Common genetic variants influence human subcortical brain structures

Global and Regional Development of the Human Cerebral Cortex: Molecular Architecture and Occupational Aptitudes

We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade-albeit in a very limited manner-to behaviors as complex as the choice of one's occupation.

Neuropsychiatric Aspects of Frontotemporal Dementia

Frontotemporal dementia (FTD) encompasses a group of clinical syndromes, including behavioral-variant FTD, nonfluent variant primary progressive aphasia, semantic variant primary progressive aphasia, FTD motor neuron disease, progressive supranuclear palsy syndrome, and corticobasal syndrome. Early on in its course, FTD is commonly seen in psychiatric clinics. We review the clinical features and diagnostic criteria in FTD spectrum disorders.

KTN1 variants and risk for attention deficit hyperactivity disorder

Individuals with attention deficit hyperactivity disorder (ADHD) show gray matter volume (GMV) reduction in the putamen. KTN1 variants may regulate kinectin 1 expression in the putamen and influence putamen structure and function. We aim to test the hypothesis that the KTN1 variants may represent a genetic risk factor of ADHD. Two independent family-based Caucasian samples were analyzed, including 922 parent-child trios (a total of 2,757 subjects with 924 ADHD children) and 735 parent-child trios (a total of 1,383 subjects with 613 ADHD children). The association between ADHD and a total of 143 KTN1 SNPs was analyzed in the first sample, and the nominally-significant (p < .05) risk SNPs were classified into independent haplotype blocks. All SNPs, including imputed SNPs within these blocks, and haplotypes across each block, were explored for replication of associations in both samples. The potential biological functions of all risk SNPs were predicted using a series of bioinformatics analyses, their regulatory effects on the putamen volumes were tested, and the KTN1 mRNA expression was examined in three independent human putamen tissue samples. We found that fifteen SNPs were nominally associated with ADHD (p < .05) in the first sample, and three of them remained significant even after correction for multiple testing (1.3 × 10^-10  ≤ p ≤ 1.2 × 10^-4 ; α = 2.5 × 10^-3 ). These 15 risk SNPs were located in five haplotype blocks, and 13 SNPs within four of these blocks were associated with ADHD in the second sample. Six haplotypes within these blocks were also significantly (1.2 × 10^-7  ≤ p ≤ .009) associated with ADHD in these samples. These risk variants were located in disease-related transposons and/or transcription-related functional regions. Major alleles of these risk variants significantly increased putamen volumes. Finally, KTN1 mRNA was significantly expressed in putamen across three independent cohorts. We concluded that the KTN1 variants were significantly associated with ADHD. KTN1 may play a functional role in the development of ADHD.

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research.

Effect of the SIRT1 gene on regional cortical grey matter density in the Han Chinese population

BACKGROUND

Our previous genome-wide association study (CONVERGE sample) identified significant association between single nucleotide polymorphisms (SNPs) near the SIRT1 gene and major depressive disorder (MDD) in Chinese populations.

AIMS

To investigate whether SNPs across the SIRT1 gene locus affect regional grey matter density in the Han Chinese population.

METHOD

T1-weighted structural magnetic resonance imaging was conducted on 92 healthy participants from Eastern China. Grey matter was segmented from the image, which consisted of voxel-wise grey matter density. The effect of SIRT1 SNPs on grey matter density was determined by a multiple linear regression framework.

RESULTS

SNP rs4746720 was significantly associated with grey matter density in two brain cortical regions: the orbital part of the right inferior frontal gyrus and the orbital part of the left inferior frontal gyrus (family-wise error-corrected P < 0.05; voxel-wise P < 0.001). Also, rs4746720 exceeded genome-wide significance in association with MDD in our CONVERGE sample (P = 3.32 × 10-08, odds ratio 1.161).

CONCLUSIONS

Our results provided evidence for a potential role of the SIRT1 gene in the brain, implying a possible pathophysiological mechanism underlying susceptibility to MDD.

Genetic correlations between subcortical brain volumes and psychiatric disorders

Psychiatric disorders as well as subcortical brain volumes are highly heritable. Large-scale genome-wide association studies (GWASs) for these traits have been performed. We investigated the genetic correlations between five psychiatric disorders and the seven subcortical brain volumes and the intracranial volume from large-scale GWASs by linkage disequilibrium score regression. We revealed weak overlaps between the genetic variants associated with psychiatric disorders and subcortical brain and intracranial volumes, such as in schizophrenia and the hippocampus and bipolar disorder and the accumbens. We confirmed shared aetiology and polygenic architecture across the psychiatric disorders and the specific subcortical brain and intracranial volume.

Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model

Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.

Effect of genetic liability to migraine on cognition and brain volume: A Mendelian randomization study

Objective

To investigate potential causality between genetic liability to migraine and Alzheimer’s disease, intelligence, and brain volume using two-sample Mendelian randomization.

Methods

The exposure consisted of independent genetic variants associated with migraine in the largest (59,674 cases/316,078 controls) published genome-wide association study. Outcomes included Alzheimer’s disease (71,880 cases/383,378 controls), a measure of general intelligence (n = 269,867), intracranial volume (n = 11,373), and seven subcortical brain volumes (n ∼ 13,000), all with available genome-wide association study summary statistics. Mendelian randomization effects were estimated using inverse-variance weighted analysis.

Results

Genetic liability to migraine did not associate with Alzheimer’s disease (odds ratio [95% confidence interval] 1.01 [1.00–1.02], p = 0.07), intelligence (standardized beta [95% confidence interval] 0.01 [0.00–0.02], p = 0.13), or any brain volume measures (all p > 0.05). No individual migraine variant associated with any of the outcomes at genome-wide significance.

Conclusions

These data do not support a causal effect of migraine liability on Alzheimer’s disease, intelligence, or brain volume.

Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence

Schizophrenia (SCZ) and bipolar disorder (BD) are severe mental disorders associated with cognitive impairment, which is considered a major determinant of functional outcome. Despite this, the etiology of the cognitive impairment is poorly understood, and no satisfactory cognitive treatments exist. Increasing evidence indicates that genetic risk for SCZ may contribute to cognitive impairment, whereas the genetic relationship between BD and cognitive function remains unclear. Here, we combined large genome-wide association study data on SCZ (n = 82,315), BD (n = 51,710), and general intelligence (n = 269,867) to investigate overlap in common genetic variants using conditional false discovery rate (condFDR) analysis. We observed substantial genetic enrichment in both SCZ and BD conditional on associations with intelligence indicating polygenic overlap. Using condFDR analysis, we leveraged this enrichment to increase statistical power and identified 75 distinct genomic loci associated with both SCZ and intelligence, and 12 loci associated with both BD and intelligence at conjunctional FDR < 0.01. Among these loci, 20 are novel for SCZ, and four are novel for BD. Most SCZ risk alleles (61 of 75, 81%) were associated with poorer cognitive performance, whereas most BD risk alleles (9 of 12, 75%) were associated with better cognitive performance. A gene set analysis of the loci shared between SCZ and intelligence implicated biological processes related to neurodevelopment, synaptic integrity, and neurotransmission; the same analysis for BD was underpowered. Altogether, the study demonstrates that both SCZ and BD share genetic influences with intelligence, albeit in a different manner, providing new insights into their genetic architectures.

Characterizing Alzheimer's Disease With Image and Genetic Biomarkers Using Supervised Topic Models

Neuroimaging and genetic biomarkers have been widely studied from discriminative perspectives towards Alzheimer's disease (AD) classification, since neuroanatomical patterns and genetic variants are jointly critical indicators for AD diagnosis. Generative methods, designed to model common occurring patterns, could potentially advance the understanding of this disease, but have not been fully explored for AD characterization. Moreover, the introduction of a supervised component into the generative process can constrain the model for more discriminative characterization. In this study, we propose an original method based on supervised topic modeling to characterize AD from a generative perspective, yet maintaining discriminative power at differentiating disease populations. Our topic modeling jointly exploits discretized image features and categorical genetic features. Diagnostic information - cognitively normal (CN), mild cognitive impairment (MCI) and AD - is introduced as a supervision variable. Experimental results on the ADNI cohort demonstrate that our model, while achieving competitive discriminative performance, can discover topics revealing both well-known and novel neuroanatomical patterns including temporal, parietal and frontal regions; as well as associations between genetic factors and neuroanatomical patterns.

Heritability of cervical spinal cord structure

OBJECTIVE

Measures of spinal cord structure can be a useful phenotype to track disease severity and development; this observational study measures the hereditability of cervical spinal cord anatomy and its correlates in healthy human beings.

METHODS

Twin data from the Human Connectome Project were analyzed with semiautomated spinal cord segmentation, evaluating test-retest reliability and broad-sense heritability with an AE model. Relationships between spinal cord metrics, general physical measures, regional brain structural measures, and motor function were assessed.

RESULTS

We found that the spinal cord C2 cross-sectional area (CSA), left-right width (LRW), and anterior-posterior width (APW) are highly heritable (85%-91%). All measures were highly correlated with the brain volume, and CSA only was positively correlated with thalamic volumes (p = 0.005) but negatively correlated with the occipital cortex area (p = 0.001). LRW was correlated with the participant's height (p = 0.00027). The subjects' sex significantly influenced these metrics. Analyses of a test-retest data set confirmed validity of the approach.

CONCLUSIONS

This study provides the evidence of genetic influence on spinal cord structure. MRI metrics of cervical spinal cord anatomy are robust and not easily influenced by nonpathological environmental factors, providing a useful metric for monitoring normal development and progression of neurodegenerative disorders affecting the spinal cord, including-but not limited to-spinal cord injury and MS.

Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group

Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD‐related differences in subcortical regions using shape analysis. In this meta‐analysis of subcortical shape from the ENIGMA‐MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta‐analysis. Relative to CTL, patients with adolescent‐onset MDD (≤ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen's d = −0.164 to −0.180). Relative to first‐episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen's d = −0.173 to −0.184). Our results suggest that previously reported MDD‐associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

Individual-Level Identification of Gene Expression Associated with Volume Differences among Neocortical Areas

The human cerebral cortex is the source of many complex behaviors and is a vulnerable target of various neuropsychiatric disorders, but transcriptional profiles linked to cerebral cortical volume (CCV) differences across brain areas remain unknown. Here, we screened CCV-related genes using an across-sample spatial correlation analysis in 6 postmortem brains and then individually validated these correlations in 1091 subjects with different ages and ethnicities. We identified 62 genes whose transcriptional profiles were repeatedly associated with CCV in more than 90% of individuals. CCV-related genes were specifically expressed in neurons and in developmental periods from middle childhood to young adulthood, were enriched in ion channels and developmental processes, and showed significant overlap with genes linked to brain functional activity and mental disorders. The identified genes represent the conserved transcriptional architecture of the human cerebral cortex, suggesting a link between conserved gene transcription and neocortical structural properties.

