Genetic analysis of psychosis Biotypes: shared Ancestry-adjusted polygenic risk and unique genomic associations.

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples.

Genetic Analysis of Psychosis Biotypes: Shared Ancestry-Adjusted Polygenic Risk and Unique Genomic Associations.

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder and bipolar disorder with psychosis. Two recently developed ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples.

Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain.

Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used.

Transcriptomic sex differences in postmortem brain samples from patients with psychiatric disorders.

Many psychiatric disorders exhibit sex differences, but the underlying mechanisms remain poorly understood. We analyzed transcriptomics data from 2160 postmortem adult prefrontal cortex brain samples from the PsychENCODE consortium in a sex-stratified study design. We compared transcriptomics data of postmortem brain samples from patients with schizophrenia (SCZ), bipolar disorder (BD), and autism spectrum disorder (ASD) with transcriptomics data of postmortem control brains from individuals without a known history of psychiatric disease.

Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data.

Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets.

CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution.

Motivation: Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.

Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry.

Cross-ancestry, cell-type-informed atlas of gene, isoform, and splicing regulation in the developing human brain.

Genomic regulatory elements active in the developing human brain are notably enriched in genetic risk for neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia, and bipolar disorder. However, prioritizing the specific risk genes and candidate molecular mechanisms underlying these genetic enrichments has been hindered by the lack of a single unified large-scale gene regulatory atlas of human brain development. Here, we uniformly process and systematically characterize gene, isoform, and splicing quantitative trait loci (xQTLs) in 672 fetal brain samples from unique subjects across multiple ancestral populations.

Analyses of the autism-associated neuroligin-3 R451C mutation in human neurons reveal a gain-of-function synaptic mechanism.

Mutations in many synaptic genes are associated with autism spectrum disorders (ASD), suggesting that synaptic dysfunction is a key driver of ASD pathogenesis. Among these mutations, the R451C substitution in the NLGN3 gene that encodes the postsynaptic adhesion molecule Neuroligin-3 is noteworthy because it was the first specific mutation linked to ASDs. In mice, the corresponding Nlgn3 R451C-knockin mutation recapitulates social interaction deficits of ASD patients and produces synaptic abnormalities, but the impact of the NLGN3 R451C mutation on human neurons has not been investigated.

Distinct Phenotypes of Inflammation Associated Macrophages and Microglia in the Prefrontal Cortex Schizophrenia Compared to Controls.

Approximately 40% of people with schizophrenia are classified as having "high inflammation." This subgroup has worse neuropathology than patients with "low inflammation." Thus, one would expect the resident microglia and possibly monocyte-derived macrophages infiltrating from the periphery to be "activated" in those with schizophrenia with elevated neuroinflammation.

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution.

Motivation: Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions.

Spatiotemporal specificity of correlated DNA methylation and gene expression pairs across different human tissues and stages of brain development.

DNA methylation (DNAm) that occurs on promoter regions is primarily considered to repress gene expression. Previous studies indicated that DNAm could also show positive correlations with gene expression. Both DNAm and gene expression profiles are known to be tissue- and development-specific.

Agonal Factors Distort Gene-Expression Patterns in Human Postmortem Brains.

Agonal factors, the conditions that occur just prior to death, can impact the molecular quality of postmortem brains, influencing gene expression results. Our study used gene expression data of 262 samples from ROSMAP with the detailed terminal state recorded for each donor, such as fever, infection, and unconsciousness. Fever and infection were the primary contributors to brain gene expression changes, brain cell-type-specific gene expression, and cell proportion changes.

Transcription factor POU3F2 regulates TRIM8 expression contributing to cellular functions implicated in schizophrenia.

Schizophrenia (SCZ) is a neuropsychiatric disorder with aberrant expression of multiple genes. However, identifying its exact causal genes remains a considerable challenge. The brain-specific transcription factor POU3F2 (POU domain, class 3, transcription factor 2) has been recognized as a risk factor for SCZ, but our understanding of its target genes and pathogenic mechanisms are still limited.

Integrative analyses prioritize GNL3 as a risk gene for bipolar disorder.

Genome-wide association studies (GWASs) have identified numerous single nucleotide polymorphisms (SNPs) associated with bipolar disorder (BD), but what the causal variants are and how they contribute to BD is largely unknown. In this study, we used FUMA, a GWAS annotation tool, to pinpoint potential causal variants and genes from the latest BD GWAS findings, and performed integrative analyses, including brain expression quantitative trait loci (eQTL), gene coexpression network, differential gene expression, protein-protein interaction, and brain intermediate phenotype association analysis to identify the functions of a prioritized gene and its connection to BD. Convergent lines of evidence prioritized protein-coding gene G Protein Nucleolar 3 (GNL3) as a BD risk gene, with integrative analyses revealing GNL3's roles in cell proliferation, neuronal functions, and brain phenotypes.

Brain Banks Spur New Frontiers in Neuropsychiatric Research and Strategies for Analysis and Validation.

Neuropsychiatric disorders affect hundreds of millions of patients and families worldwide. To decode the molecular framework of these diseases, many studies use human postmortem brain samples. These studies reveal brain-specific genetic and epigenetic patterns via high-throughput sequencing technologies.

Sex-differential DNA methylation and associated regulation networks in human brain implicated in the sex-biased risks of psychiatric disorders.

Many psychiatric disorders are characterized by a strong sex difference, but the mechanisms behind sex-bias are not fully understood. DNA methylation plays important roles in regulating gene expression, ultimately impacting sexually different characteristics of the human brain. Most previous literature focused on DNA methylation alone without considering the regulatory network and its contribution to sex-bias of psychiatric disorders.

The long noncoding RNA may regulate expression of several schizophrenia-related genes.

A number of studies indicate that rare copy number variations (CNVs) contribute to the risk of schizophrenia (SCZ). Most of these studies have focused on protein-coding genes residing in the CNVs. Here, we investigated long noncoding RNAs (lncRNAs) within 10 SCZ risk-associated CNV deletion regions (CNV-lncRNAs) and examined their potential contribution to SCZ risk.

The transcription factor POU3F2 regulates a gene coexpression network in brain tissue from patients with psychiatric disorders.

Schizophrenia and bipolar disorder are complex psychiatric diseases with risks contributed by multiple genes. Dysregulation of gene expression has been implicated in these disorders, but little is known about such dysregulation in the human brain. We analyzed three transcriptome datasets from 394 postmortem brain tissue samples from patients with schizophrenia or bipolar disorder or from healthy control individuals without a known history of psychiatric disease.

Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder.

Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in diseased brains are limited. In this work, we integrated genotypes and RNA sequencing in brain samples from 1695 individuals with autism spectrum disorder (ASD), schizophrenia, and bipolar disorder, as well as controls.

BrainEXP: a database featuring with spatiotemporal expression variations and co-expression organizations in human brains.

Summary: Gene expression changes over the lifespan and varies among different tissues or cell types. Gene co-expression also changes by sex, age, different tissues or cell types. However, gene expression under the normal state and gene co-expression in the human brain has not been fully defined and quantified.