Genome-wide association study of schizophrenia in Ashkenazi Jews

Genetic determinants of coping, resilience and self-esteem in schizophrenia suggest a primary role for social factors and hippocampal neurogenesis

Schizophrenia is a severe psychiatric disorder, associated with a reduction in life expectancy of 15-20 years. Available treatments are at least partially effective in most affected individuals, and personal resources such as resilience (successful adaptation despite adversity) and coping abilities (strategies used to deal with stressful or threatening situations), are important determinants of disease outcomes and long-term sustained recovery. Published findings support the existence of a genetic background underlying resilience and coping, with variable heritability estimates. However, genome-wide analyses concerning the genetic determinants of these personal resources, especially in the context of schizophrenia, are lacking. Here, we performed a genome-wide association study coupled with accessory analyses to investigate potential genetic determinants of resilience, coping and self-esteem in 490 schizophrenia patients. Results revealed a complex genetic background partly overlapping with that of neuroticism, worry and schizophrenia itself and support the importance of social aspects in shapingthese psychological constructs. Hippocampal neurogenesis and lipid metabolism appear to be potentially relevant biological underpinnings, and specific miRNAs such as miR-124 and miR-137 may warrant further studies as potential biomarkers. In conclusion, this study represents an important first step in the identification of genetic and biological correlates shaping resilience, coping resources and self-esteem in schizophrenia.

Sex effects on DNA methylation affect discovery in epigenome-wide association study of schizophrenia

Sex differences in the epidemiology and clinical characteristics of schizophrenia are well-known; however, the molecular mechanisms underlying these differences remain unclear. Further, the potential advantages of sex-stratified meta-analyses of epigenome-wide association studies (EWAS) of schizophrenia have not been investigated. Here, we performed sex-stratified EWAS meta-analyses to investigate whether sex stratification improves discovery, and to identify differentially methylated regions (DMRs) in schizophrenia. Peripheral blood-derived DNA methylation data from 1519 cases of schizophrenia (male n = 989, female n = 530) and 1723 controls (male n = 997, female n = 726) from three publicly available datasets, and the TOP cohort were meta-analyzed to compare sex-specific, sex-stratified, and sex-adjusted EWAS. The predictive power of each model was assessed by polymethylation score (PMS). The number of schizophrenia-associated differentially methylated positions identified was higher for the sex-stratified model than for the sex-adjusted one. We identified 20 schizophrenia-associated DMRs in the sex-stratified analysis. PMS from sex-stratified analysis outperformed that from sex-adjusted analysis in predicting schizophrenia. Notably, PMSs from the sex-stratified and female-only analyses, but not those from sex-adjusted or the male-only analyses, significantly predicted schizophrenia in males. The findings suggest that sex-stratified EWAS meta-analyses improve the identification of schizophrenia-associated epigenetic changes and highlight an interaction between sex and schizophrenia status on DNA methylation. Sex-specific DNA methylation may have potential implications for precision psychiatry and the development of stratified treatments for schizophrenia.

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA Collapse

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Grants

• R01 HL143885 NHLBI NIH HHS
• R01 HL163262 NHLBI NIH HHS
• R01 HL142302 NHLBI NIH HHS
• R01 HL151152 NHLBI NIH HHS
• N01HC25195 NHLBI NIH HHS
• R01 HG010297 NHGRI NIH HHS
• R01 HL092577 NHLBI NIH HHS
• P20 MD000170 NIMHD NIH HHS
• 75N92019D00031 NHLBI NIH HHS
• R01 HD057194 NICHD NIH HHS
• UL1 TR000371 NCATS NIH HHS
• R01 DK122503 NIDDK NIH HHS

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Affiliation(s)

Hanfei Xu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Shreyash Gupta Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ian Dinsmore Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Abbey Kollu Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC, 27599, USA
Anne Marie Cawley Marsico Lung Institute, University of North Carolina, 125 Mason Farm Rd, Chapel Hill, NC, 27599, USA
Mohammad Y. Anwar Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Hung-Hsin Chen Institute of Biomedical Sciences, Academia Sinica, No. 128, Section 2, Academia Rd., Taipei, Nangang District, 115201, Taiwan Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Lauren E. Petty Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Sudha Seshadri Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio, 8300 Floyd Curl Drive, San Antonio, TX, 78229, USA
Misa Graff Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Piper Below Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Jennifer A. Brody Department of Medicine, Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Seattle, WA, 98101, USA
Geetha Chittoor Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Susan P. Fisher-Hoch Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Nancy L. Heard-Costa Framingham Heart Study, 73 Mt Wayte Ave, Framingham, MA, 01702, USA Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA
Daniel Levy Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, 6701 Rockledge Drive, Bethesda, MD, 20892, USA
Honghuang Lin Department of Medicine, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, USA
Ruth JF. Loos Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3A, 2200, Copenhagen, Denmark
Joseph B. Mccormick Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Jerome I. Rotter Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA
Tooraj Mirshahi Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Christopher D. Still Center for Obesity and Metabolic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Anita Destefano Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA
L. Adrienne Cupples Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Karen L Mohlke Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA
Kari E. North Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Anne E. Justice Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ching-Ti Liu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA

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Emerging roles of long non-coding RNAs in human epilepsy

Genome-scale biological studies conducted in the post-genomic era have revealed that two-thirds of human genes do not encode proteins. Most functional non-coding RNA transcripts in humans are products of long non-coding RNA (lncRNA) genes, an abundant but still poorly understood class of human genes. As a result of their fundamental and multitasking regulatory roles, lncRNAs are associated with a wide range of human diseases, including neurological disorders. Approximately 40% of lncRNAs are specifically expressed in the brain, and many of them exhibit distinct spatiotemporal patterns of expression. Comparative genomics approaches have determined that 65%-75% of human lncRNA genes are primate-specific and hence can be posited as a contributing potential cause of the higher-order complexity of primates, including human, brains relative to those of other mammals. Although lncRNAs present important mechanistic examples of epileptogenic functions, the human/primate specificity of lncRNAs questions their relevance in rodent models. Here, we present an in-depth review that supports the contention that human lncRNAs are direct contributors to the etiology and pathogenesis of human epilepsy, as a means to accelerate the integration of lncRNAs into clinical practice as potential diagnostic biomarkers and therapeutic targets. Meta-analytically, the major finding of our review is the commonality of lncRNAs in epilepsy and cancer pathogenesis through mitogen-activated protein kinase (MAPK)-related pathways. In addition, neuroinflammation may be a relevant part of the common pathophysiology of cancer and epilepsy. LncRNAs affect neuroinflammation-related signaling pathways such as nuclear factor kappa- light- chain- enhancer of activated B cells (NF-κB), Notch, and phosphatidylinositol 3- kinase/ protein kinase B (Akt) (PI3K/AKT), with the NF-κB pathway being the most common. Besides the controversy over lncRNA research in non-primate models, whether neuroinflammation is triggered by injury and/or central nervous system (CNS) toxicity during epilepsy modeling in animals or is a direct consequence of epilepsy pathophysiology needs to be considered meticulously in future studies.

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.

Mapping drug biology to disease genetics to discover drug impacts on the human phenome

Motivation

Medications can have unexpected effects on disease, including not only harmful drug side effects, but also beneficial drug repurposing. These effects on disease may result from hidden influences of drugs on disease gene networks. Then, discovering how biological effects of drugs relate to disease biology can both provide insight into the mechanism of latent drug effects, and can help predict new effects.

Results

Here, we develop Draphnet, a model that integrates molecular data on 429 drugs and gene associations of nearly 200 common phenotypes to learn a network that explains drug effects on disease in terms of these molecular signals. We present evidence that our method can both predict drug effects, and can provide insight into the biology of unexpected drug effects on disease. Using Draphnet to map a drug's known molecular effects to downstream effects on the disease genome, we put forward disease genes impacted by drugs, and we suggest a new grouping of drugs based on shared effects on the disease genome. Our approach has multiple applications, including predicting drug uses and learning drug biology, with implications for personalized medicine.

