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After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics, such as the extent of pleiotropy across the genome and variation in genetic architecture across traits, are still unanswered. The current availability of hundreds of GWASs provides a unique opportunity to address these questions. We systematically analyzed 4,155 publicly available GWASs. For a subset of well-powered GWASs on 558 traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait-associated loci cover more than half of the genome, and 90% of these overlap with loci from multiple traits. We find that potential causal variants are enriched in coding and flanking regions, as well as in regulatory elements, and show variation in polygenicity and discoverability of traits. Our results provide insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource ( https://atlas.ctglab.nl ).

An enigma in studies of neuropsychiatric disorders is how to translate polygenic risk into disease biology. For schizophrenia, where > 145 significant GWAS loci have been identified and only a few genes directly implicated, addressing this issue is a particular challenge. We used a combined cellomics and proteomics approach to show that polygenic risk can be disentangled by searching for shared neuronal morphology and cellular pathway phenotypes of candidate schizophrenia risk genes. We first performed an automated high-content cellular screen to characterize neuronal morphology phenotypes of 41 candidate schizophrenia risk genes. The transcription factors Tcf4 and Tbr1 and the RNA topoisomerase Top3b shared a neuronal phenotype marked by an early and progressive reduction in synapse numbers upon knockdown in mouse primary neuronal cultures. Proteomics analysis subsequently showed that these three genes converge onto the syntaxin-mediated neurotransmitter release pathway, which was previously implicated in schizophrenia, but for which genetic evidence was weak. We show that dysregulation of multiple proteins in this pathway may be due to the combined effects of schizophrenia risk genes Tcf4, Tbr1, and Top3b. Together, our data provide new biological functions for schizophrenia risk genes and support the idea that polygenic risk is the result of multiple small impacts on common neuronal signaling pathways.

A Correction to this paper has been published: https://doi.org/10.1038/s41380-021-01049-6.

Genetic factors play a major role in Alzheimer's disease (AD) pathology, but biological mechanisms through which these factors contribute to AD remain elusive. Using a cerebrospinal fluid (CSF) proteomic approach, we examined associations between polygenic risk scores for AD (PGRS) and CSF proteomic profiles in 250 individuals with normal cognition, mild cognitive impairment, and AD-type dementia from the Alzheimer's Disease Neuroimaging Initiative. Out of 412 proteins, 201 were associated with PGRS. Hierarchical clustering analysis on proteins associated with PGRS at different single-nucleotide polymorphism p-value inclusion thresholds identified 3 clusters: (1) a protein cluster correlated with highly significant single-nucleotide polymorphisms, associated with amyloid-beta pathology and complement cascades; (2) a protein cluster associated with PGRS additionally including variants contributing to modest risk, involved in neural injury; (3) a protein cluster that also included less strongly associated variants, enriched with cytokine-cytokine interactions and cell adhesion molecules. These findings suggest that CSF protein levels reflect varying degrees of genetic liability for AD and may serve as a tool to investigate biological mechanisms in AD.

Intelligence is highly heritable 1 and a major determinant of human health and well-being 2 . Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence 3-7 , but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

Intelligence is associated with important economic and health-related life outcomes. Despite intelligence having substantial heritability (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered. Here we report a meta- A nalysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10-8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10-6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10-6). Despite the well-known difference in twin-based heritability for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regression P = 5.4 × 10-29). These findings provide new insight into the genetic architecture of intelligence.

Alzheimer’s disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD. © 2019. This is the authors' accepted and refereed manuscript to the chapter. The final authenticated version is available online at: http://dx.doi.org/10.1038/s41588-018-0311-9

