Refining the scope of genetic influences on alcohol misuse through environmental stratification and gene-environment interaction.

BACKGROUND: Gene-environment interaction (G × E) is likely an important influence shaping individual differences in alcohol misuse (AM), yet it has not been extensively studied in molecular genetic research. In this study, we use a series of genome-wide gene-environment interaction (GWEIS) and in silico annotation methods with the aim of improving gene identification and biological understanding of AM. METHODS: We carried out GWEIS for four AM phenotypes in the large UK Biobank sample (N = 360,314), with trauma exposure and socioeconomic status (SES) as moderators of the genetic effects. Exploratory analyses compared stratified genome-wide association (GWAS) and GWEIS modeling approaches. We applied functional annotation, gene- and gene-set enrichment, and polygenic score analyses to interpret the GWEIS results. RESULTS: GWEIS models showed few genetic variants with significant interaction effects across gene-environment pairs. Enrichment analyses identified moderation by SES of the genes NOXA1, DLGAP1, and UBE2L3 on drinking quantity and the gene IFIT1B on drinking frequency. Except for DLGAP1, these genes have not previously been linked to AM. The most robust results (GWEIS interaction p = 4.59e-09) were seen for SES moderating the effects of variants linked to immune-related genes on a pattern of drinking with versus without meals. CONCLUSIONS: Our results highlight several genes and a potential mechanism of immune system functioning behind the moderating effect of SES on the genetic influences on AM. Although GWEIS seems to be a preferred approach over stratified GWAS, modeling G × E effects at the molecular level remains a challenge even in large samples. Understanding these effects will require substantial effort and more in-depth phenotypic measurement.

Reduced MUNC18-1 Levels, Synaptic Proteome Changes, and Altered Network Activity in STXBP1-Related Disorder Patient Neurons.

Background: STXBP1-related disorder (STXBP1-RD) is a neurodevelopmental disorder caused by pathogenic variants in the STXBP1 gene. Its gene product MUNC18-1 organizes synaptic vesicle exocytosis and is essential for synaptic transmission. Patients present with developmental delay, intellectual disability, and/or epileptic seizures, with high clinical heterogeneity. To date, the cellular deficits of neurons of patients with STXBP1-RD are unknown. Methods: We combined live-cell imaging, electrophysiology, confocal microscopy, and mass spectrometry proteomics to characterize cellular phenotypes of induced pluripotent stem cell-derived neurons from 6 patients with STXBP1-RD, capturing shared features as well as phenotypic diversity among patients. Results: Neurons from all patients showed normal in vitro development, morphology, and synapse formation, but reduced MUNC18-1 RNA and protein levels. In addition, a proteome-wide screen identified dysregulation of proteins related to synapse function and RNA processes. Neuronal networks showed shared as well as patient-specific phenotypes in activity frequency, network irregularity, and synchronicity, especially when networks were challenged by increasing excitability. No shared effects were observed in synapse physiology of single neurons except for a few patient-specific phenotypes. Similarities between functional and proteome phenotypes suggested 2 patient clusters, not explained by gene variant type. Conclusions: Together, these data show that decreased MUNC18-1 levels, dysregulation of synaptic proteins, and altered network activity are shared cellular phenotypes of STXBP1-RD. The 2 patient clusters suggest distinctive pathobiology among subgroups of patients, providing a plausible explanation for the clinical heterogeneity. This phenotypic spectrum provides a framework for future validation studies and therapy design for STXBP1-RD.

Power and optimal study design in iPSC-based brain disease modelling.

Studies using induced pluripotent stem cells (iPSCs) are gaining momentum in brain disorder modelling, but optimal study designs are poorly defined. Here, we compare commonly used designs and statistical analysis for different research aims. Furthermore, we generated immunocytochemical, electrophysiological, and proteomic data from iPSC-derived neurons of five healthy subjects, analysed data variation and conducted power simulations. These analyses show that published case-control iPSC studies are generally underpowered. Designs using isogenic iPSC lines typically have higher power than case-control designs, but generalization of conclusions is limited. We show that, for the realistic settings used in this study, a multiple isogenic pair design increases absolute power up to 60% or requires up to 5-fold fewer lines. A free web tool is presented to explore the power of different study designs, using any (pilot) data.