Availability and implementation

Code to reproduce the analysis is available at https://github.com/RDMelamed/drug-phenome.

Identification and validation of supervariants reveal novel loci associated with human white matter microstructure

As an essential part of the central nervous system, white matter coordinates communications between different brain regions and is related to a wide range of neurodegenerative and neuropsychiatric disorders. Previous genome-wide association studies (GWASs) have uncovered loci associated with white matter microstructure. However, GWASs suffer from limited reproducibility and difficulties in detecting multi-single-nucleotide polymorphism (multi-SNP) and epistatic effects. In this study, we adopt the concept of supervariants, a combination of alleles in multiple loci, to account for potential multi-SNP effects. We perform supervariant identification and validation to identify loci associated with 22 white matter fractional anisotropy phenotypes derived from diffusion tensor imaging. To increase reproducibility, we use United Kingdom (UK) Biobank White British (n = 30,842) data for discovery and internal validation, and UK Biobank White but non-British (n = 1927) data, Europeans from the Adolescent Brain Cognitive Development study (n = 4399) data, and Europeans from the Human Connectome Project (n = 319) data for external validation. We identify 23 novel loci on the discovery set that have not been reported in the previous GWASs on white matter microstructure. Among them, three supervariants on genomic regions 5q35.1, 8p21.2, and 19q13.32 have P-values lower than 0.05 in the meta-analysis of the three independent validation data sets. These supervariants contain genetic variants located in genes that have been related to brain structures, cognitive functions, and neuropsychiatric diseases. Our findings provide a better understanding of the genetic architecture underlying white matter microstructure.

Genome-wide association studies and polygenic risk score phenome-wide association studies across complex phenotypes in the human phenotype project

BACKGROUND

Genome-wide association studies (GWASs) associate phenotypes and genetic variants across a study cohort. GWASs require large-scale cohorts with both phenotype and genetic sequencing data, limiting studied phenotypes. The Human Phenotype Project is a longitudinal study that has measured a wide range of clinical and biomolecular features from a self-assignment cohort over 5 years. The phenotypes collected are quantitative traits, providing higher-resolution insights into the genetics of complex phenotypes.

METHODS

We present the results of GWASs and polygenic risk score phenome-wide association studies with 729 clinical phenotypes and 4,043 molecular features from the Human Phenotype Project. This includes clinical traits that have not been previously associated with genetics, including measures from continuous sleep monitoring, continuous glucose monitoring, liver ultrasound, hormonal status, and fundus imaging.

FINDINGS

In GWAS of 8,706 individuals, we found significant associations between 169 clinical traits and 1,184 single-nucleotide polymorphisms. We found genes associated with both glycemic control and mental disorders, and we quantify the strength of genetic signals in serum metabolites. In polygenic risk score phenome-wide association studies for clinical traits, we found 16,047 significant associations.

CONCLUSIONS

The entire set of findings, which we disseminate publicly, provides newfound resolution into the genetic architecture of complex human phenotypes.

FUNDING

E.S. is supported by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation.

A comprehensive update on genetic inheritance, epigenetic factors, associated pathology, and recent therapeutic intervention by gene therapy in schizophrenia

Schizophrenia is a severe psychological disorder in which reality is interpreted abnormally by the patient. The symptoms of the disease include delusions and hallucinations, associated with extremely disordered behavior and thinking, which may affect the daily lives of the patients. Advancements in technology have led to understanding the dynamics of the disease and the identification of the underlying causes. Multiple investigations prove that it is regulated genetically, and epigenetically, and is affected by environmental factors. The molecular and neural pathways linked to the regulation of schizophrenia have been extensively studied. Over 180 Schizophrenic risk loci have now been recognized due to several genome-wide association studies (GWAS). It has been observed that multiple transcription factors (TF) binding-disrupting single nucleotide polymorphisms (SNPs) have been related to gene expression responsible for the disease in cerebral complexes. Copy number variation, SNP defects, and epigenetic changes in chromosomes may cause overexpression or underexpression of certain genes responsible for the disease. Nowadays, gene therapy is being implemented for its treatment as several of these genetic defects have been identified. Scientists are trying to use viral vectors, miRNA, siRNA, and CRISPR technology. In addition, nanotechnology is also being applied to target such genes. The primary aim of such targeting was to either delete or silence such hyperactive genes or induce certain genes that inhibit the expression of these genes. There are challenges in delivering the gene/DNA to the site of action in the brain, and scientists are working to resolve the same. The present article describes the basics regarding the disease, its causes and factors responsible, and the gene therapy solutions available to treat this disease.

The role of cell adhesion molecule IgSF9b at the inhibitory synapse and psychiatric disease

Understanding perturbations in synaptic function between health and disease states is crucial to the treatment of neuropsychiatric illness. While genome-wide association studies have identified several genetic loci implicated in synaptic dysfunction in disorders such as autism and schizophrenia, many have not been rigorously characterized. Here, we highlight immunoglobulin superfamily member 9b (IgSF9b), a cell adhesion molecule thought to localize exclusively to inhibitory synapses in the brain. While both pre-clinical and clinical studies suggest its association with psychiatric diseases, our understanding of IgSF9b in synaptic maintenance, neural circuits, and behavioral phenotypes remains rudimentary. Moreover, these functions wield undiscovered influences on neurodevelopment. This review evaluates current literature and publicly available gene expression databases to explore the implications of IgSF9b dysfunction in rodents and humans. Through a focused analysis of one high-risk gene locus, we identify areas requiring further investigation and unearth clues related to broader mechanisms contributing to the synaptic etiology of psychiatric disorders.

Genetics and epigenetics of diabetes and its complications in India

Diabetes mellitus (DM) has become a significant health concern with an increasing rate of morbidity and mortality worldwide. India ranks second in the number of diabetes cases in the world. The increasing burden of DM can be explained by genetic predisposition of Indians to type 2 diabetes mellitus (T2DM) coupled with rapid urbanization and socio-economic development in the last 3 decades leading to drastic changes in lifestyle. Environment and lifestyle changes contribute to T2DM development by altering epigenetic processes such as DNA methylation, histone post-translational modifications, and long non-coding RNAs, all of which regulate chromatin structure and gene expression. Although the genetic predisposition of Indians to T2DM is well established, how environmental and genetic factors interact and lead to T2DM is not well understood. In this review, we discuss the prevalence of diabetes and its complications across different states in India and how various risk factors contribute to its pathogenesis. The review also highlights the role of genetic predisposition among the Indian population and epigenetic factors involved in the etiology of diabetes. Lastly, we review current treatments and emphasize the knowledge gap with respect to genetic and epigenetic factors in the Indian context. Further understanding of the genetic and epigenetic determinants will help in risk prediction and prevention as well as therapeutic interventions, which will improve the clinical management of diabetes and associated macro- and micro-vascular complications.

Neuronal connectivity, behavioral, and transcriptional alterations associated with the loss of MARK2

Neuronal connectivity is essential for adaptive brain responses and can be modulated by dendritic spine plasticity and the intrinsic excitability of individual neurons. Dysregulation of these processes can lead to aberrant neuronal activity, which has been associated with numerous neurological disorders including autism, epilepsy, and Alzheimer's disease. Nonetheless, the molecular mechanisms underlying aberrant neuronal connectivity remains unclear. We previously found that the serine/threonine kinase Microtubule Affinity Regulating Kinase 2 (MARK2), also known as Partitioning Defective 1b (Par1b), is important for the formation of dendritic spines in vitro. However, despite its genetic association with several neurological disorders, the in vivo impact of MARK2 on neuronal connectivity and cognitive functions remains unclear. Here, we demonstrate that loss of MARK2 in vivo results in changes to dendritic spine morphology, which in turn leads to a decrease in excitatory synaptic transmission. Additionally, loss of MARK2 produces substantial impairments in learning and memory, anxiety, and social behavior. Notably, MARK2 deficiency results in heightened seizure susceptibility. Consistent with this observation, RNAseq analysis reveals transcriptional changes in genes regulating synaptic transmission and ion homeostasis. These findings underscore the in vivo role of MARK2 in governing synaptic connectivity, cognitive functions, and seizure susceptibility.

Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for the 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs. The multivariate GWASs of these GIBNs identified 74 genome-wide significant (GWS) loci (p<5×10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed with attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), and insomnia, indicating genetic predisposition to a larger SA in the specific GIBN is associated with lower genetic risk of these disorders. CT GIBNs displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping the cortex into genetically informed networks.

Ocular and neural genes jointly regulate the visuospatial working memory in ADHD children

OBJECTIVE

Working memory (WM) deficits have frequently been linked to attention deficit hyperactivity disorder (ADHD). Despite previous studies suggested its high heritability, its genetic basis, especially in ADHD, remains unclear. The current study aimed to comprehensively explore the genetic basis of visual-spatial working memory (VSWM) in ADHD using wide-ranging genetic analyses.

METHODS

The current study recruited a cohort consisted of 802 ADHD individuals, all met DSM-IV ADHD diagnostic criteria. VSWM was assessed by Rey-Osterrieth complex figure test (RCFT), which is a widely used psychological test include four memory indexes: detail delayed (DD), structure delayed (SD), structure immediate (SI), detail immediate (DI). Genetic analyses were conducted at the single nucleotide polymorphism (SNP), gene, pathway, polygenic and protein network levels. Polygenic Risk Scores (PRS) were based on summary statistics of various psychiatric disorders, including ADHD, autism spectrum disorder (ASD), major depressive disorder (MDD), schizophrenia (SCZ), obsessive compulsive disorders (OCD), and substance use disorder (SUD).

RESULTS

Analyses at the single-marker level did not yield significant results (5E-08). However, the potential signals with P values less than E-05 and their mapped genes suggested the regulation of VSWM involved both ocular and neural system related genes, moreover, ADHD-related genes were also involved. The gene-based analysis found RAB11FIP1, whose encoded protein modulates several neurodevelopment processes and visual system, as significantly associated with DD scores (P = 1.96E-06, Padj = 0.036). Candidate pathway enrichment analyses (N = 53) found that forebrain neuron fate commitment significantly enriched in DD (P = 4.78E-04, Padj = 0.025), and dopamine transport enriched in SD (P = 5.90E-04, Padj = 0.031). We also observed a significant negative relationship between DD scores and ADHD PRS scores (P = 0.0025, Empirical P = 0.048).

CONCLUSIONS

Our results emphasized the joint contribution of ocular and neural genes in regulating VSWM. The study reveals a shared genetic basis between ADHD and VSWM, with GWAS indicating the involvement of ADHD-related genes in VSWM. Additionally, the PRS analysis identifies a significant relationship between ADHD-PRS and DD scores. Overall, our findings shed light on the genetic basis of VSWM deficits in ADHD, and may have important implications for future research and clinical practice.

Clinical and genetic factors associated with post-operative nausea and vomiting after propofol anaesthesia

BACKGROUND

The incidence of post-operative nausea and vomiting (PONV) remains at about 30% despite all therapeutic efforts to reduce it. The clinical risk factors guiding the prophylactic treatment are well established, but genetic factors associated with PONV remain poorly known. The aim of this study was to explore clinical and genetic factors impacting PONV by performing a genome-wide association study (GWAS) together with relevant clinical factors as covariates, and systematically attempt to replicate previously reported PONV associations. Relevant clinical factors are explored with logistic regression model.

METHODS

This was an observational case control study in Helsinki University Hospital between 1 August 2006 and 31 December 2010. One thousand consenting women with elevated risk for PONV, undergoing breast cancer surgery with standardised propofol anaesthesia and antiemetics. After exclusions for clinical reasons and failed genotyping, 815 patients were included with 187 PONV cases and 628 controls. Emergence of PONV up to 7th post-operative day was recorded. PONV at 2-24 h after surgery was selected to be the primary outcome. The GWAS explored associations between PONV and 653 034 genetic variants. Replication attempts included 31 variants in 16 genes.

RESULTS

The overall incidence of PONV up to 7th post-operative day was 35%, where 3% had PONV at 0-2 h and 23% at 2-24 h after surgery. Age, American Society of Anaesthesiologists status, the amount of oxycodone used in the post-anaesthesia care unit, smoking status, previous PONV, and history of motion sickness were statistically significant predictive factors in the logistic model. The receiver operating characteristic-area under the curve of 0.75 (95% CI 0.71-0.79) was calculated for the model. The GWAS identified six variants with suggestive association to PONV (p < 1 × 10-5 ). Of the previously reported variants, association with the DRD2 variant rs18004972 (TaqIA) was replicated (p = .028).

CONCLUSIONS

Our GWAS approach did not identify any high-impact PONV susceptibility variants. The results provide some support for a role of dopamine D2 receptors in PONV.

Immp2l knockdown in male mice increases stimulus-driven instrumental behaviour but does not alter goal-directed learning or neuron density in cortico-striatal circuits in a model of Tourette syndrome and autism spectrum disorder

Cortico-striatal neurocircuits mediate goal-directed and habitual actions which are necessary for adaptive behaviour. It has recently been proposed that some of the core symptoms of autism spectrum disorder (ASD) and Gilles de la Tourette syndrome (GTS), such as tics and other repetitive behaviours, may emerge because of imbalances in these neurocircuits. We have recently developed a model of ASD and GTS by knocking down Immp2l, a mitochondrial gene frequently associated with these disorders. The current study sought to determine whether Immp2l knockdown (KD) in male mice alters flexible, goal- or cue- driven behaviour using procedures specifically designed to examine response-outcome and stimulus-response associations, which underlie goal-directed and habitual behaviour, respectively. Whether Immp2l KD alters neuron density in cortico-striatal neurocircuits known to regulate these behaviours was also examined. Immp2l KD mice and wild type-like mice (WT) were trained on Pavlovian and instrumental learning procedures where auditory cues predicted food delivery and lever-press responses earned a food outcome. It was demonstrated that goal-directed learning was not changed for Immp2l KD mice compared to WT mice, as lever-press responses were sensitive to changes in the value of the food outcome, and to contingency reversal and degradation. There was also no difference in the capacity of KD mice to form habitual behaviours compared to WT mice following extending training of the instrumental action. However, Immp2l KD mice were more responsive to auditory stimuli paired with food as indicated by a non-specific increase in lever response rates during Pavlovian-to-instrumental transfer. Finally, there were no alterations to neuron density in striatum or any prefrontal cortex or limbic brain structures examined. Thus, the current study suggests that Immp2l is not necessary for learned maladaptive goal or stimulus driven behaviours in ASD or GTS, but that it may contribute to increased capacity for external stimuli to drive behaviour. Alterations to stimulus-driven behaviour could potentially influence the expression of tics and repetitive behaviours, suggesting that genetic alterations to Immp2l may contribute to these core symptoms in ASD and GTS. Given that this is the first application of this battery of instrumental learning procedures to a mouse model of ASD or GTS, it is an important initial step in determining the contribution of known risk-genes to goal-directed versus habitual behaviours, which should be more broadly applied to other rodent models of ASD and GTS in the future.

