Investigating trait variability of gene co-expression network architecture in brain by controlling for genomic risk of schizophrenia.

The effect of schizophrenia (SCZ) genetic risk on gene expression in brain remains elusive. A popular approach to this problem has been the application of gene co-expression network algorithms (e.g., WGCNA). To improve reliability with this method it is critical to remove unwanted sources of variance while also preserving biological signals of interest. In this WCGNA study of RNA-Seq data from postmortem prefrontal cortex (78 neurotypical donors, EUR ancestry), we tested the effects of SCZ genetic risk on co-expression networks. Specifically, we implemented a novel design in which gene expression was adjusted by linear regression models to preserve or remove variance explained by biological signal of interest (GWAS genomic scores for SCZ risk-(GS-SCZ), and genomic scores- GS of height (GS-Ht) as a negative control), while removing variance explained by covariates of non-interest. We calculated co-expression networks from adjusted expression (GS-SCZ and GS-Ht preserved or removed), and consensus between them (representative of a "background" network free of genomic scores effects). We then tested the overlap between GS-SCZ preserved modules and background networks reasoning that modules with reduced overlap would be most affected by GS-SCZ biology. Additionally, we tested these modules for convergence of SCZ risk (i.e., enrichment in PGC3 SCZ GWAS priority genes, enrichment in SCZ risk heritability and relevant biological ontologies. Our results highlight key aspects of GS-SCZ effects on brain co-expression networks, specifically: 1) preserving/removing SCZ genetic risk alters the co-expression modules; 2) biological pathways enriched in modules affected by GS-SCZ implicate processes of transcription, translation and metabolism that converge to influence synaptic transmission; 3) priority PGC3 SCZ GWAS genes and SCZ risk heritability are enriched in modules associated with GS-SCZ effects. Overall, our results indicate that gene co-expression networks that selectively integrate information about genetic risk can reveal novel combinations of biological pathways involved in schizophrenia.

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.

Air pollution interacts with genetic risk to influence cortical networks implicated in depression.

Air pollution is a reversible cause of significant global mortality and morbidity. Epidemiological evidence suggests associations between air pollution exposure and impaired cognition and increased risk for major depressive disorders. However, the neural bases of these associations have been unclear. Here, in healthy human subjects exposed to relatively high air pollution and controlling for socioeconomic, genomic, and other confounders, we examine across multiple levels of brain network function the extent to which particulate matter (PM2.5) exposure influences putative genetic risk mechanisms associated with depression. Increased ambient PM2.5 exposure was associated with poorer reasoning and problem solving and higher-trait anxiety/depression. Working memory and stress-related information transfer (effective connectivity) across cortical and subcortical brain networks were influenced by PM2.5 exposure to differing extents depending on the polygenic risk for depression in gene-by-environment interactions. Effective connectivity patterns from individuals with higher polygenic risk for depression and higher exposures with PM2.5, but not from those with lower genetic risk or lower exposures, correlated spatially with the coexpression of depression-associated genes across corresponding brain regions in the Allen Brain Atlas. These converging data suggest that PM2.5 exposure affects brain network functions implicated in the genetic mechanisms of depression.

A multimodal deep learning model to infer cell-type-specific functional gene networks.

BACKGROUND: Functional gene networks (FGNs) capture functional relationships among genes that vary across tissues and cell types. Construction of cell-type-specific FGNs enables the understanding of cell-type-specific functional gene relationships and insights into genetic mechanisms of human diseases in disease-relevant cell types. However, most existing FGNs were developed without consideration of specific cell types within tissues. RESULTS: In this study, we created a multimodal deep learning model (MDLCN) to predict cell-type-specific FGNs in the human brain by integrating single-nuclei gene expression data with global protein interaction networks. We systematically evaluated the prediction performance of the MDLCN and showed its superior performance compared to two baseline models (boosting tree and convolutional neural network). Based on the predicted cell-type-specific FGNs, we observed that cell-type marker genes had a higher level of hubness than non-marker genes in their corresponding cell type. Furthermore, we showed that risk genes underlying autism and Alzheimer's disease were more strongly connected in disease-relevant cell types, supporting the cellular context of predicted cell-type-specific FGNs. CONCLUSIONS: Our study proposes a powerful deep learning approach (MDLCN) to predict FGNs underlying a diverse set of cell types in human brain. The MDLCN model enhances prediction accuracy of cell-type-specific FGNs compared to single modality convolutional neural network (CNN) and boosting tree models, as shown by higher areas under both receiver operating characteristic (ROC) and precision-recall curves for different levels of independent test datasets. The predicted FGNs also show evidence for the cellular context and distinct topological features (i.e. higher hubness and topological score) of cell-type marker genes. Moreover, we observed stronger modularity among disease-associated risk genes in FGNs of disease-relevant cell types. For example, the strength of connectivity among autism risk genes was stronger in neurons, but risk genes underlying Alzheimer's disease were more connected in microglia.

