Common genetic variants influence human subcortical brain structures

Human Cortical Thickness Organized into Genetically-determined Communities across Spatial Resolutions

The cerebral cortex may be organized into anatomical genetic modules, communities of brain regions with shared genetic influences via pleiotropy. Such modules could represent novel phenotypes amenable to large-scale gene discovery. This modular structure was investigated with network analysis of in vivo MRI of extended pedigrees, revealing a "multiscale" structure where smaller and larger modules exist simultaneously and in partially overlapping fashion across spatial scales, in contrast to prior work suggesting a specific number of cortical thickness modules. Inter-regional genetic correlations, gene co-expression patterns and computational models indicate that two simple organizational principles account for a large proportion of the apparent complexity in the network of genetic correlations. First, regions are strongly genetically correlated with their homologs in the opposite cerebral hemisphere. Second, regions are strongly genetically correlated with nearby regions in the same hemisphere, with an initial steep decrease in genetic correlation with anatomical distance, followed by a more gradual decline. Understanding underlying organizational principles of genetic influence is a critical step towards a mechanistic model of how specific genes influence brain anatomy and mediate neuropsychiatric risk.

Imaging Genetics Towards a Refined Diagnosis of Schizophrenia

Current diagnoses of schizophrenia and related psychiatric disorders are classified by phenomenological principles and clinical descriptions while ruling out other symptoms and conditions. Specific biomarkers are needed to assist the current diagnostic system. However, complicated gene and environment interactions induce great disease heterogeneity. This unclear etiology and heterogeneity raise difficulties in distinguishing schizophrenia-related effects. Simultaneously, the overlap in symptoms, genetic variations, and brain alterations in schizophrenia and related psychiatric disorders raises similar difficulties in determining disease-specific effects. Imaging genetics is a unique methodology to assess the impact of genetic factors on both brain structure and function. More importantly, imaging genetics builds a bridge to understand the behavioral and clinical implications of genetics and neuroimaging. By characterizing and quantifying the brain measures affected in psychiatric disorders, imaging genetics is contributing to identifying potential biomarkers for schizophrenia and related disorders. To date, candidate gene analysis, genome-wide association studies, polygenetic risk score analysis, and large-scale collaborative studies have made contributions to the understanding of schizophrenia with the potential to serve as biomarkers. Despite limitations, imaging genetics remains promising as more aggregative, clustering methods and imaging genetics-compatible clinical assessments are employed in future studies. We review imaging genetics' contribution to our understanding of the heterogeneity within schizophrenia and the commonalities across schizophrenia and other diagnostic borders, and we will discuss whether imaging genetics is ready to form its own diagnostic system.

Neandertal Introgression Sheds Light on Modern Human Endocranial Globularity

One of the features that distinguishes modern humans from our extinct relatives and ancestors is a globular shape of the braincase [1-4]. As the endocranium closely mirrors the outer shape of the brain, these differences might reflect altered neural architecture [4, 5]. However, in the absence of fossil brain tissue, the underlying neuroanatomical changes as well as their genetic bases remain elusive. To better understand the biological foundations of modern human endocranial shape, we turn to our closest extinct relatives: the Neandertals. Interbreeding between modern humans and Neandertals has resulted in introgressed fragments of Neandertal DNA in the genomes of present-day non-Africans [6, 7]. Based on shape analyses of fossil skull endocasts, we derive a measure of endocranial globularity from structural MRI scans of thousands of modern humans and study the effects of introgressed fragments of Neandertal DNA on this phenotype. We find that Neandertal alleles on chromosomes 1 and 18 are associated with reduced endocranial globularity. These alleles influence expression of two nearby genes, UBR4 and PHLPP1, which are involved in neurogenesis and myelination, respectively. Our findings show how integration of fossil skull data with archaic genomics and neuroimaging can suggest developmental mechanisms that may contribute to the unique modern human endocranial shape.

eQTL of KCNK2 regionally influences the brain sulcal widening: evidence from 15,597 UK Biobank participants with neuroimaging data

The grey and white matter volumes are known to reduce with age. This cortical shrinkage is visible on magnetic resonance images and is conveniently identified by the increased volume of cerebrospinal fluid in the sulci between two gyri. Here, we replicated this finding using the UK Biobank dataset and studied the genetic influence on these cortical features of aging. We divided all individuals genetically confirmed of British ancestry into two sub-cohorts (12,162 and 3435 subjects for discovery and replication samples, respectively). We found that the heritability of the sulcal opening ranges from 15 to 45% (SE = 4.8%). We identified 4 new loci that contribute to this opening, including one that also affects the sulci grey matter thickness. We identified the most significant variant (rs864736) on this locus as being an expression quantitative trait locus (eQTL) for the KCNK2 gene. This gene regulates the immune-cell into the central nervous system (CNS) and controls the CNS inflammation, which is implicated in cortical atrophy and cognitive decline. These results expand our knowledge of the genetic contribution to cortical shrinking and promote further investigation into these variants and genes in pathological context such as Alzheimer’s disease in which brain shrinkage is a key biomarker.

Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders

Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest genome-wide association study to date of DSM-IV-diagnosed AD. Genome-wide data on 14,904 individuals with AD and 37,944 controls from 28 case-control and family-based studies were meta-analyzed, stratified by genetic ancestry (European, n = 46,568; African, n = 6,280). Independent, genome-wide significant effects of different ADH1B variants were identified in European (rs1229984; P = 9.8 × 10-13) and African ancestries (rs2066702; P = 2.2 × 10-9). Significant genetic correlations were observed with 17 phenotypes, including schizophrenia, attention deficit-hyperactivity disorder, depression, and use of cigarettes and cannabis. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and nonpathological drinking behaviors.

Genetic architecture of hippocampal subfields on standard resolution MRI: How the parts relate to the whole

The human hippocampus can be subdivided into subfields with unique functional properties and differential vulnerability to disease or neuropsychiatric conditions. Identifying genes that confer susceptibility to such processes is an important goal in developing treatments. Recent advances in automatic subfield segmentation from magnetic resonance images make it possible to use these measures as phenotypes in large-scale genome-wide association studies. Such analyses are likely to rely largely on standard resolution (~1 mm isotropic) T₁ -weighted images acquired on 3.0T scanners. Determining whether the genetic architecture of subfields can be detected from such images is therefore an important step. We used Freesurfer v6.0 to segment hippocampal subfields in two large twin studies, the Vietnam Era Twin Study of Aging and the Human Connectome Project. We estimated heritability of subfields and the genetic overlap with total hippocampal volume. Heritability was similar across samples, but little genetic variance remained after accounting for genetic influences on total hippocampal volume. Importantly, we examined genetic relationships between subfields to determine whether subfields can be grouped based on a smaller number of underlying, genetically independent factors. We identified three genetic factors in both samples, but the high degree of cross loadings precluded formation of genetically distinct groupings of subfields. These results confirm the reliability of Freesurfer v6.0 generated subfields across samples for phenotypic analyses. However, the current results suggest that it will be difficult for large-scale genetic analyses to identify subfield-specific genes that are distinct from both total hippocampal volume and other subfields using segmentations generated from standard resolution T₁ -weighted images.