Contains fulltext : 219447.pdf (Publisher’s version ) (Open Access) Attention-deficit/hyperactivity disorder (ADHD) is a severely impairing neurodevelopmental disorder with a prevalence of 5% in children and adolescents and of 2.5% in adults. Comorbid conditions in ADHD play a key role in symptom progression, disorder course and outcome. ADHD is associated with a significantly increased risk for substance use, abuse and dependence. ADHD and cannabis use are partly determined by genetic factors; the heritability of ADHD is estimated at 70-80% and of cannabis use initiation at 40-48%. In this study, we used summary statistics from the largest available meta-analyses of genome-wide association studies (GWAS) of ADHD (n = 53,293) and lifetime cannabis use (n = 32,330) to gain insights into the genetic overlap and causal relationship of these two traits. We estimated their genetic correlation to be r2 = 0.29 (P = 1.63 x 10-5) and identified four new genome-wide significant loci in a cross-trait analysis: two in a single variant association analysis (rs145108385, P = 3.30 x 10-8 and rs4259397, P = 4.52 x 10-8) and two in a gene-based association analysis (WDPCP, P = 9.67 x 10-7 and ZNF251, P = 1.62 x 10-6). Using a two-sample Mendelian randomization approach we found support that ADHD is causal for lifetime cannabis use, with an odds ratio of 7.9 for cannabis use in individuals with ADHD in comparison to individuals without ADHD (95% CI (3.72, 15.51), P = 5.88 x 10-5). These results substantiate the temporal relationship between ADHD and future cannabis use and reinforce the need to consider substance misuse in the context of ADHD in clinical interventions. 11 p.

Item does not contain fulltext Several occurrences of the word 'schizophrenia' have been re-worded as 'liability to schizophrenia' or 'schizophrenia risk', including in the title, which should have been "GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability", as well as in Supplementary Figures 1-10 and Supplementary Tables 7-10, to more accurately reflect the findings of the work. 1 p.

Studying biological mechanisms underlying neuropsychiatric disorders is highly challenging as many risk genes are associated with these disorders. This complexity requires research approaches to reliably dissect the cell biology of the risk genes involved. Here, we describe a combined cellomics–proteomics approach that allows (a) medium-throughput functional screening and unbiased selection of important risk genes, and (b) discovery of common functional pathways and interactome connections of selected risk genes. The overlay of pathway and proteome data from selected genes in a biological context can be used to formulate new testable hypothesis of both the genetics and the biology of the disorders.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics are still unanswered, such as the extent of pleiotropy across the genome, the nature of trait-associated genetic variants and the disparate genetic architecture across human traits. The current availability of hundreds of GWAS results provide the unique opportunity to gain insight into these questions. In this study, we harmonized and systematically analysed 4,155 publicly available GWASs. For a subset of well-powered GWAS on 558 unique traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait associated loci cover more than half of the genome, and 90% of those loci are associated with multiple trait domains. We further show that potential causal genetic variants are enriched in coding and flanking regions, as well as in regulatory elements, and how trait-polygenicity is related to an estimate of the required sample size to detect 90% of causal genetic variants. Our results provide novel insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource (http://atlas.ctglab.nl).

Alzheimer’s disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (r(g)=0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.

In the version of this article originally published, the captions of Fig. 1e,f were incorrectly swapped. The error has been corrected in the HTML and PDF versions of the article.

Objectives: Genome-wide association studies (GWAS) have become increasingly popular to identify associations between single nucleotide polymorphisms (SNPs) and phenotypic traits. The GWAS method is commonly applied within the social sciences. However, statistical analyses will need to be carefully conducted and the use of dedicated genetics software will be required. This tutorial aims to provide a guideline for conducting genetic analyses. Methods: We discuss and explain key concepts and illustrate how to conduct GWAS using example scripts provided through GitHub (https://github.com/MareesAT/GWA_tutorial/). In addition to the illustration of standard GWAS, we will also show how to apply polygenic risk score (PRS) analysis. PRS does not aim to identify individual SNPs but aggregates information from SNPs across the genome in order to provide individual-level scores of genetic risk. Results: The simulated data and scripts that will be illustrated in the current tutorial provide hands-on practice with genetic analyses. The scripts are based on PLINK, PRSice, and R, which are commonly used, freely available software tools that are accessible for novice users. Conclusions: By providing theoretical background and hands-on experience, we aim to make GWAS more accessible to researchers without formal training in the field.