Molecular phenotypes associated with antipsychotic drugs in the human caudate nucleus.

Antipsychotic drugs are the current first-line of treatment for schizophrenia and other psychotic conditions. However, their molecular effects on the human brain are poorly studied, due to difficulty of tissue access and confounders associated with disease status. Here we examine differences in gene expression and DNA methylation associated with positive antipsychotic drug toxicology status in the human caudate nucleus. We find no genome-wide significant differences in DNA methylation, but abundant differences in gene expression. These gene expression differences are overall quite similar to gene expression differences between schizophrenia cases and controls. Interestingly, gene expression differences based on antipsychotic toxicology are different between brain regions, potentially due to affected cell type differences. We finally assess similarities with effects in a mouse model, which finds some overlapping effects but many differences as well. As a first look at the molecular effects of antipsychotics in the human brain, the lack of epigenetic effects is unexpected, possibly because long term treatment effects may be relatively stable for extended periods.

Apolipoprotein E gene polymorphism, posttraumatic stress disorder, and cognitive function in older U.S. veterans: Results from the National Health and Resilience in Veterans Study.

BACKGROUND: Although the ε4 allele of the apolipoprotein E (APOE) gene and posttraumatic stress disorder (PTSD) have been linked to cognitive dysfunction and dementia risk, it is unknown whether they interact to predict cognitive dysfunction. METHODS: We analyzed data from European-American (EA) veterans who participated in the National Health and Resilience in Veterans Study (NHRVS): main sample (n = 1,386) and primary replication sample (n = 509). EAs from the Yale-Penn Study cohort (n = 948) served as a second replication sample. Multivariable analyses were conducted to evaluate the predictive effects of ε4 carrier status and PTSD on cognitive functioning, with a focus on whether PTSD moderates the effect of ε4 carrier status. RESULTS: APOE ε4 allele carrier status (d = 0.15 and 0.17 in the main and primary replication NHRVS samples, respectively) and PTSD (d = 0.31 and 0.17, respectively) were independently associated with lower cognitive functioning. ε4 carriers with PTSD scored lower than those without PTSD (d = 0.68 and 1.29, respectively) with the most pronounced differences in executive function (d's = 0.75-1.50) and attention/concentration (d's = 0.62-1.33). A significant interaction was also observed in the Yale-Penn sample, with ε4 carriers with PTSD making more perseverative errors on a measure of executive function than those without PTSD (24.7% vs. 17.6%; d = 0.59). CONCLUSIONS: APOE ε4 allele carriers with PTSD have substantially greater cognitive difficulties than ε4 carriers without PTSD. These results underscore the importance of assessing, monitoring, and treating PTSD in trauma-affected individuals who are at genetic risk for cognitive decline and dementia.

Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder.

Alcohol use disorder (AUD) is a heritable complex behavior. Due to the highly polygenic nature of AUD, identifying genetic variants that comprise this heritable variation has proved to be challenging. With the exception of functional variants in alcohol metabolizing genes (e.g. ADH1B and ALDH2), few other candidate loci have been confidently linked to AUD. Genome-wide association studies (GWAS) of AUD and other alcohol-related phenotypes have either produced few hits with genome-wide significance or have failed to replicate on further study. These issues reinforce the complex nature of the genetic underpinnings for AUD and suggest that both GWAS studies with larger samples and additional analysis approaches that better harness the nominally significant loci in existing GWAS are needed. Here, we review approaches of interest in the post-GWAS era, including in silico functional analyses; functional partitioning of single nucleotide polymorphism heritability; aggregation of signal into genes and gene networks; and validation of identified loci, genes and gene networks in postmortem brain tissue and across species. These integrative approaches hold promise to illuminate our understanding of the biological basis of AUD; however, we recognize that the main challenge continues to be the extremely polygenic nature of AUD, which necessitates large samples to identify multiple loci associated with AUD liability.

