Common genetic variants influence human subcortical brain structures

Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group

Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10^-21), left fusiform gyrus (d=-0.288; P=8.25 × 10^-21) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10^-19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.

No evidence for association of MTHFR 677C>T and 1298A>C variants with placental DNA methylation

Background

5,10-Methylenetetrahydrofolate reductase (MTHFR) is a key enzyme in one-carbon metabolism that ensures the availability of methyl groups for methylation reactions. Two single-nucleotide polymorphisms (SNPs) in the MTHFR gene, 677C>T and 1298A>C, result in a thermolabile enzyme with reduced function. These variants, in both the maternal and/or fetal genes, have been associated with pregnancy complications including miscarriage, neural tube defects (NTDs), and preeclampsia (PE), perhaps due to altered capacity for DNA methylation (DNAm). In this study, we assessed the association between MTHFR 677TT and 1298CC genotypes and risk of NTDs, PE, or normotensive intrauterine growth restriction (nIUGR). Additionally, we assessed whether these high-risk genotypes are associated with altered DNAm in the placenta.

Results

In 303 placentas screened for this study, we observed no significant association between the occurrence of NTDs (N = 55), PE (early-onset: N = 28, late-onset: N = 20), or nIUGR (N = 21) and placental (fetal) MTHFR 677TT or 1298CC genotypes compared to healthy pregnancies (N = 179), though a trend of increased 677TT genotype in PE/IUGR together was observed (OR 2.53, p = 0.048). DNAm was profiled in 10 high-risk 677 (677TT + 1298AA), 10 high-risk 1298 (677CC + 1298CC), and 10 reference (677CC + 1298AA) genotype placentas. Linear modeling identified no significantly differentially methylated sites between high-risk 677 or 1298 and reference placentas at a false discovery rate < 0.05 and Δβ ≥ 0.05 using the Illumina Infinium HumanMethylation450 BeadChip. Using a differentially methylated region analysis or separating cytosine-guanine dinucleotides (CpGs) by CpG density to reduce multiple comparisons also did not identify differential methylation. Additionally, there was no consistent evidence for altered methylation of repetitive DNA between high-risk and reference placentas.

Conclusions

We conclude that large-scale, genome-wide disruption in DNAm does not occur in placentas with the high-risk MTHFR 677TT or 1298CC genotypes. Furthermore, there was no evidence for an association of the 1298CC genotype and only a tendency to higher 677TT in pregnancy complications of PE/IUGR. This may be due to small sample sizes or folate repletion in our Canadian population attenuating effects of the high-risk MTHFR variants. However, given our results and the conflicting results in the literature, investigations into alternative mechanisms that may explain the link between MTHFR variants and pregnancy complications, or in populations at risk of folate deficiencies, are warranted.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0468-1) contains supplementary material, which is available to authorized users.

Genome-wide analyses of self-reported empathy: correlations with autism, schizophrenia, and anorexia nervosa

Empathy is the ability to recognize and respond to the emotional states of other individuals. It is an important psychological process that facilitates navigating social interactions and maintaining relationships, which are important for well-being. Several psychological studies have identified difficulties in both self-report and performance-based measures of empathy in a range of psychiatric conditions. To date, no study has systematically investigated the genetic architecture of empathy using genome-wide association studies (GWAS). Here we report the results of the largest GWAS of empathy to date using a well-validated self-report measure of empathy, the Empathy Quotient (EQ), in 46,861 research participants from 23andMe, Inc. We identify 11 suggestive loci (P < 1 × 10^-6), though none were significant at P < 2.5 × 10^-8 after correcting for multiple testing. The most significant SNP was identified in the non-stratified analysis (rs4882760; P = 4.29 × 10^-8), and is an intronic SNP in TMEM132C. The EQ had a modest but significant narrow-sense heritability (0.11 ± 0.014; P = 1.7 × 10^-14). As predicted, based on earlier work, we confirmed a significant female advantage on the EQ (P < 2 × 10^-16, Cohen's d = 0.65). We identified similar SNP heritability and high genetic correlation between the sexes. Also, as predicted, we identified a significant negative genetic correlation between autism and the EQ (r_g = -0.27 ± 0.07, P = 1.63 × 10^-4). We also identified a significant positive genetic correlation between the EQ and risk for schizophrenia (r_g = 0.19 ± 0.04; P = 1.36 × 10^-5), risk for anorexia nervosa (r_g = 0.32 ± 0.09; P = 6 × 10^-4), and extraversion (r_g = 0.45 ± 0.08; 5.7 × 10^-8). This is the first GWAS of self-reported empathy. The results suggest that the genetic variations associated with empathy also play a role in psychiatric conditions and psychological traits.

Amotivation is associated with smaller ventral striatum volumes in older patients with schizophrenia

OBJECTIVE

Motivational deficits are prevalent in patients with schizophrenia, persist despite antipsychotic treatment, and predict long-term outcomes. Evidence suggests that patients with greater amotivation have smaller ventral striatum (VS) volumes. We wished to replicate this finding in a sample of older, chronically medicated patients with schizophrenia. Using structural imaging and positron emission tomography, we examined whether amotivation uniquely predicted VS volumes beyond the effects of striatal dopamine D2/3 receptor (D2/3 R) blockade by antipsychotics.

METHODS

Data from 41 older schizophrenia patients (mean age: 60.2 ± 6.7; 11 female) were reanalysed from previously published imaging data. We constructed multivariate linear stepwise regression models with VS volumes as the dependent variable and various sociodemographic and clinical variables as the initial predictors: age, gender, total brain volume, and antipsychotic striatal D2/3 R occupancy. Amotivation was included as a subsequent step to determine any unique relationships with VS volumes beyond the contribution of the covariates. In a reduced sample (n = 36), general cognition was also included as a covariate.

RESULTS

Amotivation uniquely explained 8% and 6% of the variance in right and left VS volumes, respectively (right: β = -.38, t = -2.48, P = .01; left: β = -.31, t = -2.17, P = .03). Considering cognition, amotivation levels uniquely explained 9% of the variance in right VS volumes (β = -.43, t = -0.26, P = .03).

CONCLUSION

We replicate and extend the finding of reduced VS volumes with greater amotivation. We demonstrate this relationship uniquely beyond the potential contributions of striatal D2/3 R blockade by antipsychotics. Elucidating the structural correlates of amotivation in schizophrenia may help develop treatments for this presently irremediable deficit.

Post-genomic behavioral genetics: From revolution to routine

What was once expensive and revolutionary-full-genome sequence-is now affordable and routine. Costs will continue to drop, opening up new frontiers in behavioral genetics. This shift in costs from the genome to the phenome is most notable in large clinical studies of behavior and associated diseases in cohorts that exceed hundreds of thousands of subjects. Examples include the Women's Health Initiative (www.whi.org), the Million Veterans Program (www.

RESEARCH

va.gov/MVP), the 100 000 Genomes Project (genomicsengland.co.uk) and commercial efforts such as those by deCode (www.decode.com) and 23andme (www.23andme.com). The same transition is happening in experimental neuro- and behavioral genetics, and sample sizes of many hundreds of cases are becoming routine (www.genenetwork.org, www.mousephenotyping.org). There are two major consequences of this new affordability of massive omics datasets: (1) it is now far more practical to explore genetic modulation of behavioral differences and the key role of gene-by-environment interactions. Researchers are already doing the hard part-the quantitative analysis of behavior. Adding the omics component can provide powerful links to molecules, cells, circuits and even better treatment. (2) There is an acute need to highlight and train behavioral scientists in how best to exploit new omics approaches. This review addresses this second issue and highlights several new trends and opportunities that will be of interest to experts in animal and human behaviors.

