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BackgroundThe corpus callosum (CC) is a brain structure with a high heritability and potential role in psychiatric disorders. However, the genetic architecture of the CC and the genetic link with psychiatric disorders remains largely unclear. We investigated the genetic architectures of the volume of the CC and its subregions, and the genetic overlap with psychiatric disorders.MethodsWe applied multivariate GWAS to genetic and T1-weighted MRI data of 40,894 individuals from the UK Biobank, aiming to boost genetic discovery and to assess the pleiotropic effects across volumes of the five subregions of the CC (posterior, mid posterior, central, mid anterior and anterior) obtained by FreeSurfer 7.1. Multivariate GWAS was run combining all subregions, co-varying for relevant variables. Gene-set enrichment analyses were performed using MAGMA. Linkage disequilibrium score regression (LDSC) was used to determine SNP-based heritability of total CC volume and volumes of its subregions as well as their genetic correlations with relevant psychiatric traits.ResultsWe identified 70 independent loci with distributed effects across the five subregions of the CC (p < 5 × 10−8). Additionally, we identified 33 significant loci in the anterior subregion, 23 in the mid anterior, 29 in the central, 7 in the mid posterior and 56 in the posterior subregion. Gene-set analysis revealed 156 significant genes contributing to volume of the CC subregions (p < 2.6 × 10−6). LDSC estimated the heritability of CC to (h2SNP=0.38, SE=0.03), and subregions ranging from 0.22 (SE=0.02) to 0.37 (SE=0.03). We found significant genetic correlations of total CC volume with bipolar disorder (BD, rg=-0.09, SE=0.03; p=5.9 × 10−3) and drinks consumed per week (rg=-0.09, SE=0.02; p=4.8 × 10−4), and volume of the mid anterior subregion with BD (rg=-0.12, SE=0.02; p=2.5 × 10−4), major depressive disorder (rg=-0.12, SE=0.04; p=3.6 × 10−3), drinks consumed per week (rg=-0.13, SE=0.04; p=1.8 × 10−3) and cannabis use (rg=-0.09, SE=0.03; p=8.4 × 10−3).ConclusionsOur results demonstrate that the CC has a polygenic architecture implicating multiple genes, and show that CC subregion volumes are heritable. We found distinct genetic factors are involved in the development of anterior and posterior subregions, consistent with their divergent functional specialization. Significant genetic correlation between volumes of the CC and bipolar disorder, drinks per week, major depressive disorder and cannabis consumption subregion volumes with psychiatric traits is noteworthy and deserving of further investigation.

BACKGROUND: Polygenic hazard scores (PHS) estimate age-dependent genetic risk of late-onset Alzheimer's disease (AD), but there is limited information about the performance of PHS on real-world data where the population of interest differs from the model development population and part of the model genotypes are missing or need to be imputed.OBJECTIVE: The aim of this study was to estimate age-dependent risk of late-onset AD using polygenic predictors in Nordic populations.METHODS: We used Desikan PHS model, based on Cox proportional hazards assumption, to obtain age-dependent hazard scores for AD from individual genotypes in the Norwegian DemGene cohort (n = 2,772). We assessed the risk discrimination and calibration of Desikan model and extended it by adding new genotype markers (the Desikan Nordic model). Finally, we evaluated both Desikan and Desikan Nordic models in two independent Danish cohorts: The Copenhagen City Heart Study (CCHS) cohort (n = 7,643) and The Copenhagen General Population Study (CGPS) cohort (n = 10,886).RESULTS: We showed a robust prediction efficiency of Desikan model in stratifying AD risk groups in Nordic populations, even when some of the model SNPs were missing or imputed. We attempted to improve Desikan PHS model by adding new SNPs to it, but we still achieved similar risk discrimination and calibration with the extended model.CONCLUSION: PHS modeling has the potential to guide the timing of treatment initiation based on individual risk profiles and can help enrich clinical trials with people at high risk to AD in Nordic populations.