A Fast Method for Estimating Statistical Power of Multivariate GWAS in Real Case Scenarios: Examples from the Field of Imaging Genetics

In GWAS of imaging phenotypes (e.g., by the ENIGMA and CHARGE consortia), the growing number of phenotypes considered presents a statistical challenge that other fields are not experiencing (e.g. psychiatry and the Psychiatric Genetics Consortium). However, the multivariate nature of MRI measurements may also be an advantage as many of the MRI phenotypes are correlated and multivariate methods could be considered. Here, we compared the statistical power of a multivariate GWAS versus the current univariate approach, which consists of multiple univariate analyses. To do so, we used results from twin models to estimate pertinent vectors of SNP effect sizes on brain imaging phenotypes, as well as the residual correlation matrices, necessary to estimate analytically the statistical power. We showed that for subcortical structure volumes and hippocampal subfields, a multivariate GWAS yields similar statistical power to the current univariate approach. Our analytical approach is as accurate but ~ 1000 times faster than simulations and we have released the code to facilitate the investigation of other scenarios, may they be outside the field of imaging genetics.

Decentralized temporal independent component analysis: Leveraging fMRI data in collaborative settings

The field of neuroimaging has recently witnessed a strong shift towards data sharing; however, current collaborative research projects may be unable to leverage institutional architectures that collect and store data in local, centralized data centers. Additionally, though research groups are willing to grant access for collaborations, they often wish to maintain control of their data locally. These concerns may stem from research culture as well as privacy and accountability concerns. In order to leverage the potential of these aggregated larger data sets, we require tools that perform joint analyses without transmitting the data. Ideally, these tools would have similar performance and ease of use as their current centralized counterparts. In this paper, we propose and evaluate a new Algorithm, decentralized joint independent component analysis (djICA), which meets these technical requirements. djICA shares only intermediate statistics about the data, plausibly retaining privacy of the raw information to local sites, thus making it amenable to further privacy protections, for example via differential privacy. We validate our method on real functional magnetic resonance imaging (fMRI) data and show that it enables collaborative large-scale temporal ICA of fMRI, a rich vein of analysis as of yet largely unexplored, and which can benefit from the larger-N studies enabled by a decentralized approach. We show that djICA is robust to different distributions of data over sites, and that the temporal components estimated with djICA show activations similar to the temporal functional modes analyzed in previous work, thus solidifying djICA as a new, decentralized method oriented toward the frontiers of temporal independent component analysis.

A structural equation model for imaging genetics using spatial transcriptomics

Imaging genetics deals with relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored with both dimension reduction methods and model-based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. We present a method for integrating genetic markers (single nucleotide polymorphisms) and imaging features, which is based on a causal model and, at the same time, uses the power of dimension reduction. We use structural equation models to find latent variables that explain brain volume changes in a disease context, and which are in turn affected by genetic variants. We make use of publicly available spatial transcriptome data from the Allen Human Brain Atlas to specify the model structure, which reduces noise and improves interpretability. The model is tested in a simulation setting and applied on a case study of the Alzheimer's Disease Neuroimaging Initiative.

Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity

Oscillatory activity is crucial for information processing in the brain, and has a long history as a biomarker for psychopathology. Variation in oscillatory activity is highly heritable, but current understanding of specific genetic influences remains limited. We performed the largest genome-wide association study to date of oscillatory power during eyes-closed resting electroencephalogram (EEG) across a range of frequencies (delta 1-3.75 Hz, theta 4-7.75 Hz, alpha 8-12.75 Hz, and beta 13-30 Hz) in 8,425 subjects. Additionally, we performed KGG positional gene-based analysis and brain-expression analyses. GABRA2-a known genetic marker for alcohol use disorder and epilepsy-significantly affected beta power, consistent with the known relation between GABAA interneuron activity and beta oscillations. Tissue-specific SNP-based imputation of gene-expression levels based on the GTEx database revealed that hippocampal GABRA2 expression may mediate this effect. Twenty-four genes at 3p21.1 were significant for alpha power (FDR q < .05). SNPs in this region were linked to expression of GLYCTK in hippocampal tissue, and GNL3 and ITIH4 in the frontal cortex-genes that were previously implicated in schizophrenia and bipolar disorder. In sum, we identified several novel genetic variants associated with oscillatory brain activity; furthermore, we replicated and advanced understanding of previously known genes associated with psychopathology (i.e., schizophrenia and alcohol use disorders). Importantly, these psychopathological liability genes affect brain functioning, linking the genes' expression to specific cortical/subcortical brain regions.

Maintenance, reserve and compensation: the cognitive neuroscience of healthy ageing

Cognitive ageing research examines the cognitive abilities that are preserved and/or those that decline with advanced age. There is great individual variability in cognitive ageing trajectories. Some older adults show little decline in cognitive ability compared with young adults and are thus termed 'optimally ageing'. By contrast, others exhibit substantial cognitive decline and may develop dementia. Human neuroimaging research has led to a number of important advances in our understanding of the neural mechanisms underlying these two outcomes. However, interpreting the age-related changes and differences in brain structure, activation and functional connectivity that this research reveals is an ongoing challenge. Ambiguous terminology is a major source of difficulty in this venture. Three terms in particular - compensation, maintenance and reserve - have been used in a number of different ways, and researchers continue to disagree about the kinds of evidence or patterns of results that are required to interpret findings related to these concepts. As such inconsistencies can impede progress in both theoretical and empirical research, here, we aim to clarify and propose consensual definitions of these terms.

Inflammation-related epigenetic risk and child and adolescent mental health: A prospective study from pregnancy to middle adolescence

In 785 mother-child (50% male) pairs from a longitudinal epidemiological birth cohort, we investigated associations between inflammation-related epigenetic polygenic risk scores (i-ePGS), environmental exposures, cognitive function, and child and adolescent internalizing and externalizing problems. We examined prenatal and postnatal effects. For externalizing problems, one prenatal effect was found: i-ePGS at birth associated with higher externalizing problems (ages 7-15) indirectly through lower cognitive function (age 7). For internalizing problems, we identified two effects. For a prenatal effect, i-ePGS at birth associated with higher internalizing symptoms via continuity in i-ePGS at age 7. For a postnatal effect, higher postnatal adversity exposure (birth through age 7) associated with higher internalizing problems (ages 7-15) via higher i-ePGS (age 7). Hence, externalizing problems were related mainly to prenatal effects involving lower cognitive function, whereas internalizing problems appeared related to both prenatal and postnatal effects. The present study supports a link between i-ePGS and child and adolescent mental health.

Amygdala subnuclei and healthy cognitive aging

Amygdala is a group of nuclei involved in the neural circuits of fear, reward learning, and stress. The main goal of this magnetic resonance imaging (MRI) study was to investigate the relationship between age and the amygdala subnuclei volumes in a large cohort of healthy individuals. Our second goal was to determine effects of the apolipoprotein E (APOE) and brain-derived neurotrophic factor (BDNF) polymorphisms on the amygdala structure. One hundred and twenty-six healthy participants (18-85 years old) were recruited for this study. MRI datasets were acquired on a 4.7 T system. Amygdala was manually segmented into five major subdivisions (lateral, basal, accessory basal nuclei, and cortical, and centromedial groups). The BDNF (methionine and homozygous valine) and APOE genotypes (ε2, homozygous ε3, and ε4) were obtained using single nucleotide polymorphisms. We found significant nonlinear negative associations between age and the total amygdala and its lateral, basal, and accessory basal nuclei volumes, while the cortical amygdala showed a trend. These age-related associations were found only in males but not in females. Centromedial amygdala did not show any relationship with age. We did not observe any statistically significant effects of APOE and BDNF polymorphisms on the amygdala subnuclei volumes. In contrast to APOE ε2 allele carriers, both older APOE ε4 and ε3 allele carriers had smaller lateral, basal, accessory basal nuclei volumes compared to their younger counterparts. This study indicates that amygdala subnuclei might be nonuniformly affected by aging and that age-related association might be gender specific.

