GWASdb v2: an update database for human genetic variants identified by genome-wide association studies

Causal associations of hypothyroidism with frozen shoulder: a two-sample bidirectional Mendelian randomization study

BACKGROUND

Many studies have investigated the association between hypothyroidism and frozen shoulder, but their findings have been inconsistent. Furthermore, earlier research has been primarily observational, which may introduce bias and does not establish a cause-and-effect relationship. To ascertain the causal association, we performed a two-sample bidirectional Mendelian randomization (MR) analysis.

METHODS

We obtained data on "Hypothyroidism" and "Frozen Shoulder" from Summary-level Genome-Wide Association Studies (GWAS) datasets that have been published. The information came from European population samples. The primary analysis utilized the inverse-variance weighted (IVW) method. Additionally, a sensitivity analysis was conducted to assess the robustness of the results.

RESULTS

We ultimately chose 39 SNPs as IVs for the final analysis. The results of the two MR methods we utilized in the investigation indicated that a possible causal relationship between hypothyroidism and frozen shoulder. The most significant analytical outcome demonstrated an odds ratio (OR) of 1.0577 (95% Confidence Interval (CI):1.0057-1.1123), P = 0.029, using the IVW approach. Furthermore, using the MR Egger method as a supplementary analytical outcome showed an OR of 1.1608 (95% CI:1.0318-1.3060), P = 0.017. Furthermore, the results of our sensitivity analysis indicate that there is no heterogeneity or pleiotropy in our MR analysis. In the reverse Mendelian analysis, no causal relationship was found between frozen shoulders and hypothyroidism.

CONCLUSION

Our MR analysis suggests that there may be a causal relationship between hypothyroidism and frozen shoulder.

PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis

Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA). Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells. Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.

Deep learning modeling of rare noncoding genetic variants in human motor neurons defines CCDC146 as a therapeutic target for ALS

Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective and progressive death of motor neurons (MNs). Understanding the genetic and molecular factors influencing ALS survival is crucial for disease management and therapeutics. In this study, we introduce a deep learning-powered genetic analysis framework to link rare noncoding genetic variants to ALS survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, this method prioritizes functional noncoding variants using deep learning, links cis-regulatory elements (CREs) to target genes using epigenomics data, and integrates these data through gene-level burden tests to identify survival-modifying variants, CREs, and genes. We apply this approach to analyze 6,715 ALS genomes, and pinpoint four novel rare noncoding variants associated with survival, including chr7:76,009,472:C>T linked to CCDC146. CRISPR-Cas9 editing of this variant increases CCDC146 expression in iPSC-derived MNs and exacerbates ALS-specific phenotypes, including TDP-43 mislocalization. Suppressing CCDC146 with an antisense oligonucleotide (ASO), showing no toxicity, completely rescues ALS-associated survival defects in neurons derived from sporadic ALS patients and from carriers of the ALS-associated G4C2-repeat expansion within C9ORF72. ASO targeting of CCDC146 may be a broadly effective therapeutic approach for ALS. Our framework provides a generic and powerful approach for studying noncoding genetics of complex human diseases.

Association of seafood consumption with cardiovascular disease among adults in Qingdao, China

BACKGROUND AND AIMS

The relationship between seafood consumption and cardiovascular disease (CVD) is controversial, and studies have not considered competing risk events. Our study examined the association between a full range of seafood consumption and CVD incidence and mortality based on the Qingdao Diabetes Prevention Program.

METHODS AND RESULTS

We followed up 5285 participants without CVD at baseline until December 31, 2021. CVD cases and deaths were identified through record linkage with the Qingdao CVD Surveillance System and the Qingdao Death Surveillance System, respectively. Information on seafood consumption was obtained using a food frequency questionnaire. We used the Cox proportional hazard model and the competing risk model to evaluate the association between all types of seafood consumption and CVD incidence and mortality. During a median follow-up of 11.4 years, 122 CVD cases and 75 deaths occurred. After adjustment for potential confounders, compared with nonconsumers, seafood consumption of 300-500 and > 500 g/week was associated with a lower risk of CVD incidence [hazards ratio and 95 % confidence interval (CI): 0.54 (0.29-0.99) and 0.49 (0.26-0.91), respectively]. However, seafood consumption of >500 g/week had a significantly lower risk of CVD mortality [subdistribution hazard ratio and 95 % CI: 0.40 (0.17-0.95)], but it was insignificant in other groups.

CONCLUSION

Seafood consumption of 300-500 g/week and >500 g/week was associated with a lower CVD incidence and mortality. Our findings provide evidence of the recommendations of the 2022 Dietary Guidelines for Chinese residents and may guide the promotion of strategies for CVD prevention.

Structural effect of the H992D/H418D mutation of angiotensin-converting enzyme in the Indian population: implications for health and disease

The Non synonymous SNPs (nsSNPs) of the renin-angiotensin-system (RAS) pathway, unique to the Indian population were investigated in view of its importance as an endocrine system. nsSNPs of the RAS pathway genes were mined from the IndiGenome database. Damaging nsSNPs were predicted using SIFT, PredictSNP, SNP and GO, Snap2 and Protein Variation Effect Analyzer. Loss of function was predicted based on protein stability change using I mutant, PremPS and CONSURF. The structural impact of the nsSNPs was predicted using HOPE and Missense3d followed by modeling, refinement, and energy minimization. Molecular Dynamics studies were carried out using Gromacsv2021.1. 23 Indian nsSNPs of the RAS pathway genes were selected for structural analysis and 8 were predicted to be damaging. Further sequence analysis showed that HEMGH zinc binding motif changes to HEMGD in somatic ACE-C domain (sACE-C) H992D and Testis ACE (tACE) H418D resulted in loss of zinc coordination, which is essential for enzymatic activity in this metalloprotease. There was a loss of internal interactions around the zinc coordination residues in the protein structural network. This was also confirmed by Principal Component Analysis, Free Energy Landscape and residue contact maps. Both mutations lead to broadening of the AngI binding cavity. The H992D mutation in sACE-C is likely to be favorable for cardiovascular health, but may lead to renal abnormalities with secondary impact on the heart. H418D in tACE is potentially associated with male infertility.Communicated by Ramaswamy H. Sarma.