Genome-wide association studies (GWAS) of psychological traits are generally conducted on (dichotomized) sums of items or symptoms (e.g., case-control status), and not on the individual items or symptoms themselves. We conduct large-scale GWAS on 12 neuroticism items and observe notable and replicable variation in genetic signal between items. Within samples, genetic correlations among the items range between 0.38 and 0.91 (mean rg = .63), indicating genetic heterogeneity in the full item set. Meta-analyzing the two samples, we identify 255 genome-wide significant independent genomic regions, of which 138 are item-specific. Genetic analyses and genetic correlations with 33 external traits support genetic differences between the items. Hierarchical clustering analysis identifies two genetically homogeneous item clusters denoted depressed affect and worry. We conclude that the items used to measure neuroticism are genetically heterogeneous, and that biological understanding can be gained by studying them in genetically more homogeneous clusters., Neuroticism can be assessed as a composite score of individual items. Here, Nagel et al. perform genetic association studies for 12 neuroticism items and the sum-score and demonstrate genetic heterogeneity at the item-level.

Late onset Alzheimer’s disease (AD) is the most common form of dementia with more than 35 million people affected worldwide, and no curative treatment available. AD is highly heritable and recent genome-wide meta-analyses have identified over 20 genomic loci associated with AD, yet only explaining a small proportion of the genetic variance indicating that undiscovered loci exist. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 AD cases, 383,378 controls). AD-by-proxy status is based on parental AD diagnosis, and showed strong genetic correlation with AD (rg=0.81). Genetic meta analysis identified 29 risk loci, of which 9 are novel, and implicating 215 potential causative genes. Independent replication further supports these novel loci in AD. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Furthermore, gene-set analyses indicate the genetic contribution of biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying more of the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD to guide new drug development.

Insomnia is the second-most prevalent mental disorder, with no sufficient treatment available. Despite a substantial role of genetic factors, only a handful of genes have been implicated and insight into the associated neurobiological pathways remains limited. Here, we use an unprecedented large genetic association sample (N=1,331,010) to allow detection of a substantial number of genetic variants and gain insight into biological functions, cell types and tissues involved in insomnia complaints. We identify 202 genome-wide significant loci implicating 956 genes through positional, eQTL and chromatin interaction mapping. We show involvement of the axonal part of neurons, of specific cortical and subcortical tissues, and of two specific cell-types in insomnia: striatal medium spiny neurons and hypothalamic neurons. These cell-types have been implicated previously in the regulation of reward processing, sleep and arousal in animal studies, but have never been genetically linked to insomnia in humans. We found weak genetic correlations with other sleep-related traits, but strong genetic correlations with psychiatric and metabolic traits. Mendelian randomization identified causal effects of insomnia on specific psychiatric and metabolic traits. Our findings reveal key brain areas and cells implicated in the neurobiology of insomnia and its related disorders, and provide novel targets for treatment.

Cannabis use is a heritable trait [1] that has been associated with adverse mental health outcomes. To identify risk variants and improve our knowledge of the genetic etiology of cannabis use, we performed the largest genome-wide association study (GWAS) meta-analysis for lifetime cannabis use (N=184,765) to date. We identified 4 independent loci containing genome-wide significant SNP associations. Gene-based tests revealed 29 genome-wide significant genes located in these 4 loci and 8 additional regions. All SNPs combined explained 10% of the variance in lifetime cannabis use. The most significantly associated gene, CADM2, has previously been associated with substance use and risk-taking phenotypes [2–4]. We used S-PrediXcan to explore gene expression levels and found 11 unique eGenes. LD-score regression uncovered genetic correlations with smoking, alcohol use and mental health outcomes, including schizophrenia and bipolar disorder. Mendelian randomisation analysis provided evidence for a causal positive influence of schizophrenia risk on lifetime cannabis use.

Gene-set analysis provides insight into which functional and biological properties of genes are aetiologically relevant for a particular phenotype. But genes have multiple properties, and these properties are often correlated across genes. This can cause confounding in a gene-set analysis, because one property may be statistically associated even if biologically irrelevant to the phenotype, by being correlated with gene properties that are relevant. To address this issue we present a novel conditional and interaction gene-set analysis approach, which attains considerable functional refinement of its conclusions compared to traditional gene-set analysis. We applied our approach to blood pressure phenotypes in the UK Biobank data (N = 360,243), the results of which we report here. We confirm and further refine several associations with multiple processes involved in heart and blood vessel formation but also identify novel interactions, among others with cardiovascular tissues involved in regulatory pathways of blood pressure homoeostasis.

