GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies

The expression and biological role of complement C1s in esophageal squamous cell carcinoma

The present work focused on investigating the role of the altered expression of complement C1s in proliferation and apoptosis of esophageal squamous cell carcinoma (ESCC) cells and explore its biological functions in ESCC, so as to lay a theoretical foundation and provide certain clinical reference for diagnosing and treating ESCC. Complement C1s expression within ESCC was assessed, and its clinical pathological characteristics in ESCC patients were analyzed. Subsequently, in vitro experiments were performed to further explore the mechanisms by which complement C1s affected ESCC. According to the results, complement C1s expression within ESCC markedly increased relative to adjacent non-cancerous samples. High C1s expression showed positive relation to race, residual lesion, and tumor location of ESCC patients. Complement C1s affected ESCC cell proliferation and apoptosis. Notably, C1s knockdown significantly inhibited ESCC cell proliferation and enhanced their apoptosis. C1s suppressed ESCC cell proliferation via Wnt1/β-catenin pathway and promoted their apoptosis through modulating the expression of Bcl2, Bax, and cleaved-caspase3.

Network Cluster Analysis of PPI and Phenotype Ontology for Type 1 Diabetes Mellitus

Background

Our knowledge of Type 1 Diabetes Mellitus (T1DM) etiology is incomplete; however, the pathogenesis of the disease includes T-cell-mediated destruction of β-cells.

Objective

The present study aimed to investigate the key gene pathways and co-expression networks in T1DM disease.

Material and Methods

TIDM-associated genes were identified from 13 databases, enrichment of pathways annotated with functional annotations, and analysis of protein-protein network interactions. Next, functional modules and transcription factor networks were constructed. The analysis of gene co-expression networks was conducted to discover associated pivotal modules.

Results

A total of 172 expressed genes and four variants (SNP) were filtered in the of T1DM disease; pathway enrichment analysis identified key pathways, such as inflammatory bowel disease, type I diabetes mellitus, cytokine-cytokine receptor interaction, Th17 cell differentiation, JAK-STAT signaling pathway, and graft-versus-host disease. A weighted correlation network analysis revealed one module that was strongly correlated with T1DM. Functional annotation revealed that the module was mainly enriched in pathways such as T cell activation, regulation of immune system process, and response to the organic substance. IRF2, IRF4, IRF8, and CDX2 were regulated in the module at a significant level.

Conclusion

The study identified IL-2 as a significant T1DM hotspot and highlighted the role of hub genes and transcription factors in the autoimmune disease, offering potentials for treatment and prevention.

Recovering Misidentified Samples Through Genetic Discordance Clustering

The many logistical and technical challenges associated with sample and data handling in largescale genotyping studies can increase the risk of sample misidentification, which may compromise subsequent analyses. However, the standard quality assurance methods typical for large genotyping arrays can often be further utilized to identify and recover problematic samples. This article emphasizes the importance of identifying and correcting underlying sample misidentification rather than simply excluding known discrepancies, which may potentially include undetected issues. Lastly, we provide a screening protocol to complement standard quality assessments as a guideline for identifying mismatched samples and a tool for assessing the most common causes of sample misidentification. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC.

Ethnic-specific associations between body mass index and gastric cancer: a Mendelian randomization study in European and Korean populations

BACKGROUND

Given the uncertainties surrounding the associations in previous epidemiological studies, we conducted linear and nonlinear Mendelian randomization (MR) studies to evaluate whether body mass index (BMI) associated with gastric cancer (GC) risk in European and Korean.

METHODS

Genome-wide association study-summary statistics were used from the Pan-UK Biobank, the Genetic Investigation of Anthropometric Traits consortium, the K-CHIP consortium, and BioBank Japan. BMI-associated single nucleotide polymorphisms (SNPs) were used as instrumental variables (IVs) in MR to identify the association between BMI and GC. Both linear and nonlinear MR analyses were performed. Sensitivity analyses were also conducted for individuals below or above a BMI of 24 kg/m2.

RESULTS

The study used 22 and 55 SNPs as IVs for BMI in European and Korean populations, respectively. Genetically predicted BMI was positively associated with GC risk in the European population (Odds ratio per 1 kg/m2 increase; 95% CI = 1.17; 1.01-1.36 using simple median method), but no significant association was observed in the Korean population. However, the nonlinear MR identified a U-shaped association between BMI and GC in the Korean population, with both low and high BMIs associated with increased GC risk. A BMI of 24 kg/m2 presented the lowest risk. Sensitivity analyses did not yield any genome-wide significant SNPs.

CONCLUSION

While MR analysis suggests a linear association between BMI and GC in those of European ancestry, nonlinear MR hints at a U-shaped association in Koreans. This suggests the association between BMI and GC risk may vary according to ethnic ancestry.