NCAD v1.0: a database for non-coding variant annotation and interpretation

The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. Therefore, it is urgent to improve the diagnostic yield by exploring the pathogenic mechanisms of variants in non-coding regions. However, the interpretation of non-coding variants remains a significant challenge, due to the complex functional regulatory mechanisms of non-coding regions and the current limitations of available databases and tools. Hence, we develop the non-coding variant annotation database (NCAD, http://www.ncawdb.net/), encompassing comprehensive insights into 665,679,194 variants, regulatory elements, and element interaction details. Integrating data from 96 sources, spanning both GRCh37 and GRCh38 versions, NCAD v1.0 provides vital information to support the genetic diagnosis of non-coding variants, including allele frequencies of 12 diverse populations, with a particular focus on the population frequency information for 230,235,698 variants in 20,964 Chinese individuals. Moreover, it offers prediction scores for variant functionality, five categories of regulatory elements, and four types of non-coding RNAs. With its rich data and comprehensive coverage, NCAD serves as a valuable platform, empowering researchers and clinicians with profound insights into non-coding regulatory mechanisms while facilitating the interpretation of non-coding variants.

Diabetic sensory neuropathy and insulin resistance are induced by loss of UCHL1 in Drosophila

Diabetic sensory neuropathy (DSN) is one of the most common complications of type 2 diabetes (T2D), however the molecular mechanistic association between T2D and DSN remains elusive. Here we identify ubiquitin C-terminal hydrolase L1 (UCHL1), a deubiquitinase highly expressed in neurons, as a key molecule underlying T2D and DSN. Genetic ablation of UCHL1 leads to neuronal insulin resistance and T2D-related symptoms in Drosophila. Furthermore, loss of UCHL1 induces DSN-like phenotypes, including numbness to external noxious stimuli and axonal degeneration of sensory neurons in flies' legs. Conversely, UCHL1 overexpression improves DSN-like defects of T2D model flies. UCHL1 governs insulin signaling by deubiquitinating insulin receptor substrate 1 (IRS1) and antagonizes an E3 ligase of IRS1, Cullin 1 (CUL1). Consistent with these results, genetic and pharmacological suppression of CUL1 activity rescues T2D- and DSN-associated phenotypes. Therefore, our findings suggest a complete set of genetic factors explaining T2D and DSN, together with potential remedies for the diseases.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species

Transcription factors (TFs), transcription co-factors (TcoFs) and their target genes perform essential functions in diseases and biological processes. KnockTF 2.0 (http://www.licpathway.net/KnockTF/index.html) aims to provide comprehensive gene expression profile datasets before/after T(co)F knockdown/knockout across multiple tissue/cell types of different species. Compared with KnockTF 1.0, KnockTF 2.0 has the following improvements: (i) Newly added T(co)F knockdown/knockout datasets in mice, Arabidopsis thaliana and Zea mays and also an expanded scale of datasets in humans. Currently, KnockTF 2.0 stores 1468 manually curated RNA-seq and microarray datasets associated with 612 TFs and 172 TcoFs disrupted by different knockdown/knockout techniques, which are 2.5 times larger than those of KnockTF 1.0. (ii) Newly added (epi)genetic annotations for T(co)F target genes in humans and mice, such as super-enhancers, common SNPs, methylation sites and chromatin interactions. (iii) Newly embedded and updated search and analysis tools, including T(co)F Enrichment (GSEA), Pathway Downstream Analysis and Search by Target Gene (BLAST). KnockTF 2.0 is a comprehensive update of KnockTF 1.0, which provides more T(co)F knockdown/knockout datasets and (epi)genetic annotations across multiple species than KnockTF 1.0. KnockTF 2.0 facilitates not only the identification of functional T(co)Fs and target genes but also the investigation of their roles in the physiological and pathological processes.

eRNAbase: a comprehensive database for decoding the regulatory eRNAs in human and mouse

Enhancer RNAs (eRNAs) transcribed from distal active enhancers serve as key regulators in gene transcriptional regulation. The accumulation of eRNAs from multiple sequencing assays has led to an urgent need to comprehensively collect and process these data to illustrate the regulatory landscape of eRNAs. To address this need, we developed the eRNAbase (http://bio.liclab.net/eRNAbase/index.php) to store the massive available resources of human and mouse eRNAs and provide comprehensive annotation and analyses for eRNAs. The current version of eRNAbase cataloged 10 399 928 eRNAs from 1012 samples, including 858 human samples and 154 mouse samples. These eRNAs were first identified and uniformly processed from 14 eRNA-related experiment types manually collected from GEO/SRA and ENCODE. Importantly, the eRNAbase provides detailed and abundant (epi)genetic annotations in eRNA regions, such as super enhancers, enhancers, common single nucleotide polymorphisms, expression quantitative trait loci, transcription factor binding sites, CRISPR/Cas9 target sites, DNase I hypersensitivity sites, chromatin accessibility regions, methylation sites, chromatin interactions regions, topologically associating domains and RNA spatial interactions. Furthermore, the eRNAbase provides users with three novel analyses including eRNA-mediated pathway regulatory analysis, eRNA-based variation interpretation analysis and eRNA-mediated TF-target gene analysis. Hence, eRNAbase is a powerful platform to query, browse and visualize regulatory cues associated with eRNAs.

PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs

To fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.