Genetic association test based on principal component analysis.

Many gene- and pathway-based association tests have been proposed in the literature. Among them, the SKAT is widely used, especially for rare variants association studies. In this paper, we investigate the connection between SKAT and a principal component analysis. This investigation leads to a procedure that encompasses SKAT as a special case. Through simulation studies and real data applications, we compare the proposed method with some existing tests.

Apolipoprotein E gene polymorphism, trauma burden, and posttraumatic stress symptoms in U.S. military veterans: Results from the National Health and Resilience in Veterans Study.

BACKGROUND: Previous research examining the association between apolipoprotein E (APOE) gene polymorphism and risk for posttraumatic stress disorder (PTSD) has been inconsistent due to the use of small and select samples. This study examined the relation between APOE genotype and PTSD symptoms in two nationally representative samples of U.S. military veterans. The potential effect of cumulative trauma burden and social support in moderating this association was also evaluated. METHODS: The main sample consisted of 1,386 trauma-exposed European American (EA) veterans (mean age: 62-63 years) who participated in the National Health and Resilience in Veterans Study (NHRVS) in 2011. The independent replication sample consisted of 509 trauma-exposed EA veterans from the 2013 NHRVS. RESULTS: APOE ε4 allele carriers reported significantly greater severity of PTSD symptoms than noncarriers in the main, but not the replication, sample. In both samples, the interaction of APOE ε4 carrier status and cumulative trauma burden was associated with greater severity of PTSD symptoms (F range = 2.53-8.09, all P's < .01), particularly re-experiencing/intrusion symptoms (F range = 3.59-4.24, P's < .001). Greater social support was associated with lower severity of PTSD symptoms among APOE ε4 allele carriers with greater cumulative trauma burden (ß range -.27 to -.60, P's < .05). CONCLUSION: U.S. military veterans who are APOE ε4 allele carriers and exposed to a high number of traumas may be at increased risk for developing PTSD symptoms than ε4 noncarriers. Greater social support may moderate this association, thereby highlighting the potential importance of social support promoting interventions in mitigating the effect of ε4 × cumulative trauma burden on PTSD risk.

Sex-specific linkage scans in opioid dependence.

Sex influences risk for opioid dependence (OD). We hypothesized that sex might interact with genetic loci that influence the risk for OD. Therefore we performed an analysis to identify sex-specific genomic susceptibility regions for OD using linkage. Over 6,000 single nucleotide polymorphism (SNP) markers were genotyped for 1,758 African- and European-American (AA and EA) individuals from 739 families, ascertained via affected sib-pairs with OD and/or cocaine dependence. Autosomewide non-parametric linkage scans, stratified by sex and population, were performed. We identified one significant linkage region, segregating with OD in EA men, at 71.1 cM on chromosome 4 (LOD = 3.29; point-wise P = 0.00005; empirical autosome-wide P = 0.042), which significantly differed from the linkage signal at the same location in EA women (empirical P = 0.002). Three suggestive linkage signals were identified at 181.3 cM on chromosome 7 (LOD = 2.18), 104 cM on chromosome 11 (LOD = 1.85), and 60.9 cM on chromosome 16 (LOD = 1.93) in EA women. In AA men, four suggestive linkage signals were detected at 201.1 cM on chromosome 3 (LOD = 2.32), 152.9 cM on chromosome 6 (LOD = 1.86), 16.8 cM on chromosome 7 (LOD = 1.95), and 36.1 cM on chromosome 17 (LOD = 1.99). The significant region, mapping to 4q12-4q13.1, harbors several OD candidate genes with interconnected functionality, including VEGFR, CLOCK, PDCL2, NMU, NRSF, and IGFBP7. In conclusion, these results provide an evidence for the existence of sex-specific and population-specific differences in OD. Furthermore, these results provide positional information that will facilitate the use of targeted next-generation sequencing to search for genes that contribute to sex-specific differences in OD. © 2016 Wiley Periodicals, Inc.