Persistent insomnia is among the most frequent complaints in general practice. To identify genetic factors for insomnia complaints, we performed a genome-wide association study (GWAS) and a genome-wide gene-based association study (GWGAS) in 113,006 individuals. We identify three loci and seven genes associated with insomnia complaints, with the associations for one locus and five genes supported by joint analysis with an independent sample (n = 7,565). Our top association (MEIS1, P < 5 × 10 -8) has previously been implicated in restless legs syndrome (RLS). Additional analyses favor the hypothesis that MEIS1 exhibits pleiotropy for insomnia and RLS and show that the observed association with insomnia complaints cannot be explained only by the presence of an RLS subgroup within the cases. Sex-specific analyses suggest that there are different genetic architectures between the sexes in addition to shared genetic factors. We show substantial positive genetic correlation of insomnia complaints with internalizing personality traits and metabolic traits and negative correlation with subjective well-being and educational attainment. These findings provide new insight into the genetic architecture of insomnia.

There has been an increasing interest in the identification of genetic variants causing individual differences in human behavior. Psychiatrists have contributed to the genetics field by defining the most important behavioral characteristics and by studying the association between genetic variants and behavioral differences within phenotypically well-characterized samples in which detailed assessments have been collected (e.g. neuroimaging). These samples are typically limited in size and are therefore not suitable for a genome-wide association analysis. Instead, gene association studies conducted in such samples typically focus on a few genes of interest, allowing smaller sample sizes. However, the selection of high-priority genes is not always straightforward and psychiatrists will usually have a limited background in genetics. We aim to fill this gap by (i) providing a basic introduction to genetics; (ii) showing how the selection of genes of interest can be optimized by the use of two web tools: Polysearch and Gene Prospector; (iii) illustrating how statistical power analyses can be performed and discussing the importance of sufficiently powered studies. This guide can help psychiatrists with limited experience in genetics in designing genetic studies that allow identification of specific behavioral, cognitive, or neural correlates of genetic risk variants, while avoiding common pitfalls. Copyright © 2015 John Wiley & Sons, Ltd.

BACKGROUND: Schizophrenia patients and their parents have an increased risk of immune disorders compared to population controls and their parents. This may be explained by genetic overlap in the pathogenesis of both types of disorders. The purpose of this study was to investigate the genetic overlap between schizophrenia and three immune disorders and to compare with the overlap between schizophrenia and two disorders not primarily characterized by immune dysregulation: bipolar disorder and type 2 diabetes.METHODS: We performed a polygenic risk score analysis using results from the schizophrenia Psychiatric GWAS consortium (PGC) (8922 cases and 9528 controls) and five Wellcome Trust Case Control Consortium (WTCCC) case samples as target cases: bipolar disorder (n=1998), type 1 diabetes (n=2000), Crohn's diseases (n=2005), rheumatoid arthritis (n=1999), and type 2 diabetes (n=1999). The WTCCC British Birth Cohort and National Blood Service samples (n=3004) were used as target controls. Additionally, we tested whether schizophrenia polygenic risk scores significantly differed between patients with immune disorder, bipolar disorder, and type 2 diabetes respectively.RESULTS: Polygenic risk scores for schizophrenia significantly predicted disease status in all three immune disorder samples (Nagelkerke-R(2) 1.1%-1.3%; pCONCLUSIONS: Our results suggest that genetic factors are shared between schizophrenia and immune disorders. This contributes to an accumulating body of evidence that immune processes may play a role in the etiology of schizophrenia.

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3–7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimer’s dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

Neuroticism is an important risk factor for psychiatric traits including depression1, anxiety2,3, and schizophrenia4–6. Previous genome-wide association studies7–12 (GWAS) reported 16 genomic loci10–12. Here we report the largest neuroticism GWAS meta-analysis to date (N=449,484), and identify 136 independent genome-wide significant loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts (P=3×10-8), medium spiny neurons (P=4×10-8) and serotonergic neurons (P=1×10-7). Gene-set analyses implicate three specific pathways: neurogenesis (P=4.4×10-9), behavioural response to cocaine processes (P=1.84×10-7), and axon part (P=5.26×10-8). We show that neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters13 (depressed affect and worry, the former being genetically strongly related to depression, rg=0.84), suggesting distinct causal mechanisms for subtypes of individuals. These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.