Exploring the genetic overlap between twelve psychiatric disorders.

The widespread comorbidity among psychiatric disorders demonstrated in epidemiological studies1-5 is mirrored by non-zero, positive genetic correlations from large-scale genetic studies6-10. To identify shared biological processes underpinning this observed phenotypic and genetic covariance and enhance molecular characterization of general psychiatric disorder liability11-13, we used several strategies aimed at uncovering pleiotropic, that is, cross-trait-associated, single-nucleotide polymorphisms (SNPs), genes and biological pathways. We conducted cross-trait meta-analysis on 12 psychiatric disorders to identify pleiotropic SNPs. The meta-analytic signal was driven by schizophrenia, hampering interpretation and joint biological characterization of the cross-trait meta-analytic signal. Subsequent pairwise comparisons of psychiatric disorders identified substantial pleiotropic overlap, but mainly among pairs of psychiatric disorders, and mainly at less stringent P-value thresholds. Only annotations related to evolutionarily conserved genomic regions were significant for multiple (9 out of 12) psychiatric disorders. Overall, identification of shared biological mechanisms remains challenging due to variation in power and genetic architecture between psychiatric disorders.

Cortical Pathology in Vanishing White Matter.

Vanishing white matter (VWM) is classified as a leukodystrophy with astrocytes as primary drivers in its pathogenesis. Magnetic resonance imaging has documented the progressive thinning of cortices in long-surviving patients. Routine histopathological analyses, however, have not yet pointed to cortical involvement in VWM. Here, we provide a comprehensive analysis of the VWM cortex. We employed high-resolution-mass-spectrometry-based proteomics and immunohistochemistry to gain insight into possible molecular disease mechanisms in the cortices of VWM patients. The proteome analysis revealed 268 differentially expressed proteins in the VWM cortices compared to the controls. A majority of these proteins formed a major protein interaction network. A subsequent gene ontology analysis identified enrichment for terms such as cellular metabolism, particularly mitochondrial activity. Importantly, some of the proteins with the most prominent changes in expression were found in astrocytes, indicating cortical astrocytic involvement. Indeed, we confirmed that VWM cortical astrocytes exhibit morphological changes and are less complex in structure than control cells. Our findings also suggest that these astrocytes are immature and not reactive. Taken together, we provide insights into cortical involvement in VWM, which has to be taken into account when developing therapeutic strategies.

Disentangling Genetic Risks for Metabolic Syndrome.

A quarter of the world's population is estimated to meet the criteria for metabolic syndrome (MetS), a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type 2 diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with MetS components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations of fasting glucose, HDL cholesterol, systolic blood pressure, triglycerides, and waist circumference was used, which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on MetS to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more MetS components, indicating that MetS is a complex, heterogeneous disorder. Associated loci harbor genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the MetS factor GWAS predicts 5.9% of the variance in MetS. These results provide mechanistic insights into the genetics of MetS and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple MetS components.

An integrated framework for local genetic correlation analysis.

Genetic correlation (rg) analysis is used to identify phenotypes that may have a shared genetic basis. Traditionally, rg is studied globally, considering only the average of the shared signal across the genome, although this approach may fail when the rg is confined to particular genomic regions or in opposing directions at different loci. Current tools for local rg analysis are restricted to analysis of two phenotypes. Here we introduce LAVA, an integrated framework for local rg analysis that, in addition to testing the standard bivariate local rgs between two phenotypes, can evaluate local heritabilities and analyze conditional genetic relations between several phenotypes using partial correlation and multiple regression. Applied to 25 behavioral and health phenotypes, we show considerable heterogeneity in the bivariate local rgs across the genome, which is often masked by the global rg patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations.

Genome-wide gene-environment interactions in neuroticism: an exploratory study across 25 environments.

