Psychiatric Genetics Consortium Research Articles

Genome-Wide Association Study of Obsessive-Compulsive Symptoms including 33,943 individuals from the general population.

While 1-2% of individuals meet the criteria for a clinical diagnosis of obsessive-compulsive disorder (OCD), many more (~13-38%) experience subclinical obsessive-compulsive symptoms (OCS) during their life. To characterize the genetic underpinnings of OCS and its genetic relationship to OCD, we conducted the largest genome-wide association study (GWAS) meta-analysis of parent- or self-reported OCS to date (N = 33,943 with complete phenotypic and genome-wide data), combining the results from seven large-scale population-based cohorts from Sweden, the Netherlands, England, and Canada (including six twin cohorts and one cohort of unrelated individuals). We found no genome-wide significant associations at the single-nucleotide polymorphism (SNP) or gene-level, but a polygenic risk score (PRS) based on the OCD GWAS previously published by the Psychiatric Genetics Consortium (PGC-OCD) was significantly associated with OCS (Pfixed = 3.06 × 10-5). Also, one curated gene set (Mootha Gluconeogenesis) reached Bonferroni-corrected significance (Ngenes = 28, Beta = 0.79, SE = 0.16, Pbon = 0.008). Expression of genes in this set is high at sites of insulin mediated glucose disposal. Dysregulated insulin signaling in the etiology of OCS has been suggested by a previous study describing a genetic overlap of OCS with insulin signaling-related traits in children and adolescents. We report a SNP heritability of 4.1% (P = 0.0044) in the meta-analyzed GWAS, and heritability estimates based on the twin cohorts of 33-43%. Genetic correlation analysis showed that OCS were most strongly associated with OCD (rG = 0.72, p = 0.0007) among all tested psychiatric disorders (N = 11). Of all 97 tested phenotypes, 24 showed a significant genetic correlation with OCS, and 66 traits showed concordant directions of effect with OCS and OCD. OCS have a significant polygenic contribution and share genetic risk with diagnosed OCD, supporting the hypothesis that OCD represents the extreme end of widely distributed OCS in the population.

Genetic risk for hospitalization of African American patients with severe mental illness reveals HLA loci.

Mood disorders such as major depressive and bipolar disorders, along with posttraumatic stress disorder (PTSD), schizophrenia (SCZ), and other psychotic disorders, constitute serious mental illnesses (SMI) and often lead to inpatient psychiatric care for adults. Risk factors associated with increased hospitalization rate in SMI (H-SMI) are largely unknown but likely involve a combination of genetic, environmental, and socio-behavioral factors. We performed a genome-wide association study in an African American cohort to identify possible genes associated with hospitalization due to SMI (H-SMI). Patients hospitalized for psychiatric disorders (H-SMI; n=690) were compared with demographically matched controls (n=4467). Quality control and imputation of genome-wide data were performed following the Psychiatric Genetic Consortium (PGC)-PTSD guidelines. Imputation of the Human Leukocyte Antigen (HLA) locus was performed using the HIBAG package. Genome-wide association analysis revealed a genome-wide significant association at 6p22.1 locus in the ubiquitin D (UBD/FAT10) gene (rs362514, p=9.43x10-9) and around the HLA locus. Heritability of H-SMI (14.6%) was comparable to other psychiatric disorders (4% to 45%). We observed a nominally significant association with 2 HLA alleles: HLA-A*23:01 (OR=1.04, p=2.3x10-3) and HLA-C*06:02 (OR=1.04, p=1.5x10-3). Two other genes (VSP13D and TSPAN9), possibly associated with immune response, were found to be associated with H-SMI using gene-based analyses. We observed a strong association between H-SMI and a locus that has been consistently and strongly associated with SCZ in multiple studies (6p21.32-p22.1), possibly indicating an involvement of the immune system and the immune response in the development of severe transdiagnostic SMI.

Polygenic risk of major depressive disorder as a risk factor for venous thromboembolism.

Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are associated with an increased risk of cardiovascular diseases, including venous thromboembolism (VTE). The reasons for this are complex and include obesity, smoking, and use of hormones and psychotropic medications. Genetic studies have increasingly provided evidence of the shared genetic risk of psychiatric and cardiometabolic illnesses. This study aimed to determine whether a genetic predisposition to MDD, BD, or SCZ is associated with an increased risk of VTE. Genetic correlations using the largest genome-wide genetic meta-analyses summary statistics for MDD, BD, and SCZ (Psychiatric Genetics Consortium) and a recent genome-wide genetic meta-analysis of VTE (INVENT Consortium) demonstrated a positive association between VTE and MDD but not BD or SCZ. The same summary statistics were used to construct polygenic risk scores for MDD, BD, and SCZ in UK Biobank participants of self-reported White British ancestry. These were assessed for impact on self-reported VTE risk (10 786 cases, 285 124 controls), using logistic regression, in sex-specific and sex-combined analyses. We identified significant positive associations between polygenic risk for MDD and the risk of VTE in men, women, and sex-combined analyses, independent of the known risk factors. Secondary analyses demonstrated that this association was not driven by those with lifetime experience of mental illness. Meta-analyses of individual data from 6 additional independent cohorts replicated the sex-combined association. This report provides evidence for shared biological mechanisms leading to MDD and VTE and suggests that, in the absence of genetic data, a family history of MDD might be considered when assessing the risk of VTE.

Well-being spectrum traits are associated with polygenic scores for autism.

Individuals with autism spectrum disorder (ASD) tend to experience lower well-being as demonstrated mostly for children and adolescents in epidemiological studies. A further investigation of inclusive well-being, in terms of five well-being spectrum (5-WBS) traits including neuroticism, depression, loneliness, life satisfaction, and positive affect, among adults with ASD may deepen our understanding of their well-being, and lead to the possibility to further modify societal supportive mechanisms for individuals with ASD. This study aims to investigate if a genetic predisposition for ASD is associated with 5-WBS traits using polygenic risk score (PRS) analysis. PRS for ASD were calculated based on the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and were created in the independent cohort UK Biobank. Regression analyses were performed to investigate the association between ASD PRS and 5-WBS traits in the UK Biobank population including 337,423 individuals. ASD PRS were significantly associated with all 5-WBS traits, showing a positive association with the negative WBS traits, neuroticism (max R2 = 0.04%, p < 1 × 10-4 ), depression (max R2 = 0.06%, p < 1 × 10-4 ), loneliness (max R2 = 0.04%, p < 1 × 10-4 ), and a negative association with the positive WBS traits, life satisfaction (max R2 = 0.08%, p < 1 × 10-4 ), positive affect (max R2 = 0.10%, p < 1 × 10-4 ). The findings suggest that adults carrying a high load of risk single nucleotide peptides (SNPs) for ASD are more likely to report decreased well-being. The study demonstrates a considerable connection between susceptibility to ASD, its underlying genetic etiology and well-being.

Tough times never last too long: the future of psychopharmacology.

Tough times never last too long: the future of psychopharmacology.

LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics

We present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory. It shows good control of type 1 error given an appropriate reference panel. Across 109 phenotypes (82 from the UK Biobank, 18 from the Million Veteran Program, and nine from the Psychiatric Genetics Consortium), LDAK-GBAT finds on average 19% (SE: 1%) more significant genes than the existing tool sumFREGAT-ACAT, with even greater gains in comparison with MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, and sumFREGAT-PCA.

Genetic liability to rheumatoid arthritis on autism and autistic traits: polygenic risk score and Mendelian randomization analyses

Higher prevalence of autism in offspring born to mothers with rheumatoid arthritis has been reported in observational studies. We investigated (a) the associations between maternal and offspring’s own genetic liability for rheumatoid arthritis and autism-related outcomes in the offspring using polygenic risk scores (PRS) and (b) whether the effects were causal using Mendelian randomization (MR). Using the latest genome-wide association (GWAS) summary data on rheumatoid arthritis and individual-level data from the Avon Longitudinal Study of Parents and Children, United Kingdom, we constructed PRSs for maternal and offspring genetic liability for rheumatoid arthritis (single-nucleotide polymorphism [SNP] p-value threshold 0.05). We investigated associations with autism, and autistic traits: social and communication difficulties, coherence, repetitive behaviours and sociability. We used modified Poisson regression with robust standard errors. In two-sample MR analyses, we used 40 genome-wide significant SNPs for rheumatoid arthritis and investigated the causal effects on risk for autism, in 18,381 cases and 27,969 controls of the Psychiatric Genetics Consortium and iPSYCH. Sample size ranged from 4992 to 7849 in PRS analyses. We found little evidence of associations between rheumatoid arthritis PRSs and autism-related phenotypes in the offspring (maternal PRS on autism: RR 0.89, 95%CI 0.73–1.07, p = 0.21; offspring’s own PRS on autism: RR 1.11, 95%CI 0.88–1.39, p = 0.39). MR results provided little evidence for a causal effect (IVW OR 1.01, 95%CI 0.98–1.04, p = 0.56). There was little evidence for associations between genetic liability for rheumatoid arthritis on autism-related outcomes in offspring. Lifetime risk for rheumatoid arthritis has no causal effects on autism.