Variability in Resting State Network and Functional Network Connectivity Associated With Schizophrenia Genetic Risk: A Pilot Study

Imaging genetics posits a valuable strategy for elucidating genetic influences on brain abnormalities in psychiatric disorders. However, association analysis between 2D genetic data (subject × genetic variable) and 3D first-level functional magnetic resonance imaging (fMRI) data (subject × voxel × time) has been challenging given the asymmetry in data dimension. A summary feature needs to be derived for the imaging modality to compute inter-modality association at subject level. In this work, we propose to use variability in resting state networks (RSNs) and functional network connectivity (FNC) as potential features for purpose of association analysis. We conducted a pilot study to investigate the proposed features in a dataset of 171 healthy controls and 134 patients with schizophrenia (SZ). We computed variability in RSN and FNC in a group independent component analysis framework and tested three types of variability metrics, namely Euclidean distance, Pearson correlation and Kullback-Leibler (KL) divergence. Euclidean distance and Pearson correlation metrics more effectively discriminated controls from patients than KL divergence. The group differences observed with variability in RSN and FNC were highly consistent, indicating patients presenting increased deviation from the cohort-common pattern of RSN and FNC than controls. The variability in RSN and FNC showed significant associations with network global efficiency, the more the deviation, the lower the efficiency. Furthermore, the RSN and FNC variability were found to associate with individual SZ risk SNPs as well as cumulative polygenic risk score for SZ. Collectively the current findings provide preliminary evidence for variability in RSN and FNC being promising imaging features that may find applications as biomarkers and in imaging genetic association analysis.

Convergent effects of a functional C3 variant on brain atrophy, demyelination, and cognitive impairment in multiple sclerosis

BACKGROUND

Complement system activation products are present in areas of neuroinflammation, demyelination, and neurodegeneration in brains of patients with multiple sclerosis (MS). C3 is a central element in the activation of complement cascades. A common coding variant in the C3 gene (rs2230199, C3R102G) affects C3 activity.

OBJECTIVES

To assess the effects of rs2230199 on MS severity using clinical, cognitive, and imaging measures.

METHODS

In total, 161 relapse-onset MS patients (Expanded Disability Status Scale (EDSS) ≤ 6) underwent physical assessments, cognitive tests (Paced Auditory Serial Addition Test (PASAT), Symbol Digit Modalities Test (SDMT), and California Verbal Learning Test (CVLT)), and magnetic resonance imaging (MRI). Lesion volumes were quantified semi-automatically. Voxel-wise analyses were performed to assess the effects of rs2230199 genotype on gray matter (GM) atrophy ( n = 155), white matter (WM) fractional anisotropy (FA; n = 105), and WM magnetization transfer ratio (MTR; n = 90).

RESULTS

While rs2230199 minor-allele dosage (C3-102G) showed no significant effect on EDSS and Multiple Sclerosis Functional Composite (MSFC), it was associated with worse cognitive performance ( p = 0.02), lower brain parenchymal fraction ( p = 0.003), and higher lesion burden ( p = 0.02). Moreover, voxel-wise analyses showed lower GM volume in subcortical structures and insula, and lower FA and MTR in several WM areas with higher copies of rs2230199 minor allele.

CONCLUSION

C3-rs2230199 affects white and GM damage as well as cognitive impairment in MS patients. Our findings support a causal role for complement system activity in the pathophysiology of MS.

Comprehensive integrative analyses identify GLT8D1 and CSNK2B as schizophrenia risk genes

Recent genome-wide association studies (GWAS) have identified multiple risk loci that show strong associations with schizophrenia. However, pinpointing the potential causal genes at the reported loci remains a major challenge. Here we identify candidate causal genes for schizophrenia using an integrative genomic approach. Sherlock integrative analysis shows that ALMS1, GLT8D1, and CSNK2B are schizophrenia risk genes, which are validated using independent brain expression quantitative trait loci (eQTL) data and integrative analysis method (SMR). Consistently, gene expression analysis in schizophrenia cases and controls further supports the potential role of these three genes in the pathogenesis of schizophrenia. Finally, we show that GLT8D1 and CSNK2B knockdown promote the proliferation and inhibit the differentiation abilities of neural stem cells, and alter morphology and synaptic transmission of neurons. These convergent lines of evidence suggest that the ALMS1, CSNK2B, and GLT8D1 genes may be involved in pathophysiology of schizophrenia.

More than 100 risk loci for schizophrenia have been identified by genome-wide association studies. Here, the authors apply an integrative genomic approach to prioritize risk genes and validate GLT8D1 and CSNK2B as candidate causal genes by in vitro studies in neural stem cells.

Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort

Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use, and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome-wide association study in 116,255 UK Biobank participants who responded yes/no to the question "Would you consider yourself a risk taker?" Risk takers (compared with controls) were more likely to be men, smokers, and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention-deficit hyperactivity disorder, and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait that has a major impact on a range of common physical and mental health disorders.

Cross-Tissue Exploration of Genetic and Epigenetic Effects on Brain Gray Matter in Schizophrenia

Closely linking genetics and environment factors, epigenetics has been of increasing interest in psychiatric disease studies. In this work, we integrated single nucleotide polymorphisms (SNPs), DNA methylation of blood and saliva, and brain gray matter (GM) measures to explore the role of genetic and epigenetic variation to the brain structure changes in schizophrenia (SZ). By focusing on the reported SZ genetic risk regions, we applied a multi-stage multivariate analysis to a discovery dataset (92 SZ patients and 110 controls, blood) and an independent replication dataset (93 SZ patients and 99 controls, saliva). Two pairs of SNP-methylation components were significantly correlated (r = .48 and .35) in blood DNA, and replicated (r = .46 and .29) in saliva DNA, reflecting cross-tissue SNP cis-effects. In the discovery data, both SNP-related methylation components were also associated with one GM component primarily located in cerebellum, caudate, and thalamus. Additionally, another methylation component in NOSIP gene with significant SZ patient differences (P = .009), was associated with 8 GM components (7 with patient differences) including superior, middle, and inferior frontal gyri, superior, middle, and inferior temporal gyri, cerebellum, insula, cuneus, and lingual gyrus. Of these, 5 methylation-GM associations were replicated (P < .05). In contrast, no pairwise significant associations were observed between SNP and GM components. This study strongly supports that compared to genetic variation, epigenetics show broader and more significant associations with brain structure as well as diagnosis, which can be cross-tissue, and the potential in explaining the mechanism of genetic risks in SZ.

The protocadherin 17 gene affects cognition, personality, amygdala structure and function, synapse development and risk of major mood disorders

Major mood disorders, which primarily include bipolar disorder and major depressive disorder, are the leading cause of disability worldwide and pose a major challenge in identifying robust risk genes. Here, we present data from independent large-scale clinical data sets (including 29 557 cases and 32 056 controls) revealing brain expressed protocadherin 17 (PCDH17) as a susceptibility gene for major mood disorders. Single-nucleotide polymorphisms (SNPs) spanning the PCDH17 region are significantly associated with major mood disorders; subjects carrying the risk allele showed impaired cognitive abilities, increased vulnerable personality features, decreased amygdala volume and altered amygdala function as compared with non-carriers. The risk allele predicted higher transcriptional levels of PCDH17 mRNA in postmortem brain samples, which is consistent with increased gene expression in patients with bipolar disorder compared with healthy subjects. Further, overexpression of PCDH17 in primary cortical neurons revealed significantly decreased spine density and abnormal dendritic morphology compared with control groups, which again is consistent with the clinical observations of reduced numbers of dendritic spines in the brains of patients with major mood disorders. Given that synaptic spines are dynamic structures which regulate neuronal plasticity and have crucial roles in myriad brain functions, this study reveals a potential underlying biological mechanism of a novel risk gene for major mood disorders involved in synaptic function and related intermediate phenotypes.