MethMarkerDB: a comprehensive cancer DNA methylation biomarker database

DNA methylation plays a crucial role in tumorigenesis and tumor progression, sparking substantial interest in the clinical applications of cancer DNA methylation biomarkers. Cancer-related whole-genome bisulfite sequencing (WGBS) data offers a promising approach to precisely identify these biomarkers with differentially methylated regions (DMRs). However, currently there is no dedicated resource for cancer DNA methylation biomarkers with WGBS data. Here, we developed a comprehensive cancer DNA methylation biomarker database (MethMarkerDB, https://methmarkerdb.hzau.edu.cn/), which integrated 658 WGBS datasets, incorporating 724 curated DNA methylation biomarker genes from 1425 PubMed published articles. Based on WGBS data, we documented 5.4 million DMRs from 13 common types of cancer as candidate DNA methylation biomarkers. We provided search and annotation functions for these DMRs with different resources, such as enhancers and SNPs, and developed diagnostic and prognostic models for further biomarker evaluation. With the database, we not only identified known DNA methylation biomarkers, but also identified 781 hypermethylated and 5245 hypomethylated pan-cancer DMRs, corresponding to 693 and 2172 genes, respectively. These novel potential pan-cancer DNA methylation biomarkers hold significant clinical translational value. We hope that MethMarkerDB will help identify novel cancer DNA methylation biomarkers and propel the clinical application of these biomarkers.

RAVAR: a curated repository for rare variant-trait associations

Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant-trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level. To address the issue, we present the Rare Variant Association Repository (RAVAR), a comprehensive collection of rare variant associations. RAVAR includes 95 047 high-quality rare variant associations (76186 gene-level and 18 861 variant-level associations) for 4429 reported traits which are manually curated from 245 publications. RAVAR is the first resource to collect and curate published rare variant associations in an interactive web interface with integrated visualization, search, and download features. Detailed gene and SNP information are provided for each association, and users can conveniently search for related studies by exploring the EFO tree structure and interactive Manhattan plots. RAVAR could vastly improve the accessibility of rare variant studies. RAVAR is freely available for all users without login requirement at http://www.ravar.bio.

Diabetic Retinopathy and Brain Structure, Cognition Function, and Dementia: A Bidirectional Mendelian Randomization Study

BACKGROUND

Accumulating evidence has demonstrated that hyperglycemia is a possible risk factor for mild cognitive impairment or Alzheimer's disease. Diabetic retinopathy (DR) has been identified as a risk factor for dementia in patients with diabetes.

OBJECTIVE

This study aimed to investigate the causal relationships between DR and brain structure, cognitive function, and dementia.

METHODS

We performed bidirectional two-sample Mendelian randomization for DR, brain structure, cognitive function, and dementia using the inverse-variance weighted method.

RESULTS

Inverse-variance weighted analysis showed the association of DR with vascular dementia (OR = 1.68, 95% CI: 1.01-2.82), and dementia was significantly associated with the increased risk of non-proliferative DR (NPDR) (OR = 1.76, 95% CI: 1.04-2.98). Furthermore, better cognitive performance was significantly associated with a reduced risk of NPDR (OR = 0.85, 95% CI: 0.74-0.98). No association was observed between DR and brain structure.

CONCLUSIONS

These findings suggest that the association of DR with vascular dementia. The reciprocal effect of cognitive performance and dementia on NPDR risk highlights the potential benefits of dementia prevention for reducing the burden of DR.

Causal relationship between atrial fibrillation and stroke risk: a Mendelian randomization

OBJECTIVES

This study aimed to investigate the causal relationship between Atrial Fibrillation (AF) and the risk of Stroke using a Mendelian randomization (MR) approach.

METHODS

A two-sample MR analysis was conducted using publicly available genome-wide association study (GWAS) summary statistics data. In this analysis, genetic variants associated with AF were used as instrumental variables to estimate the causal effect. The inverse-variance weighted (IVW) method, weighted median estimator, and MR-Egger regression were employed for estimation. Additionally, sensitivity analysis was performed using the leave-one-out method.

RESULTS

The analysis included 87 single nucleotide polymorphisms (SNPs) associated with AF. The results from the IVW method indicated a positive association between genetic predisposition to AF and the risk of stroke (OR 1.002, 95 % CI 1.001-1.003, P < 0.001). The weighted median and MR-Egger methods showed consistent results (weighted median: OR 1.001, 95 % CI 1.000-1.002, P = 0.034; MR-Egger: OR 1.001, 95 % CI 1.000-1.003, P = 0.086). Sensitivity analysis demonstrated that no individual SNP significantly influenced the causal inference.

CONCLUSIONS

This study provides evidence of a causal relationship between AF and an elevated risk of stroke. These findings emphasize the significance of managing AF in order to prevent and treat strokes. Additional research is required to better understand the underlying mechanisms of this causal association.

Widespread BRCA1/2-independent homologous recombination defects are caused by alterations in RNA-binding proteins

Defects in homologous recombination DNA repair (HRD) both predispose to cancer development and produce therapeutic vulnerabilities, making it critical to define the spectrum of genetic events that cause HRD. However, we found that mutations in BRCA1/2 and other canonical HR genes only identified 10%-20% of tumors that display genomic evidence of HRD. Using a networks-based approach, we discovered that over half of putative genes causing HRD originated outside of canonical DNA damage response genes, with a particular enrichment for RNA-binding protein (RBP)-encoding genes. These putative drivers of HRD were experimentally validated, cross-validated in an independent cohort, and enriched in cancer-associated genome-wide association study loci. Mechanistic studies indicate that some RBPs are recruited to sites of DNA damage to facilitate repair, whereas others control the expression of canonical HR genes. Overall, this study greatly expands the repertoire of known drivers of HRD, with implications for basic biology, genetic screening, and therapy stratification.