Genome-wide DNA methylation association study of recent and cumulative marijuana use in middle aged adults

Marijuana is a widely used psychoactive substance in the US and medical and recreational legalization has risen over the past decade. Despite the growing number of individuals using marijuana, studies investigating the association between epigenetic factors and recent and cumulative marijuana use remain limited. We therefore investigated the association between recent and cumulative marijuana use and DNA methylation levels. Participants from the Coronary Artery Risk Development in Young Adults Study with whole blood collected at examination years (Y) 15 and Y20 were randomly selected to undergo DNA methylation profiling at both timepoints using the Illumina MethylationEPIC BeadChip. Recent use of marijuana was queried at each examination and used to estimate cumulative marijuana use from Y0 to Y15 and Y20. At Y15 (n = 1023), we observed 22 and 31 methylation markers associated (FDR P ≤ 0.05) with recent and cumulative marijuana use and 132 and 16 methylation markers at Y20 (n = 883), respectively. We replicated 8 previously reported methylation markers associated with marijuana use. We further identified 640 cis-meQTLs and 198 DMRs associated with recent and cumulative use at Y15 and Y20. Differentially methylated genes were statistically overrepresented in pathways relating to cellular proliferation, hormone signaling, and infections as well as schizophrenia, bipolar disorder, and substance-related disorders. We identified numerous methylation markers, pathways, and diseases associated with recent and cumulative marijuana use in middle-aged adults, providing additional insight into the association between marijuana use and the epigenome. These results provide novel insights into the role marijuana has on the epigenome and related health conditions.

Immp2l Mutation Induces Mitochondrial Membrane Depolarization and Complex III Activity Suppression after Middle Cerebral Artery Occlusion in Mice

OBJECTIVE

We previously reported that mutations in inner mitochondrial membrane peptidase 2-like (Immp2l) increase infarct volume, enhance superoxide production, and suppress mitochondrial respiration after transient cerebral focal ischemia and reperfusion injury. The present study investigated the impact of heterozygous Immp2l mutation on mitochondria function after ischemia and reperfusion injury in mice.

METHODS

Mice were subjected to middle cerebral artery occlusion for 1 h followed by 0, 1, 5, and 24 h of reperfusion. The effects of Immp2l^+/- on mitochondrial membrane potential, mitochondrial respiratory complex III activity, caspase-3, and apoptosis-inducing factor (AIF) translocation were examined.

RESULTS

Immp2l^+/- increased ischemic brain damage and the number of TUNEL-positive cells compared with wild-type mice. Immp2l^+/- led to mitochondrial damage, mitochondrial membrane potential depolarization, mitochondrial respiratory complex III activity suppression, caspase-3 activation, and AIF nuclear translocation.

CONCLUSION

The adverse impact of Immp2l^+/- on the brain after ischemia and reperfusion might be related to mitochondrial damage that involves depolarization of the mitochondrial membrane potential, inhibition of the mitochondrial respiratory complex III, and activation of mitochondria-mediated cell death pathways. These results suggest that patients with stroke carrying Immp2l^+/- might have worse and more severe infarcts, followed by a worse prognosis than those without Immp2l mutations.

Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases

Imaging genetics is a crucial tool that is applied to explore potentially disease-related biomarkers, particularly for neurodegenerative diseases (NDs). With the development of imaging technology, the association analysis between multimodal imaging data and genetic data is gradually being concerned by a wide range of imaging genetics studies. However, multimodal data are fused first and then correlated with genetic data in traditional methods, which leads to an incomplete exploration of their common and complementary information. In addition, the inaccurate formulation in the complex relationships between imaging and genetic data and information loss caused by missing multimodal data are still open problems in imaging genetics studies. Therefore, in this study, a deep multimodality-disentangled association analysis network (DMAAN) is proposed to solve the aforementioned issues and detect the disease-related biomarkers of NDs simultaneously. First, the imaging data are nonlinearly projected into a latent space and imaging representations can be achieved. The imaging representations are further disentangled into common and specific parts by using a multimodal-disentangled module. Second, the genetic data are encoded to achieve genetic representations, and then, the achieved genetic representations are nonlinearly mapped to the common and specific imaging representations to build nonlinear associations between imaging and genetic data through an association analysis module. Moreover, modality mask vectors are synchronously synthesized to integrate the genetic and imaging data, which helps the following disease diagnosis. Finally, the proposed method achieves reasonable diagnosis performance via a disease diagnosis module and utilizes the label information to detect the disease-related modality-shared and modality-specific biomarkers. Furthermore, the genetic representation can be used to impute the missing multimodal data with our learning strategy. Two publicly available datasets with different NDs are used to demonstrate the effectiveness of the proposed DMAAN. The experimental results show that the proposed DMAAN can identify the disease-related biomarkers, which suggests the proposed DMAAN may provide new insights into the pathological mechanism and early diagnosis of NDs. The codes are publicly available at https://github.com/Meiyan88/DMAAN.

Brain transcriptome-wide association study implicates novel risk genes underlying schizophrenia risk

BACKGROUND

To identify risk genes whose expression are regulated by the reported risk variants and to explore the potential regulatory mechanism in schizophrenia (SCZ).

METHODS

We systematically integrated three independent brain expression quantitative traits (eQTLs) (CommonMind, GTEx, and BrainSeq Phase 2, a total of 1039 individuals) and GWAS data (56 418 cases and 78 818 controls), with the use of transcriptome-wide association study (TWAS). Diffusion magnetic resonance imaging was utilized to quantify the integrity of white matter bundles and determine whether polygenic risk of novel genes linked to brain structure was present in patients with first-episode antipsychotic SCZ.

RESULTS

TWAS showed that eight risk genes (CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, PCDHA8, THOC7, and TYW5) reached transcriptome-wide significance (TWS) level. These findings were confirmed by an independent integrative approach (i.e. Sherlock). We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Gene expression analysis showed that several TWS genes (including CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, THOC7 and TYW5) were dysregulated in the dorsolateral prefrontal cortex of SCZ cases compared with controls. TWS genes were mainly expressed on the surface of glutamatergic neurons, GABAergic neurons, and microglia. Finally, SCZ cases had a substantially greater TWS genes-based polygenic risk (PRS) compared to controls, and we showed that fractional anisotropy of the cingulum-hippocampus mediates the influence of TWS genes PRS on SCZ.

CONCLUSIONS

Our findings identified novel SCZ risk genes and highlighted the importance of the TWS genes in frontal-limbic dysfunctions in SCZ, indicating possible therapeutic targets.

Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders

BACKGROUND

The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown.

METHODS

We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders.

RESULTS

We identified 11 genetic loci (P < 8.3 × 10^-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories.

CONCLUSIONS

Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.

DNA methylation and general psychopathology in childhood: an epigenome-wide meta-analysis from the PACE consortium

The general psychopathology factor (GPF) has been proposed as a way to capture variance shared between psychiatric symptoms. Despite a growing body of evidence showing both genetic and environmental influences on GPF, the biological mechanisms underlying these influences remain unclear. In the current study, we conducted epigenome-wide meta-analyses to identify both probe- and region-level associations of DNA methylation (DNAm) with school-age general psychopathology in six cohorts from the Pregnancy And Childhood Epigenetics (PACE) Consortium. DNAm was examined both at birth (cord blood; prospective analysis) and during school-age (peripheral whole blood; cross-sectional analysis) in total samples of N = 2178 and N = 2190, respectively. At school-age, we identified one probe (cg11945228) located in the Bromodomain-containing protein 2 gene (BRD2) that negatively associated with GPF (p = 8.58 × 10-8). We also identified a significant differentially methylated region (DMR) at school-age (p = 1.63 × 10-8), implicating the SHC Adaptor Protein 4 (SHC4) gene and the EP300-interacting inhibitor of differentiation 1 (EID1) gene that have been previously implicated in multiple types of psychiatric disorders in adulthood, including obsessive compulsive disorder, schizophrenia, and major depressive disorder. In contrast, no prospective associations were identified with DNAm at birth. Taken together, results of this study revealed some evidence of an association between DNAm at school-age and GPF. Future research with larger samples is needed to further assess DNAm variation associated with GPF.