Role of subcortical structures on cognitive and social function in schizophrenia

Subcortical regions have a pivotal role in cognitive, affective, and social functions in humans, and the structural and functional abnormalities of the regions have been associated with various psychiatric disorders. Although previous studies focused on the neurocognitive and socio-functional consequences of prefrontal and tempolo-limbic abnormalities in psychiatric disorders, those of subcortical structures remain largely unknown. Recently, MRI volume alterations in subcortical structures in patients with schizophrenia have been replicated in large-scale meta-analytic studies. Here we investigated the relationship between volumes of subcortical structures and neurocognitive and socio-functional indices in a large sample of patients with schizophrenia. First, we replicated the results of meta-analyses: the regional volumes of the bilateral hippocampus, amygdala, thalamus and nucleus accumbens were significantly smaller for patients (N = 163) than for healthy controls (HCs, N = 620). Second, in the patient group, the right nucleus accumbens volume was significantly correlated with the Digit Symbol Coding score, which is known as a distinctively characteristic index of cognitive deficits in schizophrenia. Furthermore, the right thalamic volume was significantly correlated with social function scores. In HCs, no significant correlation was found. The results from this large-scale investigation shed light upon the role of specific subcortical nuclei on cognitive and social functioning in schizophrenia.

Fifteen Genetic Loci Associated With the Electrocardiographic P Wave

BACKGROUND

The P wave on an ECG is a measure of atrial electric function, and its characteristics may serve as predictors for atrial arrhythmias. Increased mean P-wave duration and P-wave terminal force traditionally have been used as markers for left atrial enlargement, and both have been associated with increased risk of atrial fibrillation. Here, we explore the genetic basis of P-wave morphology through meta-analysis of genome-wide association study results for P-wave duration and P-wave terminal force from 12 cohort studies.

METHODS AND RESULTS

We included 44 456 individuals, of which 6778 (16%) were of African ancestry. Genotyping, imputation, and genome-wide association study were performed at each study site. Summary-level results were meta-analyzed centrally using inverse-variance weighting. In meta-analyses of P-wave duration, we identified 6 significant (P<5×10^-8) novel loci and replicated a prior association with SCN10A. We identified 3 loci at SCN5A, TBX5, and CAV1/CAV2 that were jointly associated with the PR interval, PR segment, and P-wave duration. We identified 6 novel loci in meta-analysis of P-wave terminal force. Four of the identified genetic loci were significantly associated with gene expression in 329 left atrial samples. Finally, we observed that some of the loci associated with the P wave were linked to overall atrial conduction, whereas others identified distinct phases of atrial conduction.

CONCLUSIONS

We have identified 6 novel genetic loci associated with P-wave duration and 6 novel loci associated with P-wave terminal force. Future studies of these loci may aid in identifying new targets for drugs that may modify atrial conduction or treat atrial arrhythmias.

Preliminary evidence for genetic overlap between body mass index and striatal reward response

The reward-processing network is implicated in the aetiology of obesity. Several lines of evidence suggest obesity-linked genetic risk loci (such as DRD2 and FTO) may influence individual variation in body mass index (BMI) through neuropsychological processes reflected in alterations in activation of the striatum during reward processing. However, no study has tested the broader hypotheses that (a) the relationship between BMI and reward-related brain activation (measured through the blood oxygenation-dependent (BOLD) signal) may be observed in a large population study and (b) the overall genetic architecture of these phenotypes overlap, an assumption critical for the progression of imaging genetic studies in obesity research. Using data from the Human Connectome Project (N = 1055 healthy, young individuals: average BMI = 26.4), we first establish a phenotypic relationship between BMI and ventral striatal (VS) BOLD during the processing of rewarding (monetary) stimuli (β = 0.44, P = 0.013), accounting for potential confounds. BMI and VS BOLD were both significantly influenced by additive genetic factors (H2r = 0.57; 0.12, respectively). Further decomposition of this variance suggested that the relationship was driven by shared genetic (ρ g = 0.47, P = 0.011), but not environmental (ρ E = -0.07, P = 0.29) factors. To validate the assumption of genetic pleiotropy between BMI and VS BOLD, we further show that polygenic risk for higher BMI is also associated with increased VS BOLD response to appetitive stimuli (calorically high food images), in an independent sample (N = 81; P FWE-ROI < 0.005). Together, these observations suggest that the genetic factors link risk to obesity to alterations within key nodes of the brain's reward circuity. These observations provide a basis for future work exploring the mechanistic role of genetic loci that confer risk for obesity using the imaging genetics approach.

Imaging genetics of schizophrenia in the post-GWAS era

Imaging genetics is a research methodology studying the effect of genetic variation on brain structure, function, behavior, and risk for psychopathology. Since the early 2000s, imaging genetics has been increasingly used in the research of schizophrenia (SZ). SZ is a severe mental disorder with no precise knowledge of its underlying neurobiology, however, new genetic and neurobiological data generate a climate for new avenues. The accumulating data of genome wide association studies (GWAS) continuously decode SZ risk genes. Global neuroimaging consortia produce collections of brain phenotypes from tens of thousands of people. In this context, imaging genetics will be strategically important both for the validation and discovery of SZ related findings. Thus, the study of GWAS supported risk variants as candidate genes to validate by neuroimaging is one trend. The study of epigenetic differences in relation to variations of brain phenotypes and the study of large scale multivariate analysis of genome wide and brain wide associations are other trends. While these studies hold a big potential for understanding the neurobiology of SZ, the problem of reproducibility appears as a major challenge, which requires standardizations in study designs and compensations of methodological limitations such as sensitivity and specificity. On the other hand, advancements of neuroimaging, optical and electron microscopy along with the use of genetically encoded fluorescent probes and robust statistical approaches will not only catalyze integrative methodologies but also will help better design the imaging genetics studies. In this invited paper, I will discuss the current perspective of imaging genetics and emerging opportunities of SZ research.

Both activated and less-activated regions identified by functional MRI reconfigure to support task executions

INTRODUCTION

Functional magnetic resonance imaging (fMRI) has become very important for noninvasively characterizing BOLD signal fluctuations, which reflect the changes in neuronal firings in the brain. Unlike the activation detection strategy utilized with fMRI, which only emphasizes the synchronicity between the functional nodes (activated regions) and the task design, brain connectivity and network theory are able to decipher the interactive structure across the entire brain. However, little is known about whether and how the activated/less-activated interactions are associated with the functional changes that occur when the brain changes from the resting state to a task state. What are the key networks that play important roles in the brain state changes?

METHODS

We used the fMRI data from the Human Connectome Project S500 release to examine the changes of network efficiency, interaction strength, and fractional modularity contributions of both the local and global networks, when the subjects change from the resting state to seven different task states.

RESULTS

We found that, from the resting state to each of seven task states, both the activated and less-activated regions had significantly changed to be in line with, and comparably contributed to, a global network reconfiguration. We also found that three networks, the default mode network, frontoparietal network, and salience network, dominated the flexible reconfiguration of the brain.

CONCLUSIONS

This study shows quantitatively that contributions from both activated and less-activated regions enable the global functional network to respond when the brain switches from the resting state to a task state and suggests the necessity of considering large-scale networks (rather than only activated regions) when investigating brain functions in imaging cognitive neuroscience.