Genetic architecture of schizophrenia: a review of major advancements.

Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

Increasing the resolution and precision of psychiatric genome-wide association studies by re-imputing summary statistics using a large, diverse reference panel.

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large‐scale genome‐wide association studies (GWAS). Methods for direct imputation of GWAS summary‐statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage‐disequilibrium (LD) and that most of the methods using a small reference panel for example, ~2,500‐subject coming from the 1000 Genome‐Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: (a) uses a much larger and more diverse reference panel compared to traditional reference panels, and (b) can estimate weights of ethnic‐mixture based solely on Z‐scores, when allele frequencies are not available. We applied DISTMIX2 to GWAS summary‐statistics from the psychiatric genetic consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post‐traumatic stress disorder GWAS found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most psychiatric GWAS‐studies.

TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders.

Genetic signal detection in genome‐wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), for example, across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred to as transcriptomic wide association studies (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effects of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular basis of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to the quadratic (as a function of the number of SNPs) computational burden for computing LD across large chromosomal regions. To overcome this obstacle, we propose JEPEGMIX2‐P, a novel TWAS pathway method that (a) has a linear computational burden, (b) uses a large and diverse reference panel (33 K subjects), (c) is competitive (adjusts for background enrichment in gene TWAS statistics), and (d) is applicable as‐is to ethnically mixed‐cohorts. To underline its potential for increasing the power to uncover genetic signals over the commonly used nontranscriptomics methods, for example, MAGMA, we applied JEPEGMIX2‐P to summary statistics of most large meta‐analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

Clock gene polygenic risk score and seasonality in major depressive disorder and bipolar disorder.

Seasonal changes in mood and diurnal preference are two well-characterized chronobiological phenotypes in major depressive disorder (MDD) and bipolar disorder (BD). The biological mechanisms regulating physiological changes related to seasonality and chronotype involve several genes known as "clock" or circadian genes. Our goal was to study the relationship between the polygenic risk score (PRS) obtained from a set of clock genes and chronobiological traits in patients with mood disorders. The sample included 445 patients with mood disorders (256 MDD; 189 BD). Seasonality was assessed using the Seasonal Pattern Assessment Questionnaire (SPAQ), and chronotype was assessed using the Horne and Östberg Morningness-Eveningness Questionnaire. We selected 248 single nucleotide polymorphisms located within 19 genes. PRS for both MDD and BD was calculated using the Psychiatric Genetics Consortium latest datasets as discovery samples. Another PRS was calculated using results from a genome-wide association study focusing on chronotype. SPAQ results were significantly associated with MDD-PRS (p = 0.037) and chronotype-PRS (p = 0.019), which also showed a significant interaction with age (p = 0.039). No significant association was observed between the measured PRS and chronotype. Our results reflect that small effect variants associated with MDD and chronotype within clock genes are associated with seasonality traits in patients with mood disorders, further explaining the mechanism through which the circadian system might influence mood disorder clinical presentation. Future studies measuring PRS from specific gene sets and focusing on biological endophenotypes will help to elucidate the pathways from genetic variations to clinical outcome.

