest an important role for rare variants in suicide risk and implicate genes and gene pathways for targeted replication.

The relationship between plasma serotonin and kynurenine pathway metabolite levels and the treatment response to escitalopram and desvenlafaxine.

INTRODUCTION: The response of patients with major depressive disorders (MDD) to antidepressant treatments have been shown to be affected by multiple factors, including disease severity and inflammation. Increasing evidence indicates that the kynurenine metabolic pathway is activated by inflammation in MDD patients and plays a role in the pathophysiology of depression. Antidepressant treatments have been reported to affect kynurenine pathway metabolite levels as well. This study investigates differential associations between the antidepressant treatment outcome to escitalopram versus desvenlafaxine with the pre-treatment and post-treatment-changes in serotonin and kynurenine pathway metabolite levels. METHODS: The levels of serotonin and of kynurenine pathway metabolites were measured in plasma using liquid chromatography-mass spectrometry (LC-MS) in 161 currently depressed patients with MDD at baseline and after 8 weeks of treatment with either escitalopram or desvenlafaxine. Treatment response was defined conventionally by a reduction of at least 50% in the Hamilton Depression Rating Scale 21 item (HAMD-21) total score from baseline; remission was defined by reaching a post-treatment HAMD-21 score ≤7. RESULTS: Response to escitalopram treatment was associated with higher baseline serotonin levels (p = 0.022), lower baseline kynurenine (Kyn)/tryptophan (Trp) ratio (p = 0.008) and lower baseline quinolinic acid (QuinA)/tryptophan (Trp) ratio (p = 0.047), suggesting a lower inflammation state. Greater improvement in depression symptoms as measured by percent change of HAMD-21 score from baseline was also associated with higher baseline serotonin levels (p = 0.033) in escitalopram treatment arm. Furthermore, remitters to escitalopram treatment showed significant increases in the kynurenic acid (KynA)/3-hydroxykynurenine (3HK) ratio after treatment (p = 0.015). In contrast, response to desvenlafaxine treatment was not associated with any metabolite analyzed. We also confirmed a previous report that plasma serotonin levels are lower in MDD patients compared to healthy controls (p = 0.004) and that the kynurenine plasma level is negatively associated with depression symptom severity (p = 0.047). CONCLUSIONS: In MDD patients the antidepressant response to escitalopram was positively associated with baseline serotonin levels and inversely associated with activation of the kynurenine pathway. These results appear consistent with previous literature showing that biomarker evidence of inflammation is associated with lower response to antidepressants from the selective serotonin reuptake inhibitor class. Moreover, increases in the kynurenic acid (KynA)/3-hydroxykynurenine (3HK) ratio, which previously has been characterized as a neuroprotective index, were associated with full remission under escitalopram treatment.

A candidate-gene association study of topiramate-induced weight loss in obese patients with and without type 2 diabetes mellitus.

OBJECTIVE: Clinical response to topiramate can vary greatly in obese patients. Identifying genetic variants associated with treatment response could help gain insight into the mechanism of action of topiramate. Little is known about the relationship between genetic variability and topiramate treatment response. We performed a large-scale candidate-gene study to identify genetic risk factors predictive of topiramate-induced weight loss. METHODS: We collected DNA samples from patients who had previously participated in clinical trials to assess the efficacy of topiramate for the treatment of obesity. A custom chip containing single nucleotide polymorphisms from ∼ 480 candidate genes was utilized to genotype a discovery cohort of 445 obese patients from a clinical study. Variants predictive of topiramate-induced weight loss were identified and further tested in an independent replication cohort of drug-naive, obese patients with type 2 diabetes (N=139). RESULTS: We identified a haplotype in INSR that may contribute to differential topiramate-induced weight loss. Carriers and noncarriers of an INSR haplotype lost 9.1 and 7.0% of body weight, respectively (P = 6.5 × 10(-6), P adj = 0.001). This finding was replicated, with carriers and noncarriers losing 9.5 and 7.3% of body weight, respectively (P Bonf=0.02), in the independent replication cohort. We also identified an SNP in HNF1A that may be associated with topiramate response and an SNP in GRIA3 that may be associated with nonpharmacologic treatment response. CONCLUSION: According to our preliminary findings, genetic variation in the INSR and HNF1A genes may differentially affect weight loss in obese individuals treated with topiramate and genes related to insulin action are implicated in modulating topiramate response. However, these findings need to be further replicated in additional larger samples.

Depression-Anxiety Coupling Strength as a predictor of relapse in major depressive disorder: A CAN-BIND wellness monitoring study report.