AgingReG: a curated database of aging regulatory relationships in humans

Aging and cellular senescence are characterized by a progressive loss of physiological integrity, which could be triggered by aging factors such as physiological, pathological and external factors. Numerous studies have shown that gene regulatory events play crucial roles in aging, increasing the need for a comprehensive repository of regulatory relationships during aging. Here, we established a manually curated database of aging factors (AgingReG, https://bio.liclab.net/Aging-ReG/), focusing on the regulatory relationships during aging with experimental evidence in humans. By curating thousands of published literature, 2157 aging factor entries (1345 aging gene entries, 804 external factor entries and eight aging-related pathway entries) and related regulatory information were manually curated. The regulatory relationships were classified into four types according to their functions: (i) upregulation, which indicates that aging factors upregulate the expression of target genes during aging; (ii) downregulation, which indicates that aging factors downregulate the expression of target genes during aging; (iii) activation, which indicates that aging factors influence the activity of target genes during aging and (iv) inhibition, which indicates that aging factors inhibit the activation of target molecule activity, leading to declined or lost target activity. AgingReG involves 651 upregulating pairs, 632 downregulating pairs, 330 activation-regulating pairs and 34 inhibition-regulating pairs, covering 195 disease types and more than 800 kinds of cells and tissues from 1784 published literature studies. AgingReG provides a user-friendly interface to query, browse and visualize detailed information about the regulatory relationships during aging. We believe that AgingReG will serve as a valuable resource database in the field of aging research. Database URL: https://bio.liclab.net/Aging-ReG/.

Cis-Cardio: A comprehensive analysis platform for cardiovascular-relavant cis-regulation in human and mouse

Cis-regulatory elements are important molecular switches in controlling gene expression and are regarded as determinant hubs in the transcriptional regulatory network. Collection and processing of large-scale cis-regulatory data are urgent to decipher the potential mechanisms of cardiovascular diseases from a cis-regulatory element aspect. Here, we developed a novel web server, Cis-Cardio, which aims to document a large number of available cardiovascular-related cis-regulatory data and to provide analysis for unveiling the comprehensive mechanisms at a cis-regulation level. The current version of Cis-Cardio catalogs a total of 45,382,361 genomic regions from 1,013 human and mouse epigenetic datasets, including ATAC-seq, DNase-seq, Histone ChIP-seq, TF/TcoF ChIP-seq, RNA polymerase ChIP-seq, and Cohesin ChIP-seq. Importantly, Cis-Cardio provides six analysis tools, including region overlap analysis, element upstream/downstream analysis, transcription regulator enrichment analysis, variant interpretation, and protein-protein interaction-based co-regulatory analysis. Additionally, Cis-Cardio provides detailed and abundant (epi-) genetic annotations in cis-regulatory regions, such as super-enhancers, enhancers, transcription factor binding sites (TFBSs), methylation sites, common SNPs, risk SNPs, expression quantitative trait loci (eQTLs), motifs, DNase I hypersensitive sites (DHSs), and 3D chromatin interactions. In summary, Cis-Cardio is a valuable resource for elucidating and analyzing regulatory cues of cardiovascular-specific cis-regulatory elements. The platform is freely available at http://www.licpathway.net/Cis-Cardio/index.html.

Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481-0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984-0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837-0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766-0.5188). We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.

Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond

A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.

Processing genome-wide association studies within a repository of heterogeneous genomic datasets

BACKGROUND

Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants - typically single-nucleotide polymorphisms (SNPs) - in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions.

RESULTS

To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals.

CONCLUSIONS

As a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.

ChromLoops: a comprehensive database for specific protein-mediated chromatin loops in diverse organisms

Chromatin loops (or chromatin interactions) are important elements of chromatin structures. Disruption of chromatin loops is associated with many diseases, such as cancer and polydactyly. A few methods, including ChIA-PET, HiChIP and PLAC-Seq, have been proposed to detect high-resolution, specific protein-mediated chromatin loops. With rapid progress in 3D genomic research, ChIA-PET, HiChIP and PLAC-Seq datasets continue to accumulate, and effective collection and processing for these datasets are urgently needed. Here, we developed a comprehensive, multispecies and specific protein-mediated chromatin loop database (ChromLoops, https://3dgenomics.hzau.edu.cn/chromloops), which integrated 1030 ChIA-PET, HiChIP and PLAC-Seq datasets from 13 species, and documented 1 491 416 813 high-quality chromatin loops. We annotated genes and regions overlapping with chromatin loop anchors with rich functional annotations, such as regulatory elements (enhancers, super-enhancers and silencers), variations (common SNPs, somatic SNPs and eQTLs), and transcription factor binding sites. Moreover, we identified genes with high-frequency chromatin interactions in the collected species. In particular, we identified genes with high-frequency interactions in cancer samples. We hope that ChromLoops will provide a new platform for studying chromatin interaction regulation in relation to biological processes and disease.

GWAS Central: an expanding resource for finding and visualising genotype and phenotype data from genome-wide association studies

The GWAS Central resource gathers and curates extensive summary-level genome-wide association study (GWAS) data and puts a range of user-friendly but powerful website tools for the comparison and visualisation of GWAS data at the fingertips of researchers. Through our continued efforts to harmonise and import data received from GWAS authors and consortia, and data sets actively collected from public sources, the database now contains over 72.5 million P-values for over 5000 studies testing over 7.4 million unique genetic markers investigating over 1700 unique phenotypes. Here, we describe an update to integrate this extensive data collection with mouse disease model data to support insights into the functional impact of human genetic variation. GWAS Central has expanded to include mouse gene-phenotype associations observed during mouse gene knockout screens. To allow similar cross-species phenotypes to be compared, terms from mammalian and human phenotype ontologies have been mapped. New interactive interfaces to find, correlate and view human and mouse genotype-phenotype associations are included in the website toolkit. Additionally, the integrated browser for interrogating multiple association data sets has been updated and a GA4GH Beacon API endpoint has been added for discovering variants tested in GWAS. The GWAS Central resource is accessible at https://www.gwascentral.org/.

GWAS Atlas: an updated knowledgebase integrating more curated associations in plants and animals

GWAS Atlas (https://ngdc.cncb.ac.cn/gwas/) is a manually curated resource of genome-wide genotype-to-phenotype associations for a wide range of species. Here, we present an updated implementation of GWAS Atlas by curating and incorporating more high-quality associations, with significant improvements and advances over the previous version. Specifically, the current release of GWAS Atlas incorporates a total of 278,109 curated genotype-to-phenotype associations for 1,444 different traits across 15 species (10 plants and 5 animals) from 830 publications and 3,432 studies. A collection of 6,084 lead SNPs of 439 traits and 486 experiment-validated causal variants of 157 traits are newly added. Moreover, 1,056 trait ontology terms are newly defined, resulting in 1,172 and 431 terms for Plant Phenotype and Trait Ontology and Animal Phenotype and Trait Ontology, respectively. Additionally, it is equipped with four online analysis tools and a submission platform, allowing users to perform data analysis and data submission. Collectively, as a core resource in the National Genomics Data Center, GWAS Atlas provides valuable genotype-to-phenotype associations for a diversity of species and thus plays an important role in agronomic trait study and molecular breeding.