Genetic regulation of testosterone level in overweight males from the Kazakh population and its association with hypogonadism

Male hypogonadism and erectile dysfunction in different populations are associated with excess body weight. A key aspect in most studies is the metabolism of sexual hormones, primarily testosterone. At the same time, the binding protein sex hormone binding globulin (SHBG) can play a large role, as it determines the ratio of total and bioavailable testosterone in blood, i.e. both the hormone content and level of its production. Recent research has identified common mutations that affect SHBG levels, such as the rs727428 polymorphic locus, which is associated with alterations in histone protein function, affecting the regulation of ribonucleic acid (RNA) protein SHBG synthesis. Similar relationships have been observed for prevalent mutations, including rs5934505 and rs10822184, in diverse populations. This study involved 300 individuals of Kazakh nationality from the Eastern Kazakhstan region, examining three polymorphic variants of the SHBG gene (rs727428, rs5934505, and rs10822184). The participants were categorized into three groups: individuals with hypogonadism and obesity (group 1, n=85), those with excess body weight but no hypogonadism (group 2, n=70), and individuals with neither excess body weight nor hypogonadism (group 3, n=145). The frequency of mutant gene alleles impacting GPS (SHBG) synthesis in the Kazakh population was notably high, comparable to European and South-East Asian populations. However, the association between excess body weight and these mutations exhibited varying patterns. Hypogonadism was linked to decreased GPS levels, strongly correlating with total testosterone but not bioavailable testosterone. The retention of sexual functions in overweight men was not always directly related to BMI levels and GPS concentrations.

Chromogranin A and its derived peptides: potential regulators of cholesterol homeostasis

Chromogranin A (CHGA), a member of the granin family of proteins, has been an attractive therapeutic target and candidate biomarker for several cardiovascular, neurological, and inflammatory disorders. The prominence of CHGA stems from the pleiotropic roles of several bioactive peptides (e.g., catestatin, pancreastatin, vasostatins) generated by its proteolytic cleavage and by their wide anatomical distribution. These peptides are emerging as novel modulators of cardiometabolic diseases that are often linked to high blood cholesterol levels. However, their impact on cholesterol homeostasis is poorly understood. The dynamic nature of cholesterol and its multitudinous roles in almost every aspect of normal body function makes it an integral component of metabolic physiology. A tightly regulated coordination of cholesterol homeostasis is imperative for proper functioning of cellular and metabolic processes. The deregulation of cholesterol levels can result in several pathophysiological states. Although studies till date suggest regulatory roles for CHGA and its derived peptides on cholesterol levels, the mechanisms by which this is achieved still remain unclear. This review aims to aggregate and consolidate the available evidence linking CHGA with cholesterol homeostasis in health and disease. In addition, we also look at common molecular regulatory factors (viz., transcription factors and microRNAs) which could govern the expression of CHGA and genes involved in cholesterol homeostasis under basal and pathological conditions. In order to gain further insights into the pathways mediating cholesterol regulation by CHGA/its derived peptides, a few prospective signaling pathways are explored, which could act as primers for future studies.

PopTradeOff: A database for exploring population-specificity of adaptive evolution, disease susceptibility, and drug responsiveness

The influence of adaptive evolution on disease susceptibility has drawn attention; however, the extent of the influence, whether favored mutations also influence drug responses, and whether the associations between the three are population-specific remain unknown. Using a reported deep learning network to integrate seven statistical tests for detecting selection signals, we predicted favored mutations in the genomes of 17 human populations and integrated these favored mutations with reported GWAS sites and drug response-related variants into the database PopTradeOff (http://www.gaemons.net/PopFMIntro). The database also contains genome annotation information on the SNP, sequence, gene, and pathway levels. The preliminary data analyses suggest that substantial associations exist between adaptive evolution, disease susceptibility, and drug responses and that the associations are highly population-specific. The database may be valuable for disease studies, drug development, and personalized medicine.

Polygenic Risk Score in African populations: progress and challenges

Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects  single nucleotide polymorphisms (SNPs) that  contribute to the disease with low effect size  making it more precise at individual level risk prediction. PRS  analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with  low effect size but play an indispensable role to the observed phenotypic/trait variance.  PRS analysis has  applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies  show   that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that  lack of sufficient GWAS data and tools is  the limiting factor of applying PRS analysis to sub-Saharan populations.   We recommend developing Africa-specific PRS methods and tools for estimating and analyzing  African population data   for clinical  evaluation of PRSs of interest and predicting  rare diseases.

Polygenic Risk Score in African populations: progress and challenges

Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects  single nucleotide polymorphisms (SNPs) that  contribute to the disease with low effect size  making it more precise at individual level risk prediction. PRS  analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with  low effect size but play an indispensable role to the observed phenotypic/trait variance.  PRS analysis has  applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies  show   that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that  lack of sufficient GWAS data and tools is  the limiting factor of applying PRS analysis to sub-Saharan populations.   We recommend developing Africa-specific PRS methods and tools for estimating and analyzing  African population data   for clinical  evaluation of PRSs of interest and predicting  rare diseases.

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.