M126. THE MAIN AND INTERACTIVE EFFECTS OF ADULT STRESSFUL LIFE EVENTS WITH GENOMIC AND EXPOSOMIC LIABILITY FOR SCHIZOPHRENIA ON MENTAL AND PHYSICAL HEALTH: A PROSPECTIVE COHORT STUDY

BackgroundEvidence suggests that the impact of adult stressful life events (SLE) on health depends on an individual’s environmental and genetic predisposition to psychopathology. However, the influence of the genomic and exposomic (the sum of exposures) liabilities for schizophrenia on the association between SLE and health outcomes in the general population has not been studied. The current longitudinal study therefore aims to evaluate whether the association of recent SLE with mental as well as physical health is moderated by polygenic risk score for schizophrenia (PRS-S) and the exposome score for schizophrenia (ES-S, an aggregated environmental exposure score, akin to PRS-S).MethodsData from four waves of the Netherlands Mental Health Survey and Incidence Study-II (NEMESIS-II), a prospective survey in the Dutch general population aged 18 – 64, was used. The baseline data (T0) of NEMESIS-II, including 6646 participants, were collected from 2007 to 2009 and were followed up at year 3 (T1), year 6 (T2), and year 9 (T3). The Short-Form-36 Health Survey was used to repeatedly assess mental and physical health. Repeated assessment of SLE were based on the Brugha Life events section. Guided by our previous work, ES-S was constructed as the weighted sum of important baseline environmental factors, which were previously associated with schizophrenia. A subsample of 3099 participants (at T0) were genotyped and PRS-S was trained on the latest Psychiatric Genetics Consortium schizophrenia meta-analysis, adjusted for the first three population principal components. The analyses were conducted using random intercept multilevel regression models with age, sex, and education included as a priori covariates. Models were clustered for multiple assessments per individual, and the random intercept of mental or physical health at T-1 was added to the model to control for previous health status. We evaluated the main effects of SLE, ES-S, and PRS-S, and tested the moderating effects of ES-S and PRS-S on the impact of SLE on physical and mental health. As sensitivity analyses, we repeated the analyses using lagged SLE at T-1.ResultsSLE and ES-S were associated with decreased mental (SLE: β = -2.53, P <0.001; ES-S: β = -2.93, P <0.001) and physical health (SLE: β = -2.62, P < 0.001; ES-S, β = -3.10, P < 0.001), while PRS-S was only associated with decreased mental health (β = -1.02, P <0.001) and not with physical health (β = -0.57, P = 0.061). ES-S moderated the association of SLE with mental health (β = -0.87, P = 0.002) but not with physical health (β = -0.60, P = 0.069). There was no evidence for gene–environment correlation between SLE and PRS-S (OR = 1.02, P = 0.527); and PRS-S did not moderate the association of SLE with mental (β = -0.38, P = 0.241) or physical health (β = -0.44, P = 0.252). The sensitivity analyses with lagged SLE at the previous time point converged with the results. SLE at T-1 was associated with mental (β = -0.90, P < 0.001) and physical health (β = -1.87, P < 0.001). ES-S moderated the association of SLE at T-1 with mental (β = -0.52 P = 0.029) but not physical health (β = -0.45, P = 0.116). PRS-S did not moderate the association of SLE at T-1 with mental (β = -0.03, P = 0.917) or physical health (β = 0.43, P = 0.233).DiscussionIn support of the diathesis-stress model, we have provided novel evidence showing that exposure to adult SLE, particularly in individuals with high ES-S, is associated with decreased mental health. However, we did not find evidence for a gene-environment interaction in the context of SLExPRS-S. Our findings also add to the literature showing that genomic and exposomic liability for schizophrenia is pleiotropically associated with health outcomes in the general population.

Determination of shared genetic etiology and possible causal relations between tobacco smoking and depression.

Cigarette smoking is strongly associated with major depressive disorder (MDD). However, any genetic etiology of such comorbidity and causal relations is poorly understood, especially at the genome-wide level. In the present in silico research, we analyzed summary data from the genome-wide association study of the Psychiatric Genetic Consortium for MDD (n = 191 005) and UK Biobank for smoking (n = 337 030) by using various biostatistical methods including Bayesian colocalization analysis, LD score regression, variant effect size correlation analysis, and Mendelian randomization (MR). By adopting a gene prioritization approach, we identified 43 genes shared by MDD and smoking, which were significantly enriched in membrane potential, gamma-aminobutyric acid receptor activity, and retrograde endocannabinoid signaling pathways, indicating that the comorbid mechanisms are involved in the neurotransmitter system. According to linkage disequilibrium score regression, we found a strong positive correlation between MDD and current smoking (rg = 0.365; p = 7.23 × 10-25) and a negative correlation between MDD and former smoking (rg = -0.298; p = 1.59 × 10-24). MR analysis suggested that genetic liability for depression increased smoking. These findings inform the concomitant conditions of MDD and smoking and support the use of self-medication with smoking to counteract depression.