Cognitive Impairment Associated with Schizophrenia: From Pathophysiology to Treatment

Cognitive impairment is a core feature of schizophrenia and a major contributor to poor functional outcomes. Methods for assessment of cognitive dysfunction in schizophrenia are now well established. In addition, there has been increasing appreciation in recent years of the additional role of social cognitive impairment in driving functional outcomes and of the contributions of sensory-level dysfunction to higher-order impairments. At the neurochemical level, acute administration of N-methyl-d-aspartate receptor (NMDAR) antagonists reproduces the pattern of neurocognitive dysfunction associated with schizophrenia, encouraging the development of treatments targeted at both NMDAR and its interactome. At the local-circuit level, an auditory neurophysiological measure, mismatch negativity, has emerged both as a veridical index of NMDAR dysfunction and excitatory/inhibitory imbalance in schizophrenia and as a critical biomarker for early-stage translational drug development. Although no compounds have yet been approved for treatment of cognitive impairment associated with schizophrenia, several candidates are showing promise in early-phase testing.

Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer's disease related proteins

BACKGROUND

Alzheimer's disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The main protein components of these hallmarks include Aβ40, Aβ42, tau, phosphor-tau, and APOE. We hypothesize that genetic variants influence the levels and solubility of these AD-related proteins in the brain; identifying these may provide key insights into disease pathogenesis.

METHODS

Genome-wide genotypes were collected from 441 AD cases, imputed to the haplotype reference consortium (HRC) panel, and filtered for quality and frequency. Temporal cortex levels of five AD-related proteins from three fractions, buffer-soluble (TBS), detergent-soluble (Triton-X = TX), and insoluble (Formic acid = FA), were available for these same individuals. Variants were tested for association with each quantitative biochemical measure using linear regression, and GSA-SNP2 was used to identify enriched Gene Ontology (GO) terms. Implicated variants and genes were further assessed for association with other relevant variables.

RESULTS

We identified genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD-related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways.

CONCLUSIONS

Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.

Mapping gene by early life stress interactions on child subcortical brain structures: A genome-wide prospective study

Background

Although it is well-established that both genetics and the environment influence brain development, they are typically examined separately. Here, we aimed to prospectively investigate the interactive effects of genetic variants-from a genome-wide approach-and early life stress (ELS) on child subcortical brain structures, and their association with subsequent mental health problems.

Method

Primary analyses were conducted using data from the Generation R Study (N = 2257), including genotype and cumulative prenatal and postnatal ELS scores (encompassing life events, contextual risk, parental risk, interpersonal risk, direct victimisation). Neuroimaging data were collected at age 10 years, including intracranial and subcortical brain volumes (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus). Genome-wide association and genome-wide-by-environment interaction analyses (GWEIS, run separately for prenatal/postnatal ELS) were conducted for eight brain outcomes (i.e., 24 genome-wide analyses) in the Generation R Study (discovery). Polygenic scores (PGS) using the resulting weights were calculated in an independent (target) cohort (adolescent brain cognitive development Study; N = 10,751), to validate associations with corresponding subcortical volumes and examine links to later mother-reported internalising and externalising problems.

Results

One GWEIS-prenatal stress locus was associated with caudate volume (rs139505895, mapping onto PRSS12 and NDST3) and two GWEIS-postnatal stress loci with the accumbens (rs2397823 and rs3130008, mapping onto CUTA, SYNGAP1, and TABP). Functional annotation revealed that these genes play a role in neuronal plasticity and synaptic function, and have been implicated in neuro-developmental phenotypes, for example, intellectual disability, autism, and schizophrenia. None of these associations survived a more stringent correction for multiple testing across all analysis sets. In the validation sample, all PGSgenotype were associated with their respective brain volumes, but no PGSGxE associated with any subcortical volume. None of the PGS associated with internalising or externalising problems.

Conclusions

This study lends novel suggestive insights into gene-environment interplay on the developing brain as well as pointing to promising candidate loci for future replication and mechanistic studies.

A Genome-Wide Association Study Reveals a BDNF-Centered Molecular Network Associated with Alcohol Dependence and Related Clinical Measures

At least 50% of factors predisposing to alcohol dependence (AD) are genetic and women affected with this disorder present with more psychiatric comorbidities, probably indicating different genetic factors involved. We aimed to run a genome-wide association study (GWAS) followed by a bioinformatic functional annotation of associated genomic regions in patients with AD and eight related clinical measures. A genome-wide significant association of rs220677 with AD (p-value = 1.33 × 10^-8 calculated with the Yates-corrected χ² test under the assumption of dominant inheritance) was discovered in female patients. Associations of AD and related clinical measures with seven other single nucleotide polymorphisms listed in previous GWASs of psychiatric and addiction traits were differently replicated in male and female patients. The bioinformatic analysis showed that regulatory elements in the eight associated linkage disequilibrium blocks define the expression of 80 protein-coding genes. Nearly 68% of these and of 120 previously published coding genes associated with alcohol phenotypes directly interact in a single network, where BDNF is the most significant hub gene. This study indicates that several genes behind the pathogenesis of AD are different in male and female patients, but implicated molecular mechanisms are functionally connected. The study also reveals a central role of BDNF in the pathogenesis of AD.

Network reconstruction for trans acting genetic loci using multi-omics data and prior information

BACKGROUND

Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information.

METHODS

We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data.

RESULTS

Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses.

CONCLUSIONS

We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms.

Upper cortical layer-driven network impairment in schizophrenia

Schizophrenia is one of the most widespread and complex mental disorders. To characterize the impact of schizophrenia, we performed single-nucleus RNA sequencing (snRNA-seq) of >220,000 neurons from the dorsolateral prefrontal cortex of patients with schizophrenia and matched controls. In addition, >115,000 neurons were analyzed topographically by immunohistochemistry. Compositional analysis of snRNA-seq data revealed a reduction in abundance of GABAergic neurons and a concomitant increase in principal neurons, most pronounced for upper cortical layer subtypes, which was substantiated by histological analysis. Many neuronal subtypes showed extensive transcriptomic changes, the most marked in upper-layer GABAergic neurons, including down-regulation in energy metabolism and up-regulation in neurotransmission. Transcription factor network analysis demonstrated a developmental origin of transcriptomic changes. Last, Visium spatial transcriptomics further corroborated upper-layer neuron vulnerability in schizophrenia. Overall, our results point toward general network impairment within upper cortical layers as a core substrate associated with schizophrenia symptomatology.

Defining Specific Cell States of MPTP-Induced Parkinson's Disease by Single-Nucleus RNA Sequencing

Parkinson's disease (PD) is a neurodegenerative disease with an impairment of movement execution that is related to age and genetic and environmental factors. 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is a neurotoxin widely used to induce PD models, but the effect of MPTP on the cells and genes of PD has not been fully elucidated. By single-nucleus RNA sequencing, we uncovered the PD-specific cells and revealed the changes in their cellular states, including astrocytosis and endothelial cells' absence, as well as a cluster of medium spiny neuron cells unique to PD. Furthermore, trajectory analysis of astrocyte and endothelial cell populations predicted candidate target gene sets that might be associated with PD. Notably, the detailed regulatory roles of astrocyte-specific transcription factors Dbx2 and Sox13 in PD were revealed in our work. Finally, we characterized the cell-cell communications of PD-specific cells and found that the overall communication strength was enhanced in PD compared with a matched control, especially the signaling pathways of NRXN and NEGR. Our work provides an overview of the changes in cellular states of the MPTP-induced mouse brain.