CRdb: a comprehensive resource for deciphering chromatin regulators in human

Chromatin regulators (CRs) regulate epigenetic patterns on a partial or global scale, playing a critical role in affecting multi-target gene expression. As chromatin immunoprecipitation sequencing (ChIP-seq) data associated with CRs are rapidly accumulating, a comprehensive resource of CRs needs to be built urgently for collecting, integrating, and processing these data, which can provide abundant annotated information on CR upstream and downstream regulatory analyses as well as CR-related analysis functions. This study established an integrative CR resource, named CRdb (http://cr.liclab.net/crdb/), with the aim of curating a large number of available resources for CRs and providing extensive annotations and analyses of CRs to help biological researchers clarify the regulation mechanism and function of CRs. The CRdb database comprised a total of 647 CRs and 2,591 ChIP-seq samples from more than 300 human tissues and cell types. These samples have been manually curated from NCBI GEO/SRA and ENCODE. Importantly, CRdb provided the abundant and detailed genetic annotations in CR-binding regions based on ChIP-seq. Furthermore, CRdb supported various functional annotations and upstream regulatory information on CRs. In particular, it embedded four types of CR regulatory analyses: CR gene set enrichment, CR-binding genomic region annotation, CR-TF co-occupancy analysis, and CR regulatory axis analysis. CRdb is a useful and powerful resource that can help in exploring the potential functions of CRs and their regulatory mechanism in diseases and biological processes.

The immunogenetics of COVID-19

The worldwide coronavirus disease 2019 pandemic was sparked by the severe acute respiratory syndrome caused by coronavirus 2 (SARS-CoV-2) that first surfaced in December 2019 (COVID-19). The effects of COVID-19 differ substantially not just between patients individually but also between populations with different ancestries. In humans, the human leukocyte antigen (HLA) system coordinates immune regulation. Since HLA molecules are a major component of antigen-presenting pathway, they play an important role in determining susceptibility to infectious disease. It is likely that differential susceptibility to SARS-CoV-2 infection and/or disease course in COVID-19 in different individuals could be influenced by the variations in the HLA genes which are associated with various immune responses to SARS-CoV-2. A growing number of studies have identified a connection between HLA variation and diverse COVID-19 outcomes. Here, we review research investigating the impact of HLA on individual responses to SARS-CoV-2 infection and/or progression, also discussing the significance of MHC-related immunological patterns and its use in vaccine design.

Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits

A broad range of complex phenotypes are related to dysfunctions in brain (hereafter referred to as brain-related traits), including various mental and behavioral disorders and diseases of the nervous system. These traits in general share overlapping symptoms, pathogenesis, and genetic components. Here, we present Brain Catalog (https://ngdc.cncb.ac.cn/braincatalog), a comprehensive database aiming to delineate the genetic components of more than 500 GWAS summary statistics datasets for brain-related traits from multiple aspects. First, Brain Catalog provides results of candidate causal variants, causal genes, and functional tissues and cell types for each trait identified by multiple methods using comprehensive annotation datasets (58 QTL datasets spanning 6 types of QTLs). Second, Brain Catalog estimates the SNP-based heritability, the partitioning heritability based on functional annotations, and genetic correlations among traits. Finally, through bidirectional Mendelian randomization analyses, Brain Catalog presents inference of risk factors that are likely causal to each trait. In conclusion, Brain Catalog presents a one-stop shop for the genetic components of brain-related traits, potentially serving as a valuable resource for worldwide researchers to advance the understanding of how GWAS signals may contribute to the biological etiology of brain-related traits.

[Rare-disease data standards]

The use of standardized data formats (data standards) in healthcare supports four main goals: (1) exchange of data, (2) integration of computer systems and tools, (3) data storage and archiving, and (4) support of federated databases. Standards are especially important for rare-disease research and clinical care.In this review, we introduce healthcare standards and present a selection of standards that are commonly used in the field of rare diseases. The Human Phenotype Ontology (HPO) is the most commonly used standard for annotating phenotypic abnormalities and supporting phenotype-driven analysis of diagnostic exome and genome sequencing. Numerous standards for diseases are available that support a range of needs. Online Mendelian Inheritance in Man (OMIM) and the Orphanet Rare Disease Ontology (ORDO) are the most important standards developed specifically for rare diseases. The Mondo Disease Ontology (Mondo) is a new disease ontology that aims to integrate data from a comprehensive range of current nosologies. New standards and schemas such as the Medical Action Ontology (MAxO) and the Global Alliance for Genomics and Health (GA4GH) phenopacket are being introduced to extend the scope of standards that support rare disease research.In order to provide optimal care for patients with SE in different healthcare settings, it will be necessary to better integrate standards for rare disease with electronic healthcare resources such as the Fast Healthcare Interoperability Resources (FHIR) standard for healthcare data exchange.

Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing

Background

Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking.

Methods

By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P value of multiple associations as the significance measurement.

Results

Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones.

Conclusion

Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03637-8.

Somatic point mutations are enriched in non-coding RNAs with possible regulatory function in breast cancer

Non-coding RNAs (ncRNAs) form a large portion of the mammalian genome. However, their biological functions are poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we analyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancer samples. We identify 1030 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs significantly enrich active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 1030 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other cancers. Using breast cell line related Hi-C data, we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role of candidate ncRNA genes in the activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource (https://www.ihealthe.unsw.edu.au/research) to communicate our results with the research community. Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used to analyze somatic mutations in all cancers.

