Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration

GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis

Hundreds of inbred mouse strains and intercross populations have been used to characterize the function of genetic variants that contribute to disease. Thousands of disease-relevant traits have been characterized in mice and made publicly available. New strains and populations including consomics, the collaborative cross, expanded BXD, and inbred wild-derived strains add to existing complex disease mouse models, mapping populations, and sensitized backgrounds for engineered mutations. The genome sequences of inbred strains, along with dense genotypes from others, enable integrated analysis of trait-variant associations across populations, but these analyses are hampered by the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense variant resource by harmonizing multiple data sets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extendable to other model organisms. The result is a web- and programmatically accessible data service called GenomeMUSter, comprising single-nucleotide variants covering 657 strains at 106.8 million segregating sites. Interoperation with phenotype databases, analytic tools, and other resources enable a wealth of applications, including multitrait, multipopulation meta-analysis. We show this in cross-species comparisons of type 2 diabetes and substance use disorder meta-analyses, leveraging mouse data to characterize the likely role of human variant effects in disease. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

Mouse phenome database: curated data repository with interactive multi-population and multi-trait analyses

The Mouse Phenome Database continues to serve as a curated repository and analysis suite for measured attributes of members of diverse mouse populations. The repository includes annotation to community standard ontologies and guidelines, a database of allelic states for 657 mouse strains, a collection of protocols, and analysis tools for flexible, interactive, user directed analyses that increasingly integrates data across traits and populations. The database has grown from its initial focus on a standard set of inbred strains to include heterogeneous mouse populations such as the Diversity Outbred and mapping crosses and well as Collaborative Cross, Hybrid Mouse Diversity Panel, and recombinant inbred strains. Most recently the system has expanded to include data from the International Mouse Phenotyping Consortium. Collectively these data are accessible by API and provided with an interactive tool suite that enables users' persistent selection, storage, and operation on collections of measures. The tool suite allows basic analyses, advanced functions with dynamic visualization including multi-population meta-analysis, multivariate outlier detection, trait pattern matching, correlation analyses and other functions. The data resources and analysis suite provide users a flexible environment in which to explore the basis of phenotypic variation in health and disease across the lifespan.

CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.

Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks

A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes show substantial overlap, highlighting common mechanisms including synaptic signaling, epigenetic modification, and hormonal regulation. Genetic perturbations within these networks cause abnormal BMI phenotypes in mice, too, supporting their broad conservation across mammals. Other mechanisms appear species specific, including carbohydrate biosynthesis (humans) and glycerolipid metabolism (rodents). Finally, network co-localization also identifies cross-species convergence for height/body length. This study advances a general paradigm for determining whether and how phenotypes measured in model species recapitulate human biology.

GenomeMUSter mouse genetic variation service enables multi-trait, multi-population data integration and analyses

Hundreds of inbred laboratory mouse strains and intercross populations have been used to functionalize genetic variants that contribute to disease. Thousands of disease relevant traits have been characterized in mice and made publicly available. New strains and populations including the Collaborative Cross, expanded BXD and inbred wild-derived strains add to set of complex disease mouse models, genetic mapping resources and sensitized backgrounds against which to evaluate engineered mutations. The genome sequences of many inbred strains, along with dense genotypes from others could allow integrated analysis of trait - variant associations across populations, but these analyses are not feasible due to the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense data resource by harmonizing multiple variant datasets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extensible to other model organism species. The result is a web- and programmatically-accessible data service called GenomeMUSter ( https://muster.jax.org ), comprising allelic data covering 657 strains at 106.8M segregating sites. Interoperation with phenotype databases, analytic tools and other resources enable a wealth of applications including multi-trait, multi-population meta-analysis. We demonstrate this in a cross-species comparison of the meta-analysis of Type 2 Diabetes and of substance use disorders, resulting in the more specific characterization of the role of human variant effects in light of mouse phenotype data. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