Exonic deletions in IMMP2L in schizophrenia with enhanced glycation stress subtype

We previously identified a subtype of schizophrenia (SCZ) characterized by increased plasma pentosidine, a marker of glycation and oxidative stress (PEN-SCZ). However, the genetic factors associated with PEN-SCZ have not been fully clarified. We performed a genome-wide copy number variation (CNV) analysis to identify CNVs associated with PEN-SCZ to provide an insight into the novel therapeutic targets for PEN-SCZ. Plasma pentosidine was measured by high-performance liquid chromatography in 185 patients with SCZ harboring rare CNVs detected by array comparative genomic hybridization. In three patients with PEN-SCZ showing additional autistic features, we detected a novel deletion at 7q31.1 within exons 2 and 3 of IMMP2L, which encodes the inner mitochondrial membrane peptidase subunit 2. The deletion was neither observed in non-PEN-SCZ nor in public database of control subjects. IMMP2L is one of the SCZ risk loci genes identified in a previous SCZ genome-wide association study, and its trans-populational association was recently described. Interestingly, deletions in IMMP2L have been previously linked with autism spectrum disorder. Disrupted IMMP2L function has been shown to cause glycation/oxidative stress in neuronal cells in an age-dependent manner. To our knowledge, this is the first genome-wide CNV study to suggest the involvement of IMMP2L exons 2 and 3 in the etiology of PEN-SCZ. The combination of genomic information with plasma pentosidine levels may contribute to the classification of biological SCZ subtypes that show additional autistic features. Modifying IMMP2L functions may be useful for treating PEN-SCZ if the underlying biological mechanism can be clarified in further studies.

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery

A common strategy for the functional interpretation of genome-wide association study (GWAS) findings has been the integrative analysis of GWAS and expression data. Using this strategy, many association methods (e.g., PrediXcan and FUSION) have been successful in identifying trait-associated genes via mediating effects on RNA expression. However, these approaches often ignore the effects of splicing, which can carry as much disease risk as expression. Compared to expression data, one challenge to detect associations using splicing data is the large multiple testing burden due to multidimensional splicing events within genes. Here, we introduce a multidimensional splicing gene (MSG) approach, which consists of two stages: 1) we use sparse canonical correlation analysis (sCCA) to construct latent canonical vectors (CVs) by identifying sparse linear combinations of genetic variants and splicing events that are maximally correlated with each other; and 2) we test for the association between the genetically regulated splicing CVs and the trait of interest using GWAS summary statistics. Simulations show that MSG has proper type I error control and substantial power gains over existing multidimensional expression analysis methods (i.e., S-MultiXcan, UTMOST, and sCCA+ACAT) under diverse scenarios. When applied to the Genotype-Tissue Expression Project data and GWAS summary statistics of 14 complex human traits, MSG identified on average 83%, 115%, and 223% more significant genes than sCCA+ACAT, S-MultiXcan, and UTMOST, respectively. We highlight MSG's applications to Alzheimer's disease, low-density lipoprotein cholesterol, and schizophrenia, and found that the majority of MSG-identified genes would have been missed from expression-based analyses. Our results demonstrate that aggregating splicing data through MSG can improve power in identifying gene-trait associations and help better understand the genetic risk of complex traits.

Fut9 Deficiency Causes Abnormal Neural Development in the Mouse Cerebral Cortex and Retina

α1,3-Fucosyltransferase 9 (Fut9) is responsible for the synthesis of Lewis X [Le^X, Galβ1-4(Fucα1-3)GlcNAc] carbohydrate epitope, a marker for pluripotent or multipotent tissue-specific stem cells. Although Fut9-deficient mice show anxiety-related behaviors, structural and cellular abnormalities in the brain remain to be investigated. In this study, using in situ hybridization and immunohistochemical techniques in combination, we clarified the spatiotemporal expression of Fut9, together with Le^X, in the brain and retina. We found that Fut9-expressing cells are positive for Ctip2, a marker of neurons residing in layer V/VI, and TLE4, a marker of corticothalamic projection neurons (CThPNs) in layer VI, of the cortex. A birthdating analysis using 5-ethynyl-2'-deoxyuridine at embryonic day (E)11.5, 5-bromo-2'-deoxyuridine at E12.5, and in utero electroporation of a GFP expression plasmid at E14.5 revealed a reduction in the percentage of neurons produced at E11.5 in layer VI/subplate of the cortex and in the ganglion cell layer of the retina in P0 Fut9^-/- mice. Furthermore, this reduction in layer VI/subplate neurons persisted into adulthood, leading to a reduction in the number of Ctip2^strong/Satb2^- excitatory neurons in layer V/VI of the adult Fut9^-/- cortex. These results suggest that Fut9 plays significant roles in the differentiation, migration, and maturation of neural precursor cells in the cortex and retina.

Genome-wide variants and polygenic risk scores for cognitive impairment following blood or marrow transplantation

Cognitive impairment is prevalent in blood or marrow transplantation (BMT) recipients, albeit with inter-individual variability. We conducted a genome-wide association study of objective cognitive function assessed longitudinally in 239 adult BMT recipients for discovery and replicated in an independent cohort of 540 BMT survivors. Weighted genome-wide polygenic risk scores (PRS) were constructed using linkage disequilibrium pruned significant SNPs. Forty-four genome-wide significant SNPs were identified using additive (n = 3); codominant (n = 20) and genotype models (n = 21). Each additional copy of a risk allele was associated with a 0.28-point (p = 1.07 × 10-8) to a 1.82-point (p = 6.7 × 10-12) increase in a global deficit score. We replicated two SNPs (rs11634183 and rs12486041) with links to neural integrity. Patients in the top PRS quintile were at increased risk of cognitive impairment in discovery (RR = 1.95, 95%CI: 1.28-2.96, p = 0.002) and replication cohorts (OR = 1.84, 95%CI, 1.02-3.32, p = 0.043). Associations were stronger among individuals with lowest clinical risk for cognitive impairment. These findings support potential utility of PRS-based risk classification in the development of targeted interventions aimed at improving cognitive outcomes in BMT survivors.

Comprehensive and integrative analyses identify TYW5 as a schizophrenia risk gene

BACKGROUND

Identifying the causal genes at the risk loci and elucidating their roles in schizophrenia (SCZ) pathogenesis remain significant challenges. To explore risk variants associated with gene expression in the human brain and to identify genes whose expression change may contribute to the susceptibility of SCZ, here we report a comprehensive integrative study on SCZ.

METHODS

We systematically integrated the genetic associations from a large-scale SCZ GWAS (N = 56,418) and brain expression quantitative trait loci (eQTL) data (N = 175) using a Bayesian statistical framework (Sherlock) and Summary data-based Mendelian Randomization (SMR). We also measured brain structure of 86 first-episode antipsychotic-naive schizophrenia patients and 152 healthy controls with the structural MRI.

RESULTS

Both Sherlock (P = 3. 38 × 10^-6) and SMR (P = 1. 90 × 10^-8) analyses showed that TYW5 mRNA expression was significantly associated with risk of SCZ. Brain-based studies also identified a significant association between TYW5 protein abundance and SCZ. The single-nucleotide polymorphism rs203772 showed significant association with SCZ and the risk allele is associated with higher transcriptional level of TYW5 in the prefrontal cortex. We further found that TYW5 was significantly upregulated in the brain tissues of SCZ cases compared with controls. In addition, TYW5 expression was also significantly higher in neurons induced from pluripotent stem cells of schizophrenia cases compared with controls. Finally, combining analysis of genotyping and MRI data showed that rs203772 was significantly associated with gray matter volume of the right middle frontal gyrus and left precuneus.

CONCLUSIONS

We confirmed that TYW5 is a risk gene for SCZ. Our results provide useful information toward a better understanding of the genetic mechanism of TYW5 in risk of SCZ.

Gene-based association tests using GWAS summary statistics and incorporating eQTL

Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with.

Genome-wide analysis reveals genetic overlap between alcohol use behaviours, schizophrenia and bipolar disorder and identifies novel shared risk loci

BACKGROUND AND AIM

Schizophrenia (SCZ) and bipolar disorder (BD) have a high comorbidity of alcohol use disorder (AUD), and both comorbid AUD and excessive alcohol consumption (AC) have been linked to greater illness severity. We aimed to identify genomic loci jointly associated with SCZ, BD, AUD and AC to gain further insights into their shared genetic architecture.

DESIGN

We analysed summary data (P values and Z scores) from genome-wide association studies (GWAS) using conjunctional false discovery rate (conjFDR) analysis, which increases power to discover shared genomic loci. We functionally characterized the identified loci using publicly available biological resources.