The SomaGene tool is developed to identify non-coding RNAs (ncRNAs) mutated in breast cancer but can be used for other cancers. Candidate ncRNAs are shown to be enriched for regulatory features and to contain specific trait loci polymorphisms.

mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights

Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.

FGF21 modulates mitochondrial stress response in cardiomyocytes only under mild mitochondrial dysfunction

The mitochondrial integrated stress response (mitoISR) has emerged as a major adaptive pathway to respiratory chain deficiency, but both the tissue specificity of its regulation, and how mitoISR adapts to different levels of mitochondrial dysfunction are largely unknown. Here, we report that diverse levels of mitochondrial cardiomyopathy activate mitoISR, including high production of FGF21, a cytokine with both paracrine and endocrine function, shown to be induced by respiratory chain dysfunction. Although being fully dispensable for the cell-autonomous and systemic responses to severe mitochondrial cardiomyopathy, in the conditions of mild-to-moderate cardiac OXPHOS dysfunction, FGF21 regulates a portion of mitoISR. In the absence of FGF21, a large part of the metabolic adaptation to mitochondrial dysfunction (one-carbon metabolism, transsulfuration, and serine and proline biosynthesis) is strongly blunted, independent of the primary mitoISR activator ATF4. Collectively, our work highlights the complexity of mitochondrial stress responses by revealing the importance of the tissue specificity and dose dependency of mitoISR.

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease

Introduction

Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity.

Methods

We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs.

Results

MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect.

Discussion

Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region.

Diseases 2.0: a weekly updated database of disease–gene associations from text mining and data integration

The scientific knowledge about which genes are involved in which diseases grows rapidly, which makes it difficult to keep up with new publications and genetics datasets. The DISEASES database aims to provide a comprehensive overview by systematically integrating and assigning confidence scores to evidence for disease–gene associations from curated databases, genome-wide association studies (GWAS) and automatic text mining of the biomedical literature. Here, we present a major update to this resource, which greatly increases the number of associations from all these sources. This is especially true for the text-mined associations, which have increased by at least 9-fold at all confidence cutoffs. We show that this dramatic increase is primarily due to adding full-text articles to the text corpus, secondarily due to improvements to both the disease and gene dictionaries used for named entity recognition, and only to a very small extent due to the growth in number of PubMed abstracts. DISEASES now also makes use of a new GWAS database, Target Illumination by GWAS Analytics, which considerably increased the number of GWAS-derived disease–gene associations. DISEASES itself is also integrated into several other databases and resources, including GeneCards/MalaCards, Pharos/Target Central Resource Database and the Cytoscape stringApp. All data in DISEASES are updated on a weekly basis and is available via a web interface at https://diseases.jensenlab.org, from where it can also be downloaded under open licenses.

Database URL: https://diseases.jensenlab.org

CircleBase: an integrated resource and analysis platform for human eccDNAs

Rapid advances in high-throughput sequencing technologies have led to the discovery of thousands of extrachromosomal circular DNAs (eccDNAs) in the human genome. Loss-of-function experiments are difficult to conduct on circular and linear chromosomes, as they usually overlap. Hence, it is challenging to interpret the molecular functions of eccDNAs. Here, we present CircleBase (http://circlebase.maolab.org), an integrated resource and analysis platform used to curate and interpret eccDNAs in multiple cell types. CircleBase identifies putative functional eccDNAs by incorporating sequencing datasets, computational predictions, and manual annotations. It classifies them into six sections including targeting genes, epigenetic regulations, regulatory elements, chromatin accessibility, chromatin interactions, and genetic variants. The eccDNA targeting and regulatory networks are displayed by informative visualization tools and then prioritized. Functional enrichment analyses revealed that the top-ranked cancer cell eccDNAs were enriched in oncogenic pathways such as the Ras and PI3K-Akt signaling pathways. In contrast, eccDNAs from healthy individuals were not significantly enriched. CircleBase provides a user-friendly interface for searching, browsing, and analyzing eccDNAs in various cell/tissue types. Thus, it is useful to screen for potential functional eccDNAs and interpret their molecular mechanisms in human cancers and other diseases.

TcoFBase: a comprehensive database for decoding the regulatory transcription co-factors in human and mouse

Transcription co-factors (TcoFs) play crucial roles in gene expression regulation by communicating regulatory cues from enhancers to promoters. With the rapid accumulation of TcoF associated chromatin immunoprecipitation sequencing (ChIP-seq) data, the comprehensive collection and integrative analyses of these data are urgently required. Here, we developed the TcoFBase database (http://tcof.liclab.net/TcoFbase), which aimed to document a large number of available resources for mammalian TcoFs and provided annotations and enrichment analyses of TcoFs. TcoFBase curated 2322 TcoFs and 6759 TcoFs associated ChIP-seq data from over 500 tissues/cell types in human and mouse. Importantly, TcoFBase provided detailed and abundant (epi) genetic annotations of ChIP-seq based TcoF binding regions. Furthermore, TcoFBase supported regulatory annotation information and various functional annotations for TcoFs. Meanwhile, TcoFBase embedded five types of TcoF regulatory analyses for users, including TcoF gene set enrichment, TcoF binding genomic region annotation, TcoF regulatory network analysis, TcoF-TF co-occupancy analysis and TcoF regulatory axis analysis. TcoFBase was designed to be a useful resource that will help reveal the potential biological effects of TcoFs and elucidate TcoF-related regulatory mechanisms.

LncRNA functional annotation with improved false discovery rate achieved by disease associations

The long non‐coding RNAs (lncRNAs) play critical roles in various biological processes and are associated with many diseases. Functional annotation of lncRNAs in diseases attracts great attention in understanding their etiology. However, the traditional co-expression-based analysis usually produces a significant number of false positive function assignments. It is thus crucial to develop a new approach to obtain lower false discovery rate for functional annotation of lncRNAs. Here, a novel strategy termed DAnet which combining disease associations with cis-regulatory network between lncRNAs and neighboring protein-coding genes was developed, and the performance of DAnet was systematically compared with that of the traditional differential expression-based approach. Based on a gold standard analysis of the experimentally validated lncRNAs, the proposed strategy was found to perform better in identifying the experimentally validated lncRNAs compared with the other method. Moreover, the majority of biological pathways (40%∼100%) identified by DAnet were reported to be associated with the studied diseases. In sum, the DAnet is expected to be used to identify the function of specific lncRNAs in a particular disease or multiple diseases.