BACKGROUND: A critical challenge in the study and management of major depressive disorder (MDD) is predicting relapse. We examined the temporal correlation/coupling between depression and anxiety (called Depression-Anxiety Coupling Strength, DACS) as a predictor of relapse in patients with MDD. METHODS: We followed 97 patients with remitted MDD for an average of 394 days. Patients completed weekly self-ratings of depression and anxiety symptoms using the Quick Inventory of Depressive Symptoms (QIDS-SR) and the Generalized Anxiety Disorder 7-item scale (GAD-7). Using these longitudinal ratings we computed DACS as random slopes in a linear mixed effects model reflecting individual-specific degree of correlation between depression and anxiety across time points. We then tested DACS as an independent variable in a Cox proportional hazards model to predict relapse. RESULTS: A total of 28 patients (29 %) relapsed during the follow-up period. DACS significantly predicted confirmed relapse (hazard ratio [HR] 1.5, 95 % CI [1.01, 2.22], p = 0.043; Concordance 0.79 [SE 0.04]). This effect was independent of baseline depressive or anxiety symptoms or their average levels over the follow-up period, and was identifiable more than one month before relapse onset. LIMITATIONS: Small sample size, in a single study. Narrow phenotype and comorbidity profiles. CONCLUSIONS: DACS may offer opportunities for developing novel strategies for personalized monitoring, early detection, and intervention. Future studies should replicate our findings in larger, diverse patient populations, develop individual patient prediction models, and explore the underlying mechanisms that govern the relationship of DACS and relapse.

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS.

Background: Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD). Aims: Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis. Methods: Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses. Results: While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD. Conclusions: We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BD and, importantly, BD with depressive onset from MDD.

Personalized relapse prediction in patients with major depressive disorder using digital biomarkers.

Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a 'predict and preempt' paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core symptoms of depression and anxiety) to predict near-term relapse in MDD. The framework was evaluated on two independent longitudinal observational trials, characterized by regular bimonthly (every other month) in-person clinical assessments, weekly self-reported symptom assessments, and continuous activity monitoring data with two different wearable sensors for ≥ 1 year or until the first relapse episode. This combined passive-active relapse prediction framework achieved a balanced accuracy of ≥ 71%, false alarm rate of ≤ 2.3 alarm/patient/year with a median relapse detection time of 2-3 weeks in advance of clinical onset in both studies. The study results suggest that the proposed personalized N-of-1 prediction framework is generalizable and can help predict a majority of MDD relapses in an actionable time frame with relatively low patient and provider burden.

Predicting recurrence of major depressive episodes using the Depression Implicit Association Test: A Canadian biomarker integration network in depression (CAN-BIND) report.

Identifying clinically relevant predictors of depressive recurrence following treatment for Major Depressive Disorder (MDD) is critical for relapse prevention. Implicit self-depressed associations (SDAs), defined as implicit cognitive associations between elements of depression (e.g., sad, miserable) and oneself, often persist following depressive episodes and may represent a cognitive biomarker for future recurrences. Thus, we examined whether SDAs, and changes in SDAs over time, prospectively predict depressive recurrence among treatment responders in the CAN-BIND Wellness Monitoring for MDD Study, a prospective cohort study conducted across 5 clinical centres. A total of 96 patients with MDD responding to various treatments were followed an average of 1.01 years. Participants completed the Depression Implicit Association Test (DIAT) - a computer-based measure of SDAs - every 8 weeks on a tablet device. Survival analyses indicated that greater SDAs at baseline and increases in SDAs over time predicted shorter time to MDD recurrence, even after accounting for depressive symptom severity. The findings show that SDAs are a robust prognostic indicator of risk for MDD recurrence, and that the DIAT may be a feasible and low-cost clinical screening tool. SDAs also represent a potential mechanism underlying the course of recurrent depression and are a promising target for relapse prevention interventions.

A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study.

Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual's own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data. In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants. We implemented a data processing pipeline using open-source packages for longitudinal data thereby providing a framework for treating missing data patterns, non-wear scoring, sleep/wake scoring, and conducted a sensitivity analysis to demonstrate the impact of non-wear and missing data on the relationship between sleep variables and depressive symptoms. Compliance with actigraph wear decreased over time, with missing data proportion increasing from a mean of 4.8% in the first week to 23.6% at the end of the 12 months of data collection. Sensitivity analyses demonstrated the importance of defining a pre-processing threshold, as it substantially impacts the predictive value of variables on sleep-related outcomes. We developed a novel non-wear algorithm which outperformed several other algorithms and a capacitive wear sensor in quality control. These findings provide essential insight informing study design in digital health research.

Genetic structure of major depression symptoms across clinical and community cohorts.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and aetiological subtypes. There are several challenges to integrating symptom data from genetically-informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. We conducted genome-wide association studies of major depressive symptoms in three clinical cohorts that were enriched for affected participants (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for missing data patterns in the community cohorts (use of Depression and Anhedonia as gating symptoms). The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analysing genetic association data.

Meta-analysis of epigenome-wide association studies of major depressive disorder.