A Longitudinal Imaging Genetics Study of Neuroanatomical Asymmetry in Alzheimer's Disease

BACKGROUND

Contralateral brain structures represent a unique, within-patient reference element for disease, and asymmetries can provide a personalized measure of the accumulation of past disease processes. Neuroanatomical shape asymmetries have recently been associated with the progression of Alzheimer's disease (AD), but the biological basis of asymmetric brain changes in AD remains unknown.

METHODS

We investigated genetic influences on brain asymmetry by identifying associations between magnetic resonance imaging-derived measures of asymmetry and candidate single nucleotide polymorphisms (SNPs) that have previously been identified in genome-wide association studies for AD diagnosis and for brain subcortical volumes. For analyzing longitudinal neuroimaging data (1241 individuals, 6395 scans), we used a mixed effects model with interaction between genotype and diagnosis.

RESULTS

Significant associations between asymmetry of the amygdala, hippocampus, and putamen and SNPs in the genes BIN1, CD2AP, ZCWPW1, ABCA7, TNKS, and DLG2 were found.

CONCLUSIONS

The associations between SNPs in the genes TNKS and DLG2 and AD-related increases in shape asymmetry are of particular interest; these SNPs have previously been associated with subcortical volumes of amygdala and putamen but have not yet been associated with AD pathology. For AD candidate SNPs, we extend previous work to show that their effects on subcortical brain structures are asymmetric. This provides novel evidence about the biological underpinnings of brain asymmetry as a disease marker.

Genome-wide association studies of brain imaging phenotypes in UK Biobank

The genetic architecture of brain structure and function is largely unknown. To investigate this, we carried out genome-wide association studies of 3,144 functional and structural brain imaging phenotypes from UK Biobank (discovery dataset 8,428 subjects). Here we show that many of these phenotypes are heritable. We identify 148 clusters of associations between single nucleotide polymorphisms and imaging phenotypes that replicate at P < 0.05, when we would expect 21 to replicate by chance. Notable significant, interpretable associations include: iron transport and storage genes, related to magnetic susceptibility of subcortical brain tissue; extracellular matrix and epidermal growth factor genes, associated with white matter micro-structure and lesions; genes that regulate mid-line axon development, associated with organization of the pontine crossing tract; and overall 17 genes involved in development, pathway signalling and plasticity. Our results provide insights into the genetic architecture of the brain that are relevant to neurological and psychiatric disorders, brain development and ageing.

SJ. Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data

Individuals with psychiatric disorders have elevated rates of autoimmune comorbidity and altered immune signaling. It is unclear whether these altered immunological states have a shared genetic basis with those psychiatric disorders. The present study sought to use existing summary-level data from previous genome-wide association studies to determine if commonly varying single nucleotide polymorphisms are shared between psychiatric and immune-related phenotypes. We estimated heritability and examined pair-wise genetic correlations using the linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics methods. Using LDSC, we observed significant genetic correlations between immune-related disorders and several psychiatric disorders, including anorexia nervosa, attention deficit-hyperactivity disorder, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia, smoking behavior, and Tourette syndrome. Loci significantly mediating genetic correlations were identified for schizophrenia when analytically paired with Crohn's disease, primary biliary cirrhosis, systemic lupus erythematosus, and ulcerative colitis. We report significantly correlated loci and highlight those containing genome-wide associations and candidate genes for respective disorders. We also used the LDSC method to characterize genetic correlations among the immune-related phenotypes. We discuss our findings in the context of relevant genetic and epidemiological literature, as well as the limitations and caveats of the study.

Unravelling the GSK3β-related genotypic interaction network influencing hippocampal volume in recurrent major depressive disorder

OBJECTIVE

Glycogen synthase kinase 3β (GSK3β) has been implicated in mood disorders. We previously reported associations between a GSK3β polymorphism and hippocampal volume in major depressive disorder (MDD). We then reported similar associations for a subset of GSK3β-regulated genes. We now investigate an algorithm-derived comprehensive list of genes encoding proteins that directly interact with GSK3β to identify a genotypic network influencing hippocampal volume in MDD.

PARTICIPANTS AND METHODS

We used discovery (N=141) and replication (N=77) recurrent MDD samples. Our gene list was generated from the NetworKIN database. Hippocampal measures were derived using an optimized Freesurfer protocol. We identified interacting single nucleotide polymorphisms using the machine learning algorithm Random Forest and verified interactions using likelihood ratio tests between nested linear regression models.

RESULTS

The discovery sample showed multiple two-single nucleotide polymorphism interactions with hippocampal volume. The replication sample showed a replicable interaction (likelihood ratio test: P=0.0088, replication sample; P=0.017, discovery sample; Stouffer's combined P=0.0007) between genes associated previously with endoplasmic reticulum stress, calcium regulation and histone modifications.

CONCLUSION

Our results provide genetic evidence supporting associations between hippocampal volume and MDD, which may reflect underlying cellular stress responses. Our study provides evidence of biological mechanisms that should be further explored in the search for disease-modifying therapeutic targets for depression.

Subcortical brain volumes, cortical thickness and cortical surface area in families genetically enriched for social anxiety disorder - A multiplex multigenerational neuroimaging study

BACKGROUND

Social anxiety disorder (SAD) is a disabling psychiatric condition with a genetic background. Brain alterations in gray matter (GM) related to SAD have been previously reported, but it remains to be elucidated whether GM measures are candidate endophenotypes of SAD. Endophenotypes are measurable characteristics on the causal pathway from genotype to phenotype, providing insight in genetically-based disease mechanisms. Based on a review of existing evidence, we examined whether GM characteristics meet two endophenotype criteria, using data from a unique sample of SAD-patients and their family-members of two generations. First, we investigated whether GM characteristics co-segregate with social anxiety within families genetically enriched for SAD. Secondly, heritability of the GM characteristics was estimated.

METHODS

Families with a genetic predisposition for SAD participated in the Leiden Family Lab study on SAD; T1-weighted MRI brain scans were acquired (n = 110, 8 families). Subcortical volumes, cortical thickness and cortical surface area were determined for a-priori determined regions of interest (ROIs). Next, associations with social anxiety and heritabilities were estimated.

FINDINGS

Several subcortical and cortical GM characteristics, derived from frontal, parietal and temporal ROIs, co-segregated with social anxiety within families (uncorrected p-level) and showed moderate to high heritability.