SETTING

AUD and AC data provided by the Million Veteran Program, derived from the United States Department of Veterans Affairs Healthcare System. SCZ and BD data provided by the Psychiatric Genomics Consortium, based on cohorts from countries in Europe, North America and Australia.

PARTICIPANTS

AUD (34 658 cases, 167 346 controls), AC (n = 200 680), SCZ (31 013 cases and 38 918 controls), BD (20 352 cases and 31 358 controls). All participants were of European ancestry.

MEASUREMENTS

Genomic loci shared between alcohol traits, SCZ and BD at conjFDR <0.05.

FINDINGS

Conditional Q-Q plots showed single-nucleotide polymorphism (SNP) enrichment for both alcohol traits across different levels of significance with SCZ and BD, and vice versa. Using conjFDR analysis we leveraged this genetic enrichment and identified several loci shared between SCZ and AUD (n = 28) and AC (n = 24), BD and AUD (n = 2) and AC (n = 8) at conjFDR <0.05. Among these loci, 24 are novel for AUD, 15 are novel for AC, three are novel for SCZ and one is novel for BD. There was a mixture of same and opposite effect directions among the shared loci.

CONCLUSIONS

Alcohol use disorder and alcohol consumption share genomic loci with the psychiatric disorders schizophrenia and bipolar disorder with a mixed pattern of effect directions, indicating a complex genetic relationship between the phenotypes.

Pierre C, Bimschleger H, Gao J, Cheng R, Polesskaya O, Solberg Woods LC, Palmer AA, Chen H. Genome-Wide Association Study on Three Behaviors Tested in an Open Field in Heterogeneous Stock Rats Identifies Multiple Loci Implicated in Psychiatric Disorders

Many personality traits are influenced by genetic factors. Rodents models provide an efficient system for analyzing genetic contribution to these traits. Using 1,246 adolescent heterogeneous stock (HS) male and female rats, we conducted a genome-wide association study (GWAS) of behaviors measured in an open field, including locomotion, novel object interaction, and social interaction. We identified 30 genome-wide significant quantitative trait loci (QTL). Using multiple criteria, including the presence of high impact genomic variants and co-localization of cis-eQTL, we identified 17 candidate genes (Adarb2, Ankrd26, Cacna1c, Cacng4, Clock, Ctu2, Cyp26b1, Dnah9, Gda, Grxcr1, Eva1a, Fam114a1, Kcnj9, Mlf2, Rab27b, Sec11a, and Ube2h) for these traits. Many of these genes have been implicated by human GWAS of various psychiatric or drug abuse related traits. In addition, there are other candidate genes that likely represent novel findings that can be the catalyst for future molecular and genetic insights into human psychiatric diseases. Together, these findings provide strong support for the use of the HS population to study psychiatric disorders.

Shared genetic influences on resting‐state functional networks of the brain

The amplitude of activation in brain resting state networks (RSNs), measured with resting‐state functional magnetic resonance imaging, is heritable and genetically correlated across RSNs, indicating pleiotropy. Recent univariate genome‐wide association studies (GWASs) explored the genetic underpinnings of individual variation in RSN activity. Yet univariate genomic analyses do not describe the pleiotropic nature of RSNs. In this study, we used a novel multivariate method called genomic structural equation modeling to model latent factors that capture the shared genomic influence on RSNs and to identify single nucleotide polymorphisms (SNPs) and genes driving this pleiotropy. Using summary statistics from GWAS of 21 RSNs reported in UK Biobank (N = 31,688), the genomic latent factor analysis was first conducted in a discovery sample (N = 21,081), and then tested in an independent sample from the same cohort (N = 10,607). In the discovery sample, we show that the genetic organization of RSNs can be best explained by two distinct but correlated genetic factors that divide multimodal association networks and sensory networks. Eleven of the 17 factor loadings were replicated in the independent sample. With the multivariate GWAS, we found and replicated nine independent SNPs associated with the joint architecture of RSNs. Further, by combining the discovery and replication samples, we discovered additional SNP and gene associations with the two factors of RSN amplitude. We conclude that modeling the genetic effects on brain function in a multivariate way is a powerful approach to learn more about the biological mechanisms involved in brain function.

Mitochondrial Processing Peptidases-Structure, Function and the Role in Human Diseases

Mitochondrial proteins are encoded by both nuclear and mitochondrial DNA. While some of the essential subunits of the oxidative phosphorylation (OXPHOS) complexes responsible for cellular ATP production are synthesized directly in the mitochondria, most mitochondrial proteins are first translated in the cytosol and then imported into the organelle using a sophisticated transport system. These proteins are directed mainly by targeting presequences at their N-termini. These presequences need to be cleaved to allow the proper folding and assembly of the pre-proteins into functional protein complexes. In the mitochondria, the presequences are removed by several processing peptidases, including the mitochondrial processing peptidase (MPP), the inner membrane processing peptidase (IMP), the inter-membrane processing peptidase (MIP), and the mitochondrial rhomboid protease (Pcp1/PARL). Their proper functioning is essential for mitochondrial homeostasis as the disruption of any of them is lethal in yeast and severely impacts the lifespan and survival in humans. In this review, we focus on characterizing the structure, function, and substrate specificities of mitochondrial processing peptidases, as well as the connection of their malfunctions to severe human diseases.

Association analyses of rare variants identify two genes associated with refractive error

PURPOSE

Genetic variants identified through population-based genome-wide studies are generally of high frequency, exerting their action in the central part of the refractive error spectrum. However, the power to identify associations with variants of lower minor allele frequency is greatly reduced, requiring considerable sample sizes. Here we aim to assess the impact of rare variants on genetic variation of refractive errors in a very large general population cohort.

METHODS

Genetic association analyses of non-cyclopaedic autorefraction calculated as mean spherical equivalent (SPHE) used whole-exome sequence genotypic information from 50,893 unrelated participants in the UK Biobank of European ancestry. Gene-based analyses tested for association with SPHE using an optimised SNP-set kernel association test (SKAT-O) restricted to rare variants (minor allele frequency < 1%) within protein-coding regions of the genome. All models were adjusted for age, sex and common lead variants within the same locus reported by previous genome-wide association studies. Potentially causal markers driving association at significant loci were elucidated using sensitivity analyses by sequentially dropping the most associated variants from gene-based analyses.

RESULTS

We found strong statistical evidence for association of SPHE with the SIX6 (p-value = 2.15 x 10-10, or Bonferroni-Corrected p = 4.41x10-06) and the CRX gene (p-value = 6.65 x 10-08, or Bonferroni-Corrected p = 0.001). The SIX6 gene codes for a transcription factor believed to be critical to the eye, retina and optic disc development and morphology, while CRX regulates photoreceptor specification and expression of over 700 genes in the retina. These novel associations suggest an important role of genes involved in eye morphogenesis in refractive error.

CONCLUSION

The results of our study support previous research highlighting the importance of rare variants to the genetic risk of refractive error. We explain some of the origins of the genetic signals seen in GWAS but also report for the first time a completely novel association with the CRX gene.

Mapping the serum proteome to neurological diseases using whole genome sequencing

Despite the increasing global burden of neurological disorders, there is a lack of effective diagnostic and therapeutic biomarkers. Proteins are often dysregulated in disease and have a strong genetic component. Here, we carry out a protein quantitative trait locus analysis of 184 neurologically-relevant proteins, using whole genome sequencing data from two isolated population-based cohorts (N = 2893). In doing so, we elucidate the genetic landscape of the circulating proteome and its connection to neurological disorders. We detect 214 independently-associated variants for 107 proteins, the majority of which (76%) are cis-acting, including 114 variants that have not been previously identified. Using two-sample Mendelian randomisation, we identify causal associations between serum CD33 and Alzheimer's disease, GPNMB and Parkinson's disease, and MSR1 and schizophrenia, describing their clinical potential and highlighting drug repurposing opportunities.