Gene-environment interactions (GxE) are often suggested to play an important role in the aetiology of psychiatric phenotypes, yet so far, only a handful of genome-wide environment interaction studies (GWEIS) of psychiatric phenotypes have been conducted. Representing the most comprehensive effort of its kind to date, we used data from the UK Biobank to perform a series of GWEIS for neuroticism across 25 broadly conceptualised environmental risk factors (trauma, social support, drug use, physical health). We investigated interactions on the level of SNPs, genes, and gene-sets, and computed interaction-based polygenic risk scores (PRS) to predict neuroticism in an independent sample subset (N = 10,000). We found that the predictive ability of the interaction-based PRSs did not significantly improve beyond that of a traditional PRS based on SNP main effects from GWAS, but detected one variant and two gene-sets showing significant interaction signal after correction for the number of analysed environments. This study illustrates the possibilities and limitations of a comprehensive GWEIS in currently available sample sizes.

Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence.

The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation (rg) between BV and intelligence to be 0.24. We show that the rg is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV's shared genetic etiology with intelligence.

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.

Vanishing white matter: deregulated integrated stress response as therapy target.

OBJECTIVE: Vanishing white matter (VWM) is a fatal, stress-sensitive leukodystrophy that mainly affects children and is currently without treatment. VWM is caused by recessive mutations in eukaryotic initiation factor 2B (eIF2B) that is crucial for initiation of mRNA translation and its regulation during the integrated stress response (ISR). Mutations reduce eIF2B activity. VWM pathomechanisms remain unclear. In contrast with the housekeeping function of eIF2B, astrocytes are selectively affected in VWM. One study objective was to test our hypothesis that in the brain translation of specific mRNAs is altered by eIF2B mutations, impacting primarily astrocytes. The second objective was to investigate whether modulation of eIF2B activity could ameliorate this altered translation and improve the disease. METHODS: Mice with biallelic missense mutations in eIF2B that recapitulate human VWM were used to screen for mRNAs with altered translation in brain using polysomal profiling. Findings were verified in brain tissue from VWM patients using qPCR and immunohistochemistry. The compound ISRIB (for "ISR inhibitor") was administered to VWM mice to increase eIF2B activity. Its effect on translation, neuropathology, and clinical signs was assessed. RESULTS: In brains of VWM compared to wild-type mice we observed the most prominent changes in translation concerning ISR mRNAs; their expression levels correlated with disease severity. We substantiated these findings in VWM patients' brains. ISRIB normalized expression of mRNA markers, ameliorated brain white matter pathology and improved motor skills in VWM mice. INTERPRETATION: The present findings show that ISR deregulation is central in VWM pathomechanisms and a viable target for therapy.

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.

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.

Axonal abnormalities in vanishing white matter.

OBJECTIVE: We aimed to study the occurrence and development of axonal pathology and the influence of astrocytes in vanishing white matter. METHODS: Axons and myelin were analyzed using electron microscopy and immunohistochemistry on Eif2b4 and Eif2b5 single- and double-mutant mice and patient brain tissue. In addition, astrocyte-forebrain co-culture studies were performed. RESULTS: In the corpus callosum of Eif2b5-mutant mice, myelin sheath thickness, axonal diameter, and G-ratio developed normally up to 4 months. At 7 months, however, axons had become thinner, while in control mice axonal diameters had increased further. Myelin sheath thickness remained close to normal, resulting in an abnormally low G-ratio in Eif2b5-mutant mice. In more severely affected Eif2b4-Eif2b5 double-mutants, similar abnormalities were already present at 4 months, while in milder affected Eif2b4 mutants, few abnormalities were observed at 7 months. Additionally, from 2 months onward an increased percentage of thin, unmyelinated axons and increased axonal density were present in Eif2b5-mutant mice. Co-cultures showed that Eif2b5 mutant astrocytes induced increased axonal density, also in control forebrain tissue, and that control astrocytes induced normal axonal density, also in mutant forebrain tissue. In vanishing white matter patient brains, axons and myelin sheaths were thinner than normal in moderately and severely affected white matter. In mutant mice and patients, signs of axonal transport defects and cytoskeletal abnormalities were minimal. INTERPRETATION: In vanishing white matter, axons are initially normal and atrophy later. Astrocytes are central in this process. If therapy becomes available, axonal pathology may be prevented with early intervention.

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.