INTERPRETATION

These findings provide preliminary evidence that GM characteristics of multiple ROIs, which are distributed over the brain, are candidate endophenotypes of SAD. Thereby, they shed light on the genetic vulnerability for SAD. Future research is needed to confirm these results and to link them to functional brain alterations and to genetic variations underlying these GM changes. FUND: Leiden University Research Profile 'Health, Prevention and the Human Life Cycle'.

Field of View Normalization in Multi-Site Brain MRI

Multi-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs). Normalizing FOV is currently done manually or using ad hoc pre-processing steps, which do not always generalize well to multi-site diverse images. In this paper, we show that (a) a generic FOV normalization approach is possible in multi-site diverse images; we show experiments on images acquired from Philips, GE, Siemens scanners, from 1.0T, 1.5T, 3.0T field of strengths, and from subjects 0-90 years of ages; and (b) generic FOV normalization improves skull stripping accuracy and consistency for multiple skull stripping algorithms; we show this effect for 5 skull stripping algorithms including FSL's BET, AFNI's 3dSkullStrip, FreeSurfer's HWA, BrainSuite's BSE, and MASS. We have released our FOV normalization software at http://www.nitrc.org/projects/normalizefov .

A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis

To characterize associations between genetic and neuroimaging data, a variety of analytic methods have been proposed in neuroimaging genetic studies. These methods have achieved promising performance by taking into account inherent correlation in either the neuroimaging data or the genetic data alone. In this study, we propose a novel robust reduced rank graph regression based method in a linear regression framework by considering correlations inherent in neuroimaging data and genetic data jointly. Particularly, we model the association analysis problem in a reduced rank regression framework with the genetic data as a feature matrix and the neuroimaging data as a response matrix by jointly considering correlations among the neuroimaging data as well as correlations between the genetic data and the neuroimaging data. A new graph representation of genetic data is adopted to exploit their inherent correlations, in addition to robust loss functions for both the regression and the data representation tasks, and a square-root-operator applied to the robust loss functions for achieving adaptive sample weighting. The resulting optimization problem is solved using an iterative optimization method whose convergence has been theoretically proved. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset have demonstrated that our method could achieve competitive performance in terms of regression performance between brain structural measures and the Single Nucleotide Polymorphisms (SNPs), compared with state-of-the-art alternative methods.

Heritability and genome-wide associations studies of cerebral blood flow in the general population

Cerebral blood flow is an important process for brain functioning and its dysregulation is implicated in multiple neurological disorders. While environmental risk factors have been identified, it remains unclear to what extent the flow is regulated by genetics. Here we performed heritability and genome-wide association analyses of cerebral blood flow in a population-based cohort study. We included 4472 persons free of cortical infarcts who underwent genotyping and phase-contrast magnetic resonance flow imaging (mean age 64.8 ± 10.8 years). The flow rate, cross-sectional area of the vessel, and flow velocity through the vessel were measured in the basilar artery and bilateral carotids. We found that the flow rate of the basilar artery is most heritable (h2 (SE) = 24.1 (9.8), p-value = 0.0056), and this increased over age. The association studies revealed two significant loci for the right carotid artery area (rs12546630, p-value = 2.0 × 10^-8) and velocity (rs2971609, p-value = 1.4 × 10^-8), with the latter showing a concordant effect in an independent sample (N = 1350, p-value = 0.057, meta-analyzed p-value = 2.5 × 10^-9). These loci were also associated with other cerebral blood flow parameters below genome-wide significance, and rs2971609 lies in a known migraine locus. These findings establish that cerebral blood flow is under genetic control with potential relevance for neurological diseases.

Decentralized Analysis of Brain Imaging Data: Voxel-Based Morphometry and Dynamic Functional Network Connectivity

In the field of neuroimaging, there is a growing interest in developing collaborative frameworks that enable researchers to address challenging questions about the human brain by leveraging data across multiple sites all over the world. Additionally, efforts are also being directed at developing algorithms that enable collaborative analysis and feature learning from multiple sites without requiring the often large data to be centrally located. In this paper, we propose two new decentralized algorithms: (1) A decentralized regression algorithm for performing a voxel-based morphometry analysis on structural magnetic resonance imaging (MRI) data and, (2) A decentralized dynamic functional network connectivity algorithm which includes decentralized group ICA and sliding-window analysis of functional MRI data. We compare results against those obtained from their pooled (or centralized) counterparts on the same data i.e., as if they are at one site. Results produced by the decentralized algorithms are similar to the pooled-case and showcase the potential of performing multi-voxel and multivariate analyses of data located at multiple sites. Such approaches enable many more collaborative and comparative analysis in the context of large-scale neuroimaging studies.

A Perspective of the Cross-Tissue Interplay of Genetics, Epigenetics, and Transcriptomics, and Their Relation to Brain Based Phenotypes in Schizophrenia

Genetic association studies of psychiatric disorders have provided unprecedented insight into disease risk profiles with high confidence. Yet, the next research challenge is how to translate this rich information into mechanisms of disease, and further help interventions and treatments. Given other comprehensive reviews elsewhere, here we want to discuss the research approaches that integrate information across various tissue types. Taking schizophrenia as an example, the tissues, cells, or organisms being investigated include postmortem brain tissues or neurons, peripheral blood and saliva, in vivo brain imaging, and in vitro cell lines, particularly human induced pluripotent stem cells (iPSC) and iPSC derived neurons. There is a wealth of information on the molecular signatures including genetics, epigenetics, and transcriptomics of various tissues, along with neuronal phenotypic measurements including neuronal morphometry and function, together with brain imaging and other techniques that provide data from various spatial temporal points of disease development. Through consistent or complementary processes across tissues, such as cross-tissue methylation quantitative trait loci (QTL) and expression QTL effects, systemic integration of such information holds the promise to put the pieces of puzzle together for a more complete view of schizophrenia disease pathogenesis.

Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes

Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors, such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present a method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.

LLR: a latent low-rank approach to colocalizing genetic risk variants in multiple GWAS

Motivation

Genome-wide association studies (GWAS), which genotype millions of single nucleotide polymorphisms (SNPs) in thousands of individuals, are widely used to identify the risk SNPs underlying complex human phenotypes (quantitative traits or diseases). Most conventional statistical methods in GWAS only investigate one phenotype at a time. However, an increasing number of reports suggest the ubiquity of pleiotropy, i.e. many complex phenotypes sharing common genetic bases. This motivated us to leverage pleiotropy to develop new statistical approaches to joint analysis of multiple GWAS.

Results

In this study, we propose a latent low-rank (LLR) approach to colocalizing genetic risk variants using summary statistics. In the presence of pleiotropy, there exist risk loci that affect multiple phenotypes. To leverage pleiotropy, we introduce a low-rank structure to modulate the probabilities of the latent association statuses between loci and phenotypes. Regarding the computational efficiency of LLR, a novel expectation-maximization-path (EM-path) algorithm has been developed to greatly reduce the computational cost and facilitate model selection and inference. We demonstrate the advantages of LLR over competing approaches through simulation studies and joint analysis of 18 GWAS datasets.

Availability and implementation

The LLR software is available on https://sites.google.com/site/liujin810822.