Integrating GWASs and human protein interaction networks identifies a gene subnetwork underlying alcohol dependence.

Despite a significant genetic contribution to alcohol dependence (AD), few AD-risk genes have been identified to date. In the current study, we aimed to integrate genome-wide association studies (GWASs) and human protein interaction networks to investigate whether a subnetwork of genes whose protein products interact with one another might collectively contribute to AD. By using two discovery GWAS data sets of the Study of Addiction: Genetics and Environment (SAGE) and the Collaborative Study on the Genetics of Alcoholism (COGA), we identified a subnetwork of 39 genes that not only was enriched for genes associated with AD, but also collectively associated with AD in both European Americans (p < 0.0001) and African Americans (p = 0.0008). We replicated the association of the gene subnetwork with AD in three independent samples, including two samples of European descent (p = 0.001 and p = 0.006) and one sample of African descent (p = 0.0069). To evaluate whether the significant associations are likely to be false-positive findings and to ascertain their specificity, we examined the same gene subnetwork in three other human complex disorders (bipolar disorder, major depressive disorder, and type 2 diabetes) and found no significant associations. Functional enrichment analysis revealed that the gene subnetwork was enriched for genes involved in cation transport, synaptic transmission, and transmission of nerve impulses, all of which are biologically meaningful processes that may underlie the risk for AD. In conclusion, we identified a gene subnetwork underlying AD that is biologically meaningful and highly reproducible, providing important clues for future research into AD etiology and treatment.

Admixture mapping in lupus identifies multiple functional variants within IFIH1 associated with apoptosis, inflammation, and autoantibody production.

Systemic lupus erythematosus (SLE) is an inflammatory autoimmune disease with a strong genetic component. African-Americans (AA) are at increased risk of SLE, but the genetic basis of this risk is largely unknown. To identify causal variants in SLE loci in AA, we performed admixture mapping followed by fine mapping in AA and European-Americans (EA). Through genome-wide admixture mapping in AA, we identified a strong SLE susceptibility locus at 2q22-24 (LOD=6.28), and the admixture signal is associated with the European ancestry (ancestry risk ratio ~1.5). Large-scale genotypic analysis on 19,726 individuals of African and European ancestry revealed three independently associated variants in the IFIH1 gene: an intronic variant, rs13023380 [P(meta) = 5.20×10(-14); odds ratio, 95% confidence interval = 0.82 (0.78-0.87)], and two missense variants, rs1990760 (Ala946Thr) [P(meta) = 3.08×10(-7); 0.88 (0.84-0.93)] and rs10930046 (Arg460His) [P(dom) = 1.16×10(-8); 0.70 (0.62-0.79)]. Both missense variants produced dramatic phenotypic changes in apoptosis and inflammation-related gene expression. We experimentally validated function of the intronic SNP by DNA electrophoresis, protein identification, and in vitro protein binding assays. DNA carrying the intronic risk allele rs13023380 showed reduced binding efficiency to a cellular protein complex including nucleolin and lupus autoantigen Ku70/80, and showed reduced transcriptional activity in vivo. Thus, in SLE patients, genetic susceptibility could create a biochemical imbalance that dysregulates nucleolin, Ku70/80, or other nucleic acid regulatory proteins. This could promote antibody hypermutation and auto-antibody generation, further destabilizing the cellular network. Together with molecular modeling, our results establish a distinct role for IFIH1 in apoptosis, inflammation, and autoantibody production, and explain the molecular basis of these three risk alleles for SLE pathogenesis.

Association of the HTR2A gene with alcohol and heroin abuse.

Positive genetic associations of rs6313 (102T/C at exon 1) and rs6311 (-1438A/G) on the 5-hydroxytryptamine (serotonin) 2A receptor gene (HTR2A or 5-HT2A) were reported for alcohol and drug abuse; however, other association studies failed to produce consistent results supporting the susceptibility of the two single nucleotide polymorphisms (SNPs). To clarify the associations of the HTR2A gene with substance use disorders, we performed a meta-analysis based on the genotypes from the available candidate gene association studies of the two SNPs with alcohol and drug abuse from multiple populations. Evidence of association was found for HTR2A rs6313 in all the combined studies (e.g., allelic P = 0.0048 and OR 0.86, 95 % CI 0.77-0.95) and also in the combined studies of alcohol dependence (abuse) (e.g., allelic P = 0.0001 and OR 0.71, 95 % CI 0.59-0.85). The same association trend was also observed in the Study of Addiction: Genetics and Environment datasets. The meta-analysis supports a contribution of the HTR2A gene to the susceptibility to substance use disorders, particularly alcohol dependence.