Gene expression genetics of the striatum of Diversity Outbred mice

Brain transcriptional variation is a heritable trait that mediates complex behaviors, including addiction. Expression quantitative trait locus (eQTL) mapping reveals genomic regions harboring genetic variants that influence transcript abundance. In this study, we profiled transcript abundance in the striatum of 386 Diversity Outbred (J:DO) mice of both sexes using RNA-Seq. All mice were characterized using a behavioral battery of widely-used exploratory and risk-taking assays prior to transcriptional profiling. We performed eQTL mapping, incorporated the results into a browser-based eQTL viewer, and deposited co-expression network members in GeneWeaver. The eQTL viewer allows researchers to query specific genes to obtain allelic effect plots, analyze SNP associations, assess gene expression correlations, and apply mediation analysis to evaluate whether the regulatory variant is acting through the expression of another gene. GeneWeaver allows multi-species comparison of gene sets using statistical and combinatorial tools. This data resource allows users to find genetic variants that regulate differentially expressed transcripts and place them in the context of other studies of striatal gene expression and function in addiction-related behavior.

Polygenic influences on the behavioral effects of alcohol withdrawal in a mixed-ancestry population from the collaborative study on the genetics of alcoholism (COGA)

Alcohol withdrawal (AW) is a feature of alcohol use disorder that may occur in up to half of individuals with chronic, heavy alcohol consumption whenever alcohol use is abruptly stopped or significantly reduced. To date, few genes have been robustly associated with AW; this may be partly due to most studies defining AW as a binary construct despite the multiple symptoms and their range in severity from mild to severe. The current study examined the effects of genome-wide loci on a factor score for AW in high risk and community family samples in the Collaborative Study for the Genetics of Alcoholism (COGA). In addition, we tested whether differentially expressed genes associated with alcohol withdrawal in model organisms are enriched in human genome-wide association study (GWAS) effects. Analyses employed roughly equal numbers of males and females (mean age 35, standard deviation = 15; total N = 8009) and included individuals from multiple ancestral backgrounds. Genomic data were imputed to the HRC reference panel and underwent strict quality control procedures using Plink2. Analyses controlled for age, sex, and population stratification effects using ancestral principal components. We found support that AW is a polygenic disease (SNP-heritability = 0.08 [95 % CI = 0.01, 0.15; pedigree-based heritability = 0.12 [0.08,0.16]. We identified five single nucleotide variants that met genomewide significance, some of which have previously been associated with alcohol phenotypes. Gene-level analyses suggest a role for COL19A1 in AW; H-MAGMA analyses implicated 12 genes associated with AW. Cross-species enrichment analyses indicated that variation within genes identified in model organism studies explained <1 % of the phenotypic variability in human AW. Notably, the surrounding regulatory regions of model organism genes explained more variance than expected by chance, indicating that these regulatory regions and gene sets may be important for human AW. Lastly, when comparing the overlap in genes identified from the human GWAS and H-MAGMA analyses with the genes identified from the animal studies, there was modest overlap, indicating some convergence between the methods and organisms.

Gene expression genetics of the striatum of Diversity Outbred mice

Brain transcriptional variation is a heritable trait that mediates complex behaviors, including addiction. Expression quantitative trait locus (eQTL) mapping reveals genomic regions harboring genetic variants that influence transcript abundance. In this study, we profiled transcript abundance in the striatum of 386 Diversity Outbred (J:DO) mice of both sexes using RNA-Seq. All mice were characterized using a behavioral battery of widely-used exploratory and risk-taking assays prior to transcriptional profiling. We performed eQTL mapping, incorporated the results into a browser-based eQTL viewer, and deposited co-expression network members in GeneWeaver. The eQTL viewer allows researchers to query specific genes to obtain allelic effect plots, analyze SNP associations, assess gene expression correlations, and apply mediation analysis to evaluate whether the regulatory variant is acting through the expression of another gene. GeneWeaver allows multi-species comparison of gene sets using statistical and combinatorial tools. This data resource allows users to find genetic variants that regulate differentially expressed transcripts and place them in the context of other studies of striatal gene expression and function in addiction-related behavior.