Contact

macyang@ust.hk.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

A systematic review of genome-wide research on psychotic experiences and negative symptom traits: new revelations and implications for psychiatry

We present a systematic review of genome-wide research on psychotic experience and negative symptom (PENS) traits in the community. We integrate these new findings, most of which have emerged over the last four years, with more established behaviour genetic and epidemiological research. The review includes the first genome-wide association studies of PENS, including a recent meta-analysis, and the first SNP heritability estimates. Sample sizes of <10 000 participants mean that no genome-wide significant variants have yet been replicated. Importantly, however, in the most recent and well-powered studies, polygenic risk score prediction and linkage disequilibrium (LD) score regression analyses show that all types of PENS share genetic influences with diagnosed schizophrenia and that negative symptom traits also share genetic influences with major depression. These genetic findings corroborate other evidence in supporting a link between PENS in the community and psychiatric conditions. Beyond the systematic review, we highlight recent work on gene-environment correlation, which appears to be a relevant process for psychotic experiences. Genes that influence risk factors such as tobacco use and stressful life events are likely to be harbouring 'hits' that also influence PENS. We argue for the acceptance of PENS within the mainstream, as heritable traits in the same vein as other sub-clinical psychopathology and personality styles such as neuroticism. While acknowledging some mixed findings, new evidence shows genetic overlap between PENS and psychiatric conditions. In sum, normal variations in adolescent and adult thinking styles, such as feeling paranoid, are heritable and show genetic associations with schizophrenia and major depression.

Biospecimens and the ABCD study: Rationale, methods of collection, measurement and early data

Biospecimen collection in the Adolescent Brain Cognitive Development (ABCD) study - of hair samples, shed deciduous (baby) teeth, and body fluids - will serve dual functions of screening for study eligibility, and providing measures of biological processes thought to predict or correlate with key study outcomes on brain and cognitive development. Biosamples are being collected annually to screen for recency of drug use prior to the neuroimaging or cognitive testing visit, and to store for the following future studies: (1) on the effects of exposure to illicit and recreational drugs (including alcohol and nicotine); (2) of pubertal hormones on brain and cognitive developmental trajectories; (3) on the contribution of genomics and epigenomics to child and adolescent development and behavioral outcomes; and (4) with pre- and post-natal exposure to environmental neurotoxicants and drugs of abuse measured from novel tooth analyses. The present manuscript describes the rationales for inclusion and selection of the specific biospecimens, methodological considerations for each measure, future plans for assessment of biospecimens during follow-up visits, and preliminary ABCD data to illustrate methodological considerations.

Vitamin D, Folate and the Intracranial Volume in Schizophrenia and Bipolar Disorder and Healthy Controls

Vitamin D and folate deficiency are considered risk factors for schizophrenia and bipolar disorders, but it is unknown how vitamin D and folate influence the growing brain, cranium or the clinical phenotype. Serum vitamin D and folate levels are in part genetically regulated. We investigated whether adult vitamin D and folate levels are associated with the intracranial volume (ICV) under the hypothesis that developmental vitamin D or folate levels influence neurodevelopment and that current levels are associated with ICV. Ninety patients with severe mental disorders and 91 healthy controls underwent 3 T magnetic resonance imaging and serum sampling. Multiple linear regression was used to assess the contribution of serum vitamin D, folate and patient-control status on ICV. We show that vitamin D levels were within lower range for patients and controls (48.8 ± 22.1 nmol/l and 53.4 ± 20.0 nmol/l, respectively). A significant positive association was found between vitamin D and ICV (p = 0.003, r = 0.22), folate was trend-significantly associated with ICV. Folate and vitamin D were significantly associated (p = 0.0001, r = 0.28). There were nonsignificant patient-control differences and no interaction effects. The results suggest that Vitamin D is associated with ICV as detected in the adult. Further studies are warranted for replication and to investigate possible mechanisms and genetic associations.

Potential for Mitochondrial DNA Sequencing in the Differential Diagnosis of Gynaecological Malignancies

In the event of multiple synchronous gynecological lesions, a fundamental piece of information to determine patient management, prognosis, and therapeutic regimen choice is whether the simultaneous malignancies arise independently or as a result of metastatic dissemination. An example of synchronous primary tumors of the female genital tract most frequently described are ovarian and endometrial cancers. Surgical findings and histopathological examination aimed at resolving this conundrum may be aided by molecular analyses, although they are too often inconclusive. High mitochondrial DNA (mtDNA) variability and its propensity to accumulate mutations has been proposed by our group as a tool to define clonality. We showed mtDNA sequencing to be informative in synchronous primary ovarian and endometrial cancer, detecting tumor-specific mutations in both lesions, ruling out independence of the two neoplasms, and indicating clonality. Furthermore, we tested this method in another frequent simultaneously detected gynecological lesion type, borderline ovarian cancer and their peritoneal implants, which may be monoclonal extra-ovarian metastases or polyclonal independent masses. The purpose of this review is to provide an update on the potential use of mtDNA sequencing in distinguishing independent and metastatic lesions in gynecological cancers, and to compare the efficiency of molecular analyses currently in use with this novel method.

Significant concordance of genetic variation that increases both the risk for obsessive-compulsive disorder and the volumes of the nucleus accumbens and putamen

BACKGROUND

Many studies have identified changes in the brain associated with obsessive-compulsive disorder (OCD), but few have examined the relationship between genetic determinants of OCD and brain variation.AimsWe present the first genome-wide investigation of overlapping genetic risk for OCD and genetic influences on subcortical brain structures.

METHOD

Using single nucleotide polymorphism effect concordance analysis, we measured genetic overlap between the first genome-wide association study (GWAS) of OCD (1465 participants with OCD, 5557 controls) and recent GWASs of eight subcortical brain volumes (13 171 participants).

RESULTS

We found evidence of significant positive concordance between OCD risk variants and variants associated with greater nucleus accumbens and putamen volumes. When conditioning OCD risk variants on brain volume, variants influencing putamen, amygdala and thalamus volumes were associated with risk for OCD.

CONCLUSIONS

These results are consistent with current OCD neurocircuitry models. Further evidence will clarify the relationship between putamen volume and OCD risk, and the roles of the detected variants in this disorder.Declaration of interestThe authors have declared that no competing interests exist.

Effects of autozygosity and schizophrenia polygenic risk on cognitive and brain developmental trajectories

Cognitive and brain development are determined by dynamic interactions between genes and environment across the lifespan. Aside from marker-by-marker analyses of polymorphisms, biologically meaningful features of the whole genome (derived from the combined effect of individual markers) have been postulated to inform on human phenotypes including cognitive traits and their underlying biological substrate. Here, estimates of inbreeding and genetic susceptibility for schizophrenia calculated from genome-wide data-runs of homozygosity (ROH) and schizophrenia polygenic risk score (PGRS)-are analyzed in relation to cognitive abilities (n = 4183) and brain structure (n = 516) in a general-population sample of European-ancestry participants aged 8-22, from the Philadelphia Neurodevelopmental Cohort. The findings suggest that a higher ROH burden and higher schizophrenia PGRS are associated with higher intelligence. Cognition-ROH and cognition-PGRS associations obtained in this cohort may, respectively, evidence that assortative mating influences intelligence, and that individuals with high schizophrenia genetic risk who do not transition to disease status are cognitively resilient. Neuroanatomical data showed that the effects of schizophrenia PGRS on cognition could be modulated by brain structure, although larger imaging datasets are needed to accurately disentangle the underlying neural mechanisms linking IQ with both inbreeding and the genetic burden for schizophrenia.