Multi-Omic Investigations of a 17-19 Translocation Links MINK1 Disruption to Autism, Epilepsy and Osteoporosis

Balanced structural variants, such as reciprocal translocations, are sometimes hard to detect with sequencing, especially when the breakpoints are located in repetitive or insufficiently mapped regions of the genome. In such cases, long-range information is required to resolve the rearrangement, identify disrupted genes and, in symptomatic carriers, pinpoint the disease-causing mechanisms. Here, we report an individual with autism, epilepsy and osteoporosis and a de novo balanced reciprocal translocation: t(17;19) (p13;p11). The genomic DNA was analyzed by short-, linked- and long-read genome sequencing, as well as optical mapping. Transcriptional consequences were assessed by transcriptome sequencing of patient-specific neuroepithelial stem cells derived from induced pluripotent stem cells (iPSC). The translocation breakpoints were only detected by long-read sequencing, the first on 17p13, located between exon 1 and exon 2 of MINK1 (Misshapen-like kinase 1), and the second in the chromosome 19 centromere. Functional validation in induced neural cells showed that MINK1 expression was reduced by >50% in the patient’s cells compared to healthy control cells. Furthermore, pathway analysis revealed an enrichment of changed neural pathways in the patient’s cells. Altogether, our multi-omics experiments highlight MINK1 as a candidate monogenic disease gene and show the advantages of long-read genome sequencing in capturing centromeric translocations.

Effect of SHBG Polymorphism on the Levels of Bioavailable Testosterone and Lipid Metabolism in Older Men of the Kazakh Population

This study is aimed at investigating the effect of SHBG (rs727428; rs10822184) and LPL (rs754493647) single nucleotide polymorphisms on the concentration of the bioavailable fraction of testosterone in older men. Materials and methods To study gene mutations, 417 residents of the East Kazakhstan region of Kazakh nationality were examined. The main group included 135 men with signs of hypogonadism (AMS 37-49), and the control group consisted of 282 healthy men (AMS 17-26) of the corresponding age (p = 0.5). Single nucleotide polymorphisms rs 727428 [C / T]; rs10822184 [T / C]; rs754493647 [T / C], was determined by the TaqMan method. Results Analysis of the rs727428 polymorphism has revealed that the TT allele (rs727428) has a lower level of albumin (p = 0.03), bioavailable testosterone (p = 0.04), and free testosterone (p = 0.6) than in carriers of the CC and CT genotypes. Also, it has shown a decrease in total testosterone (p = 0.001) and an increase in SHBG levels (p = 0.07) in men with the TT genotype of the rs727428 gene polymorphism. The rs10822184 polymorphism demonstrated an increase in triglyceride and LDL levels in TT genotype (p ≤ 0.04), in comparison with CC and CT genotypes. Conclusion It has been proven that rs727428 (p = 0.001) is associated with testosterone levels and therefore can determine the concentration of bioavailable testosterone. Decreased levels of bioavailable testosterone are a sign of male hypogonadism. This study confirms the effect of rs10822184 on LDL (p = 0.01) and triglyceride (p = 0.04) levels, but its association with androgen levels has not been proven. Our results may be of interest for understanding the etiology of early development of hypogonadism and lipid metabolism disorders in men. To confirm the conclusions, a more detailed study with a large sample of men from the Kazakh population may be required.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.

STAT3-mediated allelic imbalance of novel genetic variant Rs1047643 and B-cell-specific super-enhancer in association with systemic lupus erythematosus

Mapping of allelic imbalance (AI) at heterozygous loci has the potential to establish links between genetic risk for disease and biological function. Leveraging multi-omics data for AI analysis and functional annotation, we discovered a novel functional risk variant rs1047643 at 8p23 in association with systemic lupus erythematosus (SLE). This variant displays dynamic AI of chromatin accessibility and allelic expression on FDFT1 gene in B cells with SLE. We further found a B-cell restricted super-enhancer (SE) that physically contacts with this SNP-residing locus, an interaction that also appears specifically in B cells. Quantitative analysis of chromatin accessibility and DNA methylation profiles further demonstrated that the SE exhibits aberrant activity in B cell development with SLE. Functional studies identified that STAT3, a master factor associated with autoimmune diseases, directly regulates both the AI of risk variant and the activity of SE in cultured B cells. Our study reveals that STAT3-mediated SE activity and cis-regulatory effects of SNP rs1047643 at 8p23 locus are associated with B cell deregulation in SLE.