Addressing diversity and inclusion challenges in global neuro-psychiatric and behavioral genomics research

Advancements in neuro-psychiatric and behavioral genomics offer significant opportunities for better understanding the human brain, behavior and associated disorders. Such advancements may help us prevent, manage and/or cure complex conditions. The serious challenge confronted by these disciplines however is diversity. Both fields lack diversity in terms of genomic reference datasets needed for discovery research, engagement of diverse communities in translational research and in terms of diverse and multidisciplinary scientific teams. This is a challenge because diversity is needed on all levels in order to increase representation and inclusion of all populations across the globe as we move research activities forward. The lack of diversity can translate to an inability to use scientific innovations from these fields for the benefit of all people everywhere and signifies a missed opportunity to address pervasive global health inequities. In this commentary we identify three persistent barriers to reaching diversity targets while focusing on discovery and translational science. Additionally, we propose four suggestions on how to advance efforts and rapidly move towards achieving diversity and inclusion in neuro-psychiatric and behavioral genomics. Without systematically addressing the diversity gap within these fields, the benefits of the science may not be relevant and accessible to all people.

Mouse Phenome Database: towards a more FAIR-compliant and TRUST-worthy data repository and tool suite for phenotypes and genotypes

The Mouse Phenome Database (MPD; https://phenome.jax.org; RRID:SCR_003212), supported by the US National Institutes of Health, is a Biomedical Data Repository listed in the Trans-NIH Biomedical Informatics Coordinating Committee registry. As an increasingly FAIR-compliant and TRUST-worthy data repository, MPD accepts phenotype and genotype data from mouse experiments and curates, organizes, integrates, archives, and distributes those data using community standards. Data are accompanied by rich metadata, including widely used ontologies and detailed protocols. Data are from all over the world and represent genetic, behavioral, morphological, and physiological disease-related characteristics in mice at baseline or those exposed to drugs or other treatments. MPD houses data from over 6000 strains and populations, representing many reproducible strain types and heterogenous populations such as the Diversity Outbred where each mouse is unique but can be genotyped throughout the genome. A suite of analysis tools is available to aggregate, visualize, and analyze these data within and across studies and populations in an increasingly traceable and reproducible manner. We have refined existing resources and developed new tools to continue to provide users with access to consistent, high-quality data that has translational relevance in a modernized infrastructure that enables interaction with a suite of bioinformatics analytic and data services.

Novel functional genomics approaches bridging neuroscience and psychiatry

The possibility of establishing a metric of individual genetic risk for a particular disease or trait has sparked the interest of the clinical and research communities, with many groups developing and validating genomic profiling methodologies for their potential application in clinical care. Current approaches for calculating genetic risk to specific psychiatric conditions consist of aggregating genome-wide association studies-derived estimates into polygenic risk scores, which broadly represent the number of inherited risk alleles for an individual. While the traditional approach for polygenic risk score calculation aggregates estimates of gene-disease associations, novel alternative approaches have started to consider functional molecular phenotypes that are closer to genetic variation and are less penalized by the multiple testing required in genome-wide association studies. Moving the focus from genotype-disease to genotype-gene regulation frameworks, these novel approaches incorporate prior knowledge regarding biological processes involved in disease and aggregate estimates for the association of genotypes and phenotypes using multi-omics data modalities. In this review, we discuss and list different functional genomics tools that can be used and integrated to inform researchers and clinicians for a better understanding and diagnosis of psychopathology. We suggest that these novel approaches can help generate biologically driven hypotheses for polygenic signals that can ultimately serve the clinical community as potential biomarkers of psychiatric disease susceptibility.