Repurposing new drug candidates and identifying crucial molecules underlying PCOS Pathogenesis Based On Bioinformatics Analysis

BACKGROUNDS

Polycystic ovary syndrome affects 7% of women of reproductive ages. Poor-quality oocytes, along with lower cleavage and implantation rates, reduce fertilization.

OBJECTIVE

This study aimed to determine crucial molecular mechanisms behind PCOS pathogenesis and repurpose new drug candidates interacting with them. To predict a more in-depth insight, we applied a novel bioinformatics approach to analyze interactions between the drug-related and PCOS proteins in PCOS patients.

METHODS

The newest proteomics data was retrieved from 16 proteomics datasets and was used to construct the PCOS PPI network using Cytoscape. The topological network analysis determined hubs and bottlenecks. The MCODE Plugin was used to identify highly connected regions, and the associations between PCOS clusters and drug-related proteins were evaluated using the Chi-squared/Fisher's exact test. The crucial PPI hub-bottlenecks and the shared molecules (between the PCOS clusters and drug-related proteins) were then investigated for their drug-protein interactions with previously US FDA-approved drugs to predict new drug candidates.

RESULTS

The PI3K/AKT pathway was significantly related to one PCOS subnetwork and most drugs (metformin, letrozole, pioglitazone, and spironolactone); moreover, VEGF, EGF, TGFB1, AGT, AMBP, and RBP4 were identified as the shared proteins between the PCOS subnetwork and the drugs. The shared top biochemical pathways between another PCOS subnetwork and rosiglitazone included metabolic pathways, carbon metabolism, and citrate cycle, while the shared proteins included HSPB1, HSPD1, ACO2, TALDO1, VDAC1, and MDH2. We proposed some new candidate medicines for further PCOS treatment investigations, such as copper and zinc compounds, reteplase, alteplase, gliclazide, Etc.

CONCLUSION

Some of the crucial molecules suggested by our model have already been experimentally reported as critical molecules in PCOS pathogenesis. Moreover, some repurposed medications have already shown beneficial effects on infertility treatment. These previous experimental reports confirm our suggestion for investigating our other repurposed drugs (in vitro and in vivo).

Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes

In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.

TIGA: target illumination GWAS analytics

MOTIVATION

Genome-wide association studies can reveal important genotype-phenotype associations; however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study.

RESULTS

Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite relative citation ratio, and meanRank scores, to aggregate multivariate evidence.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists.

AVAILABILITY AND IMPLEMENTATION

Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Facilitating Antiviral Drug Discovery Using Genetic and Evolutionary Knowledge

Over the course of human history, billions of people worldwide have been infected by various viruses. Despite rapid progress in the development of biomedical techniques, it is still a significant challenge to find promising new antiviral targets and drugs. In the past, antiviral drugs mainly targeted viral proteins when they were used as part of treatment strategies. Since the virus mutation rate is much faster than that of the host, such drugs feature drug resistance and narrow-spectrum antiviral problems. Therefore, the targeting of host molecules has gradually become an important area of research for the development of antiviral drugs. In recent years, rapid advances in high-throughput sequencing techniques have enabled numerous genetic studies (such as genome-wide association studies (GWAS), clustered regularly interspersed short palindromic repeats (CRISPR) screening, etc.) for human diseases, providing valuable genetic and evolutionary resources. Furthermore, it has been revealed that successful drug targets exhibit similar genetic and evolutionary features, which are of great value in identifying promising drug targets and discovering new drugs. Considering these developments, in this article the authors propose a host-targeted antiviral drug discovery strategy based on knowledge of genetics and evolution. We first comprehensively summarized the genetic, subcellular location, and evolutionary features of the human genes that have been successfully used as antiviral targets. Next, the summarized features were used to screen novel druggable antiviral targets and to find potential antiviral drugs, in an attempt to promote the discovery of new antiviral drugs.

webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study

The development of transcriptome-wide association studies (TWAS) has enabled researchers to better identify and interpret causal genes in many diseases. However, there are currently no resources providing a comprehensive listing of gene-disease associations discovered by TWAS from published GWAS summary statistics. TWAS analyses are also difficult to conduct due to the complexity of TWAS software pipelines. To address these issues, we introduce a new resource called webTWAS, which integrates a database of the most comprehensive disease GWAS datasets currently available with credible sets of potential causal genes identified by multiple TWAS software packages. Specifically, a total of 235 064 gene-diseases associations for a wide range of human diseases are prioritized from 1298 high-quality downloadable European GWAS summary statistics. Associations are calculated with seven different statistical models based on three popular and representative TWAS software packages. Users can explore associations at the gene or disease level, and easily search for related studies or diseases using the MeSH disease tree. Since the effects of diseases are highly tissue-specific, webTWAS applies tissue-specific enrichment analysis to identify significant tissues. A user-friendly web server is also available to run custom TWAS analyses on user-provided GWAS summary statistics data. webTWAS is freely available at http://www.webtwas.net.

Understanding the role of corneal biomechanics-associated genetic variants by bioinformatic analyses

PURPOSE

To analyze functions of corneal biomechanical properties (CBP)-related variants as corneal resistance factor (CRF) and corneal hysteresis (CH).

METHODS

Related single nucleotide polymorphisms (SNPs) and genes were identified from NHGRI-EBI GWAS catalog, GWASdb v2 and possible data in published studies. HaploReg v4.1 was used to find linkage SNPs. Functional annotations were performed by GWAVA, CADD and RegulomeDB. GTEx Portal database was used to find out expression quantitative trait locus (eQTL) association. Enrichr was used to annotate the function of GWAS gene and the associated signal pathway. STING (v11.0) database was utilized for protein interaction and network construction.