Paediatric population neuroimaging and the Generation R Study: the second wave

Paediatric population neuroimaging is an emerging field that falls at the intersection between developmental neuroscience and epidemiology. A key feature of population neuroimaging studies involves large-scale recruitment that is representative of the general population. One successful approach for population neuroimaging is to embed neuroimaging studies within large epidemiological cohorts. The Generation R Study is a large, prospective population-based birth-cohort in which nearly 10,000 pregnant mothers were recruited between 2002 and 2006 with repeated measurements in the children and their parents over time. Magnetic resonance imaging was included in 2009 with the scanning of 1070 6-to-9-year-old children. The second neuroimaging wave was initiated in April 2013 with a total of 4245 visiting the MRI suite and 4087 9-to-11-year-old children being scanned. The sequences included high-resolution structural MRI, 35-direction diffusion weighted imaging, and a 6 min and 2 s resting-state functional MRI scan. The goal of this paper is to provide an overview of the imaging protocol and the overlap between the neuroimaging data and metadata. We conclude by providing a brief overview of results from our first wave of neuroimaging, which highlights a diverse array of questions that can be addressed by merging the fields of developmental neuroscience and epidemiology.

Whole genome sequence analyses of brain imaging measures in the Framingham Study

Objective

We sought to identify rare variants influencing brain imaging phenotypes in the Framingham Heart Study by performing whole genome sequence association analyses within the Trans-Omics for Precision Medicine Program.

Methods

We performed association analyses of cerebral and hippocampal volumes and white matter hyperintensity (WMH) in up to 2,180 individuals by testing the association of rank-normalized residuals from mixed-effect linear regression models adjusted for sex, age, and total intracranial volume with individual variants while accounting for familial relatedness. We conducted gene-based tests for rare variants using (1) a sliding-window approach, (2) a selection of functional exonic variants, or (3) all variants.

Results

We detected new loci in 1p21 for cerebral volume (minor allele frequency [MAF] 0.005, p = 10⁻⁸) and in 16q23 for hippocampal volume (MAF 0.05, p = 2.7 × 10⁻⁸). Previously identified associations in 12q24 for hippocampal volume (rs7294919, p = 4.4 × 10⁻⁴) and in 17q25 for WMH (rs7214628, p = 2.0 × 10⁻³) were confirmed. Gene-based tests detected associations (p ≤ 2.3 × 10⁻⁶) in new loci for cerebral (5q13, 8p12, 9q31, 13q12-q13, 15q24, 17q12, 19q13) and hippocampal volumes (2p12) and WMH (3q13, 4p15) including Alzheimer disease– (UNC5D) and Parkinson disease–associated genes (GBA). Pathway analyses evidenced enrichment of associated genes in immunity, inflammation, and Alzheimer disease and Parkinson disease pathways.

Conclusions

Whole genome sequence–wide search reveals intriguing new loci associated with brain measures. Replication of novel loci is needed to confirm these findings.

The Molecular Basis of Human Brain Evolution

Humans are a remarkable species, especially because of the remarkable properties of their brain. Since the split from the chimpanzee lineage, the human brain has increased three-fold in size and has acquired abilities for vocal learning, language and intense cooperation. To better understand the molecular basis of these changes is of great biological and biomedical interest. However, all the about 16 million fixed genetic changes that occurred during human evolution are fully correlated with all molecular, cellular, anatomical and behavioral changes that occurred during this time. Hence, as humans and chimpanzees cannot be crossed or genetically manipulated, no direct evidence for linking particular genetic and molecular changes to human brain evolution can be obtained. Here, I sketch a framework how indirect evidence can be obtained and review findings related to the molecular basis of human cognition, vocal learning and brain size. In particular, I discuss how a comprehensive comparative approach, leveraging cellular systems and genomic technologies, could inform the evolution of our brain in the future.

Brain evolution and development: adaptation, allometry and constraint

Phenotypic traits are products of two processes: evolution and development. But how do these processes combine to produce integrated phenotypes? Comparative studies identify consistent patterns of covariation, or allometries, between brain and body size, and between brain components, indicating the presence of significant constraints limiting independent evolution of separate parts. These constraints are poorly understood, but in principle could be either developmental or functional. The developmental constraints hypothesis suggests that individual components (brain and body size, or individual brain components) tend to evolve together because natural selection operates on relatively simple developmental mechanisms that affect the growth of all parts in a concerted manner. The functional constraints hypothesis suggests that correlated change reflects the action of selection on distributed functional systems connecting the different sub-components, predicting more complex patterns of mosaic change at the level of the functional systems and more complex genetic and developmental mechanisms. These hypotheses are not mutually exclusive but make different predictions. We review recent genetic and neurodevelopmental evidence, concluding that functional rather than developmental constraints are the main cause of the observed patterns.

Genetic Risk Variants Associated With Comorbid Alcohol Dependence and Major Depression

Importance

Alcohol dependence (AD) and major depression (MD) are leading causes of disability that often co-occur. Genetic epidemiologic data have shown that AD and MD share a common possible genetic cause. The molecular nature of this shared genetic basis is poorly understood.

Objectives

To detect genetic risk variants for comorbid AD and MD and to determine whether polygenic risk alleles are shared with neuropsychiatric traits or subcortical brain volumes.

Design, Setting, and Participants

This genome-wide association study analyzed criterion counts of comorbid AD and MD in African American and European American data sets collected as part of the Yale-Penn study of the genetics of drug and alcohol dependence from February 14, 1999, to January 13, 2015. After excluding participants never exposed to alcohol or with missing information for any diagnostic criterion, genome-wide association studies were performed on 2 samples (the Yale-Penn 1 and Yale-Penn 2 samples) totaling 4653 African American participants and 3169 European American participants (analyzed separately). Tests were performed to determine whether polygenic risk scores derived from potentially related traits in European American participants could be used to estimate comorbid AD and MD.

Main Outcomes and Measures

Comorbid criterion counts (ranging from 0 to 14) for AD (7 criteria) and MD (9 criteria, scaled to 7) as defined by the DSM-IV.

Results

Of the 7822 participants (3342 women and 4480 men; mean [SD] age, 40.1 [10.7] years), the median comorbid criterion count was 6.2 (interquartile range, 2.3-10.9). Under the linear regression model, rs139438618 at the semaphorin 3A (SEMA3A [OMIM 603961]) locus was significantly associated with AD and MD comorbidity in African American participants in the Yale-Penn 1 sample (β = 0.89; 95% CI, 0.57-1.20; P = 2.76 × 10-8). In the independent Yale-Penn 2 sample, the association was also significant (β = 0.83; 95% CI, 0.39-1.28; P = 2.06 × 10-4). Meta-analysis of the 2 samples yielded a more robust association (β = 0.87; 95% CI, 0.61-1.12; P = 2.41 × 10-11). There was no significant association identified in European American participants. Analyses of polygenic risk scores showed that individuals with a higher risk of neuroticism (β = 1.01; 95% CI, 0.50-1.52) or depressive symptoms (β = 0.87; 95% CI, 0.32-1.42) and a lower level of subjective well-being (β = -0.94; 95% CI, -1.46 to -0.42) and educational attainment (β = -1.00, 95% CI, -1.57 to -0.44) had a higher level of AD and MD comorbidity, while larger intracranial (β = 1.07; 95% CI, 0.50 to 1.64) and smaller putamen volumes (β = -1.16; 95% CI, -1.86 to -0.46) were associated with higher risks of AD and MD comorbidity.

Conclusions and Relevance

SEMA3A variation is significantly and replicably associated with comorbid AD and MD in African American participants. Analyses of polygenic risk scores identified pleiotropy with neuropsychiatric traits and brain volumes. Further studies are warranted to understand the biological and genetic mechanisms of this comorbidity, which could facilitate development of medications and other treatments for comorbid AD and MD.