Strategies for integrated analysis in imaging genetics studies

Imaging Genetics (IG) integrates neuroimaging and genomic data from the same individual, deepening our knowledge of the biological mechanisms behind neurodevelopmental domains and neurological disorders. Although the literature on IG has exponentially grown over the past years, the majority of studies have mainly analyzed associations between candidate brain regions and individual genetic variants. However, this strategy is not designed to deal with the complexity of neurobiological mechanisms underlying behavioral and neurodevelopmental domains. Moreover, larger sample sizes and increased multidimensionality of this type of data represents a challenge for standardizing modeling procedures in IG research. This review provides a systematic update of the methods and strategies currently used in IG studies, and serves as an analytical framework for researchers working in this field. To complement the functionalities of the Neuroconductor framework, we also describe existing R packages that implement these methodologies. In addition, we present an overview of how these methodological approaches are applied in integrating neuroimaging and genetic data.

Williams Syndrome neuroanatomical score associates with GTF2IRD1 in large-scale magnetic resonance imaging cohorts: a proof of concept for multivariate endophenotypes

Despite great interest in using magnetic resonance imaging (MRI) for studying the effects of genes on brain structure in humans, current approaches have focused almost entirely on predefined regions of interest and had limited success. Here, we used multivariate methods to define a single neuroanatomical score of how William's Syndrome (WS) brains deviate structurally from controls. The score is trained and validated on measures of T1 structural brain imaging in two WS cohorts (training, n = 38; validating, n = 60). We then associated this score with single nucleotide polymorphisms (SNPs) in the WS hemi-deleted region in five cohorts of neurologically and psychiatrically typical individuals (healthy European descendants, n = 1863). Among 110 SNPs within the 7q11.23 WS chromosomal region, we found one associated locus (p = 5e-5) located at GTF2IRD1, which has been implicated in animal models of WS. Furthermore, the genetic signals of neuroanatomical scores are highly enriched locally in the 7q11.23 compared with summary statistics based on regions of interest, such as hippocampal volumes (n = 12,596), and also globally (SNP-heritability = 0.82, se = 0.25, p = 5e-4). The role of genetic variability in GTF2IRD1 during neurodevelopment extends to healthy subjects. Our approach of learning MRI-derived phenotypes from clinical populations with well-established brain abnormalities characterized by known genetic lesions may be a powerful alternative to traditional region of interest-based studies for identifying genetic variants regulating typical brain development.

Genome-wide meta-analysis of cognitive empathy: heritability, and correlates with sex, neuropsychiatric conditions and cognition

We conducted a genome-wide meta-analysis of cognitive empathy using the 'Reading the Mind in the Eyes' Test (Eyes Test) in 88,056 research volunteers of European Ancestry (44,574 females and 43,482 males) from 23andMe Inc., and an additional 1497 research volunteers of European Ancestry (891 females and 606 males) from the Brisbane Longitudinal Twin Study. We confirmed a female advantage on the Eyes Test (Cohen's d=0.21, P<2.2 × 10^-16), and identified a locus in 3p26.1 that is associated with scores on the Eyes Test in females (rs7641347, P_meta=1.58 × 10^-8). Common single nucleotide polymorphisms explained 5.8% (95% CI: 4.5%-7.2%; P=1.00 × 10^-17) of the total trait variance in both sexes, and we identified a twin heritability of 28% (95% CI: 13%-42%). Finally, we identified significant genetic correlation between the Eyes Test and anorexia nervosa, openness (NEO-Five Factor Inventory), and different measures of educational attainment and cognitive aptitude.

Systematic genetic analyses of genome-wide association study data reveal an association between the key nucleosome remodeling and deacetylase complex and bipolar disorder development

BACKGROUND

Genome-wide association studies (GWASs) are used to identify genetic variants for association with bipolar disorder (BD) risk; however, each GWAS can only reveal a small fraction of this association. This study systematically analyzed multiple GWAS data sets to provide further insights into potential causal BD processes by integrating the results of Psychiatric Genomics Consortium Phase I (PGC-I) for BD with core human pathways and functional networks.

METHODS

The i-Gsea4GwasV2 program was used to analyze data from the PGC-I GWAS for BD (the pathways came from Reactome), as well as the nominally significant pathways. We established a gene network of the significant pathways and performed a gene set analysis for each gene cluster of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) GWAS data for the volumes of the intracranial region and seven subcortical regions.

RESULTS

A total of 30 of 1816 Reactome pathways were identified and showed associations with BD risk. We further revealed 22 interconnected functional and topologically interacting clusters (Clusters 0-21) that were associated with BD risk. Moreover, we obtained brain transcriptome data from BrainSpan and found significant associations between common variants of the genes in Cluster 1 with the hippocampus (HIP; P = .026; family-wise error [FWE] correction) and amygdala (AMY; P = .016; FEW correction) in Cluster 8 with HIP (P = .022; FWE correction). The genes in Cluster 1  were  enriched  for the transcriptional co-expression profile in the prenatal AMY, and core genes (CDH4, MTA2, RBBP4, and HDAC2) were identified to be involved in regulating early brain development.

CONCLUSION

This study demonstrated that the HIP and AMY play a central role in neurodevelopment and BD risk.

Genetic Overlap Between Schizophrenia and Volumes of Hippocampus, Putamen, and Intracranial Volume Indicates Shared Molecular Genetic Mechanisms

Schizophrenia (SCZ) is associated with differences in subcortical brain volumes and intracranial volume (ICV). However, little is known about the underlying etiology of these brain alterations. Here, we explored whether brain structure volumes and SCZ share genetic risk factors. Using conditional false discovery rate (FDR) analysis, we integrated genome-wide association study (GWAS) data on SCZ (n = 82315) and GWAS data on 7 subcortical brain volumes and ICV (n = 11840). By conditioning the FDR on overlapping associations, this statistical approach increases power to discover genetic loci. To assess the credibility of our approach, we studied the identified loci in larger GWAS samples on ICV (n = 26577) and hippocampal volume (n = 26814). We observed polygenic overlap between SCZ and volumes of hippocampus, putamen, and ICV. Based on conjunctional FDR < 0.05, we identified 2 loci shared between SCZ and ICV implicating genes FOXO3 (rs10457180) and ITIH4 (rs4687658), 2 loci shared between SCZ and hippocampal volume implicating SLC4A10 (rs4664442) and SPATS2L (rs1653290), and 2 loci shared between SCZ and volume of putamen implicating DCC (rs4632195) and DLG2 (rs11233632). The loci shared between SCZ and hippocampal volume or ICV had not reached significance in the primary GWAS on brain phenotypes. Proving our point of increased power, 2 loci did reach genome-wide significance with ICV (rs10457180) and hippocampal volume (rs4664442) in the larger GWAS. Three of the 6 identified loci are novel for SCZ. Altogether, the findings provide new insights into the relationship between SCZ and brain structure volumes, suggesting that their genetic architectures are not independent.

Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function

General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P < 5 × 10-8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.

Like mother like daughter: putamen activation as a mechanism underlying intergenerational risk for depression

Having a depressed mother is one of the strongest predictors for developing depression in adolescence. Given the role of aberrant reward processing in the onset and maintenance of depression, we examined the association between mothers’ and their daughters’ neural response to the anticipation of reward and loss. Fifteen non-depressed mothers with a history of recurrent depression and their never-disordered daughters, and 23 mothers without past or current depression and their never-disordered daughters, underwent functional magnetic resonance imaging while performing the monetary incentive delay task. To assess mother-daughter concordance, we first identified ROIs involved in the anticipation of reward and loss across all mother-daughter pairs. Within each of these ROIs, we examined the association between mothers’ and daughters’ neural response, and the interaction between group status and mothers’ neural response in predicting daughters’ neural response. We found a significant association between mothers’ and daughters’ putamen response to the anticipation of loss, regardless of mother’s depression history. Furthermore, pubertal stage moderated the association between mother-daughter putamen concordance. Our findings suggest a unique role of the putamen in the maternal transmission of reward learning and have important implications for understanding disorders characterized by disturbances in reward learning and processing, such as major depression.

Genomic Approaches to Posttraumatic Stress Disorder: The Psychiatric Genomic Consortium Initiative

Posttraumatic stress disorder (PTSD) after exposure to a traumatic event is a highly prevalent psychiatric disorder. Heritability estimates from twin studies as well as from recent molecular data (single nucleotide polymorphism-based heritability) indicate moderate to high heritability, yet robust genetic variants for PTSD have not yet been identified and the genetic architecture of this polygenic disorder remains largely unknown. To date, fewer than 10 large-scale genome-wide association studies of PTSD have been published, with findings that highlight the unique challenges for PTSD genomics, including a complex diagnostic entity with contingency of PTSD diagnosis on trauma exposure and the large genetic diversity of the study populations. The Psychiatric Genomics Consortium PTSD group has brought together more than 200 scientists with the goal to increase sample size for genome-wide association studies and other genomic analyses to sufficient numbers where robust discoveries of molecular signatures can be achieved. The sample currently includes more than 32,000 PTSD cases and 100,000 trauma-exposed control subjects, and collection is ongoing. The first results found a significant shared genetic risk of PTSD with other psychiatric disorders and sex-biased heritability estimates with higher heritability in female individuals compared with male individuals. This review describes the scope and current focus of the Psychiatric Genomics Consortium PTSD group and its expansion from the initial genome-wide association study group to nine working groups, including epigenetics, gene expression, imaging, and integrative systems biology. We further briefly outline recent findings and future directions of "omics"-based studies of PTSD, with the ultimate goal of elucidating the molecular architecture of this complex disorder to improve prevention and intervention strategies.

Evidence for association between familial bipolar risk and ventral striatal volume

BACKGROUND

Recent genome-wide association studies (GWAS) of striatal volumes and bipolar disorder (BD) indicate these traits are heritable and share common genetic architecture, however little independent work has been conducted to help establish this relationship.

METHODS

Subcortical volumes (mm3) of young, healthy offspring of BD (N= 32) and major depressive disorder (MDD) patients (N= 158) were compared to larger healthy control sample (NRANGE= 925-1052) adjusting for potential confounds, using data from the latest release (S1200) of the Human Connectome Project. Based on recent GWAS findings, it was hypothesised that the accumbens and caudate would be smaller in offspring of BD, but not MDD patients.

RESULTS

After multiple comparison correction, there was a regional and BD specific relationship in the direction expected (Accumbens: F2,1067= 6.244, PFDR-CORRECTED= 0.014).

DISCUSSION

In line with recent GWAS, there was evidence supporting the hypothesis that reduced striatal volume may be part of the genetic risk for BD, but not MDD.

LIMITATIONS

It cannot be concluded whether this association was specific to BD or consistent with a broader psychosis phenotype, due to a small sample size for offspring of schizophrenia patients. Furthermore, one cannot rule out potential shared environmental influences of parental BD.

CONCLUSIONS

The common genetic architecture of BD may confer susceptibility via inherited genetic factors that affect striatal volume. Future work should establish how this relationship relates to specific BD symptomology. This work may also help to dissect clinical heterogeneity and improve diagnosis nosology.

The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) consortium: A collaborative cognitive and neuroimaging genetics project

BACKGROUND

Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection.

METHODS

We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site.

RESULTS

Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p<1×10^-10). Data from a diversity of neuropsychological tests are available for 92% of participants, and 30% have structural MRI scans (half also have diffusion-weighted MRI scans). SNP data are available for 76% of participants. The ancestry composition is 70% European, 20% East Asian, 7% African, and 3% other.

CONCLUSIONS

The Consortium is investigating the genetic contribution to brain phenotypes in a schizophrenia sample collection of >10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia.

Genome-wide association study identifies a novel locus for cannabis dependence

Despite moderate heritability, only one study has identified genome-wide significant loci for cannabis-related phenotypes. We conducted meta-analyses of genome-wide association study data on 2080 cannabis-dependent cases and 6435 cannabis-exposed controls of European descent. A cluster of correlated single-nucleotide polymorphisms (SNPs) in a novel region on chromosome 10 was genome-wide significant (lowest P=1.3E-8). Among the SNPs, rs1409568 showed enrichment for H3K4me1 and H3K427ac marks, suggesting its role as an enhancer in addiction-relevant brain regions, such as the dorsolateral prefrontal cortex and the angular and cingulate gyri. This SNP is also predicted to modify binding scores for several transcription factors. We found modest evidence for replication for rs1409568 in an independent cohort of African American (896 cases and 1591 controls; P=0.03) but not European American (EA; 781 cases and 1905 controls) participants. The combined meta-analysis (3757 cases and 9931 controls) indicated trend-level significance for rs1409568 (P=2.85E-7). No genome-wide significant loci emerged for cannabis dependence criterion count (n=8050). There was also evidence that the minor allele of rs1409568 was associated with a 2.1% increase in right hippocampal volume in an independent sample of 430 EA college students (fwe-P=0.008). The identification and characterization of genome-wide significant loci for cannabis dependence is among the first steps toward understanding the biological contributions to the etiology of this psychiatric disorder, which appears to be rising in some developed nations.

Mesocorticolimbic Connectivity and Volumetric Alterations in DCC Mutation Carriers

The axon guidance cue receptor DCC (deleted in colorectal cancer) plays a critical role in the organization of mesocorticolimbic pathways in rodents. To investigate whether this occurs in humans, we measured (1) anatomical connectivity between the substantia nigra/ventral tegmental area (SN/VTA) and forebrain targets, (2) striatal and cortical volumes, and (3) putatively associated traits and behaviors. To assess translatability, morphometric data were also collected in Dcc-haploinsufficient mice. The human volunteers were 20 DCC^+/- mutation carriers, 16 DCC^+/+ relatives, and 20 DCC^+/+ unrelated healthy volunteers (UHVs; 28 females). The mice were 11 Dcc^+/- and 16 wild-type C57BL/6J animals assessed during adolescence and adulthood. Compared with both control groups, the human DCC^+/- carriers exhibited the following: (1) reduced anatomical connectivity from the SN/VTA to the ventral striatum [DCC^+/+: p = 0.0005, r(effect size) = 0.60; UHV: p = 0.0029, r = 0.48] and ventral medial prefrontal cortex (DCC^+/+: p = 0.0031, r = 0.53; UHV: p = 0.034, r = 0.35); (2) lower novelty-seeking scores (DCC^+/+: p = 0.034, d = 0.82; UHV: p = 0.019, d = 0.84); and (3) reduced striatal volume (DCC^+/+: p = 0.0009, d = 1.37; UHV: p = 0.0054, d = 0.93). Striatal volumetric reductions were also present in Dcc^+/- mice, and these were seen during adolescence (p = 0.0058, d = 1.09) and adulthood (p = 0.003, d = 1.26). Together these findings provide the first evidence in humans that an axon guidance gene is involved in the formation of mesocorticolimbic circuitry and related behavioral traits, providing mechanisms through which DCC mutations might affect susceptibility to diverse neuropsychiatric disorders.SIGNIFICANCE STATEMENT Opportunities to study the effects of axon guidance molecules on human brain development have been rare. Here, the identification of a large four-generational family that carries a mutation to the axon guidance molecule receptor gene, DCC, enabled us to demonstrate effects on mesocorticolimbic anatomical connectivity, striatal volumes, and personality traits. Reductions in striatal volumes were replicated in DCC-haploinsufficient mice. Together, these processes might influence mesocorticolimbic function and susceptibility to diverse neuropsychiatric disorders.

Core networks and their reconfiguration patterns across cognitive loads

Different cognitively demanding tasks recruit globally distributed but functionally specific networks. However, the configuration of core networks and their reconfiguration patterns across cognitive loads remain unclear, as does whether these patterns are indicators for the performance of cognitive tasks. In this study, we analyzed functional magnetic resonance imaging data of a large cohort of 448 subjects, acquired with the brain at resting state and executing N-back working memory (WM) tasks. We discriminated core networks by functional interaction strength and connection flexibility. Results demonstrated that the frontoparietal network (FPN) and default mode network (DMN) were core networks, but each exhibited different patterns across cognitive loads. The FPN and DMN both showed strengthened internal connections at the low demand state (0-back) compared with the resting state (control level); whereas, from the low (0-back) to high demand state (2-back), some connections to the FPN weakened and were rewired to the DMN (whose connections all remained strong). Of note, more intensive reconfiguration of both the whole brain and core networks (but no other networks) across load levels indicated relatively poor cognitive performance. Collectively these findings indicate that the FPN and DMN have distinct roles and reconfiguration patterns across cognitively demanding loads. This study advances our understanding of the core networks and their reconfiguration patterns across cognitive loads and provides a new feature to evaluate and predict cognitive capability (e.g., WM performance) based on brain networks.

Pleiotropic Contribution of MECOM and AVPR1A to Aggression and Subcortical Brain Volumes

Reactive and proactive subtypes of aggression have been recognized to help parse etiological heterogeneity of this complex phenotype. With a heritability of about 50%, genetic factors play a role in the development of aggressive behavior. Imaging studies implicate brain structures related to social behavior in aggression etiology, most notably the amygdala and striatum. This study aimed to gain more insight into the pathways from genetic risk factors for aggression to aggression phenotypes. To this end, we conducted genome-wide gene-based cross-trait meta-analyses of aggression with the volumes of amygdala, nucleus accumbens and caudate nucleus to identify genes influencing both aggression and aggression-related brain volumes. We used data of large-scale genome-wide association studies (GWAS) of: (a) aggressive behavior in children and adolescents (EAGLE, N = 18,988); and (b) Magnetic Resonance Imaging (MRI)-based volume measures of aggression-relevant subcortical brain regions (ENIGMA2, N = 13,171). Second, the identified genes were further investigated in a sample of healthy adults (mean age (SD) = 25.28 (4.62) years; 43% male) who had genome-wide genotyping data and questionnaire data on aggression subtypes available (Brain Imaging Genetics, BIG, N = 501) to study their effect on reactive and proactive subtypes of aggression. Our meta-analysis identified two genes, MECOM and AVPR1A, significantly associated with both aggression risk and nucleus accumbens (MECOM) and amygdala (AVPR1A) brain volume. Subsequent in-depth analysis of these genes in healthy adults (BIG), including sex as an interaction term in the model, revealed no significant subtype-specific gene-wide associations. Using cross-trait meta-analysis of brain measures and psychiatric phenotypes, this study generated new hypotheses about specific links between genes, the brain and behavior. Results indicate that MECOM and AVPR1A may exert an effect on aggression through mechanisms involving nucleus accumbens and amygdala volumes, respectively.

ZNF804A Variation May Affect Hippocampal-Prefrontal Resting-State Functional Connectivity in Schizophrenic and Healthy Individuals

The ZNF804A variant rs1344706 has consistently been associated with schizophrenia and plays a role in hippocampal-prefrontal functional connectivity during working memory. Whether the effect exists in the resting state and in patients with schizophrenia remains unclear. In this study, we investigated the ZNF804A polymorphism at rs1344706 in 92 schizophrenic patients and 99 healthy controls of Han Chinese descent, and used resting-state functional magnetic resonance imaging to explore the functional connectivity in the participants. We found a significant main effect of genotype on the resting-state functional connectivity (RSFC) between the hippocampus and the dorsolateral prefrontal cortex (DLPFC) in both schizophrenic patients and healthy controls. The homozygous ZNF804A rs1344706 genotype (AA) conferred a high risk of schizophrenia, and also exhibited significantly decreased resting functional coupling between the left hippocampus and right DLPFC (F(2,165) = 13.43, P < 0.001). The RSFC strength was also correlated with cognitive performance and the severity of psychosis in schizophrenia. The current findings identified the neural impact of the ZNF804A rs1344706 on hippocampal-prefrontal RSFC associated with schizophrenia.

ASD restricted and repetitive behaviors associated at 17q21.33: genes prioritized by expression in fetal brains

Autism spectrum disorder (ASD) is a behaviorally defined condition that manifests in infancy or early childhood as deficits in communication skills and social interactions. Often, restricted and repetitive behaviors (RRBs) accompany this disorder. ASD is polygenic and genetically complex, so we hypothesized that focusing analyses on intermediate core component phenotypes, such as RRBs, can reduce genetic heterogeneity and improve statistical power. Applying this approach, we mined Caucasian genome-wide association studies (GWAS) data from two of the largest ASD family cohorts, the Autism Genetics Resource Exchange and Autism Genome Project (AGP). Of the 12 RRBs measured by the Autism Diagnostic Interview-Revised, seven were found to be significantly familial and substantially variable, and hence, were tested for genome-wide association in 3104 ASD-affected children from 2045 families. Using a stringent significance threshold (P<7.1 × 10-9), GWAS in the AGP revealed an association between 'the degree of the repetitive use of objects or interest in parts of objects' and rs2898883 (P<6.8 × 10-9), which resides within the sixth intron of PHB. To identify the candidate target genes of the associated single-nucleotide polymorphisms at that locus, we applied chromosome conformation studies in developing human brains and implicated three additional genes: SLC35B1, CALCOCO2 and DLX3. Gene expression, brain imaging and fetal brain expression quantitative trait locus studies prioritize SLC35B1 and PHB. These analyses indicate that GWAS of single heritable features of genetically complex disorders followed by chromosome conformation studies in relevant tissues can be successful in revealing novel risk genes for single core features of ASD.