Further confirmation of netrin 1 receptor (DCC) as a depression risk gene via integrations of multi-omics data

Genome-wide association studies (GWAS) of major depression and its relevant biological phenotypes have been extensively conducted in large samples, and transcriptome-wide analyses in the tissues of brain regions relevant to pathogenesis of depression, e.g., dorsolateral prefrontal cortex (DLPFC), have also been widely performed recently. Integrating these multi-omics data will enable unveiling of depression risk genes and even underlying pathological mechanisms. Here, we employ summary data-based Mendelian randomization (SMR) and integrative risk gene selector (iRIGS) approaches to integrate multi-omics data from GWAS, DLPFC expression quantitative trait loci (eQTL) analyses and enhancer-promoter physical link studies to prioritize high-confidence risk genes for depression, followed by independent replications across distinct populations. These integrative analyses identify multiple high-confidence depression risk genes, and numerous lines of evidence supporting pivotal roles of the netrin 1 receptor (DCC) gene in this illness across different populations. Our subsequent explorative analyses further suggest that DCC significantly predicts neuroticism, well-being spectrum, cognitive function and putamen structure in general populations. Gene expression correlation and pathway analyses in DLPFC further show that DCC potentially participates in the biological processes and pathways underlying synaptic plasticity, axon guidance, circadian entrainment, as well as learning and long-term potentiation. These results are in agreement with the recent findings of this gene in neurodevelopment and psychiatric disorders, and we thus further confirm that DCC is an important susceptibility gene for depression, and might be a potential target for new antidepressants.

A DLG2 deficiency in mice leads to reduced sociability and increased repetitive behavior accompanied by aberrant synaptic transmission in the dorsal striatum

Background

DLG2, also known as postsynaptic density protein-93 (PSD-93) or chapsyn-110, is an excitatory postsynaptic scaffolding protein that interacts with synaptic surface receptors and signaling molecules. A recent study has demonstrated that mutations in the DLG2 promoter region are significantly associated with autism spectrum disorder (ASD). Although DLG2 is well known as a schizophrenia-susceptibility gene, the mechanisms that link DLG2 gene disruption with ASD-like behaviors remain unclear.

Methods

Mice lacking exon 14 of the Dlg2 gene (Dlg2^–/– mice) were used to investigate whether Dlg2 deletion leads to ASD-like behavioral abnormalities. To this end, we performed a battery of behavioral tests assessing locomotion, anxiety, sociability, and repetitive behaviors. In situ hybridization was performed to determine expression levels of Dlg2 mRNA in different mouse brain regions during embryonic and postnatal brain development. We also measured excitatory and inhibitory synaptic currents to determine the impacts of Dlg2 deletion on synaptic transmission in the dorsolateral striatum.

Results

Dlg2^–/– mice showed hypoactivity in a novel environment. They also exhibited decreased social approach, but normal social novelty recognition, compared with wild-type animals. In addition, Dlg2^–/– mice displayed strong self-grooming, both in home cages and novel environments. Dlg2 mRNA levels in the striatum were heightened until postnatal day 7 in mice, implying potential roles of DLG2 in the development of striatal connectivity. In addition, the frequency of excitatory, but not inhibitory, spontaneous postsynaptic currents in the Dlg2^–/– dorsolateral striatum was significantly reduced.

Conclusion

These results suggest that homozygous Dlg2 deletion in mice leads to ASD-like behavioral phenotypes, including social deficits and increased repetitive behaviors, as well as reductions in excitatory synaptic input onto dorsolateral spiny projection neurons, implying that the dorsal striatum is one of the brain regions vulnerable to the developmental dysregulation of DLG2.

Malignant Evaluation and Clinical Prognostic Values of m6A RNA Methylation Regulators in Glioblastoma

N6-methyladenosine (m6A) RNA methylation, the most common form of mRNA modification and regulated by the m6A RNA methylation regulators ("writers," "erasers," and "readers"), has been reported to be associated with the progression of the malignant tumor. However, its role in glioblastoma (GBM) has been poorly known. This study aimed to identify the expression, potential functions, and prognostic values of m6A RNA methylation regulators in GBM. Here, we revealed that the 13 central m6A RNA methylation regulators were firmly related to the clinical and molecular phenotype of GBM. Taking advantage of consensus cluster analysis, we obtained two categories of GBM samples and found malignancy-related processes of m6A methylation regulators and compounds that specifically targeted the malignant processes. Besides, we also obtained a list of genes with poor prognosis in GBM. Finally, we derived a risk-gene signature with three selected m6A RNA methylation regulators, which allowed us to extend the in-depth study and dichotomized the OS of patients with GBM into high- and low-risk subgroups. Notably, this risk-gene signature could be used as independent prognostic markers and accurate clinicopathological parameter predictors. In conclusion, m6A RNA methylation regulators are a type of vital participant in the malignant progression of GBM, with a critical potential in the prognostic stratification and treatment strategies of GBM.

The role of genes affected by human evolution marker GNA13 in schizophrenia

Numerous variants associated with increased risk for SCZ have undergone positive selection and were associated with human brain development, but which brain regions and developmental stages were influenced by the positive selection for SCZ risk alleles are unclear. We analyzed SCZ using summary statistics from a genome-wide association study (GWAS) from the Psychiatric Genomics Consortium (PGC). Machine-learning scores were used to investigate two natural-selection scenarios: complete selection (loci where a selected allele has reached fixation) and incomplete selection (loci where a selected allele has not yet reached fixation). Based on the p value of single nucleotide polymorphisms (SNPs) with selection scores in the top 5%, we formed five subgroups: p < 0.0001, 0.001, 0.01, 0.05, or 0.1. We found that 48 and 29 genes (p < 0.0001) in complete and incomplete selection, respectively, were enrichedfor the transcriptionalco-expressionprofilein theprenatal dorsolateral prefrontal cortex (DFC), inferior parietal cortex (IPC), and ventrolateral prefrontal cortex (VFC). Core genes (GNA13, TBC1D19, and ZMYM4) involved in regulating early brain development were identified in these three brain regions. RNA sequencing for primary cortical neurons that were transfected Gna13 overexpressed lentivirus demonstrated that 135 gene expression levels changed in the Gna13 overexpressed groups compared with the controls. Gene-set analysis identified important associations among common variants of these 13 genes, which were associated with neurodevelopment and putamen volume [p = 0.031; family-wise error correction (FWEC)], SCZ (p = 0.022; FWEC). The study indicate that certain SCZ risk alleles were likely to undergo positive selection during human evolution due to their involvement in the development of prenatal DFC, IPC and VFC, and suggest that SCZ is related to abnormal neurodevelopment.