Genome-wide association study of subcortical brain volume in PTSD cases and trauma-exposed controls

Depending on the traumatic event, a significant fraction of trauma survivors subsequently develop PTSD. The additional variability in PTSD risk is expected to arise from genetic susceptibility. Unfortunately, several genome-wide association studies (GWAS) have failed to identify a consistent genetic marker for PTSD. The heritability of intermediate phenotypes such as regional brain volumes is often 80% or higher. We conducted a GWAS of subcortical brain volumes in a sample of recent military veteran trauma survivors (n = 157), grouped into PTSD (n = 66) and non-PTSD controls (n = 91). Covariates included PTSD diagnosis, sex, intracranial volume, ancestry, childhood trauma, SNP×PTSD diagnosis, and SNP×childhood trauma. We identified several genetic markers in high linkage disequilibrium (LD) with rs9373240 (p = 2.0 × 10-7, FDR q = 0.0375) that were associated with caudate volume. We also observed a significant interaction between rs9373240 and childhood trauma (p-values = 0.0007-0.002), whereby increased trauma exposure produced a stronger association between SNPs and increased caudate volume. We identified several SNPs in high LD with rs34043524, which is downstream of the TRAM1L1 gene that were associated with right lateral ventricular volume (p = 1.73 × 10-7; FDR q = 0.032) and were also associated with lifetime alcohol abuse or dependence (p = 2.49 × 10-7; FDR q = 0.0375). Finally, we identified several SNPs in high LD with rs13140180 (p = 2.58 × 10-7; FDR q = .0016), an intergenic region on chromosome 4, and several SNPs in the TMPRSS15 associated with right nucleus accumbens volume (p = 2.58 × 10-7; FDR q = 0.017). Both TRAM1L1 and TMPRSS15 have been previously implicated in neuronal function. Key results survived genome-wide multiple-testing correction in our sample. Leveraging neuroimaging phenotypes may offer a shortcut, relative to clinical phenotypes, in mapping the genetic architecture and neurobiological pathways of PTSD.

Neuroimaging genomics in psychiatry-a translational approach

Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders. While early work in neuroimaging genomics focused on mapping the associations of candidate gene variants with neuroimaging measures in small cohorts, the lack of reproducible results inspired better-powered and unbiased large-scale approaches. Notably, genome-wide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings. Extensions of such approaches are now addressing epigenetics, gene–gene epistasis, and gene–environment interactions, not only in brain structure, but also in brain function. Complementary developments in systems biology might facilitate the translation of findings from basic neuroscience and neuroimaging genomics to clinical practice. Here, we review recent approaches in neuroimaging genomics—we highlight the latest discoveries, discuss advantages and limitations of current approaches, and consider directions by which the field can move forward to shed light on brain disorders.

Leveraging genome characteristics to improve gene discovery for putamen subcortical brain structure

Discovering genetic variants associated with human brain structures is an on-going effort. The ENIGMA consortium conducted genome-wide association studies (GWAS) with standard multi-study analytical methodology and identified several significant single nucleotide polymorphisms (SNPs). Here we employ a novel analytical approach that incorporates functional genome annotations (e.g., exon or 5′UTR), total linkage disequilibrium (LD) scores and heterozygosity to construct enrichment scores for improved identification of relevant SNPs. The method provides increased power to detect associated SNPs by estimating stratum-specific false discovery rate (FDR), where strata are classified according to enrichment scores. Applying this approach to the GWAS summary statistics of putamen volume in the ENIGMA cohort, a total of 15 independent significant SNPs were identified (conditional FDR < 0.05). In contrast, 4 SNPs were found based on standard GWAS analysis (P < 5 × 10⁻⁸). These 11 novel loci include GATAD2B, ASCC3, DSCAML1, and HELZ, which are previously implicated in various neural related phenotypes. The current findings demonstrate the boost in power with the annotation-informed FDR method, and provide insight into the genetic architecture of the putamen.

Multimodal neuroimaging of male and female brain structure in health and disease across the life span

Sex differences in brain development and aging are important to identify, as they may help to understand risk factors and outcomes in brain disorders that are more prevalent in one sex compared with the other. Brain imaging techniques have advanced rapidly in recent years, yielding detailed structural and functional maps of the living brain. Even so, studies are often limited in sample size, and inconsistent findings emerge, one example being varying findings regarding sex differences in the size of the corpus callosum. More recently, large‐scale neuroimaging consortia such as the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium have formed, pooling together expertise, data, and resources from hundreds of institutions around the world to ensure adequate power and reproducibility. These initiatives are helping us to better understand how brain structure is affected by development, disease, and potential modulators of these effects, including sex. This review highlights some established and disputed sex differences in brain structure across the life span, as well as pitfalls related to interpreting sex differences in health and disease. We also describe sex‐related findings from the ENIGMA consortium, and ongoing efforts to better understand sex differences in brain circuitry. © 2016 The Authors. Journal of Neuroscience Research Published by Wiley Periodicals, Inc.

Sex-specific effects of the Huntington gene on normal neurodevelopment

Huntington disease is a neurodegenerative disorder caused by a gene (HTT) with a unique feature of trinucleotide repeats ranging from 10 to 35 in healthy people; when expanded beyond 39 repeats, Huntington disease develops. Animal models demonstrate that HTT is vital to brain development; however, this has not been studied in humans. Moreover, evidence suggests that triplet repeat genes may have been vital in evolution of the human brain. Here we evaluate brain structure using magnetic resonance imaging and brain function using cognitive tests in a sample of school-aged children ages 6 to 18 years old. DNA samples were processed to quantify the number of CAG repeats within HTT. We find that the number of repeats in HTT, below disease threshold, confers advantageous changes in brain structure and general intelligence (IQ): the higher the number of repeats, the greater the change in brain structure, and the higher the IQ. The pattern of structural brain changes associated with HTT is strikingly different between males and females. HTT may confer an advantage or a disadvantage depending on the repeat length, playing a key role in either the evolution of a superior human brain or development of a uniquely human brain disease. © 2016 Wiley Periodicals, Inc.

Chromatin-state discovery and genome annotation with ChromHMM

Noncoding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 d.

Inter-individual variation in genes governing human hippocampal progenitor differentiation in vitro is associated with hippocampal volume in adulthood

Hippocampal volumes are smaller in psychiatric disorder patients and lower levels of hippocampal neurogenesis are the hypothesized cause. Understanding which molecular processes regulate hippocampal progenitor differentiation might aid in the identification of novel drug targets that can promote larger hippocampal volumes. Here we use a unique human cell line to assay genome-wide expression changes when hippocampal progenitor cells differentiate. RNA was extracted from proliferating cells versus differentiated neural cells and applied to Illumina Human HT-12 v4 Expression BeadChips. Linear regressions were used to determine the effect of differentiation on probe expression and we assessed enrichment for gene ontology (GO) terms. Genetic pathway analysis (MAGMA) was used to evaluate the relationship between hippocampal progenitor cell differentiation and adult hippocampal volume, using results from the imaging genomics consortium, ENIGMA. Downregulated transcripts were enriched for mitotic processes and upregulated transcripts were enriched for cell differentiation. Upregulated (differentiation) transcripts specifically, were also predictive of adult hippocampal volume; with Early growth response protein 2 identified as a hub transcription factor within the top GO term, and a potential drug target. Our results suggest that genes governing differentiation, rather than mitosis, have an impact on adult hippocampal volume and that these genes represent important drug targets.