The functional domain of the human cerebellum has expanded beyond motor control to also include cognitive and affective functions. In line with this notion, cerebellar volume has increased over recent primate evolution and cerebellar alterations have been linked to heritable mental disorders. In order to map the genetic architecture of human cerebellar morphology, we studied a large imaging genetics sample from the UK Biobank (n discovery = 27,302; n replication: 11,264) with state-of-the art neuroimaging and biostatistics tools. Multivariate GWAS (MOSTest) on empirically derived regional cerebellar MRI features yielded 351 significant genetic loci (228 novel, 94% replicated) showing both shared and specific effects across regions. Gene level analyses revealed enrichment for genes associated with human-specific evolution over the last ∼6-8 million years, to a similar or higher degree than cerebral comparison phenotypes. We also observed genetic overlap between cerebellar morphology and major mental disorders, supporting cerebellar involvement in psychopathology.

Epigenetic modifications influenced by environmental exposures are molecular sources of phenotypic heterogeneity found in schizophrenia and bipolar disorder and may contribute to shared etiopathogenetic mechanisms of these two disorders. Newborns who experienced birth asphyxia have suffered reduced oxygen delivery to the brain around the time of birth, which increases the risk of later psychiatric diagnosis. This study aimed to investigate DNA methylation in blood cells for associations with a history of birth asphyxia, a neurologically harmful condition occurring within the biological environment of birth. We utilized prospective data from the Medical Birth Registry of Norway to identify incidents of birth asphyxia in 643 individuals with schizophrenia or bipolar disorder and 676 healthy controls. We performed an epigenome wide association study to distinguish differentially methylated positions associated with birth asphyxia. We found an interaction between methylation and exposure to birth asphyxia on case–control status, wherein having a history of birth asphyxia was associated with an increase of methylation in healthy controls and a decrease of methylation in patients on 4 regions of DNA important for brain development and function. The differentially methylated regions were observed in genes involved in oligodendrocyte survival and axonal myelination and functional recovery (LINGO3); assembly, maturation and maintenance of the brain (BLCAP;NNAT and NANOS2) and axonal transport processes and neural plasticity (SLC2A14). These findings are consistent with the notion that an opposite epigenetic response to birth asphyxia, in patients compared with controls, may contribute to molecular mechanisms of risk for schizophrenia and bipolar disorder.

The placenta plays a role in fetal brain development, and pregnancy and birth complications can be signs of placental dysfunction. Birth asphyxia is associated with smaller head size and higher risk of developing schizophrenia (SZ), but whether birth asphyxia and placental genetic risk factors associated with SZ are related and how they might impact brain development is unclear. 433 adult patients with SZ and 870 healthy controls were clinically evaluated and underwent brain magnetic resonance imaging. Pregnancy and birth information were obtained from the Medical Birth Registry of Norway. Polygenic risk scores (PRS) from the latest genome-wide association study in SZ were differentiated into placental PRS (PlacPRS) and non-placental PRS. If the interaction between PRSs and birth asphyxia on case-control status was significant, neonatal head circumference (nHC) and adult intracranial volume (ICV) were further evaluated with these variables using multiple regression. PlacPRS in individuals with a history of birth asphyxia was associated with a higher likelihood of being a patient with SZ (t=2.10, p=.018). We found a significant interaction between PlacPRS and birth asphyxia on nHC in the whole sample (t=-2.14, p=.017), with higher placental PRS for SZ associated with lower nHC in those with birth asphyxia. This relationship was specific to males (t=-2.41, p=.010) and also found with their adult ICV (t=-1.97, p=.028). These findings suggest that placental pathophysiology and birth asphyxia may affect early and late trajectories of brain development, particularly in males with a higher vulnerability to SZ. This knowledge might lead to new strategies of treatment and prevention in SZ.

Discoveries from genome-wide association studies often contain large clusters of highly correlated genetic variants, which makes them hard to interpret. In such cases, finemapping the underlying causal variants become important. Here we present a new method, the Finemap-MiXeR, based on a variational Bayesian approach for finemapping genomic data, i.e., determining the causal single nucleotide polymorphisms (SNPs) associated with a trait at a given locus after controlling for correlation among genetic variants due to linkage disequilibrium. Our approach is based on the optimization of Evidence Lower Bound of the likelihood function obtained from the MiXeR model. The optimization is done using Adaptive Moment Estimation Algorithm, allowing to obtain posterior probability of each SNP to be a causal variant. We tested Finemap-MiXeR in a range of different scenarios, using both synthetic and real data from the UK Biobank, using standing height phenotype as an example. In comparison to the existing finemapping methods FINEMAP and SuSiE methods, we observed that Finemap-MiXeR in most cases has better accuracy. Furthermore, it is computationally efficient, and unlike other methods the complexity is not increasing as the number of causal SNPs or the heritability increases. We show that our finemapping algorithm identifies a small number of genetic variants per locus which are informative for predicting the phenotype in an independent sample.