MicroRNAs in the Onset of Schizophrenia

Mounting evidence implicates microRNAs (miRNAs) in the pathology of schizophrenia. These small noncoding RNAs bind to mRNAs containing complementary sequences and promote their degradation and/or inhibit protein synthesis. A single miRNA may have hundreds of targets, and miRNA targets are overrepresented among schizophrenia-risk genes. Although schizophrenia is a neurodevelopmental disorder, symptoms usually do not appear until adolescence, and most patients do not receive a schizophrenia diagnosis until late adolescence or early adulthood. However, few studies have examined miRNAs during this critical period. First, we examine evidence that the miRNA pathway is dynamic throughout adolescence and adulthood and that miRNAs regulate processes critical to late neurodevelopment that are aberrant in patients with schizophrenia. Next, we examine evidence implicating miRNAs in the conversion to psychosis, including a schizophrenia-associated single nucleotide polymorphism in MIR137HG that is among the strongest known predictors of age of onset in patients with schizophrenia. Finally, we examine how hemizygosity for DGCR8, which encodes an obligate component of the complex that synthesizes miRNA precursors, may contribute to the onset of psychosis in patients with 22q11.2 microdeletions and how animal models of this disorder can help us understand the many roles of miRNAs in the onset of schizophrenia.

Impact of depression and stress on placental DNA methylation in ethnically diverse pregnant women

Aim: To investigate the association between placental genome-wide methylation at birth and antenatal depression and stress during pregnancy. Methods: We examined the association between placental genome-wide DNA methylation (n = 301) and maternal depression and stress assessed at six gestation periods during pregnancy. Correlation between DNA methylation at the significantly associated CpGs and expression of nearby genes in the placenta was tested. Results: Depression and stress were associated with methylation of 16 CpGs and two CpGs, respectively, at a 5% false discovery rate. Methylation levels at two of the CpGs associated with depression were significantly associated with expression of ADAM23 and CTDP1, genes implicated in neurodevelopment and neuropsychiatric diseases. Conclusion: Placental epigenetic changes linked to antenatal depression suggest potential fetal brain programming. Clinical trial registration number: NCT00912132 (ClinicalTrials.gov).

Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing

Pleiotropy has important implication on genetic connection among complex phenotypes and facilitates our understanding of disease etiology. Genome-wide association studies provide an unprecedented opportunity to detect pleiotropic associations; however, efficient pleiotropy test methods are still lacking. We here consider pleiotropy identification from a methodological perspective of high-dimensional composite null hypothesis and propose a powerful gene-based method called MAIUP. MAIUP is constructed based on the traditional intersection-union test with two sets of independent P-values as input and follows a novel idea that was originally proposed under the high-dimensional mediation analysis framework. The key improvement of MAIUP is that it takes the composite null nature of pleiotropy test into account by fitting a three-component mixture null distribution, which can ultimately generate well-calibrated P-values for effective control of family-wise error rate and false discover rate. Another attractive advantage of MAIUP is its ability to effectively address the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate that compared with other methods, only MAIUP can maintain correct type I error control and has higher power across a wide range of scenarios. We apply MAIUP to detect shared associated genes among 14 psychiatric disorders with summary statistics and discover many new pleiotropic genes that are otherwise not identified if failing to account for the issue of composite null hypothesis testing. Functional and enrichment analyses offer additional evidence supporting the validity of these identified pleiotropic genes associated with psychiatric disorders. Overall, MAIUP represents an efficient method for pleiotropy identification.

mRNA Expression of the CUB and Sushi Multiple Domains 1 (CSMD1) and Its Serum Protein Level as Predictors for Psychosis in the Familial High-Risk Children and Young Adults

Background: Schizophrenia (SCZ) is still a challenging, refractory, and severe disorder. It is not a fully understood disease with genetic and epigenetic susceptibility and about 80% substantial heritability. The CUB and Sushi multiple domains 1 (CSMD1) gene is implicated in neurogenesis, memory, immunity, neuropsychology, and monoamine metabolism. Thus, it is one of the powerful genes involved in the pathogenesis of SCZ. Purpose: To evaluate the possible role of the CSMD1 gene's mRNA expression and its serum protein as markers for the early diagnosis of the first-episode SCZ in familial high-risk (FHR) Egyptian children and young adults. Subjects and methods: This case-control study included 80 first-episode drug-naïve SCZ patients from FHR Egyptian children and young adults and 80 healthy participants, as controls, from the FHR-susceptible children and young adults but did not develop SCZ. In this study, the CSMD1 gene's mRNA expression and CSMD1 serum levels were measured in the peripheral blood, and these levels were correlated with the lipid profile of the study populations. Results: The CSMD1 gene's mRNA expression and its' protein levels were significantly decreased in the SCZ patients compared to controls. The receiver operating characteristic (ROC) curve analysis succeeded in distinguishing SCZ patients from those not having SCZ using cutoff points of ≤0.711 and ≤4.83 ng/mL for the CSMD1 gene's mRNA expression and serum protein level, respectively. At these levels, the diagnostic sensitivities were 93.75 and 91.25%; specificity was 92.5%; positive predictive value (PPV) were 92.6 and 92.4%; and negative predictive values (NPVs) were 93.7 and 91.4%, respectively. Also, the ROC curve analysis succeeded in discriminating those with suicidal tendencies. Conclusion: CSMD1 gene's mRNA expression might be a reliable and early diagnostic predictor of first-episode SCZ in the FHR Egyptian children and young adults.

A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space

We propose a novel optimization framework that integrates imaging and genetics data for simultaneous biomarker identification and disease classification. The generative component of our model uses a dictionary learning framework to project the imaging and genetic data into a shared low dimensional space. We have coupled both the data modalities by tying the linear projection coefficients to the same latent space. The discriminative component of our model uses logistic regression on the projection vectors for disease diagnosis. This prediction task implicitly guides our framework to find interpretable biomarkers that are substantially different between a healthy and disease population. We exploit the interconnectedness of different brain regions by incorporating a graph regularization penalty into the joint objective function. We also use a group sparsity penalty to find a representative set of genetic basis vectors that span a low dimensional space where subjects are easily separable between patients and controls. We have evaluated our model on a population study of schizophrenia that includes two task fMRI paradigms and single nucleotide polymorphism (SNP) data. Using ten-fold cross validation, we compare our generative-discriminative framework with canonical correlation analysis (CCA) of imaging and genetics data, parallel independent component analysis (pICA) of imaging and genetics data, random forest (RF) classification, and a linear support vector machine (SVM). We also quantify the reproducibility of the imaging and genetics biomarkers via subsampling. Our framework achieves higher class prediction accuracy and identifies robust biomarkers. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia.

Neonatal DNA methylation and childhood low prosocial behavior: An epigenome-wide association meta-analysis

Low prosocial behavior in childhood has been consistently linked to later psychopathology, with evidence supporting the influence of both genetic and environmental factors on its development. Although neonatal DNA methylation (DNAm) has been found to prospectively associate with a range of psychological traits in childhood, its potential role in prosocial development has yet to be investigated. This study investigated prospective associations between cord blood DNAm at birth and low prosocial behavior within and across four longitudinal birth cohorts from the Pregnancy And Childhood Epigenetics (PACE) Consortium. We examined (a) developmental trajectories of "chronic-low" versus "typical" prosocial behavior across childhood in a case-control design (N = 2,095), and (b) continuous "low prosocial" scores at comparable cross-cohort time-points (N = 2,121). Meta-analyses were performed to examine differentially methylated positions and regions. At the cohort-specific level, three CpGs were found to associate with chronic low prosocial behavior; however, none of these associations was replicated in another cohort. Meta-analysis revealed no epigenome-wide significant CpGs or regions. Overall, we found no evidence for associations between DNAm patterns at birth and low prosocial behavior across childhood. Findings highlight the importance of employing multi-cohort approaches to replicate epigenetic associations and reduce the risk of false positive discoveries.