Auto-CORPus: A Natural Language Processing Tool for Standardizing and Reusing Biomedical Literature

To analyse large corpora using machine learning and other Natural Language Processing (NLP) algorithms, the corpora need to be standardized. The BioC format is a community-driven simple data structure for sharing text and annotations, however there is limited access to biomedical literature in BioC format and a lack of bioinformatics tools to convert online publication HTML formats to BioC. We present Auto-CORPus (Automated pipeline for Consistent Outputs from Research Publications), a novel NLP tool for the standardization and conversion of publication HTML and table image files to three convenient machine-interpretable outputs to support biomedical text analytics. Firstly, Auto-CORPus can be configured to convert HTML from various publication sources to BioC. To standardize the description of heterogenous publication sections, the Information Artifact Ontology is used to annotate each section within the BioC output. Secondly, Auto-CORPus transforms publication tables to a JSON format to store, exchange and annotate table data between text analytics systems. The BioC specification does not include a data structure for representing publication table data, so we present a JSON format for sharing table content and metadata. Inline tables within full-text HTML files and linked tables within separate HTML files are processed and converted to machine-interpretable table JSON format. Finally, Auto-CORPus extracts abbreviations declared within publication text and provides an abbreviations JSON output that relates an abbreviation with the full definition. This abbreviation collection supports text mining tasks such as named entity recognition by including abbreviations unique to individual publications that are not contained within standard bio-ontologies and dictionaries. The Auto-CORPus package is freely available with detailed instructions from GitHub at: https://github.com/omicsNLP/Auto-CORPus.

Metabolomics and the Multi-Omics View of Cancer

Cancer is widely regarded to be a genetic disease. Indeed, over the past five decades, the genomic perspective on cancer has come to almost completely dominate the field. However, this genome-only view is incomplete and tends to portray cancer as a disease that is highly heritable, driven by hundreds of complex genetic interactions and, consequently, difficult to prevent or treat. New evidence suggests that cancer is not as heritable or purely genetic as once thought and that it really is a multi-omics disease. As highlighted in this review, the genome, the exposome, and the metabolome all play roles in cancer’s development and manifestation. The data presented here show that >90% of cancers are initiated by environmental exposures (the exposome) which lead to cancer-inducing genetic changes. The resulting genetic changes are, then, propagated through the altered DNA of the proliferating cancer cells (the genome). Finally, the dividing cancer cells are nourished and sustained by genetically reprogrammed, cancer-specific metabolism (the metabolome). As shown in this review, all three “omes” play roles in initiating cancer. Likewise, all three “omes” interact closely, often providing feedback to each other to sustain or enhance tumor development. Thanks to metabolomics, these multi-omics feedback loops are now much more evident and their roles in explaining the hallmarks of cancer are much better understood. Importantly, this more holistic, multi-omics view portrays cancer as a disease that is much more preventable, easier to understand, and potentially, far more treatable.

Genetic Determinants of Plasma Low-Density Lipoprotein Cholesterol Levels: Monogenicity, Polygenicity, and "Missing" Heritability

Changes in plasma low-density lipoprotein cholesterol (LDL-c) levels relate to a high risk of developing some common and complex diseases. LDL-c, as a quantitative trait, is multifactorial and depends on both genetic and environmental factors. In the pregenomic age, targeted genes were used to detect genetic factors in both hyper- and hypolipidemias, but this approach only explained extreme cases in the population distribution. Subsequently, the genetic basis of the less severe and most common dyslipidemias remained unknown. In the genomic age, performing whole-exome sequencing in families with extreme plasma LDL-c values identified some new candidate genes, but it is unlikely that such genes can explain the majority of inexplicable cases. Genome-wide association studies (GWASs) have identified several single-nucleotide variants (SNVs) associated with plasma LDL-c, introducing the idea of a polygenic origin. Polygenic risk scores (PRSs), including LDL-c-raising alleles, were developed to measure the contribution of the accumulation of small-effect variants to plasma LDL-c. This paper discusses other possibilities for unexplained dyslipidemias associated with LDL-c, such as mosaicism, maternal effect, and induced epigenetic changes. Future studies should consider gene-gene and gene-environment interactions and the development of integrated information about disease-driving networks, including phenotypes, genotypes, transcription, proteins, metabolites, and epigenetics.

Targeting the Non-Coding Genome for the Diagnosis of Disorders of Sex Development

Disorders of sex development (DSD) are a complex group of conditions with highly variable clinical phenotypes, most often caused by failure of gonadal development. DSD are estimated to occur in around 1.7% of all live births. Whilst the understanding of genes involved in gonad development has increased exponentially, approximately 50% of patients with a DSD remain without a genetic diagnosis, possibly implicating non-coding genomic regions instead. Here, we review how variants in the non-coding genome of DSD patients can be identified using techniques such as array comparative genomic hybridization (CGH) to detect copy number variants (CNVs), and more recently, whole genome sequencing (WGS). Once a CNV in a patient's non-coding genome is identified, putative regulatory elements such as enhancers need to be determined within these vast genomic regions. We will review the available online tools and databases that can be used to refine regions with potential enhancer activity based on chromosomal accessibility, histone modifications, transcription factor binding site analysis, chromatin conformation, and disease association. We will also review the current in vitro and in vivo techniques available to demonstrate the functionality of the identified enhancers. The review concludes with a clinical update on the enhancers linked to DSD.