RESULTS

The integration of 302 CH-associated and 420 CRF-associated lead SNPs has produced 531 CBP-associated lead SNPs. A total of 5,324 proxy variants identified using the HaploReg v4.1 and lead SNPs were functionally annotated. Based on the threshold (CADD ≥ 10, GWAVA ≥ 0.4 and RegulomeDB < rank 3), 23 prioritized putative regulatory SNPs were identified. Eight prioritized eQTL variants (rs75203695, rs34861673, rs846766, rs11024102, rs1377416, rs3829492, rs9934438 and rs197912) were found with strong potential of CBP regulation. It was indicated that CBP-associated genes were significantly enriched in extracellular matrix receptor interaction pathway, closely related to the phenotype of corneal dystrophy and keratoconus. COL1A1, SMAD3, BMP4 and RUNX2 occupied the core position in the co-expression network.

CONCLUSIONS

Data integrative analysis can evaluate CBP variations and explore collagen and extracellular matrix pathways in CBP regulation, which is a promising tool to investigate biological process of corneal diseases.

Seeing the forest through the trees: prioritising potentially functional interactions from Hi-C

Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data, however, is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that prioritise potentially functional interactions. We classify three groups of approaches: structural-based discovery methods, e.g. A/B compartments and topologically associated domains, detection of statistically significant chromatin interactions, and the use of epigenomic data integration to narrow down useful interaction information. Careful use of these three approaches is crucial to successfully identifying potentially functional interactions within the genome.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

Enhancer release and retargeting activates disease-susceptibility genes

The functional engagement between an enhancer and its target promoter ensures precise gene transcription1. Understanding the basis of promoter choice by enhancers has important implications for health and disease. Here we report that functional loss of a preferred promoter can release its partner enhancer to loop to and activate an alternative promoter (or alternative promoters) in the neighbourhood. We refer to this target-switching process as 'enhancer release and retargeting'. Genetic deletion, motif perturbation or mutation, and dCas9-mediated CTCF tethering reveal that promoter choice by an enhancer can be determined by the binding of CTCF at promoters, in a cohesin-dependent manner-consistent with a model of 'enhancer scanning' inside the contact domain. Promoter-associated CTCF shows a lower affinity than that at chromatin domain boundaries and often lacks a preferred motif orientation or a partnering CTCF at the cognate enhancer, suggesting properties distinct from boundary CTCF. Analyses of cancer mutations, data from the GTEx project and risk loci from genome-wide association studies, together with a focused CRISPR interference screen, reveal that enhancer release and retargeting represents an overlooked mechanism that underlies the activation of disease-susceptibility genes, as exemplified by a risk locus for Parkinson's disease (NUCKS1-RAB7L1) and three loci associated with cancer (CLPTM1L-TERT, ZCCHC7-PAX5 and PVT1-MYC).

The Causal Relationship Between Body Mass Index and the Risk of Osteoarthritis

Objective

The study aimed to explore the causal effect of body mass index (BMI) on osteoarthritis.

Methods

The genome-wide association data of BMI and osteoarthritis were obtained via the Mendelian randomization (MR)-base platform. Single nucleotide polymorphisms (SNPs) significantly associated with BMI were identified and used as instrumental variables, and the causal relationship between BMI and osteoarthritis was examined using the two-sample MR research method. Three statistical methods including inverse-variance weighted (IVW) method, weighted median estimator, and MR-Egger regression were employed.

Results

A total of 79 SNPs significantly associated with BMI were identified in the study (P<5×10⁻⁸; linkage disequilibrium r² <0.1). Consistent association between BMI and osteoarthritis was observed when evaluated by different methods (IVW: odds ratio (OR) 1.028, 95% confidence interval (CI) 1.021–1.036; weighted median estimator: OR 1.028, 95% CI 1.019–1.037; MR-Egger regression: OR 1.028, 95% CI 1.009–1.046), which suggests that BMI is positively associated with increased risk of osteoarthritis. There was no evidence that the observed causal effect between BMI and the risk of osteoarthritis was affected by genetic pleiotropy (MR-Egger intercept 1.3×10⁻⁵, P=0.959).

Conclusion

The MR analysis provided the strong evidence to indicate that BMI might be causally associated with the risk of osteoarthritis.

Systematic review of genome-wide association studies of abdominal aortic aneurysm

BACKGROUND AND AIMS

Abdominal aortic aneurysm (AAA) is an important cause of death worldwide and has an estimated heritability between 70 and 77%. Genome-wide association studies (GWAS) are an established way to discover genetic risk variants. The aim of this study was to systematically review the findings and quality of previous AAA GWAS.

METHODS

The Medline, PubMed, Web of Science and relevant genetic databases were searched to identify previous AAA GWAS. A framework was developed to grade the methodological quality of the GWAS. Data from included studies were extracted to assess methods and findings.

RESULTS

Eight case-control studies were included. Thirty-three of the 38 total single nucleotide polymorphisms (SNPs) previously reported were associated with AAA diagnosis at genome-wide significance (p < 5.0 × 10^-8). The CDKN2B antisense RNA-1 gene had the most significant association with AAA diagnosis (p = 6.94 × 10^-29 and p = 1.54 × 10^-33 for rs4007642 and rs10757274 respectively). Age, sex and smoking history were not reported for the complete cohort in any of the included studies, although five of the eight studies adjusted or matched for at least two confounding variables. All included studies had important design limitations including lack of sample size estimation, inconsistent case and control ascertainment and limited phenotyping of the AAAs. AAA growth was assessed in one GWAS, however, no significant associations with the reported SNPs were found.

CONCLUSIONS

This systematic review identified 33 SNPs associated with AAA diagnosis at genome-wide significance previously validated in multiple cohorts. The association between SNPs and AAA growth was not adequately examined. Previous GWAS have a number of design limitations.

Prioritizing Pain-Associated Targets with Machine Learning

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.