Epigenetic mechanisms have been hypothesized to play a role in the etiology of major depressive disorder (MDD). In this study, we performed a meta-analysis between two case-control MDD cohorts to identify differentially methylated positions (DMPs) and differentially methylated regions (DMRs) in MDD. Using samples from two Cohorts (a total of 298 MDD cases and 63 controls with repeated samples, on average ~ 1.8 samples/subject), we performed an EWAS meta-analysis. Multiple cytosine-phosphate-guanine sites annotated to TNNT3 were associated with MDD reaching study-wide significance, including cg08337959 (p = 2.3 × 10-11). Among DMPs with association p values less than 0.0001, pathways from REACTOME such as Ras activation upon Ca2+ influx through the NMDA receptor (p = 0.0001, p-adjusted = 0.05) and long-term potentiation (p = 0.0002, p-adjusted = 0.05) were enriched in this study. A total of 127 DMRs with Sidak-corrected p value < 0.05 were identified from the meta-analysis, including DMRs annotated to TNNT3 (chr11: 1948933 to 1949130 [6 probes], Sidak corrected P value = 4.32 × 10-41), S100A13 (chr1: 153599479 to 153600972 [22 probes], Sidak corrected P value = 5.32 × 10-18), NRXN1 (chr2: 50201413 to 50201505 [4 probes], Sidak corrected P value = 1.19 × 10-11), IL17RA (chr22: 17564750 to 17565149, Sidak corrected P value = 9.31 × 10-8), and NPFFR2 (chr4: 72897565 to 72898212, Sidak corrected P value = 8.19 × 10-7). Using 2 Cohorts of depression case-control samples, we identified DMPs and DMRs associated with MDD. The molecular pathways implicated by these data include mechanisms involved in neuronal synaptic plasticity, calcium signaling, and inflammation, consistent with reports from previous genetic and protein biomarker studies indicating that these mechanisms are involved in the neurobiology of depression.

Circulating microRNA associated with future relapse status in major depressive disorder.

Major depressive disorder (MDD) is an episodic condition with relapsing and remitting disease course. Elucidating biomarkers that can predict future relapse in individuals responding to an antidepressant treatment holds the potential to identify those patients who are prone to illness recurrence. The current study explored relationships between relapse risk in recurrent MDD and circulating microRNAs (miRNAs) that participate in RNA silencing and post-transcriptional regulation of gene expression. Serum samples were acquired from individuals with a history of recurrent MDD who were followed longitudinally in the observational study, OBSERVEMDD0001 (ClinicalTrials.gov Identifier: NCT02489305). Circulating miRNA data were obtained in 63 participants who relapsed ("relapsers") and 154 participants who did not relapse ("non-relapsers") during follow-up. The miRNA was quantified using the ID3EAL™ miRNA Discovery Platform from MiRXES measuring 575 circulating miRNAs using a patented qPCR technology and normalized with a standard curve from spike-in controls in each plate. The association between miRNAs and subsequent relapse was tested using a linear model, adjusting for age, gender, and plate. Four miRNAs were nominally associated with relapse status during the observational follow-up phase with a false discover rate adjusted p-value < 0.1. Enrichment analysis of experimentally validated targets revealed 112 significantly enriched pathways, including neurogenesis, response to cytokine, neurotrophin signaling, vascular endothelial growth factor signaling, relaxin signaling, and cellular senescence pathways. These data suggest these miRNAs putatively associated with relapse status may have the potential to regulate genes involved in multiple signaling pathways that have previously been associated with MDD. If shown to be significant in a larger, independent sample, these data may hold potential for developing a miRNA signature to identify patients likely to relapse, allowing for earlier intervention.

Daily steps and depressive symptoms: A longitudinal evaluation of patients with major depressive disorder in the precision medicine in mental health care study.

BACKGROUND: Although the benefits of exercise on Major Depressive Disorder (MDD) are well established, longitudinal studies of objectively measured activity in clinical populations are needed to establish specific guidelines for exercise by persons with moderate-to-severe depression. This study examines the association between objectively assessed daily step count and depressive symptoms over a 24-week follow- up period in outpatients receiving treatment for moderate-to-severe depression. METHODS: Participants were US Veterans with MDD enrolled in the Precision Medicine in Mental Health Care study (PRIME Care), a pragmatic, multi-site, randomized, controlled trial that examines the utility of genetic testing in the context of pharmacotherapy for MDD. Participants were a subset (N = 66) enrolled in actigraphy (using GT9X ActiGraph) monitoring component of the trial. Daily steps were examined as a predictor of depressive symptoms over 4-, 8-, 12-, 18-, and 24-weeks. RESULTS: On average, participants took 3,460 (±1,768) steps per day. In generalized linear mixed models, an increase in 1,000 steps per day was associated with a 0.6-point decrease in depressive symptom severity at the subsequent follow-up assessment. LIMITATIONS: Activity monitoring was observational and causal inferences cannot be made between daily steps and subsequent depressive symptom severity. Results may not generalize to non-treatment-seeking populations. CONCLUSIONS: Study findings provide an initial metric for persons with clinically significant MDD, of whom most do not get sufficient daily activity. The findings can inform future trials aimed at determining how much daily activity is needed to improve symptoms in individuals with MDD.