Disease-associated genetic variants in the regulatory regions of human genes: mechanisms of action on transcription and genomic resources for dissecting these mechanisms

Whole genome and whole exome sequencing technologies play a very important role in the studies of the genetic aspects of the pathogenesis of various diseases. The ample use of genome-wide and exome-wide association study methodology (GWAS and EWAS) made it possible to identify a large number of genetic variants associated with diseases. This information is accumulated in the databases like GWAS central, GWAS catalog, OMIM, ClinVar, etc. Most of the variants identified by the GWAS technique are located in the noncoding regions of the human genome. According to the ENCODE project, the fraction of regions in the human genome potentially involved in transcriptional control is many times greater than the fraction of coding regions. Thus, genetic variation in noncoding regions of the genome can increase the susceptibility to diseases by disrupting various regulatory elements (promoters, enhancers, silencers, insulator regions, etc.). However, identification of the mechanisms of influence of pathogenic genetic variants on the diseases risk is difficult due to a wide variety of regulatory elements. The present review focuses on the molecular genetic mechanisms by which pathogenic genetic variants affect gene expression. At the same time, attention is concentrated on the transcriptional level of regulation as an initial step in the expression of any gene. A triggering event mediating the effect of a pathogenic genetic variant on the level of gene expression can be, for example, a change in the functional activity of transcription factor binding sites (TFBSs) or DNA methylation change, which, in turn, affects the functional activity of promoters or enhancers. Dissecting the regulatory roles of polymorphic loci have been impossible without close integration of modern experimental approaches with computer analysis of a growing wealth of genetic and biological data obtained using omics technologies. The review provides a brief description of a number of the most well-known public genomic information resources containing data obtained using omics technologies, including (1) resources that accumulate data on the chromatin states and the regions of transcription factor binding derived from ChIP-seq experiments; (2) resources containing data on genomic loci, for which allele-specific transcription factor binding was revealed based on ChIP-seq technology; (3) resources containing in silico predicted data on the potential impact of genetic variants on the transcription factor binding sites

Knowledge Representation and Management: Interest in New Solutions for Ontology Curation

Objective: To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020.

Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2020, based on PubMed queries. This review was conducted according to the IMIA Yearbook guidelines.

Results: Four best papers were selected among 1,175 publications. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design. The usual KRM application domains (bioinformatics, machine learning, and electronic health records) were also represented.

Conclusion: In 2020, ontology curation emerges as a significant topic of research interest. Bioinformatics, machine learning, and electronics health records remain significant research areas in the KRM community with various applications. Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. As in 2019, representations serve a great variety of applications across many medical domains, with actionable results and now with growing adhesion to the open science initiative.

Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells

To reveal the dynamic features of cellular systems, such as the correlation among phenotypes, a time or condition series set of samples is typically required. Here, we propose intra-ramanome correlation analysis (IRCA) to achieve this goal from just one snapshot of an isogenic population, via pairwise correlation among the cells of the thousands of Raman peaks in single-cell Raman spectra (SCRS), i.e., by taking advantage of the intrinsic metabolic heterogeneity among individual cells. For example, IRCA of Chlamydomonas reinhardtii under nitrogen depletion revealed metabolite conversions at each time point plus their temporal dynamics, such as protein-to-starch conversion followed by starch-to-triacylglycerol (TAG) conversion, and conversion of membrane lipids to TAG. Such among-cell correlations in SCRS vanished when the starch-biosynthesis pathway was knocked out yet were fully restored by genetic complementation. Extension of IRCA to 64 microalgal, fungal, and bacterial ramanomes suggests the IRCA-derived metabolite conversion network as an intrinsic metabolic signature of isogenic cellular population that is reliable, species-resolved, and state-sensitive. The high-throughput, low cost, excellent scalability, and general extendibility of IRCA suggest its broad applications.

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.

Joint disease-specificity at the regulatory base-pair level

Given the pleiotropic nature of coding sequences and that many loci exhibit multiple disease associations, it is within non-coding sequence that disease-specificity likely exists. Here, we focus on joint disorders, finding among replicated loci, that GDF5 exhibits over twenty distinct associations, and we identify causal variants for two of its strongest associations, hip dysplasia and knee osteoarthritis. By mapping regulatory regions in joint chondrocytes, we pinpoint two variants (rs4911178; rs6060369), on the same risk haplotype, which reside in anatomical site-specific enhancers. We show that both variants have clinical relevance, impacting disease by altering morphology. By modeling each variant in humanized mice, we observe joint-specific response, correlating with GDF5 expression. Thus, we uncouple separate regulatory variants on a common risk haplotype that cause joint-specific disease. By broadening our perspective, we finally find that patterns of modularity at GDF5 are also found at over three-quarters of loci with multiple GWAS disease associations.