BackgroundGenome-wide Association Studies (GWAS) have reshaped our understanding of the genetic bases of complex diseases in general and neurodegenerative diseases in particular. Despite being a common disorder, dementia with Lewy bodies (DLB), which, together with Parkinson’s disease dementia (PDD), comprise the umbrella term Lewy body dementias (LBD), is far from being well-characterized genetically. This is primarily due to a lack of familial cases and difficulty recruiting large, deeply characterized cohorts, given the high rate of misdiagnosis. By performing the largest GWAS in DLB, we aimed to identify novel risk loci to gain a better understanding of this disease’s pathobiology.MethodsHere, we conducted the largest meta-analysis of genome-wide association studies performed in LBD, using a total of 5,119 cases and 20,988 controls, from five independent datasets, aggregating all previously published DLB genome-wide association results to date, as well as two previously undescribed cohorts. Additionally, we performed a sex stratified GWAS using the discovery datasets. We updated the heritability estimates for DLB and, to fine map these estimates, we used local heritability analysis. We calculated genetic correlation estimates between DLB and a range of other diseases and traits to identify potential pleiotropy. We also performed gene-set analysis to identify genes with excess burden of rare variability and pathway analysis. Lastly, we used the UK Biobank data to perform a PheWas using individuals at the extremes of genetic risk for DLB.FindingsBetween November 2018 and September 2022 we analyzed 8.6 million single nucleotide polymorphisms in 3293 DLB cases, 1826 LBD cases and 20,988 controls, as well as phenotypes from the UK Biobank dataset. Despite more than doubling the sample size from the previous GWAS in DLB, we did not identify significant loci in addition to those previously reported atGBA, SNCA, STX1B, andAPOE. However, the sex-stratified analysis revealed that theGBAandSNCAsignals are mainly driven by males, suggesting a sex-specific genetic architecture of disease. Using only clinical and neuropathologically diagnosed cases, we highlight four loci surpassing the significance threshold. Using the largest cohort of DLB we update our heritability estimates to 13% and fine map these results highlighting regions of the genome with high heritability but no genome-wide significant result so far.InterpretationThese data provide the most comprehensive analysis of genetic variability in DLB to date. The fact that no novel risk loci have been identified after doubling the cohort size indicates the potentially significant role of rare variants in the genetic architecture of DLB and stresses the urgent need for larger, well-characterized cohorts of this disease for genetic studies. The sex-stratified analysis shows that males and females have different signatures of genetic risk for DLB. These results have widespread implications for clinical practice and clinical trials’ design in DLB.

Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR MAP2K1 and ZDBF2. Further, polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment.

Background Mood and anxiety disorders account for a large share of the global burden of disability. Some studies suggest that early signs may emerge already in childhood. However, there is a lack of well-powered, prospective studies investigating how and when childhood mental traits and trajectories relate to adolescent mood and anxiety disorders. Methods We here examine cross-sectional and longitudinal association between maternally reported temperamental traits, emotional and behavioral problems in childhood (0.5–8 years) and clinical diagnosis of mood or anxiety (“emotional”) disorders in adolescence (10–18 years), using the prospective Norwegian Mother, Father and Child Cohort Study (MoBa) of 110,367 children. Results Logistic regression analyses showed consistent and increasing associations between childhood negative emotionality, behavioral and emotional problems and adolescent diagnosis of emotional disorders, present from 6 months of age (negative emotionality). Latent profile analysis incorporating latent growth models identified five developmental profiles of emotional and behavioral problems. A profile of early increasing behavioral and emotional problems with combined symptoms at 8 years (1.3% of sample) was the profile most strongly associated with emotional disorders in adolescence (OR vs. reference: 5.00, 95% CI: 3.70–6.30). Conclusions We found a consistent and increasing association between negative emotionality, behavioral and emotional problems in early to middle childhood and mood and anxiety disorders in adolescence. A developmental profile coherent with early and increasing disruptive mood dysregulation across childhood was the profile strongest associated with adolescent emotional disorders. Our results highlight the importance of early emotional dysregulation and childhood as a formative period in the development of adolescent mood and anxiety disorders, supporting potential for prevention and early intervention initiatives.