Comparison of shape alterations of the thalamus and caudate nucleus between drug-naïve major depressive disorder patients and healthy controls

BACKGROUND

Although structural alterations have been reported in patients with major depressive disorder (MDD), very few studies have compared the shape alterations of the subcortical regions between drug-naïve MDD patients and healthy controls (HCs). Therefore, we investigated and compared the subcortical shape alterations and volumetric changes between drug-naïve MDD patients and HCs in this study.

METHODS

This study included 45 drug-naïve MDD patients and 83 HCs, who underwent three-dimensional (3-D) T1-weighted structural magnetic resonance imaging. Surface-based vertex analysis (SVA) was performed with automated segmentation of the bilateral caudate nuclei, putamina, nuclei accumbens, thalami, pallidum, hippocampi, amygdalae, and brainstem. SVA revealed regional contractions of the thalamus (bilateral medial and lateral nuclei) and right caudate nucleus (medial wall and anterosuperior areas) in the drug-naïve MDD patients when compared to HCs RESULTS: In volume analysis, the drug-naïve MDD patients showed a significant decrease in the volume of bilateral thalami compared with HCs (after Bonferroni correction p < 0.003). We identified morphometric contractions in bilateral thalami and right caudate nucleus in the drug-naïve MDD patients (p < 0.05).

CONCLUSIONS

The present study implied that with cortical shape changes, the subcortical brain alterations could contribute to emotional dysregulation in the drug-naïve MDD patients.

CHIMGEN: a Chinese imaging genetics cohort to enhance cross-ethnic and cross-geographic brain research

The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese neuroimaging genetics cohort and aims to identify genetic and environmental factors and their interactions that are associated with neuroimaging and behavioral phenotypes. This study prospectively collected genomic, neuroimaging, environmental, and behavioral data from more than 7000 healthy Chinese Han participants aged 18-30 years. As a pioneer of large-sample neuroimaging genetics cohorts of non-Caucasian populations, this cohort can provide new insights into ethnic differences in genetic-neuroimaging associations by being compared with Caucasian cohorts. In addition to micro-environmental measurements, this study also collects hundreds of quantitative macro-environmental measurements from remote sensing and national survey databases based on the locations of each participant from birth to present, which will facilitate discoveries of new environmental factors associated with neuroimaging phenotypes. With lifespan environmental measurements, this study can also provide insights on the macro-environmental exposures that affect the human brain as well as their timing and mechanisms of action.

Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia

Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = -0.71 to -1.37; P < 0.0005). In an independent sample, consistent results were obtained, with significant effects in the pallidum (β = -0.95, P = 0.0042). The two data sets combined showed significant negative dose-response for the accumbens, caudate, pallidum, putamen and ICV (P = 0.0032, 8.9 × 10-6, 1.7 × 10-9, 3.5 × 10-12 and 1.0 × 10-4, respectively). Full scale IQ was lower in both deletion and duplication carriers compared to non-carriers. This is the first brain MRI study of the impact of the 16p11.2 distal CNV, and we demonstrate a specific effect on subcortical brain structures, suggesting a neuropathological pattern underlying the neurodevelopmental syndromes.

Shared genetic etiology between anxiety disorders and psychiatric and related intermediate phenotypes

BACKGROUND

Psychiatric disorders and related intermediate phenotypes are highly heritable and have a complex, overlapping polygenic architecture. A large-scale genome-wide association study (GWAS) of anxiety disorders identified genetic variants that are significant on a genome-wide. The current study investigated the genetic etiological overlaps between anxiety disorders and frequently cooccurring psychiatric disorders and intermediate phenotypes.

METHODS

Using case-control and factor score models, we investigated the genetic correlations of anxiety disorders with eight psychiatric disorders and intermediate phenotypes [the volumes of seven subcortical brain regions, childhood cognition, general cognitive ability and personality traits (subjective well-being, loneliness, neuroticism and extraversion)] from large-scale GWASs (n = 7556-298 420) by linkage disequilibrium score regression.

RESULTS

Among psychiatric disorders, the risk of anxiety disorders was positively genetically correlated with the risks of major depressive disorder (MDD) (rg ± standard error = 0.83 ± 0.16, p = 1.97 × 10-7), schizophrenia (SCZ) (0.28 ± 0.09, p = 1.10 × 10-3) and attention-deficit/hyperactivity disorder (ADHD) (0.34 ± 0.13, p = 8.40 × 10-3). Among intermediate phenotypes, significant genetic correlations existed between the risk of anxiety disorders and neuroticism (0.81 ± 0.17, p = 1.30 × 10-6), subjective well-being (-0.73 ± 0.18, p = 4.89 × 10-5), general cognitive ability (-0.23 ± 0.08, p = 4.70 × 10-3) and putamen volume (-0.50 ± 0.18, p = 5.00 × 10-3). No other significant genetic correlations between anxiety disorders and psychiatric or intermediate phenotypes were observed (p > 0.05). The case-control model yielded stronger genetic effect sizes than the factor score model.

CONCLUSIONS

Our findings suggest that common genetic variants underlying the risk of anxiety disorders contribute to elevated risks of MDD, SCZ, ADHD and neuroticism and reduced quality of life, putamen volume and cognitive performance. We suggest that the comorbidity of anxiety disorders is partly explained by common genetic variants.