LincSNP 3.0: an updated database for linking functional variants to human long non-coding RNAs, circular RNAs and their regulatory elements

We describe an updated comprehensive database, LincSNP 3.0 (http://bioinfo.hrbmu.edu.cn/LincSNP), which aims to document and annotate disease or phenotype-associated variants in human long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) or their regulatory elements. LincSNP 3.0 has updated with several novel features, including (i) more types of variants including single nucleotide polymorphisms (SNPs), linkage disequilibrium SNPs (LD SNPs), somatic mutations and RNA editing sites have been expanded; (ii) more regulatory elements including transcription factor binding sites (TFBSs), enhancers, DNase I hypersensitive sites (DHSs), topologically associated domains (TADs), footprintss, methylations and open chromatin regions have been added; (iii) the associations among circRNAs, regulatory elements and variants have been identified; (iv) more experimentally supported variant-lncRNA/circRNA-disease/phenotype associations have been manually collected; (v) the sources of lncRNAs, circRNAs, SNPs, somatic mutations and RNA editing sites have been updated. Moreover, four flexible online tools including Genome Browser, Variant Mapper, Circos Plotter and Functional Annotation have been developed to retrieve, visualize and analyze the data. Collectively, LincSNP 3.0 provides associations among functional variants, regulatory elements, lncRNAs and circRNAs in diseases. It will serve as an important and continually updated resource for investigating functions and mechanisms of lncRNAs and circRNAs in diseases.

PheLiGe: an interactive database of billions of human genotype-phenotype associations

Genome-wide association studies have provided a vast array of publicly available SNP × phenotype association results. However, they are often in disparate repositories and formats, making downstream analyses difficult and time consuming. PheLiGe (https://phelige.com) is a database that provides easy access to such results via a web interface. The underlying database currently stores >75 billion genotype-phenotype associations from 7347 genome-wide and 1.2 million region-wide (e.g. cis-eQTL) association scans. The web interface allows for investigation of regional genotype-phenotype associations across many phenotypes, giving insights into the biological function affected by the variant in question. Furthermore, PheLiGe can compare regional patterns of association between different traits. This analysis can ascertain whether a co-association is due to pleiotropy or linkage. Moreover, comparison of association patterns for a complex trait of interest and gene expression and protein levels can implicate causal genes.

Novel susceptibility loci identified in a genome-wide association study of type 2 diabetes complications in population of Latvia

BACKGROUND

Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally. Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications.

METHODS

We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications.

RESULTS

The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02-3.64) and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87-3.34), rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71-3.01), rs6032 (F5, OR = 2.12; 95% CI = 1.63-2.77), rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69-3.01) and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66-2.87) and ophthalmic complications. By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy.

CONCLUSIONS

Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions. Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations.

Candidate gene family-based and case-control studies of susceptibility to high Schistosoma mansoni worm burden in African children: a protocol

Background: Approximately 25% of the risk of Schistosoma mansoni is associated with host genetic variation. We will test 24 candidate genes, mainly in the T _h2 and T _h17 pathways, for association with S. mansoni infection intensity in four African countries, using family based and case-control approaches. Methods: Children aged 5-15 years will be recruited in S. mansoni endemic areas of Ivory Coast, Cameroon, Uganda and the Democratic Republic of Congo (DRC). We will use family based (study 1) and case-control (study 2) designs. Study 1 will take place in Ivory Coast, Cameroon, Uganda and the DRC. We aim to recruit 100 high worm burden families from each country except Uganda, where a previous study recruited at least 40 families. For phenotyping, cases will be defined as the 20% of children in each community with heaviest worm burdens as measured by the circulating cathodic antigen (CCA) assay. Study 2 will take place in Uganda. We will recruit 500 children in a highly endemic community. For phenotyping, cases will be defined as the 20% of children with heaviest worm burdens as measured by the CAA assay, while controls will be the 20% of infected children with the lightest worm burdens. Deoxyribonucleic acid (DNA) will be genotyped on the Illumina H3Africa SNP (single nucleotide polymorphisms) chip and genotypes will be converted to sets of haplotypes that span the gene region for analysis. We have selected 24 genes for genotyping that are mainly in the Th2 and Th17 pathways and that have variants that have been demonstrated to be or could be associated with Schistosoma infection intensity.   Analysis: In the family-based design, we will identify SNP haplotypes disproportionately transmitted to children with high worm burden. Case-control analysis will detect overrepresentation of haplotypes in extreme phenotypes with correction for relatedness by using whole genome principal components.

Candidate gene family-based and case-control studies of susceptibility to high Schistosoma mansoni worm burden in African children: a protocol

Background: Approximately 25% of the risk of Schistosoma mansoni is associated with host genetic variation. We will test 24 candidate genes, mainly in the T _h2 and T _h17 pathways, for association with S. mansoni infection intensity in four African countries, using family based and case-control approaches. Methods: Children aged 5-15 years will be recruited in S. mansoni endemic areas of Ivory Coast, Cameroon, Uganda and the Democratic Republic of Congo (DRC). We will use family based (study 1) and case-control (study 2) designs. Study 1 will take place in Ivory Coast, Cameroon, Uganda and the DRC. We aim to recruit 100 high worm burden families from each country except Uganda, where a previous study recruited at least 40 families. For phenotyping, cases will be defined as the 20% of children in each community with heaviest worm burdens as measured by the circulating cathodic antigen (CCA) assay. Study 2 will take place in Uganda. We will recruit 500 children in a highly endemic community. For phenotyping, cases will be defined as the 20% of children with heaviest worm burdens as measured by the CAA assay, while controls will be the 20% of infected children with the lightest worm burdens. Deoxyribonucleic acid (DNA) will be genotyped on the Illumina H3Africa SNP (single nucleotide polymorphisms) chip and genotypes will be converted to sets of haplotypes that span the gene region for analysis. We have selected 24 genes for genotyping that are mainly in the Th2 and Th17 pathways and that have variants that have been demonstrated to be or could be associated with Schistosoma infection intensity.   Analysis: In the family-based design, we will identify SNP haplotypes disproportionately transmitted to children with high worm burden. Case-control analysis will detect overrepresentation of haplotypes in extreme phenotypes with correction for relatedness by using whole genome principal components.

