A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data

Knowledge-guided learning methods for integrative analysis of multi-omics data

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework

BACKGROUND

Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms.

METHODS

Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages.

FINDINGS

The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer's disease were found to share genetic architecture with schizophrenia, bipolar disorder and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated.

INTERPRETATION

iGOAT can successfully leverage epigenomic data, phenotype-genotype associations, and protein-protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches.

FUNDING

The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm

Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient's quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes. However, the high dimensionality of genetic data presents a great challenge to accurately modeling the data. To tackle the current challenges, this study presents a novel feature selection strategy that utilizes heuristic feature fusion and a multi-objective optimization genetic algorithm. The goal is to improve classification performance and identify the key gene subset for schizophrenia diagnostics. Traditional gene screening techniques are inadequate for accurately determining the precise number of key genes associated with schizophrenia. Our innovative approach integrates a filter-based feature selection method to reduce data dimensionality and a multi-objective optimization genetic algorithm for improved classification tasks. By combining the filtering and wrapper methods, our strategy leverages their respective strengths in a deliberate manner, leading to superior classification accuracy and a more efficient selection of relevant genes. This approach has demonstrated significant improvements in classification results across 11 out of 14 relevant datasets. The performance on the remaining three datasets is comparable to the existing methods. Furthermore, visual and enrichment analyses have confirmed the practicality of our proposed method as a promising tool for the early detection of schizophrenia.

Genetic predisposition of BDNF (rs6265) gene is susceptible to Schizophrenia: A prospective study and updated meta-analysis

INTRODUCTION

Genetic polymorphism in the BDNF gene has been found to cause neuronal alterations and has been identified as a causal factor for many neuropsychiatric disorders. Therefore, various neurological case-control studies and meta-analyses have been conducted to find the possible link between BDNF and susceptibility to schizophrenia.

METHOD

This meta-analysis gathered data from 25 case-control studies including a total of 8384 patients with schizophrenia and 8821 controls in order to identify the relationship between the rs6265 single nucleotide polymorphism and the disease, evaluating the combined odds ratio and 95% confidence intervals under 5 different genetic models. Validation followed the "Leave one out" method, and we used the Egger test and Begg's funnel plot to identify publication bias.

RESULTS

Research into the rs6265 (G/A) polymorphism revealed a non-significant association with schizophrenia in all 5 genetic models; in the subgroup analysis, no association was found between white and Asian populations, with a p value>.05.

CONCLUSIONS

Overall, the updated meta-analysis revealed that rs6265 exonic polymorphisms do not increase susceptibility to this disease. However, to better understand the pathogenesis of the disease, there is a need for further case-control studies into the BDNF polymorphism including larger sample sizes and different ethnic groups.

State of the Interactomes: an evaluation of molecular networks for generating biological insights

Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

Transplantation of gut microbiota derived from patients with schizophrenia induces schizophrenia-like behaviors and dysregulated brain transcript response in mice

Schizophrenia (SCZ), as a neurodevelopmental disorder and devastating disease, affects approximately 1% of the world population. Although numerous studies have attempted to elucidate the causes of SCZ occurrence, it is not clearly understood. Recently, the emerging roles of the gut microbiota in a range of brain disorders, including SCZ, have attracted much attention. While the molecular mechanism of gut microbiota in regulating the pathogenesis of SCZ is still lacking. Here, we first confirmed the difference of gut microbiome between SCZ patients and healthy controls, and then, we performed fecal microbiota transplantation (FMT) to clarify the roles of SCZ patients-derived microbiota in a specific pathogen free (SPF) mice model. 16 S rDNA sequencing confirmed that a significant difference of gut microbiome was present between two groups of FMT mice, which has a similar trend with the above human gut microbiome. Furthermore, we found that transplantation of fecal microbiota from SCZ patients into SPF mice was sufficient to induce schizophrenia-like (SCZ-like) symptoms, such as deficits in sociability and hyperactivity. Furthermore, the brains of mice colonized with SCZ microbiota displayed dysregulated transcript response and alternative splicing of SCZ-relevant genes. Moreover, 10 key genes were identified to be correlated with SCZ by an integrative transcriptome data analysis. Finally, 4 key genes were identified to be correlated with the 12 differential genera between two groups of FMT mice. Our results thus demonstrated that the gut microbiome might modify the transcriptomic profile in the brain, thereby modulating social behavior, and our present study can help better understand the link between gut microbiota and SCZ pathogenesis through the gut-brain axis.

Are trait-associated genes clustered together in a gene network?

Genome-wide association studies (GWAS) have provided an abundance of information about the genetic variants and their loci that are associated to complex traits and diseases. However, due to linkage disequilibrium (LD) and noncoding regions of loci, it remains a challenge to pinpoint the causal genes. Gene network-based approaches, paired with network diffusion methods, have been proposed to prioritize causal genes and to boost statistical power in GWAS based on the assumption that trait-associated genes are clustered in a gene network. Due to the difficulty in mapping trait-associated variants to genes in GWAS, this assumption has never been directly or rigorously tested empirically. On the other hand, whole exome sequencing (WES) data focuses on the protein-coding regions, directly identifying trait-associated genes. In this study, we tested the assumption by leveraging the recently available exome-based association statistics from the UK Biobank WES data along with two types of networks. We found that almost all trait-associated genes were significantly more proximal to each other than randomly selected genes within both networks. These results support the assumption that trait-associated genes are clustered in gene networks, which can be further leveraged to boost the power of GWAS such as by introducing less stringent p value thresholds.

Prioritization of risk genes in colorectal cancer by integrative analysis of multi-omics data and gene networks

Genome-wide association studies (GWASs) have identified over 140 colorectal cancer (CRC)-associated loci; however, target genes at the majority of loci and underlying molecular mechanisms are poorly understood. Here, we utilized a Bayesian approach, integrative risk gene selector (iRIGS), to prioritize risk genes at CRC GWAS loci by integrating multi-omics data. As a result, a total of 105 high-confidence risk genes (HRGs) were identified, which exhibited strong gene dependencies for CRC and enrichment in the biological processes implicated in CRC. Among the 105 HRGs, CEBPB, located at the 20q13.13 locus, acted as a transcription factor playing critical roles in cancer. Our subsequent assays indicated the tumor promoter function of CEBPB that facilitated CRC cell proliferation by regulating multiple oncogenic pathways such as MAPK, PI3K-Akt, and Ras signaling. Next, by integrating a fine-mapping analysis and three independent case-control studies in Chinese populations consisting of 8,039 cases and 12,775 controls, we elucidated that rs1810503, a putative functional variant regulating CEBPB, was associated with CRC risk (OR=0.90, 95%CI=0.86-0.93, P=1.07×10-7). The association between rs1810503 and CRC risk was further validated in three additional multi-ancestry populations consisting of 24,254 cases and 58,741 controls. Mechanistically, the rs1810503 A to T allele change weakened the enhancer activity in an allele-specific manner to decrease CEBPB expression via long-range promoter-enhancer interactions, mediated by the transcription factor, REST, and thus decreased CRC risk. In summary, our study provides a genetic resource and a generalizable strategy for CRC etiology investigation, and highlights the biological implications of CEBPB in CRC tumorigenesis, shedding new light on the etiology of CRC.

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.

SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach

Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to (i) infer information of how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and (ii) predict how the diversity and functional profile of that microbiome would change under other variable conditions. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of Bayesian network scenarios created by SAMBA using data from a microbial synthetic community, and the pan-microbiome of gilthead sea bream (Sparus aurata) in different feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems as it can be used for modelling microbiome-host network relationships of any vertebrate organism, including humans, in any system and/or ecosystem.

Environmental carcinogens disproportionally mutate genes implicated in neurodevelopmental disorders

Introduction

De novo mutations contribute to a large proportion of sporadic psychiatric and developmental disorders, yet the potential role of environmental carcinogens as drivers of causal de novo mutations in neurodevelopmental disorders is poorly studied.

Methods

To explore environmental mutation vulnerability of disease-associated gene sets, we analyzed publicly available whole genome sequencing datasets of mutations in human induced pluripotent stem cell clonal lines exposed to 12 classes of environmental carcinogens, and human lung cancers from individuals living in highly polluted regions. We compared observed rates of exposure-induced mutations in disease-related gene sets with the expected rates of mutations based on control genes randomly sampled from the genome using exact binomial tests. To explore the role of sequence characteristics in mutation vulnerability, we modeled the effects of sequence length, gene expression, and percent GC content on mutation rates of entire genes and gene coding sequences using multivariate Quasi-Poisson regressions.

Results

We demonstrate that several mutagens, including radiation and polycyclic aromatic hydrocarbons, disproportionately mutate genes related to neurodevelopmental disorders including autism spectrum disorders, schizophrenia, and attention deficit hyperactivity disorder. Other disease genes including amyotrophic lateral sclerosis, Alzheimer's disease, congenital heart disease, orofacial clefts, and coronary artery disease were generally not mutated more than expected. Longer sequence length was more strongly associated with elevated mutations in entire genes compared with mutations in coding sequences. Increased expression was associated with decreased coding sequence mutation rate, but not with the mutability of entire genes. Increased GC content was associated with increased coding sequence mutation rates but decreased mutation rates in entire genes.

Discussion

Our findings support the possibility that neurodevelopmental disorder genetic etiology is partially driven by a contribution of environment-induced germ line and somatic mutations.

Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images

Cardiovascular health interacts with cognitive and mental health in complex ways, yet little is known about the phenotypic and genetic links of heart-brain systems. We quantified heart-brain connections using multiorgan magnetic resonance imaging (MRI) data from more than 40,000 subjects. Heart MRI traits displayed numerous association patterns with brain gray matter morphometry, white matter microstructure, and functional networks. We identified 80 associated genomic loci (P < 6.09 × 10-10) for heart MRI traits, which shared genetic influences with cardiovascular and brain diseases. Genetic correlations were observed between heart MRI traits and brain-related traits and disorders. Mendelian randomization suggests that heart conditions may causally contribute to brain disorders. Our results advance a multiorgan perspective on human health by revealing heart-brain connections and shared genetic influences.

Evolution-strengthened knowledge graph enables predicting the targetability and druggability of genes

Identifying promising targets is a critical step in modern drug discovery, with causative genes of diseases that are an important source of successful targets. Previous studies have found that the pathogeneses of various diseases are closely related to the evolutionary events of organisms. Accordingly, evolutionary knowledge can facilitate the prediction of causative genes and further accelerate target identification. With the development of modern biotechnology, massive biomedical data have been accumulated, and knowledge graphs (KGs) have emerged as a powerful approach for integrating and utilizing vast amounts of data. In this study, we constructed an evolution-strengthened knowledge graph (ESKG) and validated applications of ESKG in the identification of causative genes. More importantly, we developed an ESKG-based machine learning model named GraphEvo, which can effectively predict the targetability and the druggability of genes. We further investigated the explainability of the ESKG in druggability prediction by dissecting the evolutionary hallmarks of successful targets. Our study highlights the importance of evolutionary knowledge in biomedical research and demonstrates the potential power of ESKG in promising target identification. The data set of ESKG and the code of GraphEvo can be downloaded from https://github.com/Zhankun-Xiong/GraphEvo.

Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions

Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.

A gene regulatory network approach harmonizes genetic and epigenetic signals and reveals repurposable drug candidates for multiple sclerosis

Multiple sclerosis (MS) is a complex dysimmune disorder of the central nervous system. Genome-wide association studies (GWAS) have identified 233 genetic variations associated with MS at the genome-wide significant level. Epigenetic studies have pinpointed differentially methylated CpG sites in MS patients. However, the interplay between genetic risk factors and epigenetic regulation remains elusive. Here, we employed a network model to integrate GWAS summary statistics of 14 802 MS cases and 26 703 controls with DNA methylation profiles from 140 MS cases and 139 controls and the human interactome. We identified differentially methylated genes by aggregating additive effects of differentially methylated CpG sites within promoter regions. We reconstructed a gene regulatory network (GRN) using literature-curated transcription factor knowledge. Colocalization of the MS GWAS and methylation quantitative trait loci (mQTL) was performed to assess the GRN. The resultant MS-associated GRN highlighted several single nucleotide polymorphisms with GWAS-mQTL colocalization: rs6032663, rs6065926 and rs2024568 of CD40 locus, rs9913597 of STAT3 locus, and rs887864 and rs741175 of CIITA locus. Moreover, synergistic mQTL and expression QTL signals were identified in CD40, suggesting gene expression alteration was likely induced by epigenetic changes. Web-based Cell-type Specific Enrichment Analysis of Genes (WebCSEA) indicated that the GRN was enriched in T follicular helper cells (P-value = 0.0016). Drug target enrichment analysis of annotations from the Therapeutic Target Database revealed the GRN was also enriched with drug target genes (P-value = 3.89 × 10-4), revealing repurposable candidates for MS treatment. These candidates included vorinostat (HDAC1 inhibitor) and sivelestat (ELANE inhibitor), which warrant further investigation.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

An integrated strategy to identify COVID-19 causal genes and characteristics represented by LRRC37A2

Genome-wide association study (GWAS) could identify host genetic factors associated with coronavirus disease 2019 (COVID-19). The genes or functional DNA elements through which genetic factors affect COVID-19 remain uncharted. The expression quantitative trait locus (eQTL) provides a path to assess the correlation between genetic variations and gene expression. Here, we firstly annotated GWAS data to describe genetic effects, obtaining genome-wide mapped genes. Subsequently, the genetic mechanisms and characteristics of COVID-19 were investigated by an integrated strategy that included three GWAS-eQTL analysis approaches. It was found that 20 genes were significantly associated with immunity and neurological disorders, including prior and novel genes such as OAS3 and LRRC37A2. The findings were then replicated in single-cell datasets to explore the cell-specific expression of causal genes. Furthermore, associations between COVID-19 and neurological disorders were assessed as a causal relationship. Finally, the effects of causal protein-coding genes of COVID-19 were discussed using cell experiments. The results revealed some novel COVID-19-related genes to emphasize disease characteristics, offering a broader insight into the genetic architecture underlying the pathophysiology of COVID-19.

Mapping miRNA Research in Schizophrenia: A Scientometric Review

Micro RNA (miRNA) research has great implications in uncovering the aetiology of neuropsychiatric conditions due to the role of miRNA in brain development and function. Schizophrenia, a complex yet devastating neuropsychiatric disorder, is one such condition that had been extensively studied in the realm of miRNA. Although a relatively new field of research, this area of study has progressed sufficiently to warrant dozens of reviews summarising findings from past to present. However, as a majority of reviews cannot encapsulate the full body of research, there is still a need to synthesise the diversity of publications made in this area in a systematic but easy-to-understand manner. Therefore, this study adopted bibliometrics and scientometrics, specifically document co-citation analysis (DCA), to review the literature on miRNAs in the context of schizophrenia over the course of history. From a literature search on Scopus, 992 papers were found and analysed with CiteSpace. DCA analysis generated a network of 13 major clusters with different thematic focuses within the subject area. Finally, these clusters are qualitatively discussed. miRNA research has branched into schizophrenia, among other medical and psychiatric conditions, due to previous findings in other forms of non-coding RNA. With the rise of big data, bioinformatics analyses are increasingly common in this field of research. The future of research is projected to rely more heavily on interdisciplinary collaboration. Additionally, it can be expected that there will be more translational studies focusing on the application of these findings to the development of effective treatments.

Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships

Leveraging molecular networks to discover disease-relevant modules is a long-standing challenge. With the accumulation of interactomes, there is a pressing need for powerful computational approaches to handle the inevitable noise and context-specific nature of biological networks. Here, we introduce Graphene, a two-step self-supervised representation learning framework tailored to concisely integrate multiple molecular networks and adapted to gene functional analysis via downstream re-training. In practice, we first leverage GNN (graph neural network) pre-training techniques to obtain initial node embeddings followed by re-training Graphene using a graph attention architecture, achieving superior performance over competing methods for pathway gene recovery, disease gene reprioritization, and comorbidity prediction. Graphene successfully recapitulates tissue-specific gene expression across disease spectrum and demonstrates shared heritability of common mental disorders. Graphene can be updated with new interactomes or other omics features. Graphene holds promise to decipher gene function under network context and refine GWAS (genome-wide association study) hits and offers mechanistic insights via decoding diseases from genome to networks to phenotypes.

Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes

Advances and reduction of costs in various sequencing technologies allow for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omic data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omic assays and analytic approaches toward xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic and metabolomic data. In this review, we first discuss the recent development of xQTL from multi-omic findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.

TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning

MOTIVATION

Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of human diseases, we hypothesize that rare non-coding variants discovered in WGS play a regulatory role in predisposing disease risk.

RESULTS

With thousands of tissue- and cell-type-specific epigenomic features, we propose TVAR. This multi-label learning-based deep neural network predicts the functionality of non-coding variants in the genome based on eQTLs across 49 human tissues in the GTEx project. TVAR learns the relationships between high-dimensional epigenomics and eQTLs across tissues, taking the correlation among tissues into account to understand shared and tissue-specific eQTL effects. As a result, TVAR outputs tissue-specific annotations, with an average AUROC of 0.77 across these tissues. We evaluate TVAR's performance on four complex diseases (coronary artery disease, breast cancer, Type 2 diabetes and Schizophrenia), using TVAR's tissue-specific annotations, and observe its superior performance in predicting functional variants for both common and rare variants, compared with five existing state-of-the-art tools. We further evaluate TVAR's G-score, a scoring scheme across all tissues, on ClinVar, fine-mapped GWAS loci, Massive Parallel Reporter Assay (MPRA) validated variants and observe the consistently better performance of TVAR compared with other competing tools.

AVAILABILITY AND IMPLEMENTATION

The TVAR source code and its scores on the ClinVar catalog, fine mapped GWAS Loci, high confidence eQTLs from GTEx dataset, and MPRA validated functional variants are available at https://github.com/haiyang1986/TVAR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data

Host genetics affect both the susceptibility and response to viral infection. Searching for host genes that contribute to COVID-19, the Host Genetics Initiative (HGI) was formed to investigate the genetic factors involved in COVID-19 via genome-wide association studies (GWAS). The GWAS suffer from limited statistical power and in general, only a few genes can pass the conventional significance thresholds. This statistical limitation may be overcome by boosting weak association signals through integrating independent functional information such as molecular interactions. Additionally, the boosted results can be evaluated by various independent data for further connections to COVID-19. We present COVID-GWAB, a web-based tool to boost original GWAS signals from COVID-19 patients by taking the signals of the interactome neighbors. COVID-GWAB takes summary statistics from the COVID-19 HGI or user input data and reprioritizes candidate host genes for COVID-19 using HumanNet, a co-functional human gene network. The current version of COVID-GWAB provides the pre-processed data of releases 5, 6, and 7 of the HGI. Additionally, COVID-GWAB provides web interfaces for a summary of augmented GWAS signals, prediction evaluations by appearance frequency in COVID-19 literature, single-cell transcriptome data, and associated pathways. The web server also enables browsing the candidate gene networks.

Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics

Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. In the present study, we introduce sc-linker, a framework for integrating single-cell RNA-sequencing, epigenomic SNP-to-gene maps and genome-wide association study summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. The inferred disease enrichments recapitulated known biology and highlighted notable cell-disease relationships, including γ-aminobutyric acid-ergic neurons in major depressive disorder, a disease-dependent M-cell program in ulcerative colitis and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease-dependent immune cell-type programs were associated, whereas only disease-dependent epithelial cell programs were prominent, suggesting a role in disease response rather than initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

Impact of schizophrenia GWAS loci converge onto distinct pathways in cortical interneurons vs glutamatergic neurons during development

Remarkable advances have been made in schizophrenia (SCZ) GWAS, but gleaning biological insight from these loci is challenging. Genetic influences on gene expression (e.g., eQTLs) are cell type-specific, but most studies that attempt to clarify GWAS loci's influence on gene expression have employed tissues with mixed cell compositions that can obscure cell-specific effects. Furthermore, enriched SCZ heritability in the fetal brain underscores the need to study the impact of SCZ risk loci in specific developing neurons. MGE-derived cortical interneurons (cINs) are consistently affected in SCZ brains and show enriched SCZ heritability in human fetal brains. We identified SCZ GWAS risk genes that are dysregulated in iPSC-derived homogeneous populations of developing SCZ cINs. These SCZ GWAS loci differential expression (DE) genes converge on the PKC pathway. Their disruption results in PKC hyperactivity in developing cINs, leading to arborization deficits. We show that the fine-mapped GWAS locus in the ATP2A2 gene of the PKC pathway harbors enhancer marks by ATACseq and ChIPseq, and regulates ATP2A2 expression. We also generated developing glutamatergic neurons (GNs), another population with enriched SCZ heritability, and confirmed their functionality after transplantation into the mouse brain. Then, we identified SCZ GWAS risk genes that are dysregulated in developing SCZ GNs. GN-specific SCZ GWAS loci DE genes converge on the ion transporter pathway, distinct from those for cINs. Disruption of the pathway gene CACNA1D resulted in deficits of Ca2+ currents in developing GNs, suggesting compromised neuronal function by GWAS loci pathway deficits during development. This study allows us to identify cell type-specific and developmental stage-specific mechanisms of SCZ risk gene function, and may aid in identifying mechanism-based novel therapeutic targets.

Population-level variation in enhancer expression identifies disease mechanisms in the human brain

Identification of risk variants for neuropsychiatric diseases within enhancers underscores the importance of understanding population-level variation in enhancer function in the human brain. Besides regulating tissue-specific and cell-type-specific transcription of target genes, enhancers themselves can be transcribed. By jointly analyzing large-scale cell-type-specific transcriptome and regulome data, we cataloged 30,795 neuronal and 23,265 non-neuronal candidate transcribed enhancers. Examination of the transcriptome in 1,382 brain samples identified robust expression of transcribed enhancers. We explored gene-enhancer coordination and found that enhancer-linked genes are strongly implicated in neuropsychiatric disease. We identified expression quantitative trait loci (eQTLs) for both genes and enhancers and found that enhancer eQTLs mediate a substantial fraction of neuropsychiatric trait heritability. Inclusion of enhancer eQTLs in transcriptome-wide association studies enhanced functional interpretation of disease loci. Overall, our study characterizes the gene-enhancer regulome and genetic mechanisms in the human cortex in both healthy and diseased states.

CACNA1C (CaV1.2) and other L-type calcium channels in the pathophysiology and treatment of psychiatric disorders: Advances from functional genomics and pharmacoepidemiology

A role for voltage-gated calcium channels (VGCCs) in psychiatric disorders has long been postulated as part of a broader involvement of intracellular calcium signalling. However, the data were inconclusive and hard to interpret. We review three areas of research that have markedly advanced the field. First, there is now robust genomic evidence that common variants in VGCC subunit genes, notably CACNA1C which encodes the L-type calcium channel (LTCC) Ca_V1.2 subunit, are trans-diagnostically associated with psychiatric disorders including schizophrenia and bipolar disorder. Rare variants in these genes also contribute to the risk. Second, pharmacoepidemiological evidence supports the possibility that calcium channel blockers, which target LTCCs, might have beneficial effects on the onset or course of these disorders. This is especially true for calcium channel blockers that are brain penetrant. Third, long-range sequencing is revealing the repertoire of full-length LTCC transcript isoforms. Many novel and abundant CACNA1C isoforms have been identified in human and mouse brain, including some which are enriched compared to heart or aorta, and predicted to encode channels with differing functional and pharmacological properties. These isoforms may contribute to the molecular mechanisms of genetic association to psychiatric disorders. They may also enable development of therapeutic agents that can preferentially target brain LTCC isoforms and be of potential value for psychiatric indications.

Unfolding and identification of membrane proteins in situ

Single-molecule force spectroscopy (SMFS) uses the cantilever tip of an AFM to apply a force able to unfold a single protein. The obtained force-distance curve encodes the unfolding pathway, and from its analysis it is possible to characterize the folded domains. SMFS has been mostly used to study the unfolding of purified proteins, in solution or reconstituted in a lipid bilayer. Here, we describe a pipeline for analyzing membrane proteins based on SMFS, that involves the isolation of the plasma membrane of single cells and the harvesting of force-distance curves directly from it. We characterized and identified the embedded membrane proteins combining, within a Bayesian framework, the information of the shape of the obtained curves, with the information from Mass Spectrometry and proteomic databases. The pipeline was tested with purified/reconstituted proteins and applied to five cell types where we classified the unfolding of their most abundant membrane proteins. We validated our pipeline by overexpressing 4 constructs, and this allowed us to gather structural insights of the identified proteins, revealing variable elements in the loop regions. Our results set the basis for the investigation of the unfolding of membrane proteins in situ, and for performing proteomics from a membrane fragment.

Impaired migration of autologous induced neural stem cells from patients with schizophrenia and implications for genetic risk for psychosis

Stem cell technologies have presented explicit evidence of the neurodevelopmental hypothesis of schizophrenia. However, few studies investigated relevance of the schizophrenia genetic liability and the use of genetic reprogramming on pluripotent stem cells to the impaired neurodevelopment shown by stem cells. Therefore, this study sought to investigate the cellular phenotypes of induced neural stem cells (iNSCs) derived without genetic modification from patients with schizophrenia and from genetic high risk (GHR) individuals. Three patients with a diagnosis of schizophrenia, 3 GHR individuals who had two or more relatives with schizophrenia, and 3 healthy volunteers participated. iNSCs were derived using a small molecule-based lineage switch method, and their gene expression levels and migration capabilities were examined. Demographic characteristics were not different among the groups (age, χ² = 5.637, P = .060; education, χ² = 2.111, P = .348). All participants stayed well during the follow-up except one GHR individual who developed psychosis 1.5 years later. Migration capacity was impaired in iNSCs from patients with schizophrenia (SZ-iNSCs) compared to iNSCs from GHR individuals or controls (P < .001). iNSCs from a GHR individual who later developed schizophrenia showed migratory impairment that was similar to SZ-iNSCs. Gene expression levels of Sox2 in SZ-iNSCs were significantly lower than those in controls (P = .028). Defective migration in genetically unmodified SZ-iNSCs is the first direct demonstration of neurodevelopmental abnormalities in schizophrenia. Additionally, alterations in gene expression in SZ-iNSCs suggest mechanisms by which genetic liability leads to aberrant neurodevelopment.

A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data

With the advances in high-throughput biotechnologies, high-dimensional multi-layer omics data become increasingly available. They can provide both confirmatory and complementary information to disease risk and thus have offered unprecedented opportunities for risk prediction studies. However, the high-dimensionality and complex inter/intra-relationships among multi-omics data have brought tremendous analytical challenges. Here we present a computationally efficient penalized linear mixed model with generalized method of moments estimator (MpLMMGMM) for the prediction analysis on multi-omics data. Our method extends the widely used linear mixed model proposed for genomic risk predictions to model multi-omics data, where kernel functions are used to capture various types of predictive effects from different layers of omics data and penalty terms are introduced to reduce the impact of noise. Compared with existing penalized linear mixed models, the proposed method adopts the generalized method of moments estimator and it is much more computationally efficient. Through extensive simulation studies and the analysis of positron emission tomography imaging outcomes, we have demonstrated that MpLMMGMM can simultaneously consider a large number of variables and efficiently select those that are predictive from the corresponding omics layers. It can capture both linear and nonlinear predictive effects and achieves better prediction performance than competing methods.

SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease

We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.

Predicting causal genes from psychiatric genome-wide association studies using high-level etiological knowledge

Genome-wide association studies have discovered hundreds of genomic loci associated with psychiatric traits, but the causal genes underlying these associations are often unclear, a research gap that has hindered clinical translation. Here, we present a Psychiatric Omnilocus Prioritization Score (PsyOPS) derived from just three binary features encapsulating high-level assumptions about psychiatric disease etiology - namely, that causal psychiatric disease genes are likely to be mutationally constrained, be specifically expressed in the brain, and overlap with known neurodevelopmental disease genes. To our knowledge, PsyOPS is the first method specifically tailored to prioritizing causal genes at psychiatric GWAS loci. We show that, despite its extreme simplicity, PsyOPS achieves state-of-the-art performance at this task, comparable to a prior domain-agnostic approach relying on tens of thousands of features. Genes prioritized by PsyOPS are substantially more likely than other genes at the same loci to have convergent evidence of direct regulation by the GWAS variant according to both DNA looping assays and expression or splicing quantitative trait locus (QTL) maps. We provide examples of genes hundreds of kilobases away from the lead variant, like GABBR1 for schizophrenia, that are prioritized by all three of PsyOPS, DNA looping and QTLs. Our results underscore the power of incorporating high-level knowledge of trait etiology into causal gene prediction at GWAS loci, and comprise a resource for researchers interested in experimentally characterizing psychiatric gene candidates.

Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics

Background: Machine learning methods showed excellent predictive ability in a wide range of fields. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics influence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and find the most suitable machine learning prediction method. Methods: The HNSC clinical data and multi-omics data were downloaded from the TCGA database. The important variables were screened by the LASSO algorithm. We used a total of 12 supervised machine learning models to predict the outcome of HNSC survival and compared the results. In vitro qPCR was performed to verify core genes predicted by the random forest algorithm. Results: For omics of HNSC, the results of the twelve models showed that the performance of multi-omics was better than each single-omic alone. Results were presented, which showed that the Bayesian network(BN) model (area under the curve [AUC] 0.8250, F1 score=0.7917) and random forest(RF) model (area under the curve [AUC] 0.8002,F1 score=0.7839) played good prediction performance in HNSC multi-omics data. The results of in vitro qPCR were consistent with the RF algorithm. Conclusion: Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the BN model and the RF model were the most superior. Moreover, the forecast result of multi-omics was better than single-omic alone in HNSC.

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.

Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies

The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal–offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant–metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal–offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal–offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.

Application of Bayesian genomic prediction methods to genome-wide association analyses

Background

Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses.

Results

By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance.

Conclusions

Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.

Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment

BACKGROUND

Multiple sclerosis (MS) is a debilitating immune-mediated disease of the central nervous system that affects over 2 million people worldwide, resulting in a heavy burden to families and entire communities. Understanding the genetic basis underlying MS could help decipher the pathogenesis and shed light on MS treatment. We refined a recently developed Bayesian framework, Integrative Risk Gene Selector (iRIGS), to prioritize risk genes associated with MS by integrating the summary statistics from the largest GWAS to date (n = 115,803), various genomic features, and gene-gene closeness.

RESULTS

We identified 163 MS-associated prioritized risk genes (MS-PRGenes) through the Bayesian framework. We replicated 35 MS-PRGenes through two-sample Mendelian randomization (2SMR) approach by integrating data from GWAS and Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) of 19 tissues. We demonstrated that MS-PRGenes had more substantial deleterious effects and disease risk. Moreover, single-cell enrichment analysis indicated MS-PRGenes were more enriched in activated macrophages and microglia macrophages than non-activated ones in control samples. Biological and drug enrichment analyses highlighted inflammatory signaling pathways.

CONCLUSIONS

In summary, we predicted and validated a high-confidence MS risk gene set from diverse genomic, epigenomic, eQTL, single-cell, and drug data. The MS-PRGenes could further serve as a benchmark of MS GWAS risk genes for future validation or genetic studies.

Abnormal synaptic plasticity and impaired cognition in schizophrenia

Schizophrenia (SCZ) is a severe mental illness that affects several brain domains with relation to cognition and behaviour. SCZ symptoms are typically classified into three categories, namely, positive, negative, and cognitive. The etiology of SCZ is thought to be multifactorial and poorly understood. Accumulating evidence has indicated abnormal synaptic plasticity and cognitive impairments in SCZ. Synaptic plasticity is thought to be induced at appropriate synapses during memory formation and has a critical role in the cognitive symptoms of SCZ. Many factors, including synaptic structure changes, aberrant expression of plasticity-related genes, and abnormal synaptic transmission, may influence synaptic plasticity and play vital roles in SCZ. In this article, we briefly summarize the morphology of the synapse, the neurobiology of synaptic plasticity, and the role of synaptic plasticity, and review potential mechanisms underlying abnormal synaptic plasticity in SCZ. These abnormalities involve dendritic spines, postsynaptic density, and long-term potentiation-like plasticity. We also focus on cognitive dysfunction, which reflects impaired connectivity in SCZ. Additionally, the potential targets for the treatment of SCZ are discussed in this article. Therefore, understanding abnormal synaptic plasticity and impaired cognition in SCZ has an essential role in drug therapy.

The shallow cognitive map hypothesis: A hippocampal framework for thought disorder in schizophrenia

Memories are not formed in isolation. They are associated and organized into relational knowledge structures that allow coherent thought. Failure to express such coherent thought is a key hallmark of Schizophrenia. Here we explore the hypothesis that thought disorder arises from disorganized Hippocampal cognitive maps. In doing so, we combine insights from two key lines of investigation, one concerning the neural signatures of cognitive mapping, and another that seeks to understand lower-level cellular mechanisms of cognition within a dynamical systems framework. Specifically, we propose that multiple distinct pathological pathways converge on the shallowing of Hippocampal attractors, giving rise to disorganized Hippocampal cognitive maps and driving conceptual disorganization. We discuss the available evidence at the computational, behavioural, network, and cellular levels. We also outline testable predictions from this framework, including how it could unify major chemical and psychological theories of schizophrenia and how it can provide a rationale for understanding the aetiology and treatment of the disease.

Common variants contribute to intrinsic human brain functional networks

The human brain forms functional networks of correlated activity, which have been linked with both cognitive and clinical outcomes. However, the genetic variants affecting brain function are largely unknown. Here, we used resting-state functional magnetic resonance images from 47,276 individuals to discover and validate common genetic variants influencing intrinsic brain activity. We identified 45 new genetic regions associated with brain functional signatures (P < 2.8 × 10^-11), including associations to the central executive, default mode, and salience networks involved in the triple-network model of psychopathology. A number of brain activity-associated loci colocalized with brain disorders (e.g., the APOE ε4 locus with Alzheimer's disease). Variation in brain function was genetically correlated with brain disorders, such as major depressive disorder and schizophrenia. Together, our study provides a step forward in understanding the genetic architecture of brain functional networks and their genetic links to brain-related complex traits and disorders.

Multi-omics research in sarcopenia: Current progress and future prospects

Sarcopenia is a systemic disease with progressive and generalized skeletal muscle dysfunction defined by age-related low muscle mass, high content of muscle slow fibers, and low muscle function. Muscle phenotypes and sarcopenia risk are heritable; however, the genetic architecture and molecular mechanisms underlying sarcopenia remain largely unclear. In recent years, significant progress has been made in determining susceptibility loci using genome-wide association studies. In addition, recent advances in omics techniques, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, offer new opportunities to identify novel targets to help us understand the pathophysiology of sarcopenia. However, each individual technology cannot capture the entire view of the biological complexity of this disorder, while integrative multi-omics analyses may be able to reveal new insights. Here, we review the latest findings of multi-omics studies for sarcopenia and provide an in-depth summary of our current understanding of sarcopenia pathogenesis. Leveraging multi-omics data could give us a holistic understanding of sarcopenia etiology that may lead to new clinical applications. This review offers guidance and recommendations for fundamental research, innovative perspectives, and preventative and therapeutic interventions for sarcopenia.

Leveraging Gene-Level Prediction as Informative Covariate in Hypothesis Weighting Improves Power for Rare Variant Association Studies

Gene-based rare variant association studies (RVASs) have low power due to the infrequency of rare variants and the large multiple testing burden. To correct for multiple testing, traditional false discovery rate (FDR) procedures which depend solely on P-values are often used. Recently, Independent Hypothesis Weighting (IHW) was developed to improve the detection power while maintaining FDR control by leveraging prior information for each hypothesis. Here, we present a framework to increase power of gene-based RVASs by incorporating prior information using IHW. We first build supervised machine learning models to assign each gene a prediction score that measures its disease risk, using the input of multiple biological features, fed with high-confidence risk genes and local background genes selected near GWAS significant loci as the training set. Then we use the prediction scores as covariates to prioritize RVAS results via IHW. We demonstrate the effectiveness of this framework through applications to RVASs in schizophrenia and autism spectrum disorder. We found sizeable improvements in the number of significant associations compared to traditional FDR approaches, and independent evidence supporting the relevance of the genes identified by our framework but not traditional FDR, demonstrating the potential of our framework to improve power of gene-based RVASs.

Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19

Background

Understanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown.

Methods

We constructed a computational framework to characterize the host genetics that influence immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets containing healthy controls and patients with mild, moderate, and severe symptom (N = 606,534 cells). We collected 10 predefined gene sets including inflammatory and cytokine genes to calculate cell state score for evaluating the immunological features of individual immune cells.

Results

We found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly expressed genes increased with the severity of COVID-19. Three cell subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were highly expressed in these three cell types, respectively. CCR1⁺CD16+monocytes and ABO⁺ megakaryocytes with significantly up-regulated genes, including S100A12, S100A8, S100A9, and IFITM1, confer higher risk to the dysregulated immune response among severe patients. CXCR6⁺ memory CD8+ T cells exhibit a notable polyfunctionality including elevation of proliferation, migration, and chemotaxis. Moreover, we observed an increase in cell-cell interactions of both CCR1⁺ CD16+monocytes and CXCR6⁺ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues. The enhanced interactions of CXCR6⁺ memory CD8+T cells with epithelial cells facilitate the recruitment of this specific population of T cells to airways, promoting CD8+T cell-mediated immunity against COVID-19 infection.

