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

Cannabidiol prevents disruptions in sensorimotor gating induced by psychotomimetic drugs that last for 24-h with probable involvement of epigenetic changes in the ventral striatum

Cannabidiol (CBD), a major non-psychotomimetic component of the Cannabis sativa plant, shows therapeutic potential in several psychiatric disorders, including schizophrenia. The molecular mechanisms underlying the antipsychotic-like effects of CBD are not fully understood. Schizophrenia and antipsychotic treatment can modulate DNA methylation in the blood and brain, resulting in altered expression of diverse genes associated with this complex disorder. However, to date, the possible involvement of DNA methylation in the antipsychotic-like effects of CBD has not been investigated. Therefore, this study aimed at evaluating in mice submitted to the prepulse inhibition (PPI) model: i) the effects of a single injection of CBD or clozapine followed by AMPH or MK-801 on PPI and global DNA methylation changes in the ventral striatum and prefrontal cortex (PFC); and ii). if the acute antipsychotic-like effects of CBD would last for 24-h. AMPH (5 mg/kg) and MK-801 (0.5 mg/kg) impaired PPI. CBD (30 and 60 mg/kg), similar to clozapine (5 mg/kg), attenuated AMPH- and MK801-induced PPI disruption. AMPH, but not MK-801, increased global DNA methylation in the ventral striatum, an effect prevented by CBD. CBD and clozapine increased, by themselves, DNA methylation in the prefrontal cortex. The acute effects of CBD (30 or 60 mg/kg) on the PPI impairment induced by AMPH or MK-801 was also detectable 24 h later. Altogether, the results show that CBD induces acute antipsychotic-like effects that last for 24-h. It also modulates DNA methylation in the ventral striatum, suggesting a new potential mechanism for its antipsychotic-like effects.

Vertical integration methods for gene expression data analysis

Gene expression data have played an essential role in many biomedical studies. When the number of genes is large and sample size is limited, there is a 'lack of information' problem, leading to low-quality findings. To tackle this problem, both horizontal and vertical data integrations have been developed, where vertical integration methods collectively analyze data on gene expressions as well as their regulators (such as mutations, DNA methylation and miRNAs). In this article, we conduct a selective review of vertical data integration methods for gene expression data. The reviewed methods cover both marginal and joint analysis and supervised and unsupervised analysis. The main goal is to provide a sketch of the vertical data integration paradigm without digging into too many technical details. We also briefly discuss potential pitfalls, directions for future developments and application notes.

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

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

Brain Imaging Genomics: Integrated Analysis and Machine Learning

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

The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders

Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.