The GWAS-MAP platform for aggregation of results of genome-wide association studies and the GWAS-MAP|homo database of 70 billion genetic associations of human traits

Hundreds of genome-wide association studies (GWAS) of human traits are performed each year. The results of GWAS are often published in the form of summary statistics. Information from summary statistics can be used for multiple purposes – from fundamental research in biology and genetics to the search for potential biomarkers and therapeutic targets. While the amount of GWAS summary statistics collected by the scientific community is rapidly increasing, the use of this data is limited by the lack of generally accepted standards. In particular, the researchers who would like to use GWAS summary statistics in their studies have to become aware that the data are scattered across multiple websites, are presented in a variety of formats, and, often, were not quality controlled. Moreover, each available summary statistics analysis tools will ask for data to be presented in their own internal format. To address these issues, we developed GWAS-MAP, a high-throughput platform for aggregating, storing, analyzing, visualizing and providing access to a database of big data that result from region- and genome-wide association studies. The database currently contains information on more than 70 billion associations between genetic variants and human diseases, quantitative traits, and “omics” traits. The GWAS-MAP platform and database can be used for studying the etiology of human diseases, building predictive risk models and finding potential biomarkers and therapeutic interventions. In order to demonstrate a typical application of the platform as an approach for extracting new biological knowledge and establishing mechanistic hypotheses, we analyzed varicose veins, a disease affecting on average every third adult in Russia. The results of analysis confirmed known epidemiologic associations for this disease and led us to propose a hypothesis that increased levels of MICB and CD209 proteins in human plasma may increase susceptibility to varicose veins.

Data-Driven Modeling of Knowledge Assemblies in Understanding Comorbidity Between Type 2 Diabetes Mellitus and Alzheimer's Disease

Background:

Recent studies have suggested comorbid association between Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) through identification of shared molecular mechanisms. However, the inference is pre-dominantly literature-based and lacks interpretation of pre-disposed genomic variants and transcriptomic measurables.

Objective:

In this study, we aim to identify shared genetic variants and dysregulated genes in AD and T2DM and explore their functional roles in the comorbidity between the diseases.

Methods:

The genetic variants for AD and T2DM were retrieved from GWAS catalog, GWAS central, dbSNP, and DisGeNet and subjected to linkage disequilibrium analysis. Next, shared variants were prioritized using RegulomeDB and Polyphen-2. Afterwards, a knowledge assembly embedding prioritized variants and their corresponding genes was created by mining relevant literature using Biological Expression Language. Finally, coherently perturbed genes from gene expression meta-analysis were mapped to the knowledge assembly to pinpoint biological entities and processes and depict a mechanistic link between AD and T2DM.

Results:

Our analysis identified four genes (i.e., ABCG1, COMT, MMP9, and SOD2) that could have dual roles in both AD and T2DM. Using cartoon representation, we have illustrated a set of causal events surrounding these genes which are associated to biological processes such as oxidative stress, insulin resistance, apoptosis and cognition.

Conclusion:

Our approach of using data as the driving force for unraveling disease etiologies eliminates literature bias and enables identification of novel entities that serve as the bridge between comorbid conditions.

Multi-layered epigenetic regulation of IRS2 expression in the liver of obese individuals with type 2 diabetes

AIMS/HYPOTHESIS

IRS2 is an important molecular switch that mediates insulin signalling in the liver. IRS2 dysregulation is responsible for the phenomenon of selective insulin resistance that is observed in type 2 diabetes. We hypothesise that epigenetic mechanisms are involved in the regulation of IRS2 in the liver of obese and type 2 diabetic individuals.

METHODS

DNA methylation of seven CpG sites was studied by bisulphite pyrosequencing and mRNA and microRNA (miRNA) expression was assessed by quantitative real-time PCR in liver biopsies of 50 obese non-diabetic and 31 obese type 2 diabetic participants, in a cross-sectional setting. Methylation-sensitive luciferase assays and electrophoretic mobility shift assays were performed. Furthermore, HepG2 cells were treated with insulin and high glucose concentrations to induce miRNA expression and IRS2 downregulation.