Healthy minds from 0-100 years: Optimising the use of European brain imaging cohorts ("Lifebrain")

The main objective of "Lifebrain" is to identify the determinants of brain, cognitive and mental (BCM) health at different stages of life. By integrating, harmonising and enriching major European neuroimaging studies across the life span, we will merge fine-grained BCM health measures of more than 5,000 individuals. Longitudinal brain imaging, genetic and health data are available for a major part, as well as cognitive and mental health measures for the broader cohorts, exceeding 27,000 examinations in total. By linking these data to other databases and biobanks, including birth registries, national and regional archives, and by enriching them with a new online data collection and novel measures, we will address the risk factors and protective factors of BCM health. We will identify pathways through which risk and protective factors work and their moderators. Exploiting existing European infrastructures and initiatives, we hope to make major conceptual, methodological and analytical contributions towards large integrative cohorts and their efficient exploitation. We will thus provide novel information on BCM health maintenance, as well as the onset and course of BCM disorders. This will lay a foundation for earlier diagnosis of brain disorders, aberrant development and decline of BCM health, and translate into future preventive and therapeutic strategies. Aiming to improve clinical practice and public health we will work with stakeholders and health authorities, and thus provide the evidence base for prevention and intervention.

Cognitive genomics: Searching for the genetic roots of neuropsychological functioning

OBJECTIVE

Human cognition has long been known to be under substantial genetic control. With the complete mapping of the human genome, genome-wide association studies for many complex traits have proliferated; however, the highly polygenic nature of intelligence has made the identification of the precise genes that influence both global and specific cognitive abilities more difficult than anticipated.

METHOD

Here, we review the latest developments in the genomics of cognition, including a discussion of methodological advances in the genetic analysis of complex traits, and shared genetic contributions to cognitive abilities and neuropsychiatric disorders.

RESULTS

A wealth of twin and family studies have provided compelling evidence for a strong heritable component of both global and specific cognitive abilities, and for the existence of "generalist genes" responsible for a large portion of the variance in diverse cognitive abilities. Increasingly sophisticated analytic tools and ever-larger sample sizes are now facilitating the identification of specific genetic and molecular underpinnings of cognitive abilities, leading to optimism regarding possibilities for novel treatments for illnesses related to cognitive function.

CONCLUSIONS

We conclude with a set of future directions for the field, which will further accelerate discoveries regarding the biological pathways relevant to cognitive abilities. These, in turn, may be further interrogated in order to link biological mechanisms to behavior. (PsycINFO Database Record

The association between gene variants and longitudinal structural brain changes in psychosis: a systematic review of longitudinal neuroimaging genetics studies

Evidence suggests that genetic variation might influence structural brain alterations in psychotic disorders. Longitudinal genetic neuroimaging (G-NI) studies are designed to assess the association between genetic variants, disease progression and brain changes. There is a paucity of reviews of longitudinal G-NI studies in psychotic disorders. A systematic search of PubMed from inception until November 2016 was conducted to identify longitudinal G-NI studies examining the link between Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI)-based brain measurements and specific gene variants (SNPs, microsatellites, haplotypes) in patients with psychosis. Eleven studies examined seven genes: BDNF, COMT, NRG1, DISC1, CNR1, GAD1, and G72. Eight of these studies reported at least one association between a specific gene variant and longitudinal structural brain changes. Genetic variants associated with longitudinal brain volume or cortical thickness loss included a 4-marker haplotype in G72, a microsatellite and a SNP in NRG1, and individual SNPs in DISC1, CNR1, BDNF, COMT and GAD1. Associations between genotype and progressive brain changes were most frequently observed in frontal regions, with five studies reporting significant interactions. Effect sizes for significant associations were generally of small or intermediate magnitude (Cohen’s d < 0.8). Only two genes (BDNF and NRG1) were assessed in more than one study, with great heterogeneity of the results. Replication studies and studies exploring additional genetic variants identified by large-scale genetic analysis are warranted to further ascertain the role of genetic variants in longitudinal brain changes in psychosis.

Lateralized Feeding Behavior is Associated with Asymmetrical Neuroanatomy and Lateralized Gene Expressions in the Brain in Scale-Eating Cichlid Fish

Lateralized behavior ("handedness") is unusual, but consistently found across diverse animal lineages, including humans. It is thought to reflect brain anatomical and/or functional asymmetries, but its neuro-molecular mechanisms remain largely unknown. Lake Tanganyika scale-eating cichlid fish, Perissodus microlepis show pronounced asymmetry in their jaw morphology as well as handedness in feeding behavior-biting scales preferentially only from one or the other side of their victims. This makes them an ideal model in which to investigate potential laterality in neuroanatomy and transcription in the brain in relation to behavioral handedness. After determining behavioral handedness in P. microlepis (preferred attack side), we estimated the volume of the hemispheres of brain regions and captured their gene expression profiles. Our analyses revealed that the degree of behavioral handedness is mirrored at the level of neuroanatomical asymmetry, particularly in the tectum opticum. Transcriptome analyses showed that different brain regions (tectum opticum, telencephalon, hypothalamus, and cerebellum) display distinct expression patterns, potentially reflecting their developmental interrelationships. For numerous genes in each brain region, their extent of expression differences between hemispheres was found to be correlated with the degree of behavioral lateralization. Interestingly, the tectum opticum and telencephalon showed divergent biases on the direction of up- or down-regulation of the laterality candidate genes (e.g., grm2) in the hemispheres, highlighting the connection of handedness with gene expression profiles and the different roles of these brain regions. Hence, handedness in predation behavior may be caused by asymmetric size of brain hemispheres and also by lateralized gene expressions in the brain.

HPA Axis Genes, and Their Interaction with Childhood Maltreatment, are Related to Cortisol Levels and Stress-Related Phenotypes

Stress responses are controlled by the hypothalamus pituitary adrenal (HPA)-axis and maladaptive stress responses are associated with the onset and maintenance of stress-related disorders such as major depressive disorder (MDD). Genes that play a role in the HPA-axis regulation may likely contribute to the relation between relevant neurobiological substrates and stress-related disorders. Therefore, we performed gene-wide analyses for 30 a priori literature-based genes involved in HPA-axis regulation in 2014 subjects (34% male; mean age: 42.5) to study the relations with lifetime MDD diagnosis, cortisol awakening response, and dexamethasone suppression test (DST) levels (subsample N=1472) and hippocampal and amygdala volume (3T MR images; subsample N=225). Additionally, gene by childhood maltreatment (CM) interactions were investigated. Gene-wide significant results were found for dexamethasone suppression (CYP11A1, CYP17A1, POU1F1, AKR1D1), hippocampal volume (CYP17A1, CYP11A1, HSD3B2, PROP1, AVPRA1, SRD5A1), amygdala volume (POMC, CRH, HSD3B2), and lifetime MDD diagnosis (FKBP5 and CRH), all permutation p-values<0.05. Interactions with CM were found for several genes; the strongest interactions were found for NR3C2, where the minor allele of SNP rs17581262 was related to smaller hippocampal volume, smaller amygdala volume, higher DST levels, and higher odds of MDD diagnosis only in participants with CM. As hypothesized, several HPA-axis genes are associated with stress-related endophenotypes including cortisol response and reduced brain volumes. Furthermore, we found a pleiotropic interaction between CM and the mineralocorticoid receptor gene, suggesting that this gene plays an important moderating role in stress and stress-related disorders.