Sex differences in the etiology of human trait variation are a major topic of interest in the social and medical sciences given its far-reaching implications. For example, in genetic research, the presence of sex-specific effects would require sex-stratified analysis, and in clinical practice sex-specific treatments would be warranted. Here, we present a study of 2,335,920 twin pairs, in which we tested sex differences in genetic and environmental contributions to variation in 2,608 reported human traits, clustered in 50 trait categories. Monozygotic and dizygotic male and female twin correlations were used to test whether the amount of genetic and environmental influences was equal between the sexes. By comparing dizygotic opposite sex twin correlations with dizygotic same sex twin correlations we could also test whether sex-specific genetic or environmental factors were involved. We observed for only 3% of all trait categories sex differences in the amount of etiological influences. Sex-specific genetic factors were observed for 25% of trait categories, often involving obviously sex-dependent trait categories such as puberty-related disorders. Our findings show that for most traits the number of sex-specific genetic variants will be small. For those traits where we do report sexual dimorphism, sex-specific approaches may aid in future gene-finding efforts.

Background Intelligence is associated with important economic and health-related life outcomes. Despite substantial heritability (0.54) and confirmed polygenic nature, initial genetic studies were mostly underpowered. We recently conducted a meta-analysis for intelligence of 78,308 individuals, and report 18 genomic loci, of which 15 are novel and 52 genes, of which 40 are novel. We expect to have increased this sample size further by October 2017. Methods The combined data currently available data yielded GWAS information for intelligence for 78,308 unrelated individuals from 13 cohorts. All association studies were performed on individuals of European descent; standard quality-control procedures included correcting for population stratification and filtering on minor allele frequency and imputation quality. As eight out of the 13 cohorts consisted of children (aged Results We identify 336 single nucleotide polymorphisms (SNPs) (METAL P Discussion Of all 52 genes that were implicated, 35 were reported in the GWAS catalog for a previous association with at least one of 67 distinct traits. Nine genes (ATP2A1, NEGR1, SKAP1, FOXO3, COL16A1, YIPF7, DCC, SH2B1 and TUFM) were previously implicated with body mass index, seven (CYP2D6, NAGA, NDUFA6, TCF20 and SEPT3, FAM109B and MEF2C) with schizophrenia0 and four (NEGR1, SH2B1, DCC and WNT4) with obesity. EXOC4 and MEF2C have been associated previously with Alzheimer's disease. This is the largest GWAS for intelligence so far and for the first time shows multiple robust associations for intelligence, suggesting several functional mechanisms, such as neuronal development and regulation of cell death. These findings provide novel insight into the genetic architecture of intelligence, which may also be important to various psychiatric traits such as schizophrenia and autism spectrum disorder.

According to the World Health Organization, about 2 billion people drink alcohol. Excessive alcohol consumption can result in alcohol addiction, which is one of the most prevalent neuropsychiatric diseases afflicting our society today. Prevention and intervention of alcohol binging in adolescents and treatment of alcoholism are major unmet challenges affecting our health-care system and society alike. Our newly formed German SysMedAlcoholism consortium is using a new systems medicine approach and intends (1) to define individual neurobehavioral risk profiles in adolescents that are predictive of alcohol use disorders later in life and (2) to identify new pharmacological targets and molecules for the treatment of alcoholism. To achieve these goals, we will use omics-information from epigenomics, genetics transcriptomics, neurodynamics, global neurochemical connectomes and neuroimaging (IMAGEN; Schumann et al. ) to feed mathematical prediction modules provided by two Bernstein Centers for Computational Neurosciences (Berlin and Heidelberg/Mannheim), the results of which will subsequently be functionally validated in independent clinical samples and appropriate animal models. This approach will lead to new early intervention strategies and identify innovative molecules for relapse prevention that will be tested in experimental human studies. This research program will ultimately help in consolidating addiction research clusters in Germany that can effectively conduct large clinical trials, implement early intervention strategies and impact political and healthcare decision makers.