RESULTS

We found a significant downregulation of IRS2 expression in the liver of obese individuals with type 2 diabetes (0.84 ± 0.08-fold change; p = 0.0833; adjusted p value [p_a] = 0.0417; n = 31) in comparison with non-diabetic obese participants (n = 50). This downregulation correlated with hepatic IRS2 DNA methylation at CpG5. Additionally, CpG6, which is located in intron 1 of IRS2, was hypomethylated in type 2 diabetes; this site spans the sterol regulatory element binding transcription factor 1 (SREBF1) recognition motif, which likely acts as transcriptional repressor. The adjacent polymorphism rs4547213 (G>A) was significantly associated with DNA methylation at a specificity-protein-1 (SP1) binding site (CpG3). Moreover, DNA methylation of cg25924746, a CpG site located in the shore region of the IRS2 promoter-associated CpG island, was increased in the liver of individuals with type 2 diabetes, as compared with those without diabetes. A second epigenetic mechanism, upregulation of hepatic miRNA hsa-let-7e-5p (let-7e-5p) in obese individuals with type 2 diabetes (n = 29) vs non-diabetic obese individuals (n = 49) (1.2 ± 0.08-fold change; p = 0.0332; p_a = 0.0450), is likely to act synergistically with altered IRS2 DNA methylation to decrease IRS2 expression. Mechanistic in vitro experiments demonstrated an acute upregulation of let-7e-5p expression and simultaneous IRS2 downregulation in a liver (HepG2) cell line upon hyperinsulinaemic and hyperglycaemic conditions.

CONCLUSIONS/INTERPRETATION

Our study highlights a new multi-layered epigenetic network that could be involved in subtle dysregulation of IRS2 in the liver of individuals with type 2 diabetes. This might lead to fine-tuning of IRS2 expression and is likely to be supplementary to the already known factors regulating IRS2 expression. Thereby, our findings could support the discovery of new diagnostic and therapeutic strategies for type 2 diabetes. Graphical abstract.

The Dawn of the Age of Multi-Parent MAGIC Populations in Plant Breeding: Novel Powerful Next-Generation Resources for Genetic Analysis and Selection of Recombinant Elite Material

The compelling need to increase global agricultural production requires new breeding approaches that facilitate exploiting the diversity available in the plant genetic resources. Multi-parent advanced generation inter-cross (MAGIC) populations are large sets of recombinant inbred lines (RILs) that are a genetic mosaic of multiple founder parents. MAGIC populations display emerging features over experimental bi-parental and germplasm populations in combining significant levels of genetic recombination, a lack of genetic structure, and high genetic and phenotypic diversity. The development of MAGIC populations can be performed using “funnel” or “diallel” cross-designs, which are of great relevance choosing appropriate parents and defining optimal population sizes. Significant advances in specific software development are facilitating the genetic analysis of the complex genetic constitutions of MAGIC populations. Despite the complexity and the resources required in their development, due to their potential and interest for breeding, the number of MAGIC populations available and under development is continuously growing, with 45 MAGIC populations in different crops being reported here. Though cereals are by far the crop group where more MAGIC populations have been developed, MAGIC populations have also started to become available in other crop groups. The results obtained so far demonstrate that MAGIC populations are a very powerful tool for the dissection of complex traits, as well as a resource for the selection of recombinant elite breeding material and cultivars. In addition, some new MAGIC approaches that can make significant contributions to breeding, such as the development of inter-specific MAGIC populations, the development of MAGIC-like populations in crops where pure lines are not available, and the establishment of strategies for the straightforward incorporation of MAGIC materials in breeding pipelines, have barely been explored. The evidence that is already available indicates that MAGIC populations will play a major role in the coming years in allowing for impressive gains in plant breeding for developing new generations of dramatically improved cultivars.

The 27th annual Nucleic Acids Research database issue and molecular biology database collection

The 2020 Nucleic Acids Research Database Issue contains 148 papers spanning molecular biology. They include 59 papers reporting on new databases and 79 covering recent changes to resources previously published in the issue. A further ten papers are updates on databases most recently published elsewhere. This issue contains three breakthrough articles: AntiBodies Chemically Defined (ABCD) curates antibody sequences and their cognate antigens; SCOP returns with a new schema and breaks away from a purely hierarchical structure; while the new Alliance of Genome Resources brings together a number of Model Organism databases to pool knowledge and tools. Major returning nucleic acid databases include miRDB and miRTarBase. Databases for protein sequence analysis include CDD, DisProt and ELM, alongside no fewer than four newcomers covering proteins involved in liquid-liquid phase separation. In metabolism and signaling, Pathway Commons, Reactome and Metabolights all contribute papers. PATRIC and MicroScope update in microbial genomes while human and model organism genomics resources include Ensembl, Ensembl genomes and UCSC Genome Browser. Immune-related proteins are covered by updates from IPD-IMGT/HLA and AFND, as well as newcomers VDJbase and OGRDB. Drug design is catered for by updates from the IUPHAR/BPS Guide to Pharmacology and the Therapeutic Target Database. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been revised, updating 305 entries, adding 65 new resources and eliminating 125 discontinued URLs; so bringing the current total to 1637 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.