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.

DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory variants impacting DNAm levels in specific brain cell types, leveraging existing single-nucleus DNAm data from the human brain. We show that INTERACT accurately predicts cell type-specific DNAm profiles, achieving an average area under the Receiver Operating Characteristic curve of 0.98 across cell types. Furthermore, INTERACT predicts cell type-specific DNAm regulatory variants, which reflect cellular context and enrich the heritability of brain-related traits in relevant cell types. Importantly, we demonstrate that incorporating predicted variant effects and DNAm levels of CpG sites enhances the fine mapping for three brain disorders-schizophrenia, depression, and Alzheimer's disease-and facilitates mapping causal genes to particular cell types. Our study highlights the power of deep learning in identifying cell type-specific regulatory variants, which will enhance our understanding of the genetics of complex traits.

Classification of Schizophrenia, Bipolar Disorder and Major Depressive Disorder with Comorbid Traits and Deep Learning Algorithms.

Recent GWASs have demonstrated that comorbid disorders share genetic liabilities. But whether and how these shared liabilities can be used for the classification and differentiation of comorbid disorders remains unclear. In this study, we use polygenic risk scores (PRSs) estimated from 42 comorbid traits and the deep neural networks (DNN) architecture to classify and differentiate schizophrenia (SCZ), bipolar disorder (BIP) and major depressive disorder (MDD). Multiple PRSs were obtained for individuals from the schizophrenia (SCZ) (cases = 6,317, controls = 7,240), bipolar disorder (BIP) (cases = 2,634, controls 4,425) and major depressive disorder (MDD) (cases = 1,704, controls = 3,357) datasets, and classification models were constructed with and without the inclusion of PRSs of the target (SCZ, BIP or MDD). Models with the inclusion of target PRSs performed well as expected. Surprisingly, we found that SCZ could be classified with only the PRSs from 35 comorbid traits (not including the target SCZ and directly related traits) (accuracy 0.760 ± 0.007, AUC 0.843 ± 0.005). Similar results were obtained for BIP (33 traits, accuracy 0.768 ± 0.007, AUC 0.848 ± 0.009), and MDD (36 traits, accuracy 0.794 ± 0.010, AUC 0.869 ± 0.004). Furthermore, these PRSs from comorbid traits alone could effectively differentiate unaffected controls, SCZ, BIP, and MDD patients (average categorical accuracy 0.861 ± 0.003, average AUC 0.961 ± 0.041). These results suggest that the shared liabilities from comorbid traits alone may be sufficient to classify SCZ, BIP and MDD. More importantly, these results imply that a data-driven and objective diagnosis and differentiation of SCZ, BIP and MDD may be feasible.

scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia.

DNA methylation (DNAm), a crucial epigenetic mark, plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable the profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across four single-nucleus DNAm datasets generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. Furthermore, we applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, and we expect scMeFormer to be a valuable tool for single-cell DNAm studies.

Smoking-informed methylation and expression QTLs in human brain and colocalization with smoking-associated genetic loci.

Smoking is a leading cause of preventable morbidity and mortality. Smoking is heritable, and genome-wide association studies (GWASs) of smoking behaviors have identified hundreds of significant loci. Most GWAS-identified variants are noncoding with unknown neurobiological effects. We used genome-wide genotype, DNA methylation, and RNA sequencing data in postmortem human nucleus accumbens (NAc) to identify cis-methylation/expression quantitative trait loci (meQTLs/eQTLs), investigate variant-by-cigarette smoking interactions across the genome, and overlay QTL evidence at smoking GWAS-identified loci to evaluate their regulatory potential. Active smokers (N = 52) and nonsmokers (N = 171) were defined based on cotinine biomarker levels and next-of-kin reporting. We simultaneously tested variant and variant-by-smoking interaction effects on methylation and expression, separately, adjusting for biological and technical covariates and correcting for multiple testing using a two-stage procedure. We found >2 million significant meQTL variants (padj < 0.05) corresponding to 41,695 unique CpGs. Results were largely driven by main effects, and five meQTLs, mapping to NUDT12, FAM53B, RNF39, and ADRA1B, showed a significant interaction with smoking. We found 57,683 significant eQTL variants for 958 unique eGenes (padj < 0.05) and no smoking interactions. Colocalization analyses identified loci with smoking-associated GWAS variants that overlapped meQTLs/eQTLs, suggesting that these heritable factors may influence smoking behaviors through functional effects on methylation/expression. One locus containing MUSTN1 and ITIH4 colocalized across all data types (GWAS, meQTL, and eQTL). In this first genome-wide meQTL map in the human NAc, the enriched overlap with smoking GWAS-identified genetic loci provides evidence that gene regulation in the brain helps explain the neurobiology of smoking behaviors.