Opportunities for an enhanced integration of neuroscience and genomics

Neuroimaging and genetics are two rapidly expanding fields of research. Thoughtful integration of these areas is critical for ongoing large-scale research into the genetic mechanisms underlying brain structure, function, and development. Neuroimaging genetics has been slow to evolve relative to psychiatric genetics research, and some may be unaware that new statistical methods allow for the genomic analysis of more modestly-sized imaging samples. We present a broad overview of the extant imaging genetics literature, provide an interpretation of the major problems surrounding the integration of neuroimaging and genetics, discuss the influence and impact of genetics consortia, and suggest statistical genetic analyses that expand the repertoire of imaging researchers amassing rich behavioral data in modestly-sized samples. Specific attention is paid to the creative use of polygenic risk scoring in imaging genetic analyses, with primers on the most current risk scoring applications.

Perceived stress is associated with smaller hippocampal volume in adolescence

Perceived stress has been associated with decreased hippocampal, amygdala, and prefrontal cortex volume, as well as decreased memory and executive functioning performance in adulthood. Parents' perceived stress has been linked to decreased hippocampal volume in young children. However, no studies have investigated the links between self-perceived stress and brain structure or function in adolescents. Additionally, findings from previous research with younger or older samples are inconsistent, likely in part due to inconsistencies in participants' age range. In this study, we investigated the associations among self-perceived stress, family socioeconomic factors (family income, parental education), subcortical (hippocampus, amygdala) volumes, prefrontal cortical thickness and surface area, and memory and executive functioning performance in adolescents. One hundred and forty-three participants (12-20 years old) were administered a cognitive battery, a questionnaire to assess perceived stress, and a structural MRI scan. Higher levels of perceived stress were associated with decreased adolescent hippocampal volume. This study provides empirical evidence of how experience may shape brain development in adolescence-a period of plasticity during which it may be possible to intervene and prevent negative developmental outcomes.

Variation in a range of mTOR-related genes associates with intracranial volume and intellectual disability

De novo mutations in specific mTOR pathway genes cause brain overgrowth in the context of intellectual disability (ID). By analyzing 101 mMTOR-related genes in a large ID patient cohort and two independent population cohorts, we show that these genes modulate brain growth in health and disease. We report the mTOR activator gene RHEB as an ID gene that is associated with megalencephaly when mutated. Functional testing of mutant RHEB in vertebrate animal models indicates pathway hyperactivation with a concomitant increase in cell and head size, aberrant neuronal migration, and induction of seizures, concordant with the human phenotype. This study reveals that tight control of brain volume is exerted through a large community of mTOR-related genes. Human brain volume can be altered, by either rare disruptive events causing hyperactivation of the pathway, or through the collective effects of common alleles.

Genome-wide association study of paliperidone efficacy

Supplemental Digital Content is available in the text.

Objective

Clinical response to the atypical antipsychotic paliperidone is known to vary among schizophrenic patients. We carried out a genome-wide association study to identify common genetic variants predictive of paliperidone efficacy.

Methods

We leveraged a collection of 1390 samples from individuals of European ancestry enrolled in 12 clinical studies investigating the efficacy of the extended-release tablet paliperidone ER (n₁=490) and the once-monthly injection paliperidone palmitate (n₂=550 and n₃=350). We carried out a genome-wide association study using a general linear model (GLM) analysis on three separate cohorts, followed by meta-analysis and using a mixed linear model analysis on all samples. The variations in response explained by each single nucleotide polymorphism (h²_SNP) were estimated.

Results

No SNP passed genome-wide significance in the GLM-based analyses with suggestive signals from rs56240334 [P=7.97×10⁻⁸ for change in the Clinical Global Impression Scale-Severity (CGI-S); P=8.72×10⁻⁷ for change in the total Positive and Negative Syndrome Scale (PANSS)] in the intron of ADCK1. The mixed linear model-based association P-values for rs56240334 were consistent with the results from GLM-based analyses and the association with change in CGI-S (P=4.26×10⁻⁸) reached genome-wide significance (i.e. P<5×10⁻⁸). We also found suggestive evidence for a polygenic contribution toward paliperidone treatment response with estimates of heritability, h²_SNP, ranging from 0.31 to 0.43 for change in the total PANSS score, the PANSS positive Marder factor score, and CGI-S.

Conclusion

Genetic variations in the ADCK1 gene may differentially predict paliperidone efficacy in schizophrenic patients. However, this finding should be replicated in additional samples.

Towards Automatic Generation of Portions of Scientific Papers for Large Multi-Institutional Collaborations Based on Semantic Metadata

Scientific collaborations involving multiple institutions are increasingly commonplace. It is not unusual for publications to have dozens or hundreds of authors, in some cases even a few thousands. Gathering the information for such papers may be very time consuming, since the author list must include authors who made different kinds of contributions and whose affiliations are hard to track. Similarly, when datasets are contributed by multiple institutions, the collection and processing details may also be hard to assemble due to the many individuals involved. We present our work to date on automatically generating author lists and other portions of scientific papers for multi-institutional collaborations based on the metadata created to represent the people, data, and activities involved. Our initial focus is ENIGMA, a large international collaboration for neuroimaging genetics.

Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex

The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders.

FGWAS: Functional genome wide association analysis

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.

Genome-wide imaging association study implicates functional activity and glial homeostasis of the caudate in smoking addiction

Background

Nearly 6 million deaths and over a half trillion dollars in healthcare costs worldwide are attributed to tobacco smoking each year. Extensive research efforts have been pursued to elucidate the molecular underpinnings of smoking addiction and facilitate cessation. In this study, we genotyped and obtained both resting state and task-based functional magnetic resonance imaging from 64 non-smokers and 42 smokers. Smokers were imaged after having smoked normally (“sated”) and after having not smoked for at least 12 h (“abstinent”).

Results

While abstinent smokers did not differ from non-smokers with respect to pairwise resting state functional connectivities (RSFCs) between 12 brain regions of interest, RSFCs involving the caudate and putamen of sated smokers significantly differed from those of non-smokers (P < 0.01). Further analyses of caudate and putamen activity during elicited experiences of reward and disappointment show that caudate activity during reward (CR) correlated with smoking status (P = 0.015). Moreover, abstinent smokers with lower CR experienced greater withdrawal symptoms (P = 0.024), which suggests CR may be related to smoking urges. Associations between genetic variants and CR, adjusted for smoking status, were identified by genome-wide association study (GWAS). Genes containing or exhibiting caudate-specific expression regulation by these variants were enriched within Gene Ontology terms that describe cytoskeleton functions, synaptic organization, and injury response (P < 0.001, FDR < 0.05).

Conclusions

By integrating genomic and imaging data, novel insights into potential mechanisms of caudate activation and homeostasis are revealed that may guide new directions of research toward improving our understanding of addiction pathology.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4124-5) contains supplementary material, which is available to authorized users.

Brain imaging genetics in ADHD and beyond - Mapping pathways from gene to disorder at different levels of complexity

Attention-deficit/hyperactivity disorder (ADHD) is a common and often persistent neurodevelopmental disorder. Beyond gene-finding, neurobiological parameters, such as brain structure, connectivity, and function, have been used to link genetic variation to ADHD symptomatology. We performed a systematic review of brain imaging genetics studies involving 62 ADHD candidate genes in childhood and adult ADHD cohorts. Fifty-one eligible research articles described studies of 13 ADHD candidate genes. Almost exclusively, single genetic variants were studied, mostly focussing on dopamine-related genes. While promising results have been reported, imaging genetics studies are thus far hampered by methodological differences in study design and analysis methodology, as well as limited sample sizes. Beyond reviewing imaging genetics studies, we also discuss the need for complementary approaches at multiple levels of biological complexity and emphasize the importance of combining and integrating findings across levels for a better understanding of biological pathways from gene to disease. These may include multi-modal imaging genetics studies, bioinformatic analyses, and functional analyses of cell and animal models.