The molecular pathophysiology of mood disorders: From the analysis of single molecular layers to multi-omic integration

Next-generation sequencing now enables the rapid and affordable production of reliable biological data at multiple molecular levels, collectively referred to as "omics". To maximize the potential for discovery, computational biologists have created and adapted integrative multi-omic analytical methods. When applied to diseases with traceable pathophysiology such as cancer, these new algorithms and statistical approaches have enabled the discovery of clinically relevant molecular mechanisms and biomarkers. In contrast, these methods have been much less applied to the field of molecular psychiatry, although diagnostic and prognostic biomarkers are similarly needed. In the present review, we first briefly summarize main findings from two decades of studies that investigated single molecular processes in relation to mood disorders. Then, we conduct a systematic review of multi-omic strategies that have been proposed and used more recently. We also list databases and types of data available to researchers for future work. Finally, we present the newest methodologies that have been employed for multi-omics integration in other medical fields, and discuss their potential for molecular psychiatry studies.

Back-translating GWAS findings to animal models reveals a role for Hgfac and Slc39a8 in alcohol and nicotine consumption

Alcohol and tobacco are the most commonly used addictive substances, with high comorbidity rates between alcohol use disorder and tobacco use disorder. Risk for alcohol and nicotine addiction is highly heritable, and they share common genetic factors. A GWAS in over 1 million individuals has revealed 566 genetic variants in 406 loci associated with multiple stages of alcohol and tobacco use. Three novel genes-SLC39A8, GRK4 and HGFAC-within loci associated with altered alcoholic drinks per week (ADW) or cigarettes per day (CPD) were selected to further study their role in alcohol and tobacco use disorder. The role of these genes was assessed using the two-bottle choice addiction paradigm in transgenic mice for each of the genes. We found significant decreases in chronic alcohol consumption and preference in female Hgfac knockout (KO) mice, and decreased nicotine preference in male Hgfac KO compared with wild-type (WT) mice. Additionally, male Slc39a8 hypomorph mice showed greater overall nicotine preference compared with WT mice, while no differences were detected for Grk4 KO mice in alcohol or nicotine consumption and preference in either sex. Thus, this study implicates Hgfac and Slc39a8 in alcohol and tobacco use in a sex-specific manner.

Research on Frequent Itemset Mining of Imaging Genetics GWAS in Alzheimer’s Disease

As an efficient method, genome-wide association study (GWAS) is used to identify the association between genetic variation and pathological phenotypes, and many significant genetic variations founded by GWAS are closely associated with human diseases. However, it is not enough to mine only a single marker effect variation on complex biological phenotypes. Mining highly correlated single nucleotide polymorphisms (SNP) is more meaningful for the study of Alzheimer's disease (AD). In this paper, we used two frequent pattern mining (FPM) framework, the FP-Growth and Eclat algorithms, to analyze the GWAS results of functional magnetic resonance imaging (fMRI) phenotypes. Moreover, we applied the definition of confidence to FP-Growth and Eclat to enhance the FPM framework. By calculating the conditional probability of identified SNPs, we obtained the corresponding association rules to provide support confidence between these important SNPs. The resulting SNPs showed close correlation with hippocampus, memory, and AD. The experimental results also demonstrate that our framework is effective in identifying SNPs and provide candidate SNPs for further research.

Corticolimbic DCC gene co-expression networks as predictors of impulsivity in children