Oxytocin (OT) is a neuropeptide associated with several psychological and somatic processes across vertebrates. Although OT homologs have been identified in many species, the evolutionary timeline of the entire OT signaling gene pathway has yet to be described, which can help inform the current purpose of the OT pathway. Using protein sequence similarity searches, microsynteny and phylostratigraphy, we assigned the genes to different phylostrata. We show that the majority (64%) of genes in the pathway are more modern, as they evolved around the emergence of vertebrates and jawed vertebrates (540 - 530 million years ago, "mya"), including OXTR, OXT and CD38. 18% of the genes in the OT pathway are more ancient, dating back to cellular organisms and ophistokonta (3500 - 1100 mya). In humans, medium-aged pathway genes are highly expressed in contractile organs (e.g., blood vessel), while genes that evolved more recently are primarily expressed in the brain and muscle tissue. Altogether, we show that the most ancient genes in the pathway support basic cellular signaling functions in humans, whereas the recent ones are linked to adaptive functions, bone maturation and preservation, and are up-regulated in the brain.

Schizophrenia is a highly heritable brain disorder with a typical symptom onset in early adulthood. The two-hit hypothesis posits that schizophrenia results from deviant early neurodevelopment, predisposing an individual, followed by a disruption of later brain maturational processes that trigger the onset of symptoms. Here, we investigate how the timing of expression of 345 putative schizophrenia risk genes may aid in understanding the interplay of neurobiological processes in the pathophysiology of schizophrenia. Clustering of brain transcriptomic data across the lifespan revealed a set of 183 genes that was significantly upregulated prenatally and downregulated postnatally and 162 genes that showed the opposite pattern. The prenatally upregulated set of genes was functionally annotated to fundamental cell cycle processes, while the postnatally upregulated set was associated with the immune system and neuronal communication. We subsequently calculated two set-specific polygenic risk scores for 743 individuals with schizophrenia and 743 sex- and age-matched healthy controls. We found an interaction between the two scores; higher prenatal polygenic risk was only significantly associated with schizophrenia diagnosis and severity, at higher levels of postnatal polygenic risk. We therefore provide genetics-based evidence in favor of the two-hit hypothesis, supporting that schizophrenia may be shaped by disruptions of separable biological processes acting at distinct phases of neurodevelopment.

MotivationGenome-wide association studies (GWAS) have been successful in identifying genetic variants associated with a particular phenotype. However, many complex phenotypes are influenced by multiple genetic variants with small effects. Detecting the genetic pleiotropy can provide insights into biological mechanisms influencing complex human phenotypes. The recently developed Multivariate Omnibus Statistical Test (MOSTest) has proven to be efficient and powerful, suited for complex large-scale data. The method substantially increased discovery of genetic variants associated with brain MRI phenotypes in the UK Biobank compared to conventionally use multivariate approach. Here we extend the MOSTest to meta-analysis (Meta-MOSTest), facilitating data analysis of multiple phenotypes across multiple cohorts. We evaluated our updated approach in the UK Biobank using brain MRI phenotypes, by comparing the discovery yield of the single-cohort MOSTest versus Meta-MOSTest through simulating sub-cohorts of different sample sizes from 265 to 26501 subjects.ResultsOur method works efficiently on large-scale cohorts with a large number of MRI phenotypes. We found that lower per-cohort sample sizes resulted in a reduced discovery yield indicating a loss of statistical power. However, with a minimum sample size of 250 subjects across cohorts, Meta-MOSTest was equivalent to MOSTest on discovery yield while maintaining a well-calibrated type I error and equivalent statistical power. We conclude that Meta-MOSTest is a useful tool for multivariate analysis across separate brain imaging genetics cohorts.Availability and implementationAll codes are freely available on GitHub: MOSTest and Meta-MOSTest.