Analysis of gene expression in the postmortem brain of neurotypical Black Americans reveals contributions of genetic ancestry.

Ancestral differences in genomic variation affect the regulation of gene expression; however, most gene expression studies have been limited to European ancestry samples or adjusted to identify ancestry-independent associations. Here, we instead examined the impact of genetic ancestry on gene expression and DNA methylation in the postmortem brain tissue of admixed Black American neurotypical individuals to identify ancestry-dependent and ancestry-independent contributions. Ancestry-associated differentially expressed genes (DEGs), transcripts and gene networks, while notably not implicating neurons, are enriched for genes related to the immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson disease and 30% of heritability for Alzheimer's disease. Ancestry-associated DEGs also show general enrichment for the heritability of diverse immune-related traits but depletion for psychiatric-related traits. We also compared Black and non-Hispanic white Americans, confirming most ancestry-associated DEGs. Our results delineate the extent to which genetic ancestry affects differences in gene expression in the human brain and the implications for brain illness risk.

Alcohol Use Disorder-Associated DNA Methylation in the Nucleus Accumbens and Dorsolateral Prefrontal Cortex.

Background: Alcohol use disorder (AUD) has a profound public health impact. However, understanding of the molecular mechanisms underlying the development and progression of AUD remain limited. Here, we interrogate AUD-associated DNA methylation (DNAm) changes within and across addiction-relevant brain regions: the nucleus accumbens (NAc) and dorsolateral prefrontal cortex (DLPFC). Methods: Illumina HumanMethylation EPIC array data from 119 decedents of European ancestry (61 cases, 58 controls) were analyzed using robust linear regression, with adjustment for technical and biological variables. Associations were characterized using integrative analyses of public gene regulatory data and published genetic and epigenetic studies. We additionally tested for brain region-shared and -specific associations using mixed effects modeling and assessed implications of these results using public gene expression data. Results: At a false discovery rate ≤ 0.05, we identified 53 CpGs significantly associated with AUD status for NAc and 31 CpGs for DLPFC. In a meta-analysis across the regions, we identified an additional 21 CpGs associated with AUD, for a total of 105 unique AUD-associated CpGs (120 genes). AUD-associated CpGs were enriched in histone marks that tag active promoters and our strongest signals were specific to a single brain region. Of the 120 genes, 23 overlapped with previous genetic associations for substance use behaviors; all others represent novel associations. Conclusions: Our findings identify AUD-associated methylation signals, the majority of which are specific within NAc or DLPFC. Some signals may constitute predisposing genetic and epigenetic variation, though more work is needed to further disentangle the neurobiological gene regulatory differences associated with AUD.

An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus.

The hippocampus contains many unique cell types, which serve the structure's specialized functions, including learning, memory and cognition. These cells have distinct spatial topography, morphology, physiology, and connectivity, highlighting the need for transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. We defined molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization and transfer learning, we integrated these data to define gene expression patterns within the snRNA-seq data and infer the expression of these patterns in the SRT data. With this approach, we leveraged existing rodent datasets that feature information on circuit connectivity and neural activity induction to make predictions about axonal projection targets and likelihood of ensemble recruitment in spatially-defined cellular populations of the human hippocampus. Finally, we integrated genome-wide association studies with transcriptomic data to identify enrichment of genetic components for neurodevelopmental, neuropsychiatric, and neurodegenerative disorders across cell types, spatial domains, and gene expression patterns of the human hippocampus. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications.