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Introduction: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, and it significantly increases the risk of cardiovascular complications and morbidity, even with appropriate treatment. Tissue remodeling has been a significant topic, while its systematic transcriptional signature remains unclear in AF. Objectives: Our study aims to systematically investigate the molecular characteristics of AF at the cellular-level. Methods: We conducted single-nuclei RNA-sequencig (snRNA-seq) analysis using nuclei isolated from the left atrial appendage (LAA) of AF patients and sinus rhythm. Pathological staining was performed to validate the key findings of snRNA-seq. Results: A total of 30 cell subtypes were identified among 80, 592 nuclei. Within the LAA of AF, we observed a specific subtype of dedifferentiated cardiomyocytes (CMs) characterized by reduced expression of cardiac contractile proteins (TTN and TRDN) and heightened expression of extracellular-matrix related genes (COL1A2 and FBN1). Transcription factor prediction analysis revealed that gene expression patterns in dedifferentiated CMs were primarily regulated by CEBPG and GISLI. Additionally, we identified a distinct subtype of endothelial progenitor cells (EPCs) demonstrating elevated expression of PROM1 and KDR, a population decreased within the LAA of AF. Epicardial adipocytes disclosed a reduced release of the anti-inflammatory and anti-fibrotic factor PRG4, and an augmented secretion of VEGF signals targeting CMs. Additionally, we noted accumulation of M2-like macrophages and CD8+ T cells with high pro-inflammatory score in LAA of AF. Furthermore, the analysis of intercellular communication revealed specific pathways related to AF, such as inflammation, extracellular matrix, and vascular remodeling signals. Conclusions: This study has discovered the presence of dedifferentiated CMs, a decrease in endothelial progenitor cells, a shift in the secretion profile of adipocytes, and an amplified inflammatory response in AF. These findings could offer crucial insights for future research on AF and serve as valuable references for investigating novel therapeutic approaches for AF.

Abstract Background Osteoporotic vertebral compression fractures (OVCF) in the elderly increase refracture risk post-surgery, leading to higher mortality rates. Genome-wide association studies (GWAS) have identified susceptibility genes for osteoporosis, but the phenotypic variance explained by these genes has been limited, indicating the need to explore additional causal factors. Epigenetic modifications, such as DNA methylation, may influence osteoporosis and refracture risk. However, prospective cohorts for assessing epigenetic alterations in Chinese elderly patients are lacking. Here, we propose to conduct a prospective cohort study to investigate the causal network of DNA polymorphisms, DNA methylation, and environmental factors on the development of osteoporosis and the risk of refracture. Methods We will collect vertebral and peripheral blood from 500 elderly OVCF patients undergoing surgery, extract DNA, and generate whole genome genotype data and DNA methylation data. Observation indicators will be collected and combined with one-year follow-up data. A healthy control group will be selected from a natural population cohort. Epigenome-wide association studies (EWAS) of osteoporosis and bone mineral density will be conducted. Differential methylation analysis will compare candidate gene methylation patterns in patients with and without refracture. Multi-omics prediction models using genetic variants and DNA methylation sites will be built to predict OVCF risk. Discussion This study will be the first large-scale population-based study of osteoporosis and bone mineral density phenotypes based on genome-wide data, multi-time point methylation data, and phenotype data. By analyzing methylation changes related to osteoporosis and bone mineral density in OVCF patients, the study will explore the feasibility of DNA methylation in evaluating postoperative osteoporosis intervention effects. The findings may identify new molecular markers for effective anti-osteoporosis treatment and inform individualized prevention and treatment strategies. Trial registration chictr.org.cn ChiCTR2200065316, 02/11/2022.

Summary: Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the “casual” variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, to assess the functional impact of variants by SNP activity difference (SAD) scores. The DeepFace model is trained with 204 epigenomic assays from crucial human embryonic craniofacial developmental stages of post-conception week (pcw) 4 to pcw 10. The Pearson correlation coefficient between the predicted and actual values for 12 epigenetic features achieved a median range of 0.50–0.83. Specifically, our model revealed that SNPs significantly associated with OFCs tended to exhibit higher SAD scores across various variant categories compared to less related groups, indicating a context-specific impact of OFC-related SNPs. Notably, we identified six SNPs with a significant linear relationship to SAD scores throughout developmental progression, suggesting that these SNPs could play a temporal regulatory role. Furthermore, our cell-type specificity analysis pinpointed the trophoblast cell as having the highest enrichment of risk signals associated with OFCs. Overall, DeepFace can harness distal regulatory signals from extensive epigenomic assays, offering new perspectives for prioritizing OFC variants using contextualized functional genomic features. We expect DeepFace to be instrumental in accessing and predicting the regulatory roles of variants associated with OFCs, and the model can be extended to study other complex diseases or traits.

Summary: Orofacial clefts (OFCs) are common congenital birth defects with various etiologies, including genetic variants. Online Mendelian Inheritance in Man (OMIM) annotated several hundred genes involving OFCs. Furthermore, several hundreds of de novo variants (DNVs) have been identified from individuals with OFCs. Some DNVs are related to known OFC genes or pathways, but there are still many DNVs whose relevance to OFC development is unknown. To explore novel gene functions and their cellular expression profiles, we focused on DNVs in genes that were not listed in OMIM. We collected 960 DNVs in 853 genes from published studies and curated these genes, based on the DNVs’ deleteriousness, into 230 and 23 genes related to cleft lip with or without cleft palate (CL/P) and cleft palate only (CPO), respectively. For comparison, we curated 178 CL/P and 277 CPO genes from OMIM. In CL/P, the pathways enriched in DNV and OMIM genes were significantly overlapped (p = 0.002). Single-cell RNA sequencing (scRNA-seq) analysis of mouse lip development revealed that both gene sets had abundant expression in the ectoderm (DNV genes: adjusted p = 0.032, OMIM genes: adjusted p

The incidence of bladder cancer and patient survival vary greatly among different populations, but the influence of the associated molecular features and evolutionary processes on its clinical treatment and prognostication remains unknown. Here, we analyze the genomic architectures of 505 bladder cancer patients from Asian/Black/White populations. We identify a previously unknown association between AHNAK mutations and activity of the APOBEC‐a mutational signature, the activity of which varied substantially across populations. All significantly mutated genes but only half of arm‐level somatic copy number alterations (SCNAs) are enriched with clonal events, indicating large‐scale SCNAs as rich sources of bladder cancer clonal diversities. The prevalence of TP53 and ATM clonal mutations as well as the associated burden of SCNAs is significantly higher in Whites/Blacks than in Asians. We identify a trans‐ancestry prognostic subtype of bladder cancer characterized by enrichment of non‐muscle‐invasive patients and muscle‐invasive patients with good prognosis, increased CREBBP/FGFR3/HRAS/NFE2L2 mutations, decreased intra‐tumor heterogeneity and genome instability, and an activated tumor microenvironment.

The dysregulation of the m6A epitranscriptomic networks have been reported to contribute to the development of gliomas. Here, the authors utilize induced pluripotent stem cell-derived astrocytes with a p53 mutation and demonstrate that mutant p53 upregulates the m6A reader YTHDF2, resulting in the initiation of gliomas.

Abstract Background The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes. Results scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants. Conclusions We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.

Summary: Ischemia reperfusion injury (IRI), often related to surgical procedures, is one of the important causes of acute kidney injury (AKI). To decipher the dynamic process of AKI caused by IRI (with prolonged ischemia phase), we performed single-cell RNA sequencing (scRNA-seq) of clinically relevant IRI murine model with different ischemic intervals. We discovered that Slc5a2hi proximal tubular cells were susceptible to AKI and highly expressed neutral amino acid transporter gene Slc6a19, which was dramatically decreased over the time course. With the usage of mass spectrometry–based metabolomic analysis, we detected that the level of neutral amino acid isoleucine dropped off in AKI mouse plasma metabolites. And the reduction of plasma isoleucine was also verified in patients with cardiac surgery–associated acute kidney injury (CSA-AKI). The findings advanced the understanding of dynamic process of AKI and introduced reduction of isoleucine as a potential biomarker for CSA-AKI.

Drug response in cancer patients vary dramatically due to inter- and intra-tumor heterogeneity and transcriptome context plays a significant role in shaping the actual treatment outcome. Here, the authors develop a deep variational autoencoder model to compress gene signatures into latent vectors and accurately impute drug response.

Amyotrophic lateral sclerosis (ALS) is a fatal progressive multisystem disorder with limited therapeutic options. Although genome-wide association studies (GWASs) have revealed multiple ALS susceptibility loci, the exact identities of causal variants, genes, cell types, tissues, and their functional roles in the development of ALS remain largely unknown. Here, we reported a comprehensive post-GWAS analysis of the recent large ALS GWAS (n = 80,610), including functional mapping and annotation (FUMA), transcriptome-wide association study (TWAS), colocalization (COLOC), and summary data-based Mendelian randomization analyses (SMR) in extensive multi-omics datasets. Gene property analysis highlighted inhibitory neuron 6, oligodendrocytes, and GABAergic neurons (Gad1/Gad2) as functional cell types of ALS and confirmed cerebellum and cerebellar hemisphere as functional tissues of ALS. Functional annotation detected the presence of multiple deleterious variants at three loci (9p21.2, 12q13.3, and 12q14.2) and highlighted a list of SNPs that are potentially functional. TWAS, COLOC, and SMR identified 43 genes at 24 loci, including 23 novel genes and 10 novel loci, showing significant evidence of causality. Integrating multiple lines of evidence, we further proposed that rs2453555 at 9p21.2 and rs229243 at 14q12 functionally contribute to the development of ALS by regulating the expression of C9orf72 in pituitary and SCFD1 in skeletal muscle, respectively. Together, these results advance our understanding of the biological etiology of ALS, feed into new therapies, and provide a guide for subsequent functional experiments.

Testicular germ cell tumours (TGCTs) are the most common cancer in young male adults (aged 15 to 40). Unlike most other cancer types, identification of molecular signatures in TGCT has rarely reported. In this study, we developed a novel integrative analysis framework to identify co-methylated and co-expressed genes [mRNAs and microRNAs (miRNAs)] modules in two TGCT subtypes: non-seminoma (NSE) and seminoma (SE). We first integrated DNA methylation and mRNA/miRNA expression data and then used a statistical method, CoMEx (Combined score of DNA Methylation and Expression), to assess differentially expressed and methylated (DEM) genes/miRNAs. Next, we identified co-methylation and co-expression modules by applying WGCNA (Weighted Gene Correlation Network Analysis) tool to these DEM genes/miRNAs. The module with the highest average Pearson’s Correlation Coefficient (PCC) after considering all pair-wise molecules (genes/miRNAs) included 91 molecules. By integrating both transcription factor and miRNA regulations, we constructed subtype-specific regulatory networks for NSE and SE. We identified four hub miRNAs (miR-182-5p, miR-520b, miR-520c-3p, and miR-7-5p), two hub TFs (MYC and SP1), and two genes (RECK and TERT) in the NSE-specific regulatory network, and two hub miRNAs (miR-182-5p and miR-338-3p), five hub TFs (ETS1, HIF1A, HNF1A, MYC, and SP1), and three hub genes (CDH1, CXCR4, and SNAI1) in the SE-specific regulatory network. miRNA (miR-182-5p) and two TFs (MYC and SP1) were common hubs of NSE and SE. We further examined pathways enriched in these subtype-specific networks. Our study provides a comprehensive view of the molecular signatures and co-regulation in two TGCT subtypes.

Abstract Background Psychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BIP), major depressive disorder (MDD), attention deficit-hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) are often related to brain development. Both shared and unique biological and neurodevelopmental processes have been reported to be involved in these disorders. Methods In this work, we developed an integrative analysis framework to seek for the sensitive spatiotemporal point during brain development underlying each disorder. Specifically, we first identified spatiotemporal gene co-expression modules for four brain regions three developmental stages (prenatal, birth to 11 years old, and older than 13 years), totaling 12 spatiotemporal sites. By integrating GWAS summary statistics and the spatiotemporal co-expression modules, we characterized the risk genes and their co-expression partners for five disorders. Results We found that SCZ and BIP, ASD and ADHD tend to cluster with each other and keep a distance from other psychiatric disorders. At the gene level, we identified several genes that were shared among the most significant modules, such as CTNNB1 and LNX1, and a hub gene, ATF2, in multiple modules. Moreover, we pinpointed two spatiotemporal points in the prenatal stage with active expression activities and highlighted one postnatal point for BIP. Further functional analysis of the disorder-related module highlighted the apoptotic signaling pathway for ASD and the immune-related and cell-cell adhesion function for SCZ, respectively. Conclusion Our study demonstrated the dynamic changes of disorder-related genes at the network level, shedding light on the spatiotemporal regulation during brain development.

Long non-coding RNAs (lncRNAs) have recently been found to be important in gene regulation. lncRNA H19 has been reported to play an oncogenic role in many human cancers. Its specific regulatory role is still elusive. In this study, we developed a novel analytic approach by integrating the synergistic regulation among lncRNAs (e.g., H19), transcription factors (TFs), target genes, and microRNAs (miRNAs) and then applied it to the pan-cancer expression datasets from The Cancer Genome Atlas (TCGA). Using linear regression models, we identified 88 H19-TF-gene co-regulatory triplets, in which 93% of the TF-gene pairs were related to cancer, indicating that our approach was effective to identify disease-related lncRNA-TF-gene co-regulation mechanisms. lncRNAs can function as miRNA sponges. Our further experiments found that H19 might regulate SP1-TGFBR2 through let-7b and miR-200b, ETS1-TGFBR2 through miR-29a and miR-200b, and STAT3-KLF11 through miR-17 in breast cancer cell lines. Our work suggests that miRNA-mediated lncRNA-TF-gene co-regulation is complicated yet important in cancer.

Abstract Background Multiple sclerosis (MS) is a complex disease in which the immune system attacks the central nervous system. The molecular mechanisms contributing to the etiology of MS remain poorly understood. Genome-wide association studies (GWAS) of MS have identified a small number of genetic loci significant at the genome level, but they are mainly non-coding variants. Network-assisted analysis may help better interpret the functional roles of the variants with association signals and potential translational medicine application. The Dense Module Searching of GWAS tool (dmGWAS version 2.4) developed in our team is applied to 2 MS GWAS datasets (GeneMSA and IMSGC GWAS) using the human protein interactome as the reference network. A dual evaluation strategy is used to generate results with reproducibility. Results Approximately 7500 significant network modules were identified for each independent GWAS dataset, and 20 significant modules were identified from the dual evaluation. The top modules included GRB2, HDAC1, JAK2, MAPK1, and STAT3 as central genes. Top module genes were enriched with functional terms such as “regulation of glial cell differentiation” (adjusted p-value = 2.58 × 10− 3), “T-cell costimulation” (adjusted p-value = 2.11 × 10− 6) and “virus receptor activity” (adjusted p-value = 1.67 × 10− 3). Interestingly, top gene networks included several MS FDA approved drug target genes HDAC1, IL2RA, KEAP1, and RELA, Conclusions Our dmGWAS network analyses highlighted several genes (GRB2, HDAC1, IL2RA, JAK2, KEAP1, MAPK1, RELA and STAT3) in top modules that are promising to interpret GWAS signals and link to MS drug targets. The genes enriched with glial cell differentiation are important for understanding neurodegenerative processes in MS and for remyelination therapy investigation. Importantly, our identified genetic signals enriched in T cell costimulation and viral receptor activity supported the viral infection onset hypothesis for MS.

Abstract Background Cleft lip with or without cleft palate (CL/P) is one of the most common congenital human birth defects. A combination of genetic and epidemiology studies has contributed to a better knowledge of CL/P-associated candidate genes and environmental risk factors. However, the etiology of CL/P remains not fully understood. In this study, to identify new CL/P-associated genes, we conducted an integrative analysis using our in-house network tools, dmGWAS [dense module search for Genome-Wide Association Studies (GWAS)] and EW_dmGWAS (Edge-Weighted dmGWAS), in a combination with GWAS data, the human protein-protein interaction (PPI) network, and differential gene expression profiles. Results A total of 87 genes were consistently detected in both European and Asian ancestries in dmGWAS. There were 31.0% (27/87) showed nominal significance with CL/P (gene-based p

Abstract Background Heart failure (HF) has been recognized as a global pandemic with a high rate of hospitalization, morbidity, and mortality. Although numerous advances have been made, its representative molecular signatures remain largely unknown, especially the role of genes in HF progression. The aim of the present prospective follow-up study was to reveal potential biomarkers associated with the progression of heart failure. Methods We generated multi-level transcriptomic data from a cohort of left ventricular heart tissue collected from 21 HF patients and 9 healthy donors. By using Masson staining to calculate the fibrosis percentage for each sample, we applied lasso regression model to identify the genes associated with fibrosis as well as progression. The genes were further validated by immunohistochemistry (IHC) staining in the same cohort and qRT-PCR using another independent cohort (20 HF and 9 healthy donors). Enzyme-linked immunosorbent assay (ELISA) was used to measure the plasma level in a validation cohort (139 HF patients) for predicting HF progression. Results Based on the multi-level transcriptomic data, we examined differentially expressed genes [mRNAs, microRNAs, and long non-coding RNAs (lncRNAs)] in the study cohort. The follow-up functional annotation and regulatory network analyses revealed their potential roles in regulating extracellular matrix. We further identified several genes that were associated with fibrosis. By using the survival time before transplantation, COL1A1 was identified as a potential biomarker for HF progression and its upregulation was confirmed by both IHC and qRT-PCR. Furthermore, COL1A1 content ≥ 256.5 ng/ml in plasma was found to be associated with poor survival within 1 year of heart transplantation from heart failure [hazard ratio (HR) 7.4, 95% confidence interval (CI) 3.5 to 15.8, Log-rank p value

Abstract Background Cleft lip (CL), one of the most common congenital birth defects, shows considerable geographic and ethnic variation, with contribution of both genetic and environmental factors. Mouse genetic studies have identified several CL-associated genes. However, it remains elusive how these CL-associated genes are regulated and involved in CL. Environmental factors may regulate these genes at the post-transcriptional level through the regulation of non-coding microRNAs (miRNAs). In this study, we sought to identify miRNAs associated with CL in mice. Results Through a systematic literature review and a Mouse Genome Informatics (MGI) database search, we identified 55 genes that were associated with CL in mice. Subsequent bioinformatic analysis of these genes predicted that a total of 33 miRNAs target multiple CL-associated genes, with 20 CL-associated genes being potentially regulated by multiple miRNAs. To experimentally validate miRNA function in cell proliferation, we conducted cell proliferation/viability assays for the selected five candidate miRNAs (miR-124-3p, let-7a-5p, let-7b-5p, let-7c-5p, and let-7d-5p). Overexpression of miR-124-3p, but not of the others, inhibited cell proliferation through suppression of CL-associated genes in cultured mouse embryonic lip mesenchymal cells (MELM cells) isolated from the developing mouse lip region. By contrast, miR-124-3p knockdown had no effect on MELM cell proliferation. This miRNA-gene regulatory mechanism was mostly conserved in O9–1 cells, an established cranial neural crest cell line. Expression of miR-124-3p was low in the maxillary processes at E10.5, when lip mesenchymal cells proliferate, whereas it was greatly increased at later developmental stages, suggesting that miR-124-3p expression is suppressed during the proliferation phase in normal palate development. Conclusions Our findings indicate that upregulated miR-124-3p inhibits cell proliferation in cultured lip cells through suppression of CL-associated genes. These results will have a significant impact, not only on our knowledge about lip morphogenesis, but also on the development of clinical approaches for the diagnosis and prevention of CL.

Abstract Background The etiology of cleft lip with or without cleft palate (CL/P), a common congenital birth defect, is complex and involves the contribution of genetic and environmental factors. Although many candidate genes have been identified, the regulation and interaction of these genes in CL/P remain unclear. In addition, the contribution of microRNAs (miRNAs), non-coding RNAs that regulate the expression of multiple genes, to the etiology of CL/P is largely unknown. Methods To identify the signatures of causative biological pathways for human CL/P, we conducted a systematic literature review for human CL/P candidate genes and subsequent bioinformatics analyses. Functional enrichment analyses of the candidate CL/P genes were conducted using the pathway databases GO and KEGG. The miRNA-mediated post-transcriptional regulation of the CL/P candidate genes was analyzed with miRanda, PITA, and TargetScan, and miRTarbase. Genotype-phenotype association analysis was conducted using GWAS. The functional significance of the candidate miRNAs was evaluated experimentally in cell proliferation and target gene regulation assays in human lip fibroblasts. Results Through an extensive search of the main biomedical databases, we mined 177 genes with mutations or association/linkage reported in individuals with CL/P, and considered them as candidate genes for human CL/P. The genotype-phenotype association study revealed that mutations in 12 genes (ABCA4, ADAM3A, FOXE1, IRF6, MSX2, MTHFR, NTN1, PAX7, TP63, TPM1, VAX1, and WNT9B) were significantly associated with CL/P. In addition, our bioinformatics analysis predicted 16 microRNAs (miRNAs) to be post-transcriptional regulators of CL/P genes. To validate the bioinformatics results, the top six candidate miRNAs (miR-124-3p, miR-369-3p, miR-374a-5p, miR-374b-5p, miR-497-5p, and miR-655-3p) were evaluated by cell proliferation/survival assays and miRNA-gene regulation assays in cultured human lip fibroblasts. We found that miR-497-5p and miR-655-3p significantly suppressed cell proliferation in these cells. Furthermore, the expression of the predicted miRNA-target genes was significantly downregulated by either miR-497-5p or miR-655-3p mimic. Conclusion Expression of miR-497-5p and miR-655-3p suppresses cell proliferation through the regulation of human CL/P-candidate genes. This study provides insights into the role of miRNAs in the etiology of CL/P and suggests possible strategies for the diagnosis of CL/P.

Clear cell renal cell carcinoma (ccRCC) is a urogenital cancer with a well-defined genetic landscape. Here, the authors analyse the clonal architecture of ccRCC patients from three populations, and find prognostic subtypes linked to immune infiltrates and clonal architecture.

Abstract Approximately 15% of human cancers are estimated to be attributed to viruses. Virus sequences can be integrated into the host genome, leading to genomic instability and carcinogenesis. Here, a new deep convolutional neural network (CNN) model is developed with attention architecture, namely DeepVISP, for accurately predicting oncogenic virus integration sites (VISs) in the human genome. Using the curated benchmark integration data of three viruses, hepatitis B virus (HBV), human herpesvirus (HPV), and Epstein‐Barr virus (EBV), DeepVISP achieves high accuracy and robust performance for all three viruses through automatically learning informative features and essential genomic positions only from the DNA sequences. In comparison, DeepVISP outperforms conventional machine learning methods by 8.43–34.33% measured by area under curve (AUC) value enhancement in three viruses. Moreover, DeepVISP can decode cis‐regulatory factors that are potentially involved in virus integration and tumorigenesis, such as HOXB7, IKZF1, and LHX6. These findings are supported by multiple lines of evidence in literature. The clustering analysis of the informative motifs reveales that the representative k‐mers in clusters could help guide virus recognition of the host genes. A user‐friendly web server is developed for predicting putative oncogenic VISs in the human genome using DeepVISP.

Background: Genome-wide association studies have successfully identified variants associated with multiple conditions. However, generalizing discoveries across diverse populations remains challenging due to large variations in genetic composition. Methods that perform gene expression imputation have attempted to address the transferability of gene discoveries across populations, but with limited success. Methods: Here, we introduce a pipeline that combines gene expression imputation with gene module discovery, including a dense gene module search and a gene set variation analysis, to address the transferability issue. Our method feeds association probabilities of imputed gene expression with a selected phenotype into tissue-specific gene-module discovery over protein interaction networks to create higher-level gene modules. Results: We demonstrate our method’s utility in three case-control studies of Alzheimer’s disease (AD) for three different race/ethnic populations (Whites, African descent and Hispanics). We discovered 182 AD-associated genes from gene modules shared between these populations, highlighting new gene modules associated with AD. Conclusions: Our innovative framework has the potential to identify robust discoveries across populations based on gene modules, as demonstrated in AD.

With steady increase of new COVID-19 cases around the world, especially in the United States, health care resources in areas with the disease outbreak are quickly exhausted by overwhelming numbers of COVID-19 patients. Therefore, strategies that can effectively and quickly predict the disease progression and stratify patients for appropriate health care arrangements are urgently needed. We explored the features and evolutionary difference of viral gene expression in the SARS-CoV-2 infected cells from the bronchoalveolar lavage fluids of patients with moderate and severe COVID-19 using both single cell and bulk tissue transcriptome data. We found SARS-CoV-2 sequences were detectable in 8 types of immune related cells, including macrophages, T cells, and NK cells. We first reported that the SARS-CoV-2 ORF10 gene was differentially expressed in the severe vs. moderate samples. Specifically, ORF10 was abundantly expressed in infected cells of severe cases, while it was barely detectable in the infected cells of moderate cases. Consequently, the expression ratio of ORF10 to nucleocapsid (N) was significantly higher in severe than moderate cases (p = 0.0062). Moreover, we found transcription regulatory sequences (TRSs) of the viral leader sequence-independent fusions with a 5’ joint point at position 1073 of SARS-CoV-2 genome were detected mainly in the patients with death outcome, suggesting its potential indication of clinical outcome. Finally, we identified the motifs in TRS of the viral leader sequence-dependent fusion events of SARS-CoV-2 and compared with that in SARS-CoV, suggesting its evolutionary trajectory. These results implicated potential roles and predictive features of viral transcripts in the pathogenesis of COVID-19 moderate and severe patients. Such features and evolutionary patterns require more data to validate in future.

Abstract Background Genome-wide association studies (GWAS) have been successful in identifying disease-associated genetic variants. Recently, an increasing number of GWAS summary statistics have been made available to the research community, providing extensive repositories for studies of human complex diseases. In particular, cross-trait associations at the genetic level can be beneficial from large-scale GWAS summary statistics by using genetic variants that are associated with multiple traits. However, direct assessment of cross-trait associations using susceptibility loci has been challenging due to the complex genetic architectures in most diseases, calling for advantageous methods that could integrate functional interpretation and imply biological mechanisms. Results We developed an analytical framework for systematic integration of cross-trait associations. It incorporates two different approaches to detect enriched pathways and requires only summary statistics. We demonstrated the framework using 25 traits belonging to four phenotype groups. Our results revealed an average of 54 significantly associated pathways (ranged between 18 and 175) per trait. We further proved that pathway-based analysis provided increased power to estimate cross-trait associations compared to gene-level analysis. Based on Fisher’s Exact Test (FET), we identified a total of 24 (53) pairs of trait-trait association at adjusted p FET