Objective Autism Spectrum Disorder (ASD) is a highly heritable condition with a large variation in cognitive function. Here we investigated the shared genetic architecture between cognitive traits (intelligence (INT) and educational attainment (EDU)), and risk loci jointly associated with ASD and the cognitive traits. Methods We included data from genome-wide association studies (GWAS) of INT (n = 269,867), EDU (n = 766,345) and ASD (cases n = 18,381, controls n = 27,969). We used the bivariate causal mixture model (MiXeR) to estimate the total number of shared genetic variants, and conditional and conjunctional false discovery rate (cond/conjFDR) to identify specific overlapping loci. Results The MiXeR indicated 12.7k genetic variants associated with ASD, with 12.0k shared with EDU and 11.1k shared with INT (Dice: 0.90–0.91), with both positive and negative relationships within overlapping variants. The majority (59%-68%) of estimated shared loci have concordant effect directions, with a positive, albeit modest, genetic correlation between ASD and EDU (rg=0.21, p = 2e-13) and INT (rg=0.22, p = 4e-12). We discovered 43 loci jointly associated with ASD and cognitive traits (conjFDR

BackgroundMood and anxiety disorders, often emerging during adolescence, account for a large share of the global burden of disability. Prospectively assessed premorbid early signs and trajectories can provide useful insights for early detection and development of these disorders.MethodsUsing the health registry linked Norwegian Mother, Father and Child Cohort Study (MoBa) of 110,367 children, we here examine cross-sectional and longitudinal association between temperamental traits, emotional and behavioral problems in childhood (0.5-8 years) and diagnosis of mood or anxiety (emotional) disorders in adolescence (10-18 years). We included birth year and sex, retrieved from the Medical Birth Registry of Norway, as covariates in all analyses.ResultsLogistic regression analyses showed consistent and increasing associations between childhood negative emotionality, behavioral and emotional problems and adolescent diagnosis of emotional disorders, present from 6 months of age (negative emotionality) and with similar magnitude of association for the associated traits. Latent profile analysis incorporating latent growth models identified five developmental profiles of emotional and behavioral problems. A profile of early increasing behavioral and emotional problems with combined symptoms at 8 years (1.3% of sample) was the profile most strongly associated with emotional disorders in adolescence (OR vs. reference: 5.00, 95% CI: 3.73-6.30).ConclusionsWe found a consistent and increasing association between negative emotionality, behavioral and emotional problems in early to middle childhood and mood and anxiety disorders in adolescence. A developmental profile coherent with early and increasing disruptive mood dysregulation across childhood was most predictive of adolescent emotional disorders. Our results highlight the importance of early emotional dysregulation and childhood as a formative period in the development of adolescent mood and anxiety disorders, supporting a potential for prevention and early intervention initiatives.

Alcohol use disorder (AUD) is a pervasive and devastating mental illness with high comorbidity rates with other mental disorders. We aimed to characterize the sharedvs. unique polygenicity of AUD, alcohol consumption (AC) and mood instability (MOOD), a relevant transdiagnostic factor, using large genome-wide association studies (GWASs) data. We hypothesize that cross-analyzing these phenotypes would shed light on their unique and shared polygenicity, increase our knowledge regarding the genetic basis of the comorbidity between AUD and mood disorders, and boost discovery for jointly-associated loci. Summary statistics for MOOD, AC and AUD GWASs (Ns =363,705; 200,680 and 200,004; respectively) were analysed to characterize the cross-phenotype associations between MOOD and AC, MOOD and AUD and AC and AUD, respectively. To do so, we used a newly-established pipeline that combines (i) the bivariate causal mixture model (MiXeR) to quantify the cross-phenotype polygenic overlap and (ii) the conjunctional false discovery rate (conjFDR) to discover specific jointly-associated genomic loci. These loci were functionally characterized and mapped to genes and biological functions. We also performed validation in independent samples and phenotypic analyses. MOOD was highly polygenic (10,400 single nucleotide polymorphisms, SNPs) compared to AC and AUD (4,900 SNPs, SD =600 and 4,300 SNPs, SD =2,000; respectively). The polygenic overlap of MOOD and AC was much larger than that of MOOD and AUD (98%vs. 49%) and genetic correlation was opposite (−0.2vs. 0.23), which was confirmed in independent samples. MOOD&AUD causal SNPs were significantly enriched for brain genes, conversely to MOOD&AC. Among 38 loci identified in the joint analysis, sixteen were novel for MOOD, AC and AUD. Similarly distinct patterns were evidenced for SNP localization, function and previous GWAS associations outside of the phenotypes that were currently studied. MOOD, AC and AUD were also strongly associated at the phenotypic level. Overall, using multilevel polygenic quantification, joint loci discovery and functional annotation methods, we evidenced that the polygenic overlap between MOOD and AC/AUD implicated shared biological underpinnings but clearly distinct functional patterns between MOOD&AC and MOOD&AUD. Using the MOOD endophenotype, the current study suggests new mechanisms for the comorbidity of AUD with mood disorders.