Polygenic liability for schizophrenia and childhood adversity influences daily-life emotion dysregulation and psychosis proneness.

ObjectiveTo test whether polygenic risk score for schizophrenia (PRS‐S) interacts with childhood adversity and daily‐life stressors to influence momentary mental state domains (negative affect, positive affect, and subtle psychosis expression) and stress‐sensitivity measures.MethodsThe data were retrieved from a general population twin cohort including 593 adolescents and young adults. Childhood adversity was assessed using the Childhood Trauma Questionnaire. Daily‐life stressors and momentary mental state domains were measured using ecological momentary assessment. PRS‐S was trained on the latest Psychiatric Genetics Consortium schizophrenia meta‐analysis. The analyses were conducted using multilevel mixed‐effects tobit regression models.ResultsBoth childhood adversity and daily‐life stressors were associated with increased negative affect, decreased positive affect, and increased subtle psychosis expression, while PRS‐S was only associated with increased positive affect. No gene–environment correlation was detected. There is novel evidence for interaction effects between PRS‐S and childhood adversity to influence momentary mental states [negative affect (b = 0.07, P = 0.013), positive affect (b = −0.05, P = 0.043), and subtle psychosis expression (b = 0.11, P = 0.007)] and stress‐sensitivity measures.ConclusionExposure to childhood adversities, particularly in individuals with high PRS‐S, is pleiotropically associated with emotion dysregulation and psychosis proneness.

Effects of Genetic Variants on Stroke Risk.

Effects of Genetic Variants on Stroke Risk.

Polygenic risk for psychiatric disorder and singleness in patients with severe mental illness and controls

We aimed to investigate whether the polygenic risk score (PRS) for schizophrenia influences time in couple relationships for patients with severe mental illness and controls. We combined the nationwide Danish registers with genetic information from dried neonatal blood spots. We included 2,599 individuals with schizophrenia, 1,446 with bipolar disorder, 20,315 with depression, and 6,963 controls. PRS for schizophrenia, depression, and bipolar disorder were estimated using data from the Psychiatric Genetics Consortium and analyzed both as a scale-predictor and as highest versus other deciles. The main outcome was number of days in couple relationships. Patients with schizophrenia had markedly fewer days/year in couple relationships: 64 (95% CI; 61–69) than patients with depression: 119 (95% CI; 117–121), bipolar disorder: 103 (95% CI 97–110), and controls: 136 (95% CI 133–139). PRS for schizophrenia was associated with fewer days in couple relationships in patients with schizophrenia (scale-PRS: IRR = 0.95 (0.93–0.97)) or depression (highest decile: IRR = 0.93 (0.87–0.98)). PRS for bipolar disorder (as scale) was also associated with fewer days in couple relationships in patients with depression (IRR = 0.99 (0.99–1.00)) or bipolar disorder (IRR = 0.96 (0.94–0.99)) and controls (IRR = 0.99 (0.97–1.00), and IRR = 0.89 (0.81–0.98) for the highest decile). Due to the number of statistical tests, however, it cannot be concluded definitely that some of these may not be spurious findings. In conclusion, our findings implicate high genetic loading for schizophrenia as a predisposing factor to singleness in patients with schizophrenia or depression, and genetic loading for bipolar disorder a similar predisposing factor in patients with depression, bipolar disorder or controls.

Major depression and small vessel stroke: a Mendelian randomization analysis.

Although observational studies have reported a positive association between depression and ischemic stroke, causality remains inconclusive. We aimed to assess the causal relationship of major depressive disorder (MDD) with ischemic stroke, especially with the small vessel stroke (SVS) subtype. We used 72 independent single-nucleotide polymorphisms associated with MDD in a genome-wide association study (GWAS) from the Psychiatric Genetics Consortium as instrumental variables. The corresponding data for ischemic stroke and its subtypes of European ancestry were available from the MEGASTROKE consortium of 34,217 ischemic stroke cases and 406,111 controls. Primary Mendelian randomization estimates were calculated with inverse-variance weighted method, and several alternate methods and multiple sensitivity analyses were also performed. We found that genetic predisposition to higher risk of MDD was associated with higher risk of SVS, with an odds ratio of 1.33 (95% confidence interval, 1.08-1.65; p = 0.009) per log-odds increment in MDD risk, but not with large artery stroke (OR, 1.08; 95% CI 0.83-1.41; p = 0.559), cardioembolic stroke (OR, 0.98; 95% CI 0.80-1.20; p = 0.833), or all ischemic stroke (OR, 1.03; 95% CI 0.92-1.15; p = 0.633). The association of MDD with SVS was overall robust to sensitivity analyses. We provided evidence for a possible causal effect of MDD on increased risk of SVS. Future researches are required to investigate whether rational intervention on depression may help to reduce societal burden of SVS.

Genetic Overlap Between Alzheimer's Disease and Bipolar Disorder Implicates the MARK2 and VAC14 Genes.

Background: Alzheimer’s disease (AD) and bipolar disorder (BIP) are complex traits influenced by numerous common genetic variants, most of which remain to be detected. Clinical and epidemiological evidence suggest that AD and BIP are related. However, it is not established if this relation is of genetic origin. Here, we applied statistical methods based on the conditional false discovery rate (FDR) framework to detect genetic overlap between AD and BIP and utilized this overlap to increase the power to identify common genetic variants associated with either or both traits.Methods: We obtained genome wide association studies data from the International Genomics of Alzheimer’s Project part 1 (17,008 AD cases and 37,154 controls) and the Psychiatric Genetic Consortium Bipolar Disorder Working Group (20,352 BIP cases and 31,358 controls). We used conditional QQ-plots to assess overlap in common genetic variants between AD and BIP. We exploited the genetic overlap to re-rank test-statistics for AD and BIP and improve detection of genetic variants using the conditional FDR framework.Results: Conditional QQ-plots demonstrated a polygenic overlap between AD and BIP. Using conditional FDR, we identified one novel genomic locus associated with AD, and nine novel loci associated with BIP. Further, we identified two novel loci jointly associated with AD and BIP implicating the MARK2 gene (lead SNP rs10792421, conjunctional FDR = 0.030, same direction of effect) and the VAC14 gene (lead SNP rs11649476, conjunctional FDR = 0.022, opposite direction of effect).Conclusion: We found polygenic overlap between AD and BIP and identified novel loci for each trait and two jointly associated loci. Further studies should examine if the shared loci implicating the MARK2 and VAC14 genes could explain parts of the shared and distinct features of AD and BIP.

Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism

Alcohol exposure triggers changes in gene expression and biological pathways in human brain. We explored alterations in gene expression in the Pre-Frontal Cortex (PFC) of 65 alcoholics and 73 controls of European descent, and identified 129 genes that showed altered expression (FDR < 0.05) in subjects with alcohol dependence. Differentially expressed genes were enriched for pathways related to interferon signaling and Growth Arrest and DNA Damage-inducible 45 (GADD45) signaling. A coexpression module (thistle2) identified by weighted gene co-expression network analysis (WGCNA) was significantly correlated with alcohol dependence, alcohol consumption, and AUDIT scores. Genes in the thistle2 module were enriched with genes related to calcium signaling pathways and showed significant downregulation of these pathways, as well as enrichment for biological processes related to nicotine response and opioid signaling. A second module (brown4) showed significant upregulation of pathways related to immune signaling. Expression quantitative trait loci (eQTLs) for genes in the brown4 module were also enriched for genetic associations with alcohol dependence and alcohol consumption in large genome-wide studies included in the Psychiatric Genetic Consortium and the UK Biobank’s alcohol consumption dataset. By leveraging multi-omics data, this transcriptome analysis has identified genes and biological pathways that could provide insight for identifying therapeutic targets for alcohol dependence.

Correction to: Polygenic Risk for Depression Increases Risk of Ischemic Stroke From the Stroke Genetics Network Study.

Correction to: Polygenic Risk for Depression Increases Risk of Ischemic Stroke From the Stroke Genetics Network Study.