Conclusions

We uncover a major genetics-modulated immunological shift between mild and severe infection, including an elevated expression of genetics-risk genes, increase in inflammatory cytokines, and of functional immune cell subsets aggravating disease severity, which provides novel insights into parsing the host genetic determinants that influence peripheral immune cells in severe COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01021-1.

Prediction of New Risk Genes and Potential Drugs for Rheumatoid Arthritis from Multiomics Data

Rheumatoid arthritis (RA) is an autoimmune and inflammatory disease for which there is a lack of therapeutic options. Genome-wide association studies (GWASs) have identified over 100 genetic loci associated with RA susceptibility; however, the most causal risk genes (RGs) associated with, and molecular mechanism underlying, RA remain unknown. In this study, we collected 95 RA-associated loci from multiple GWASs and detected 87 candidate high-confidence risk genes (HRGs) from these loci via integrated multiomics data (the genome-scale chromosome conformation capture data, enhancer-promoter linkage data, and gene expression data) using the Bayesian integrative risk gene selector (iRIGS). Analysis of these HRGs indicates that these genes were indeed, markedly associated with different aspects of RA. Among these, 36 and 46 HRGs have been reported to be related to RA and autoimmunity, respectively. Meanwhile, most novel HRGs were also involved in the significantly enriched RA-related biological functions and pathways. Furthermore, drug repositioning prediction of the HRGs revealed three potential targets (ERBB2, IL6ST, and MAPK1) and nine possible drugs for RA treatment, of which two IL-6 receptor antagonists (tocilizumab and sarilumab) have been approved for RA treatment and four drugs (trastuzumab, lapatinib, masoprocol, and arsenic trioxide) have been reported to have a high potential to ameliorate RA. In summary, we believe that this study provides new clues for understanding the pathogenesis of RA and is important for research regarding the mechanisms underlying RA and the development of therapeutics for this condition.

Current and Future Perspectives of Noncoding RNAs in Brain Function and Neuropsychiatric Disease

Noncoding RNAs (ncRNAs) represent the majority of the transcriptome and play important roles in regulating neuronal functions. ncRNAs are exceptionally diverse in both structure and function and include enhancer RNAs, long ncRNAs, and microRNAs, all of which demonstrate specific temporal and regional expression in the brain. Here, we review recent studies demonstrating that ncRNAs modulate chromatin structure, act as chaperone molecules, and contribute to synaptic remodeling and behavior. In addition, we discuss ncRNA function within the context of neuropsychiatric diseases, particularly focusing on addiction and schizophrenia, and the recent methodological developments that allow for better understanding of ncRNA function in the brain. Overall, ncRNAs represent an underrecognized molecular contributor to complex neuronal processes underlying neuropsychiatric disorders.

Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease

BACKGROUND

Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful.

METHODS

To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells.

RESULTS

Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD.

CONCLUSIONS

In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

Prioritizing Autism Risk Genes using Personalized Graphical Models Estimated from Single Cell RNA-seq Data

Hundreds of autism risk genes have been reported recently, mainly based on genetic studies where these risk genes have more de novo mutations in autism subjects than healthy controls. However, as a complex disease, autism is likely associated with more risk genes and many of them may not be identifiable through de novo mutations. We hypothesize that more autism risk genes can be identified through their connections with known autism risk genes in personalized gene-gene interaction graphs. We estimate such personalized graphs using single cell RNA sequencing (scRNA-seq) while appropriately modeling the cell dependence and possible zero-inflation in the scRNA-seq data. The sample size, which is the number of cells per individual, ranges from 891 to 1,241 in our case study using scRNA-seq data in autism subjects and controls. We consider 1,500 genes in our analysis. Since the number of genes is larger or comparable to the sample size, we perform penalized estimation. We score each gene's relevance by applying a simple graph kernel smoothing method to each personalized graph. The molecular functions of the top-scored genes are related to autism diseases. For example, a candidate gene RYR2 that encodes protein ryanodine receptor 2 is involved in neurotransmission, a process that is impaired in ASD patients. While our method provides a systemic and unbiased approach to prioritize autism risk genes, the relevance of these genes needs to be further validated in functional studies.

Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer's disease

We developed an endophenotype disease module-based methodology for Alzheimer's disease (AD) drug repurposing and identified sildenafil as a potential disease risk modifier. Based on retrospective case-control pharmacoepidemiologic analyses of insurance claims data for 7.23 million individuals, we found that sildenafil usage was significantly associated with a 69% reduced risk of AD (hazard ratio = 0.31, 95% confidence interval 0.25-0.39, P<1.0×10^-8). Propensity score stratified analyses confirmed that sildenafil is significantly associated with a decreased risk of AD across all four drug cohorts we tested (diltiazem, glimepiride, losartan and metformin) after adjusting age, sex, race, and disease comorbidities. We also found that sildenafil increases neurite growth and decreases phospho-tau expression in AD patient-induced pluripotent stem cells-derived neuron models, supporting mechanistically its potential beneficial effect in Alzheimer's disease. The association between sildenafil use and decreased incidence of AD does not establish causality or its direction, which requires a randomized clinical trial approach.