Potential contribution of the neurodegenerative disorders risk loci to cognitive performance in an elderly male gout population

Cognitive impairment has been described in elderly subjects with high normal concentrations of serum uric acid. However, it remains unclear if gout confers an increased poorer cognition than those in individuals with asymptomatic hyperuricemia. The present study aimed at evaluating cognitive function in patients suffering from gout in an elderly male population, and further investigating the genetic contributions to the risk of cognitive function.This study examined the cognitive function as assessed by Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) in 205 male gout patients and 204 controls. The genetic basis of these cognitive measures was evaluated by genome-wide association study (GWAS) data in 102 male gout patients. Furthermore, 7 loci associated with cognition in GWAS were studied for correlation with gout in 1179 male gout patients and 1848 healthy male controls.Compared with controls, gout patients had significantly lower MoCA scores [22.78 ± 3.01 vs 23.42 ± 2.95, P = .023, adjusted by age, body mass index (BMI), education, and emotional disorder]. GWAS revealed 7 single-nucleotide polymorphisms (SNPs) associations with MoCA test at a level of conventional genome-wide significance (P < 9.6 × 10). The most significant association was observed between rs12895072 and rs12434554 within the KTN1 gene (Padjusted = 4.2 × 10, Padjusted = 4.7 × 10) at 14q22. The next best signal was in RELN gene (rs155333, Padjusted = 1.3 × 10) at 7q22, while the other variants at rs17458357 (Padjusted = 3.98 × 10), rs2572683 (Padjusted = 8.9 × 10), rs12555895 (Padjusted = 2.6 × 10), and rs3764030 (Padjusted = 9.4 × 10) were also statistically significant. The 7 SNPs were not associated with gout in further analysis (all P > .05).Elderly male subjects with gout exhibit accelerated decline in cognition performance. Several neurodegenerative disorders risk loci were identified for genetic contributors to cognitive performance in our Chinese elderly male gout population. Larger prospective studies of the cognitive performance and genetic analysis in gout subjects are recommended.

Enhancing the Informativeness and Replicability of Imaging Genomics Studies

Imaging genomics is a new field of investigation that seeks to gain insights into the impact of human genetic variation on the structure, chemistry, and function of neural systems in health and disease. Because publications in this field have increased over the past decade, increasing concerns have been raised about false-positive results entering the literature. Here, we provide an overview of the field of imaging genomic and genetic approaches and discuss factors related to research design and analysis that can enhance the informativeness and replicability of these studies. We conclude that imaging genetic studies can provide important insights into the role of human genetic variation on neural systems and circuits, both in the context of normal quantitative variation and in relation to neuropsychiatric disease. We also argue that demonstrating genetic association to imaging-derived traits is subject to the same constraints as any other genetic study, including stringent type I error control. Adequately powered studies are necessary; however, there are currently limited data available to allow precise estimates of effect sizes for candidate gene studies. Independent replication is necessary before a result can be considered definitive, and for studies with small sample sizes it is necessary before publication. Increased transparency of methods and enhanced data sharing will further enhance replicability.

Imaging Genetics and Genomics in Psychiatry: A Critical Review of Progress and Potential

Imaging genetics and genomics research has begun to provide insight into the molecular and genetic architecture of neural phenotypes and the neural mechanisms through which genetic risk for psychopathology may emerge. As it approaches its third decade, imaging genetics is confronted by many challenges, including the proliferation of studies using small sample sizes and diverse designs, limited replication, problems with harmonization of neural phenotypes for meta-analysis, unclear mechanisms, and evidence that effect sizes may be more modest than originally posited, with increasing evidence of polygenicity. These concerns have encouraged the field to grow in many new directions, including the development of consortia and large-scale data collection projects and the use of novel methods (e.g., polygenic approaches, machine learning) that enhance the quality of imaging genetic studies but also introduce new challenges. We critically review progress in imaging genetics and offer suggestions and highlight potential pitfalls of novel approaches. Ultimately, the strength of imaging genetics and genomics lies in their translational and integrative potential with other research approaches (e.g., nonhuman animal models, psychiatric genetics, pharmacologic challenge) to elucidate brain-based pathways that give rise to the vast individual differences in behavior as well as risk for psychopathology.

Elevated CYP2C19 expression is associated with depressive symptoms and hippocampal homeostasis impairment

The polymorphic CYP2C19 enzyme metabolizes psychoactive compounds and is expressed in the adult liver and fetal brain. Previously, we demonstrated that the absence of CYP2C19 is associated with lower levels of depressive symptoms in 1472 Swedes. Conversely, transgenic mice carrying the human CYP2C19 gene (2C19TG) have shown an anxious phenotype and decrease in hippocampal volume and adult neurogenesis. The aims of this study were to: (1) examine whether the 2C19TG findings could be translated to humans, (2) evaluate the usefulness of the 2C19TG strain as a tool for preclinical screening of new antidepressants and (3) provide an insight into the molecular underpinnings of the 2C19TG phenotype. In humans, we found that the absence of CYP2C19 was associated with a bilateral hippocampal volume increase in two independent healthy cohorts (N=386 and 1032) and a lower prevalence of major depressive disorder and depression severity in African-Americans (N=3848). Moreover, genetically determined high CYP2C19 enzymatic capacity was associated with higher suicidality in depressed suicide attempters (N=209). 2C19TG mice showed high stress sensitivity, impaired hippocampal Bdnf homeostasis in stress, and more despair-like behavior in the forced swim test (FST). After the treatment with citalopram and 5-HT_1A receptor agonist 8OH-DPAT, the reduction in immobility time in the FST was more pronounced in 2C19TG mice compared with WTs. Conversely, in the 2C19TG hippocampus, metabolic turnover of serotonin was reduced, whereas ERK1/2 and GSK3β phosphorylation was increased. Altogether, this study indicates that elevated CYP2C19 expression is associated with depressive symptoms, reduced hippocampal volume and impairment of hippocampal serotonin and BDNF homeostasis.

Do regional brain volumes and major depressive disorder share genetic architecture? A study of Generation Scotland (n=19 762), UK Biobank (n=24 048) and the English Longitudinal Study of Ageing (n=5766)

Major depressive disorder (MDD) is a heritable and highly debilitating condition. It is commonly associated with subcortical volumetric abnormalities, the most replicated of these being reduced hippocampal volume. Using the most recent published data from Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium's genome-wide association study of regional brain volume, we sought to test whether there is shared genetic architecture between seven subcortical brain volumes and intracranial volume (ICV) and MDD. We explored this using linkage disequilibrium score regression, polygenic risk scoring (PRS) techniques, Mendelian randomisation (MR) analysis and BUHMBOX. Utilising summary statistics from ENIGMA and Psychiatric Genomics Consortium, we demonstrated that hippocampal volume was positively genetically correlated with MDD (r_G=0.46, P=0.02), although this did not survive multiple comparison testing. None of the other six brain regions studied were genetically correlated and amygdala volume heritability was too low for analysis. Using PRS analysis, no regional volumetric PRS demonstrated a significant association with MDD or recurrent MDD. MR analysis in hippocampal volume and MDD identified no causal association, however, BUHMBOX analysis identified genetic subgrouping in GS:SFHS MDD cases only (P=0.00281). In this study, we provide some evidence that hippocampal volume and MDD may share genetic architecture in a subgroup of individuals, albeit the genetic correlation did not survive multiple testing correction and genetic subgroup heterogeneity was not replicated. In contrast, we found no evidence to support a shared genetic architecture between MDD and other regional subcortical volumes or ICV.