Combined cellomics and proteomics analysis reveals shared neuronal morphology and molecular pathway phenotypes for multiple schizophrenia risk genes.

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.

Attention-deficit/hyperactivity disorder and lifetime cannabis use: genetic overlap and causality.

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 × 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 × 10-8 and rs4259397, P = 4.52 × 10-8) and two in a gene-based association analysis (WDPCP, P = 9.67 × 10-7 and ZNF251, P = 1.62 × 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 × 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.

MIR137 schizophrenia-associated locus controls synaptic function by regulating synaptogenesis, synapse maturation and synaptic transmission.

The MIR137 locus is a replicated genetic risk factor for schizophrenia. The risk-associated allele is reported to increase miR-137 expression and miR-137 overexpression alters synaptic transmission in mouse hippocampus. We investigated the cellular mechanisms underlying these observed effects in mouse hippocampal neurons in culture. First, we correlated the risk allele to expression of the genes in the MIR137 locus in human postmortem brain. Some evidence for increased MIR137HG expression was observed, especially in hippocampus of the disease-associated genotype. Second, in mouse hippocampal neurons, we confirmed previously observed changes in synaptic transmission upon miR-137 overexpression. Evoked synaptic transmission and spontaneous release were 50% reduced. We identified defects in release probability as the underlying cause. In contrast to previous observations, no evidence was obtained for selective synaptic vesicle docking defects. Instead, ultrastructural morphometry revealed multiple effects of miR-137 overexpression on docking, active zone length and total vesicle number. Moreover, proteomic analyses of neuronal protein showed that expression of Syt1 and Cplx1, previously reported as downregulated upon miR-137 overexpression, was unaltered. Immunocytochemistry of synapses overexpressing miR-137 showed normal Synaptotagmin1 and Complexin1 protein levels. Instead, our proteomic analyses revealed altered expression of genes involved in synaptogenesis. Concomitantly, synaptogenesis assays revealed 31% reduction in synapse formation. Taken together, these data show that miR-137 regulates synaptic function by regulating synaptogenesis, synaptic ultrastructure and synapse function. These effects are plausible contributors to the increased schizophrenia risk associated with miR-137 overexpression.

What Cure Models Can Teach us About Genome-Wide Survival Analysis.

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.

A systems medicine research approach for studying alcohol addiction.

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.

Bayesian inference for the information gain model.

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.

Degree of genetic liability for Alzheimer's disease associated with specific proteomic profiles in cerebrospinal fluid.

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.

Author Correction: A global overview of pleiotropy and genetic architecture in complex traits.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Author Correction: Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A global overview of pleiotropy and genetic architecture in complex traits.

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 ).

Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways.

Insomnia is the second most prevalent mental disorder, with no sufficient treatment available. Despite substantial heritability, insight into the associated genes and neurobiological pathways remains limited. Here, we use a large genetic association sample (n = 1,331,010) to detect novel loci and gain insight into the pathways, tissue and cell types involved in insomnia complaints. We identify 202 loci implicating 956 genes through positional, expression quantitative trait loci, and chromatin mapping. The meta-analysis explained 2.6% of the variance. We show gene set enrichments for the axonal part of neurons, cortical and subcortical tissues, and specific cell types, including striatal, hypothalamic, and claustrum neurons. We found considerable genetic correlations with psychiatric traits and sleep duration, and modest correlations with other sleep-related traits. Mendelian randomization identified the causal effects of insomnia on depression, diabetes, and cardiovascular disease, and the protective effects of educational attainment and intracranial volume. Our findings highlight key brain areas and cell types implicated in insomnia, and provide new treatment targets.

Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk.

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.

Author Correction: GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability.

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.

Author Correction: Majority of human traits do not show evidence for sex-specific genetic and environmental effects.

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.

Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure.

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.

Item-level analyses reveal genetic heterogeneity in neuroticism.

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.

A tutorial on conducting genome-wide association studies: Quality control and statistical analysis.

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.

Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways.

Neuroticism is an important risk factor for psychiatric traits, including depression1, anxiety2,3, and schizophrenia4-6. At the time of analysis, previous genome-wide association studies7-12 (GWAS) reported 16 genomic loci associated to neuroticism10-12. Here we conducted a large GWAS meta-analysis (n = 449,484) of neuroticism and identified 136 independent genome-wide significant loci (124 new at the time of analysis), which implicate 599 genes. Functional follow-up analyses showed enrichment in several brain regions and involvement of specific cell types, including dopaminergic neuroblasts (P = 3.49 × 10-8), medium spiny neurons (P = 4.23 × 10-8), and serotonergic neurons (P = 1.37 × 10-7). Gene set analyses implicated three specific pathways: neurogenesis (P = 4.43 × 10-9), behavioral 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'), suggesting distinct causal mechanisms for subtypes of individuals. Mendelian randomization analysis showed unidirectional and bidirectional effects between neuroticism and multiple psychiatric traits. These results enhance neurobiological understanding of neuroticism and provide specific leads for functional follow-up experiments.