26 www.thelancet.com Vol 388 July 2, 2016 disappears. However, if unhappiness were to affect mortality, it would probably do so by fi rst aff ecting health. As the authors discuss, health and happiness are strongly correlated. Since the two variables were measured at the same timepoint, what drives this strong correlation is impossible to tell from the reported data. In other words, are unhealthy people unhappy because they are unhealthy or can unhappiness also decrease health? Adjustment for health does not answer this fundamental question. For example, health has a decreased effect on mortality after correction for happiness. This result simply reflects the strong correlation between health and happiness and would not warrant the conclusion that the eff ect of health on mortality is smaller than what was previously thought. To answer this question, health and happiness should be recorded at several timepoints and whether or not unhappy people tend to become unhealthy after adjustment for unhealthy behaviour should be tested. Therefore, to interpret the interesting results of Liu and colleagues as a defi nitive contradiction of previous results, suggesting that happiness can affect health and mortality, would be premature.

partly overlapping individuals were genotyped, phenotyped and their data analyzed in genetic association studies, reflecting a huge communal effort by the substance use/addiction genetics community. These genome-wide association study (GWAS) efforts considered different stages of substance use: lifetime use (ever versus never use) was analyzed for cannabis and smoking, quantity of use (in users) was analyzed for coffee, alcohol, and smoking and age of initiation and cessation were analyzed for smoking. There are other GWA efforts and publications in the realm of addiction (see ref. 5), but here we limit ourselves to the largest meta-analyses per substance in order to maximize power. The GWA meta-analyses of substance-related traits identified many substance-specific genetic variants of moderate to small effect, which provided insight in the genetic etiology of substance use and its comorbidities. There are substantial phenotypic correlations among use of different substances, and both twin and polygenic risk prediction studies have shown that these phenotypic correlations are partly due to common genetic influences. 6, 7 Here we estimate genetic correlations (rg) between substance use-related variables based on the GWA summary statistics. These estimates of rg are based on all polygenic effects captured by single nucleotide polymorphisms. We used the recently developed linkage disequilibrium (LD) score regression method to estimate the proportion of covariance between traits that is due to single nucleotide polymorphisms, based on the expected relationship between LD and strength of association under a polygenic model. 8,9 The genetic correlation matrix revealed important information about common versus substance-specific genetic effects as well as specific patterns of cross-substance comorbidity (Figure 1). The substantial negative correlation between smoking cessation and smoking initiation reveals that the genes that predispose to initiation are negative predictors of success at cessation. Likewise, the genes that predispose individuals to smoke more cigarettes per day are negative predictors of successful cessation. Age at first cigarette is only associated with smoking initiation, not with cigarettes per day or smoking cessation. Interestingly, high genetic correlations are also observed across substance, between cannabis initiation and smoking initiation (rg=0.83, se=0.148), but also between quantity of nicotine consumption (cigarettes per day) and quantity of coffee consumed (cups per day) (rg=0.44, se=0.151), between coffee consumed and nicotine consumption (rg=0.38, se=0.16), and between alcohol consumption (alcohol per week) and cigarettes per day (rg=0.44, se=0.17). Most significant cross-substance correlations reflect genetic correlations within stage. However, both coffee per day and cigarettes per day are negatively associated with successful smoking cessation, indicating that frequent use, irrespective of substance, is genetically related to more problematic use of a different substance. The pattern of correlations observed implies a genetic model for substance use where both substance-specific and stagespecific genetic effects play a role. GWA meta-analyses of smoking, alcohol, cannabis and coffee use have shed light on the specific genetic effects for each substance. Here we show substance- and stage-specific GWAS results can be leveraged to elucidate the genetic architecture of substance use vulnerability in general. The next generation of large well-powered substance use GWA studies should systematically target all stages of use, for a broad spectrum of substances (e.g., cocaine and sugar rich foods) or addictive behavior (e.g., gambling, gaming and compulsive Internet use). Such an effort can aid in distinguishing between genes that are substance specific from genes that contribute to a specific stage of use, irrespective of substance or addictive behavior.

The aim of logistic regression is to estimate genetic effects on disease risk, while survival analysis aims to determine effects on age of onset. In practice, genetic variants may affect both types of outcomes. A cure survival model analyzes logistic and survival effects simultaneously. The aim of this simulation study is to assess the performance of logistic regression and traditional survival analysis under a cure model and to investigate the benefits of cure survival analysis. We simulated data under a cure model and varied the percentage of subjects at risk for disease (cure fraction), the logistic and survival effect sizes, and the contribution of genetic background risk factors. We then computed the error rates and estimation bias of logistic, Cox proportional hazards (PH), and cure PH analysis, respectively. The power of logistic and Cox PH analysis is sensitive to the cure fraction and background heritability. Our results show that traditional Cox PH analysis may erroneously detect age of onset effects if no such effects are present in the data. In the presence of genetic background risk even the cure model results in biased estimates of both the odds ratio and the hazard ratio. Cure survival analysis takes cure fractions into account and can be used to simultaneously estimate the effect of genetic variants on disease risk and age of onset. Since genome-wide cure survival analysis is not computationally feasible, we recommend this analysis for genetic variants that are significant in a traditional survival analysis.

Item does not contain fulltext Background: Cannabis is the most frequently used and abused illicit drug worldwide and cannabis (ab)use is associated with social, physical, and psychological problems. Twin and family studies have shown that cannabis use and abuse are heritable traits. The International Cannabis Consortium was initiated with the aim of identifying genetic risk variants for cannabis use phenotypes by meta-analysing data from many contributing cohorts. The (partly preliminary) results of the International Cannabis Consortium will be presented: genome-wide association (GWA) meta-analyses on lifetime cannabis use and age at initiation of use. Additionally, findings from several follow-up studies will be presented, including the genetic association of cannabis use with use of other substances, schizophrenia, and conduct disorder. Methods: GWA analyses of lifetime cannabis use were performed by each contributing group independently (13 groups, total N=32,330) and were subsequently meta-analysed. We tested for replication, and the SNP results were used to perform a gene-based test of association. We also estimated the total SNP-based heritability and the genetic correlation between lifetime cannabis use and cigarette use based on LD-score regression analysis. Secondly, we meta-analysed GWA results of age at initiation of cannabis use from 8 groups (N=24,222) using a survival analysis. Again SNP results are followed up by a gene-based test of association and an estimate of SNP-based heritability. In follow-up projects, LD-score regression analyses were used to determine the genetic correlation of cannabis use with nicotine, alcohol, and caffeine use, as well as schizophrenia and conduct disorder. We also created polygenic risk scores for cannabis use in an independent target sample and determined to what extent these polygenic scores predicted conduct symptoms. Results: Although none of the SNPs were significantly associated with lifetime cannabis use, the gene-based analysis identified 4 significantly associated genes, including NCAM1, CADM2, SCOC and KCNT2. Interestingly, NCAM1 was previously reported to be associated with nicotine and other substance use. All SNPs combined explained 20% of the liability of lifetime cannabis use. For age at initiation of cannabis use, we identified five SNPs (in high linkage-disequilibrium) that were genome-wide significant. Results of the gene-based test and SNP-based heritability are not available yet. Follow-up studies show a significant genetic correlation of cannabis use with smoking initiation, alcohol use per week, as well as with schizophrenia. Furthermore, polygenic risk scores for cannabis use were significantly associated with symptoms of conduct disorder. Discussion: The findings of the two largest meta-analyses of GWA studies of cannabis use phenotypes are presented. Several interesting genetic loci were identified, revealing important new candidate genes for cannabis use. Future functional studies should explore the impact of the identified genes on the biological mechanisms of cannabis use. We also show that genes underlying cannabis use are in part overlapping with genes underlying use of other substances and mental health phenotypes, including nicotine and alcohol use, schizophrenia, and symptoms of conduct disorder. 2 p.

There has been an increasing interest in the identification of genetic variants causing individual differences in human behavior. Psychiatrists have contributed to the genetics field by defining the most important behavioral characteristics and by studying the association between genetic variants and behavioral differences within phenotypically well‐characterized samples in which detailed assessments have been collected (e.g. neuroimaging). These samples are typically limited in size and are therefore not suitable for a genome‐wide association analysis. Instead, gene association studies conducted in such samples typically focus on a few genes of interest, allowing smaller sample sizes. However, the selection of high‐priority genes is not always straightforward and psychiatrists will usually have a limited background in genetics. We aim to fill this gap by (i) providing a basic introduction to genetics; (ii) showing how the selection of genes of interest can be optimized by the use of two web tools: Polysearch and Gene Prospector; (iii) illustrating how statistical power analyses can be performed and discussing the importance of sufficiently powered studies. This guide can help psychiatrists with limited experience in genetics in designing genetic studies that allow identification of specific behavioral, cognitive, or neural correlates of genetic risk variants, while avoiding common pitfalls. Copyright © 2015 John Wiley & Sons, Ltd.

From the view of statistical genetics, we explore four future directions in the genetics of psychiatric disorders with the overall goal of application to genomic medicine. The themes are (1) defining genomic variation, (2) response to treatment genomics, (3) genetic risk prediction and (4) data collection for individuals measured for phenotype, genotype and environment. We conclude that genotyping technology is no longer a limiting step and that full progression of applications of genomic medicine in psychiatry is dependent on the availability of suitable individual level data and will ultimately depend on integration of genomic information as part of accessible electronic health records.

For most complex traits, results from genome-wide association studies show that the proportion of the phenotypic variance attributable to the additive effects of individual SNPs, that is, the heritability explained by the SNPs, is substantially less than the estimate of heritability obtained by standard methods using correlations between relatives. This difference has been called the "missing heritability". One explanation is that heritability estimates from family (including twin) studies are biased upwards. Zuk et al. revisited overestimation of narrow sense heritability from twin studies as a result of confounding with non-additive genetic variance. They propose a limiting pathway (LP) model that generates significant epistatic variation and its simple parametrization provides a convenient way to explore implications of epistasis. They conclude that over-estimation of narrow sense heritability from family data ('phantom heritability') may explain an important proportion of missing heritability. We show that for highly heritable quantitative traits large phantom heritability estimates from twin studies are possible only if a large contribution of common environment is assumed. The LP model is underpinned by strong assumptions that are unlikely to hold, including that all contributing pathways have the same mean and variance and are uncorrelated. Here, we relax the assumptions that underlie the LP model to be more biologically plausible. Together with theoretical, empirical, and pragmatic arguments we conclude that in outbred populations the contribution of additive genetic variance is likely to be much more important than the contribution of non-additive variance.

One of the most popular paradigms to use for studying human reasoning involves the Wason card selection task. In this task, the participant is presented with four cards and a conditional rule (e.g., “If there is an A on one side of the card, there is always a 2 on the other side”). Participants are asked which cards should be turned to verify whether or not the rule holds. In this simple task, participants consistently provide answers that are incorrect according to formal logic. To account for these errors, several models have been proposed, one of the most prominent being the information gain model (Oaksford & Chater, Psychological Review, 101, 608–631, 1994). This model is based on the assumption that people independently select cards based on the expected information gain of turning a particular card. In this article, we present two estimation methods to fit the information gain model: a maximum likelihood procedure (programmed in R) and a Bayesian procedure (programmed in WinBUGS). We compare the two procedures and illustrate the flexibility of the Bayesian hierarchical procedure by applying it to data from a meta-analysis of the Wason task (Oaksford & Chater, Psychological Review, 101, 608–631, 1994). We also show that the goodness of fit of the information gain model can be assessed by inspecting the posterior predictives of the model. These Bayesian procedures make it easy to apply the information gain model to empirical data. Supplemental materials may be downloaded along with this article from www.springerlink.com. Electronic supplementary material The online version of this article (doi:10.3758/s13428-010-0057-5) contains supplementary material, which is available to authorized users.

Emotional labour and psychological well-being in teachers Emotional labour and psychological well-being in teachers S. Stringer, E. Ouweneel, P. Le Blanc, A. Cheriakova & J. Smulders, Gedrag & Organisatie, volume 22, September 2009, nr. 3, pp. 214-231 Emotional labour could have negative as well as positive effects on the employees' psychological well-being. In this cross-sectional study among 149 high school teachers, the relationship between emotional labour – in this study conceptualized as emotional demands – and emotional exhaustion and work engagement respectively was studied. First of all, it was examined whether emotional dissonance mediates the relationship between emotional demands and both emotional exhaustion and work engagement. Next, the possible moderating effect of two emotion regulation strategies, surface acting and deep acting, on the relationship between emotional dissonance and the two outcome variables was studied. The results showed that emotional dissonance partially mediated the relationship between emotional demands and emotional exhaustion. Furthermore, it was found that deep acting had a buffer effect on the negative relationship between emotional dissonance and work engagement. Finally, the implications of the results are discussed and suggestions for further research are mentioned.

Contains fulltext : 195179.pdf (Publisher’s version ) (Closed access) Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health–related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health. 10 p.