Personality and cognition are heritable mental traits, and their genetic determinants may be distributed across interconnected brain functions. However, previous studies have employed univariate approaches which reduce complex traits to summary measures. We applied the “pleiotropy-informed” multivariate omnibus statistical test (MOSTest) to genome-wide association studies (GWAS) of 35 item and task-level measures of neuroticism and cognition from the UK Biobank (n=336,993). We identified 431 significant genetic loci and found evidence of abundant pleiotropy across personality and cognitive domains. Functional characterisation implicated genes with significant tissue-specific expression in all tested brain tissues and enriched in brain-specific gene-sets. We conditioned independent GWAS of the Big 5 personality traits and cognition on our multivariate findings, which boosted genetic discovery in other personality traits and improved polygenic prediction. These findings advance our understanding of the polygenic architecture of complex mental traits, indicating a prominence of pleiotropic genetic effects across higher-order domains of mental function.Graphical abstract

Introduction Patients with predementia Alzheimer's disease (AD) and at-risk subjects are targets for promising disease-modifying treatments, and improved polygenic risk scores (PRSs) could improve early-stage case selection. Methods Phenotype-informed PRSs were developed by selecting AD-associated variants conditional on relevant inflammatory or cardiovascular traits. The primary outcome was longitudinal changes in measures of AD pathology, namely development of pathological amyloid deposition, medial temporal lobe atrophy, and cognitive decline in a prospective cohort study including 394 adults without AD dementia. Results High-risk groups defined by phenotype-informed AD PRSs had significantly steeper volume decline in medial temporal cortices, and the high-risk group defined by the cardiovascular-informed AD PRS had significantly increased hazard ratio of pathological amyloid deposition, compared to low-risk groups. Discussion AD PRSs informed by inflammatory disorders or cardiovascular risk factors and diseases are associated with development of AD pathology markers and may improve identification of subjects at risk for progression of AD.

Recent genome-wide association studies of mood instability (MOOD) have found significant positive genetic correlation with major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. GWAS summary statistics for schizophrenia (SCZ, n = 105,318), bipolar disorder (BIP, n = 413,466), DEP (n = 450,619), attention-deficit hyperactivity disorder (ADHD, n = 53,293), and MOOD (n = 363,705) were analyzed using the bivariate causal mixture model and conjunctional false discovery rate methods. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (rg = 0.10–0.62). Of 10.4 K genomic variants influencing MOOD, 4 K–9.4 K influenced psychiatric disorders. Furthermore, MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25. Fifty-three jointly associated loci were overlapping across two or more disorders, seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene-set, “synapse organization.” The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions may relate to differences in the core clinical features of each disorder. publishedVersion

The problem of social costs, negative externalities and collateral damage (especially related to environmental issues) has remained in the focus of economics for over a century. These issues have become critically important for economic governance in the context of climate change. Ronald Coase has proposed the most efficient market-based approach to the problem of social costs. This approach has introduced the concept of limited right to perform activities harmful to the third party and provided basis for a peer-to-peer (P2P) settlement of reciprocal damage. Decentralized P2P approach is necessary for evaluation of negative impact costs and mitigation benefits. Advance of decentralized arbitration technologies, such as Blockchain, crucially decreases transaction costs for P2P interactions and thus provides for further development of Coase paradigm. Application of Blockchain technology to mitigat collateral socio-environmental damage of economic activities relies on a concept of decentralized P2P and public evaluation of negative impact, distribution of liability, and settlement in the form of mitigation. Decentralized autonomous organization integral platform for climate initiatives (DAO IPCI) is designed to be truly decentralized public Blockchain-ecosystem and aims at creation of common business space to attract financing from investors not limited by financial capacity, location, or legal status with minimization of transaction costs, increased reliability and transparency of the whole process, which would be free of interference, interventions, manipulations, or falsifications. Although the initial objective is to provide existing markets with a “Blockchain option” or “Blockchain representation,” the goal is also to create a way for environmental markets to evolve toward truly decentralized and free personal market choice model.