Inhibitory control deficits are prevalent in multiple neuropsychiatric conditions. The communication- as well as the connectivity- between corticolimbic regions of the brain are fundamental for eliciting inhibitory control behaviors, but early markers of vulnerability to this behavioral trait are yet to be discovered. The gradual maturation of the prefrontal cortex (PFC), in particular of the mesocortical dopamine innervation, mirrors the protracted development of inhibitory control; both are present early in life, but reach full maturation by early adulthood. Evidence suggests the involvement of the Netrin-1/DCC signaling pathway and its associated gene networks in corticolimbic development. Here we investigated whether an expression-based polygenic score (ePRS) based on corticolimbic-specific DCC gene co-expression networks associates with impulsivity-related phenotypes in community samples of children. We found that lower ePRS scores associate with higher measurements of impulsive choice in 6-year-old children tested in the Information Sampling Task and with impulsive action in 6- and 10-year-old children tested in the Stop Signal Task. We also found the ePRS to be a better overall predictor of impulsivity when compared to a conventional PRS score comparable in size to the ePRS (4515 SNPs in our discovery cohort) and derived from the latest GWAS for ADHD. We propose that the corticolimbic DCC-ePRS can serve as a novel type of marker for impulsivity-related phenotypes in children. By adopting a systems biology approach based on gene co-expression networks and genotype-gene expression (rather than genotype-disease) associations, these results further validate our methodology to construct polygenic scores linked to the overall biological function of tissue-specific gene networks.

Genes identified in rodent studies of alcohol intake are enriched for heritability of human substance use

BACKGROUND

Rodent paradigms and human genome-wide association studies (GWAS) on drug use have the potential to provide biological insight into the pathophysiology of addiction.

METHODS

Using GeneWeaver, we created rodent alcohol and nicotine gene-sets derived from 19 gene expression studies on alcohol and nicotine outcomes. We partitioned the SNP-heritability of these gene-sets using four large human GWAS: 1) alcoholic drinks per week, 2) problematic alcohol use, 3) cigarettes per day and 4) smoking cessation. We benchmarked our findings with curated human alcoholism and nicotine addiction gene-sets and performed specificity analyses using other rodent gene-sets (e.g., locomotor behavior) and other human GWAS (e.g., height).

RESULTS

The rodent alcohol gene-set was enriched for heritability of drinks per week, cigarettes per day, and smoking cessation, but not problematic alcohol use. However, the rodent nicotine gene-set was not significantly associated with any of these traits. Both rodent gene-sets showed enrichment for several non-substance use GWAS, and the extent of this relationship tended to increase as a function of trait heritability. In general, larger gene-sets demonstrated more significant enrichment. Finally, when evaluating human traits with similar heritabilities, both rodent gene-sets showed greater enrichment for substance use traits.

CONCLUSION

Our results suggest that rodent gene expression studies can help to identify genes that contribute to heritability of some substance use traits in humans, yet there was less specificity than expected. We outline various limitations, interpretations and considerations for future research.

Genetics of substance use disorders: a review

Substance use disorders (SUDs) are prevalent and result in an array of negative consequences. They are influenced by genetic factors (h2 = ~50%). Recent years have brought substantial progress in our understanding of the genetic etiology of SUDs and related traits. The present review covers the current state of the field for SUD genetics, including the epidemiology and genetic epidemiology of SUDs, findings from the first-generation of SUD genome-wide association studies (GWAS), cautions about translating GWAS findings to clinical settings, and suggested prioritizations for the next wave of SUD genetics efforts. Recent advances in SUD genetics have been facilitated by the assembly of large GWAS samples, and the development of state-of-the-art methods modeling the aggregate effect of genome-wide variation. These advances have confirmed that SUDs are highly polygenic with many variants across the genome conferring risk, the vast majority of which are of small effect. Downstream analyses have enabled finer resolution of the genetic architecture of SUDs and revealed insights into their genetic relationship with other psychiatric disorders. Recent efforts have also prioritized a closer examination of GWAS findings that have suggested non-uniform genetic influences across measures of substance use (e.g. consumption) and problematic use (e.g. SUD). Additional highlights from recent SUD GWAS include the robust confirmation of loci in alcohol metabolizing genes (e.g. ADH1B and ALDH2) affecting alcohol-related traits, and loci within the CHRNA5-CHRNA3-CHRNB4 gene cluster influencing nicotine-related traits. Similar successes are expected for cannabis, opioid, and cocaine use disorders as sample sizes approach those assembled for alcohol and nicotine.

Integration of evidence across human and model organism studies: A meeting report

The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.