Impact of modifiable risk factors on Alzheimer's disease: A two-sample Mendelian randomization study

With the steadily increasing prevalence of Alzheimer's disease (AD) and great difficulties encountered for AD drug development presently, much interest has been devoted to identifying modifiable risk factors to lower the risk of AD, while the causal associations between risk factors and AD remain inconclusive. The present study conducted a comprehensive evaluation of the causal associations between risk factors and AD development by taking the recent advancements of Mendelian randomization (MR). Inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode were used for complementary calculation. A total of 45 risk factors and corresponding studies were covered in the study. This two-sample MR (2SMR) analysis provided a suggestive association between genetically predicted higher years of schooling and reduced risks of AD, and each standard deviation (3.71 years) increased in years of schooling was associated with a 41% reduction in the risk of AD (IVW, OR: 0.59, 95% CI: 0.45-0.77). At the same time, it was genetically predicted that urate might be a risk factor in AD, and it was found that each standard deviation increase in urate levels (1.33 mg/dL) was associated with a 0.09-fold increase in the risk of AD (IVW, OR: 1.09, 95% CI: 1.01-1.18). To summarize, the 2SMR analysis indicated a suggestive association between genetically predicted higher years of schooling and reduced risks of AD, and between genetically predicted higher urate levels and increased risks of AD. The findings provide useful clues to help combat AD and warrants future studies.

The Netrin-1/DCC guidance system: dopamine pathway maturation and psychiatric disorders emerging in adolescence

Axon guidance molecules direct growing axons toward their targets, assembling the intricate wiring of the nervous system. One of these molecules, Netrin-1, and its receptor, DCC (deleted in colorectal cancer), has profound effects, in laboratory animals, on the adolescent expansion of mesocorticolimbic pathways, particularly dopamine. Now, a rapidly growing literature suggests that (1) these same alterations could occur in humans, and (2) genetic variants in Netrin-1 and DCC are associated with depression, schizophrenia, and substance use. Together, these findings provide compelling evidence that Netrin-1 and DCC influence mesocorticolimbic-related psychopathological states that emerge during adolescence.

Identifying biological markers for improved precision medicine in psychiatry

Mental disorders represent an increasing personal and financial burden and yet treatment development has stagnated in recent decades. Current disease classifications do not reflect psychobiological mechanisms of psychopathology, nor the complex interplay of genetic and environmental factors, likely contributing to this stagnation. Ten years ago, the longitudinal IMAGEN study was designed to comprehensively incorporate neuroimaging, genetics, and environmental factors to investigate the neural basis of reinforcement-related behavior in normal adolescent development and psychopathology. In this article, we describe how insights into the psychobiological mechanisms of clinically relevant symptoms obtained by innovative integrative methodologies applied in IMAGEN have informed our current and future research aims. These aims include the identification of symptom groups that are based on shared psychobiological mechanisms and the development of markers that predict disease course and treatment response in clinical groups. These improvements in precision medicine will be achieved, in part, by employing novel methodological tools that refine the biological systems we target. We will also implement our approach in low- and medium-income countries to understand how distinct environmental, socioeconomic, and cultural conditions influence the development of psychopathology. Together, IMAGEN and related initiatives strive to reduce the burden of mental disorders by developing precision medicine approaches globally.

A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol

Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

Association of relative brain age with tobacco smoking, alcohol consumption, and genetic variants

Brain age is a metric that quantifies the degree of aging of a brain based on whole-brain anatomical characteristics. While associations between individual human brain regions and environmental or genetic factors have been investigated, how brain age is associated with those factors remains unclear. We investigated these associations using UK Biobank data. We first trained a statistical model for obtaining relative brain age (RBA), a metric describing a subject's brain age relative to peers, based on whole-brain anatomical measurements, from training set subjects (n = 5,193). We then applied this model to evaluation set subjects (n = 12,115) and tested the association of RBA with tobacco smoking, alcohol consumption, and genetic variants. We found that daily or almost daily consumption of tobacco and alcohol were both significantly associated with increased RBA (P < 0.001). We also found SNPs significantly associated with RBA (p-value < 5E-8). The SNP most significantly associated with RBA is located in MAPT gene. Our results suggest that both environmental and genetic factors are associated with structural brain aging.

Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer's Disease

Variations in the human genome have been found to be an essential factor that affects susceptibility to Alzheimer's disease. Genome-wide association studies (GWAS) have identified genetic loci that significantly contribute to the risk of Alzheimers. The availability of genetic data, coupled with brain imaging technologies have opened the door for further discoveries, by using data integration methodologies and new study designs. Although methods have been proposed for integrating image characteristics and genetic information for studying Alzheimers, the measurement of disease is often taken at a single time point, therefore, not allowing the disease progression to be taken into consideration. In longitudinal settings, we analyzed neuroimaging and single nucleotide polymorphism datasets obtained from the Alzheimer's Disease Neuroimaging Initiative for three clinical stages of the disease, including healthy control, early mild cognitive impairment and Alzheimer's disease subjects. We conducted a GWAS regressing the absolute change of global connectivity metrics on the genetic variants, and used the GWAS summary statistics to compute the gene and pathway scores. We observed significant associations between the change in structural brain connectivity defined by tractography and genes, which have previously been reported to biologically manipulate the risk and progression of certain neurodegenerative disorders, including Alzheimer's disease.

m6A Methylation Analysis of Differentially Expressed Genes in Skin Tissues of Coarse and Fine Type Liaoning Cashmere Goats

N6-methyladenosine (m6A) is the most common internal modification in mRNAs of all higher eukaryotes. Here we perform two high-throughput sequencing methods, m6A-modified RNA immunoprecipitation sequence (MeRIP-seq) and RNA sequence (RNA-seq) to identify key genes with m6A modification in cashmere fiber growth. A total of 9,085 m6A sites were differentially RNA m6A methylated as reported from by MeRIP-seq, including 7,170 upregulated and 1,915 downregulated. In addition, by comparing m6A-modified genes between the fine-type Liaoning cashmere goat (FT-LCG) and coarse-type Liaoning Cashmere Goat (CT-LCG) skin samples, we obtain 1,170 differentially expressed genes. In order to identify the differently methylated genes related to cashmere fiber growth, 19 genes were selected to validate by performing qRT-PCR in FT-LCG and CT-LCG. In addition, GO enrichment analysis shows that differently methylated genes are mainly involved in keratin filament and intermediate filament. These findings provide a theoretical basis for future research on the function of m6A modification during the growth of cashmere fiber.

FAM222A encodes a protein which accumulates in plaques in Alzheimer's disease

Alzheimer's disease (AD) is characterized by amyloid plaques and progressive cerebral atrophy. Here, we report FAM222A as a putative brain atrophy susceptibility gene. Our cross-phenotype association analysis of imaging genetics indicates a potential link between FAM222A and AD-related regional brain atrophy. The protein encoded by FAM222A is predominantly expressed in the CNS and is increased in brains of patients with AD and in an AD mouse model. It accumulates within amyloid deposits, physically interacts with amyloid-β (Aβ) via its N-terminal Aβ binding domain, and facilitates Aβ aggregation. Intracerebroventricular infusion or forced expression of this protein exacerbates neuroinflammation and cognitive dysfunction in an AD mouse model whereas ablation of this protein suppresses the formation of amyloid deposits, neuroinflammation and cognitive deficits in the AD mouse model. Our data support the pathological relevance of protein encoded by FAM222A in AD.

Whole Exome Sequencing Study of Parkinson Disease and Related Endophenotypes in the Italian Population

Parkinson Disease (PD) is a complex neurodegenerative disorder characterized by large genetic heterogeneity and missing heritability. Since the genetic background of PD can partly vary among ethnicities and neurological scales have been scarcely investigated in a PD setting, we performed an exploratory Whole Exome Sequencing (WES) analysis of 123 PD patients from mainland Italy, investigating scales assessing motor (UPDRS), cognitive (MoCA), and other non-motor symptoms (NMS). We performed variant prioritization, followed by targeted association testing of prioritized variants in 446 PD cases and 211 controls. Then we ran Exome-Wide Association Scans (EWAS) within sequenced PD cases (N = 113), testing both motor and non-motor PD endophenotypes, as well as their associations with Polygenic Risk Scores (PRS) influencing brain subcortical volumes. We identified a variant associated with PD, rs201330591 in GTF2H2 (5q13; alternative T allele: OR [CI] = 8.16[1.08; 61.52], FDR = 0.048), which was not replicated in an independent cohort of European ancestry (1,148 PD cases, 503 controls). In the EWAS, polygenic analyses revealed statistically significant multivariable associations of amygdala- [β(SE) = -0.039(0.013); FDR = 0.039] and caudate-PRS [0.043(0.013); 0.028] with motor symptoms. All subcortical PRSs in a multivariable model notably increased the variance explained in motor (adjusted-R2 = 38.6%), cognitive (32.2%) and other non-motor symptoms (28.9%), compared to baseline models (~20%). Although, the small sample size warrants further replications, these findings suggest shared genetic architecture between PD symptoms and subcortical structures, and provide interesting clues on PD genetic and neuroimaging features.

Gene expression and DNA methylation are extensively coordinated with MRI-based brain microstructural characteristics

Cognitive function relies on both molecular levels and cellular structures. However, systematic relationships between these two components of cognitive function, and their joint contribution to disease, are largely unknown. We utilize postmortem neuroimaging in tandem with gene expression and DNA methylation, from 222 deeply-phenotyped persons in a longitudinal aging cohort. Expression of hundreds of genes and methylation at thousands of loci are related to the microstructure of extensive regions of this same set of brains, as assessed by MRI. The genes linked to brain microstructure perform functions related to cell motility, transcriptional regulation and nuclear processes, and are selectively associated with Alzheimer’s phenotypes. Similar methodology can be applied to other diseases to identify their joint molecular and structural basis, or to infer molecular levels in the brain on the basis of neuroimaging for precision medicine applications.

The genome-wide risk alleles for psychiatric disorders at 3p21.1 show convergent effects on mRNA expression, cognitive function, and mushroom dendritic spine

Schizophrenia and bipolar disorder (BPD) are believed to share clinical features, etiological factors, and disease pathologies (such as impaired cognitive functions and dendritic spine pathology). Meanwhile, there is growing evidence of shared genetic risk between schizophrenia and BPD, despite that our knowledge of the functional risk variations and biological mechanisms is still limited. Here, we conduct summary data-based Mendelian randomization (SMR) analyses through combining the statistical data from genome-wide association studies (GWAS) of both schizophrenia and BPD and multiple expression quantitative trait loci (eQTL) datasets of the human brain dorsolateral prefrontal cortex (DLPFC) tissues. These integrative investigations identify a lead risk locus at the chromosome 3p21.1 region, which contains numerous single-nucleotide polymorphisms (SNPs) in varied linkage disequilibrium (LD) and encompasses more than 20 genes. Further analyses suggest that many SNPs at 3p21.1 are significantly associated with both schizophrenia and BPD, and even depression, and the psychiatric risk alleles at 3p21.1 are correlated with mRNA expression of multiple genes such as NEK4, GNL3, and PBRM1. We also identify a 335-bp functional Alu polymorphism rs71052682 in significant LD with the psychiatric GWAS risk SNP rs2251219, and confirm the regulatory effects of this Alu polymorphism on transcription activities. We then explore the involvement of the 3p21.1 locus in the common clinical features and etiology of these illnesses. We reveal that psychiatric risk alleles at 3p21.1 in low-to-high LD consistently predict worse cognitive functions in humans, and manipulating the gene expression (NEK4, GNL3, and PBRM1) linked with higher genetic risk could reduce the density of mushroom dendritic spines in rat primary cortical neurons, mirroring the spine pathology in the prefrontal cortex of psychiatric patients. Our results find that, although the risk alleles at 3p21.1 are in low-to-moderate LD spanning a large genomic area, their underlying biological mechanisms in psychiatric disorders likely converge. These results provide essential insights into the neural mechanisms underlying the chromosome 3p21.1 risk locus in the shared pathological and etiological features of both schizophrenia and BPD.

Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer's disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields' genetic architecture. T1-weighted brain scans (n = 21,297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, co-varying for whole hippocampal volume. We further calculated the single-nucleotide polymorphism (SNP)-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features and with AD and schizophrenia. All outcome measures were corrected for age, sex and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from 0.14 to 0.27, all p < 1 × 10^-16) and clustered together based on their genetic correlations compared with other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.

Brain Imaging Genomics: Integrated Analysis and Machine Learning

Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.

CACNA1C Gene rs11832738 Polymorphism Influences Depression Severity by Modulating Spontaneous Activity in the Right Middle Frontal Gyrus in Patients With Major Depressive Disorder

OBJECTIVES

This study aimed to examine whether the CACNA1C gene rs11832738 polymorphism and major depressive disorder (MDD) have an interactive effect on the untreated regional amplitude of low-frequency fluctuation (ALFF) and to determine whether regional ALFF mediates the association between CACNA1C rs11832738 and MDD.

METHODS

A total of 116 patients with MDD and 66 normal controls (NCs) were recruited. The MDD and NC groups were further divided into two groups according to genotype: carriers of the G allele (G-carrier group, GG/GA genotypes; MDD, n = 61; NC, n = 26) and AA homozygous group (MDD, n = 55; NC, n = 40). MDD was diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Depression severity was assessed using the Hamilton Depression Scale-24 (HAMD-24) at baseline and follow-up (after 2 and 8 weeks of treatment). All subjects underwent functional MRI (fMRI) scans at baseline, and the ALFF was calculated to reflect spontaneous brain activity. The interactions between MDD and CACNA1C single nucleotide polymorphism rs11832738 were determined using two-way factorial analysis of covariance, with age, sex, education, and head motion as covariates. We performed mediation analysis to further determine whether regional ALFF strength could mediate the associations between rs11832738 and depression severity, MDD treatment efficacy.

RESULTS

MDD had a main effect on regional ALFF distribution in three brain areas: the right medial frontal gyrus (MFG_R), the left anterior cingulate cortex (ACC_L), and the right cerebellum posterior lobe (CPL_R); CACNA1C showed a significant interactive effect with MDD on the ALFF of MFG_R. For CACNA1C G allele carriers, the ALFF of MFG_R had a significant positive correlation with the baseline HAMD-24 score. Exploratory mediation analysis revealed that the intrinsic ALFF in MFG_R significantly mediated the association between the CACNA1C rs11832738 polymorphism and baseline HAMD-24 score.

CONCLUSIONS

A genetic variant in CACNA1C rs11832738 may influence depression severity in MDD patients by moderating spontaneous MFG_R activity.

Altered Cortical Brain Structure and Increased Risk for Disease Seen Decades After Perinatal Exposure to Maternal Smoking: A Study of 9000 Adults in the UK Biobank

Secondhand smoke exposure is a major public health risk that is especially harmful to the developing brain, but it is unclear if early exposure affects brain structure during middle age and older adulthood. Here we analyzed brain MRI data from the UK Biobank in a population-based sample of individuals (ages 44-80) who were exposed (n = 2510) or unexposed (n = 6079) to smoking around birth. We used robust statistical models, including quantile regressions, to test the effect of perinatal smoke exposure (PSE) on cortical surface area (SA), thickness, and subcortical volumes. We hypothesized that PSE would be associated with cortical disruption in primary sensory areas compared to unexposed (PSE-) adults. After adjusting for multiple comparisons, SA was significantly lower in the pericalcarine (PCAL), inferior parietal (IPL), and regions of the temporal and frontal cortex of PSE+ adults; these abnormalities were associated with increased risk for several diseases, including circulatory and endocrine conditions. Sensitivity analyses conducted in a hold-out group of healthy participants (exposed, n = 109, unexposed, n = 315) replicated the effect of PSE on SA in the PCAL and IPL. Collectively our results show a negative, long term effect of PSE on sensory cortices that may increase risk for disease later in life.

The depression GWAS risk allele predicts smaller cerebellar gray matter volume and reduced SIRT1 mRNA expression in Chinese population

Major depressive disorder (MDD) is recognized as a primary cause of disability worldwide, and effective management of this illness has been a great challenge. While genetic component is supposed to play pivotal roles in MDD pathogenesis, the genetic and phenotypic heterogeneity of the illness has hampered the discovery of its genetic determinants. In this study, in an independent Han Chinese sample (1824 MDD cases and 3031 controls), we conducted replication analyses of two genetic loci highlighted in a previous Chinese MDD genome-wide association study (GWAS), and confirmed the significant association of a single nucleotide polymorphism (SNP) rs12415800 near SIRT1. Subsequently, using hypothesis-free whole-brain analysis in two independent Han Chinese imaging samples, we found that individuals carrying the MDD risk allele of rs12415800 exhibited aberrant gray matter volume in the left posterior cerebellar lobe compared with those carrying the non-risk allele. Besides, in independent Han Chinese postmortem brain and peripheral blood samples, the MDD risk allele of rs12415800 predicted lower SIRT1 mRNA levels, which was consistent with the reduced expression of this gene in MDD patients compared with healthy subjects. These results provide further evidence for the involvement of SIRT1 in MDD, and suggest that this gene might participate in the illness via affecting the development of cerebellum, a brain region that is potentially underestimated in previous MDD studies.

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

BACKGROUND

Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease.

METHODS

We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation.

FINDINGS

Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16-36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10^-7).

INTERPRETATION

These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data.

FUNDING

The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources).

Associations between rare microglia-linked Alzheimer's disease risk variants and subcortical brain volumes in young individuals

INTRODUCTION

Recent exome sequencing studies have identified three novel risk variants associated with Alzheimer's disease (AD). However, the mechanisms by which these variants confer risk are largely unknown.

METHODS

In the present study, the impact of these rare coding variants (in ABI3, PLCG2, and TREM2) on all subcortical volumes is determined in a large sample of young healthy individuals (N = 756-765; aged 22-35 years).

RESULTS

After multiple testing correction (P CORRECTED < .05), rare variants were associated with basal ganglia volumes (TREM2 and PLCG2 effects within the putamen and pallidum, respectively). Nominal associations between TREM2 and reduced hippocampal and thalamic volumes were also observed.

DISCUSSION

Our observations suggest that rare variants in microglia-mediated immunity pathway may contribute to the subcortical alterations observed in AD cases. These observations provide further evidence that genetic risk for AD may influence the volume of subcortical volumes and increase AD risk in early life processes.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

TCF7L2 polymorphisms are associated with amygdalar volume in elderly individuals with Type 2 Diabetes

The association between several Single Nucleotide Polymorphisms (SNPs) within the transcription factor 7-like 2 (TCF7L2) gene and Type 2 Diabetes (T2D) as well as additional T2D-related traits is well established. Since alteration in total and regional brain volumes are consistent findings among T2D individuals, we studied the association of four T2D susceptibility SNPS within TCF7L2 (rs7901695, rs7903146, rs11196205, and rs12255372) with volumes of white matter hyperintensities (WMH), gray matter, and regional volumes of amygdala and hippocampus obtained from structural MRI among 191 T2D elderly Jewish individuals. Under recessive genetic model (controlling for age, sex and intracranial volume), we found that for all four SNPs, carriers of two copies of the T2D risk allele (homozygous genotype) had significantly smaller amygdalar volume: rs7901695- CC genotype vs. CT + TT genotypes, p = 0.002; rs7903146-TT vs. TC + CC, p = 0.003; rs11196205- CC vs. CG + GG, p = 0.0003; and rs12255372- TT vs. TG + GG, p = 0.003. Adjusting also for T2D-related covariates, body mass index (BMI), and ancestry did not change the results substantively (rs7901695, p = 0.003; rs7903146, p = 0.005; rs11196205, p = 0.001; and rs12255372, p = 0.005). Conditional analysis demonstrated that only rs11196205 was independently associated with amygdalar volume at a significant level. Separate analysis of left and right amygdala revealed stronger results for left amygdalar volume. Taken together, we report association of TCF7L2 SNPs with amygdalar volume among T2D elderly Jewish patients. Further studies in other populations are required to support these findings and reach more definitive conclusions.

Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits

Volumetric variations of the human brain are heritable and are associated with many brain-related complex traits. Here we performed genome-wide association studies (GWAS) of 101 brain volumetric phenotypes using the UK Biobank sample including 19,629 participants. GWAS identified 365 independent genetic variants exceeding a significance threshold of 4.9 × 10-10, adjusted for testing multiple phenotypes. A gene-based association study found 157 associated genes (124 new), and functional gene mapping analysis linked 146 additional genes. Many of the discovered genetic variants and genes have previously been implicated in cognitive and mental health traits. Through genome-wide polygenic-risk-score prediction, more than 6% of the phenotypic variance (P = 3.13 × 10-24) in four other independent studies could be explained by the UK Biobank GWAS results. In conclusion, our study identifies many new genetic associations at the variant, locus and gene levels and advances our understanding of the pleiotropy and genetic co-architecture between brain volumes and other traits.

Genetic architecture of subcortical brain structures in 38,851 individuals

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.

Toward Robust Functional Neuroimaging Genetics of Cognition

A commonly held assumption in cognitive neuroscience is that, because measures of human brain function are closer to underlying biology than distal indices of behavior/cognition, they hold more promise for uncovering genetic pathways. Supporting this view is an influential fMRI-based study of sentence reading/listening by Pinel et al. (2012), who reported that common DNA variants in specific candidate genes were associated with altered neural activation in language-related regions of healthy individuals that carried them. In particular, different single-nucleotide polymorphisms (SNPs) of FOXP2 correlated with variation in task-based activation in left inferior frontal and precentral gyri, whereas a SNP at the KIAA0319/TTRAP/THEM2 locus was associated with variable functional asymmetry of the superior temporal sulcus. Here, we directly test each claim using a closely matched neuroimaging genetics approach in independent cohorts comprising 427 participants, four times larger than the original study of 94 participants. Despite demonstrating power to detect associations with substantially smaller effect sizes than those of the original report, we do not replicate any of the reported associations. Moreover, formal Bayesian analyses reveal substantial to strong evidence in support of the null hypothesis (no effect). We highlight key aspects of the original investigation, common to functional neuroimaging genetics studies, which could have yielded elevated false-positive rates. Genetic accounts of individual differences in cognitive functional neuroimaging are likely to be as complex as behavioral/cognitive tests, involving many common genetic variants, each of tiny effect. Reliable identification of true biological signals requires large sample sizes, power calculations, and validation in independent cohorts with equivalent paradigms.SIGNIFICANCE STATEMENT A pervasive idea in neuroscience is that neuroimaging-based measures of brain function, being closer to underlying neurobiology, are more amenable for uncovering links to genetics. This is a core assumption of prominent studies that associate common DNA variants with altered activations in task-based fMRI, despite using samples (10-100 people) that lack power for detecting the tiny effect sizes typical of genetically complex traits. Here, we test central findings from one of the most influential prior studies. Using matching paradigms and substantially larger samples, coupled to power calculations and formal Bayesian statistics, our data strongly refute the original findings. We demonstrate that neuroimaging genetics with task-based fMRI should be subject to the same rigorous standards as studies of other complex traits.