Common Disease Genes Research Articles

PWAS Hub for exploring gene-based associations of common complex diseases.

PWAS (proteome-wide association study) is an innovative genetic association approach that complements widely used methods like GWAS (genome-wide association study). The PWAS approach involves consecutive phases. Initially, machine learning modeling and probabilistic considerations quantify the impact of genetic variants on protein-coding genes' biochemical functions. Secondly, for each individual, aggregating the variants per gene determines a gene-damaging score. Finally, standard statistical tests are activated in the case-control setting to yield statistically significant genes per phenotype. The PWAS Hub offers a user-friendly interface for an in-depth exploration of gene-disease associations from the UK Biobank (UKB). Results from PWAS cover 99 common diseases and conditions, each with over 10,000 diagnosed individuals per phenotype. Users can explore genes associated with these diseases, with separate analyses conducted for males and females. For each phenotype, the analyses account for sex-based genetic effects, inheritance modes (dominant and recessive), and the pleiotropic nature of associated genes. The PWAS Hub showcases its usefulness for asthma by navigating through proteomic-genetic analyses. Inspecting PWAS asthma-listed genes (a total of 27) provide insights into the underlying cellular and molecular mechanisms. Comparison of PWAS-statistically significant genes for common diseases to the Open Targets benchmark shows partial but significant overlap in gene associations for most phenotypes. Graphical tools facilitate comparing genetic effects between PWAS and coding GWAS results, aiding in understanding the sex-specific genetic impact on common diseases. This adaptable platform is attractive to clinicians, researchers, and individuals interested in delving into gene-disease associations and sex-specific genetic effects.

Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles.

Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.

A novel therapeutic concern: Antibiotic resistance genes in common chronic diseases.

Infections caused by multidrug-resistant bacteria carrying antibiotic resistance genes pose a severe threat to global public health and human health. In clinical practice, it has been found that human gut microbiota act as a "reservoir" of antibiotic resistance genes (ARGs) since gut microbiota contain a wide variety of ARGs, and that the structure of the gut microbiome is influenced by the profile of the drug resistance genes present. In addition, ARGs can spread within and between species of the gut microbiome in multiple ways. To better understand gut microbiota ARGs and their effects on patients with chronic diseases, this article reviews the generation of ARGs, common vectors that transmit ARGs, the characteristics of gut microbiota ARGs in common chronic diseases, their impact on prognosis, the current state of treatment for ARGs, and what should be addressed in future research.

Myocarditis-like Episodes in Patients with Arrhythmogenic Cardiomyopathy: A Systematic Review on the So-Called Hot-Phase of the Disease.

Arrhythmogenic cardiomyopathy (ACM) is a genetically determined myocardial disease, characterized by myocytes necrosis with fibrofatty substitution and ventricular arrhythmias that can even lead to sudden cardiac death. The presence of inflammatory cell infiltrates in endomyocardial biopsies or in autoptic specimens of ACM patients has been reported, suggesting a possible role of inflammation in the pathophysiology of the disease. Furthermore, chest pain episodes accompanied by electrocardiographic changes and troponin release have been observed and defined as the “hot-phase” phenomenon. The aim of this critical systematic review was to assess the clinical features of ACM patients presenting with “hot-phase” episodes. According to PRISMA guidelines, a search was run in the PubMed, Scopus and Web of Science electronic databases using the following keywords: “arrhythmogenic cardiomyopathy”; “myocarditis” or “arrhythmogenic cardiomyopathy”; “troponin” or “arrhythmogenic cardiomyopathy”; and “hot-phase”. A total of 1433 titles were retrieved, of which 65 studies were potentially relevant to the topic. Through the application of inclusion and exclusion criteria, 9 papers reporting 103 ACM patients who had experienced hot-phase episodes were selected for this review. Age at time of episodes was available in 76% of cases, with the mean age reported being 26 years ± 14 years (min 2–max 71 years). Overall, 86% of patients showed left ventricular epicardial LGE. At the time of hot-phase episodes, 49% received a diagnosis of ACM (Arrhythmogenic left ventricular cardiomyopathy in the majority of cases), 19% of dilated cardiomyopathy and 26% of acute myocarditis. At the genetic study, Desmoplakin (DSP) was the more represented disease-gene (69%), followed by Plakophillin-2 (9%) and Desmoglein-2 (6%). In conclusion, ACM patients showing hot-phase episodes are usually young, and DSP is the most common disease gene, accounting for 69% of cases. Currently, the role of “hot-phase” episodes in disease progression and arrhythmic risk stratification remains to be clarified.

Genetic and Functional Analysis of Glycosyltransferase 8 Domain-Containing Protein 1 in Taiwanese Patients With Amyotrophic Lateral Sclerosis.

Background and ObjectivesTo investigate the frequency, spectrum, and molecular functional effect of glycosyltransferase 8 domain-containing protein 1 (GLT8D1) variations in Taiwanese patients with amyotrophic lateral sclerosis (ALS).MethodsWe performed genetic analyses of GLT8D1 in 410 unrelated patients with ALS by Sanger sequencing. The 410 patients were selected from a cohort of 477 unrelated patients with ALS after excluding variations in common ALS disease genes. Functional effects of the GLT8D1 variation were investigated by in vitro functional analysis.ResultsWe identified a novel heterozygous missense variation in GLT8D1, p.I290M (c.870C>G), in 1 single patient with familial ALS. The patient with the p.I290M variation had a spinal-onset ALS with disease onset at age 60 years and a survival of 6 years. Functional studies demonstrated that the variant I290M GLT8D1 protein was mislocalized to the endoplasmic reticulum (ER), provoked ER stress and unfolded protein response, compromised the glycosyltransferase activity, and led to an increased cytotoxicity.DiscussionGLT8D1 variations account for 0.2% (1/477) of the patients with ALS in Taiwan. These findings expand the spectrum of GLT8D1 variation and support the pathogenic role of GLT8D1 variations in ALS.

A novel systems biology approach to evaluate mouse models of late-onset Alzheimer\u2019s disease

BackgroundLate-onset Alzheimer’s disease (LOAD) is the most common form of dementia worldwide. To date, animal models of Alzheimer’s have focused on rare familial mutations, due to a lack of frank neuropathology from models based on common disease genes. Recent multi-cohort studies of postmortem human brain transcriptomes have identified a set of 30 gene co-expression modules associated with LOAD, providing a molecular catalog of relevant endophenotypes.ResultsThis resource enables precise gene-based alignment between new animal models and human molecular signatures of disease. Here, we describe a new resource to efficiently screen mouse models for LOAD relevance. A new NanoString nCounter® Mouse AD panel was designed to correlate key human disease processes and pathways with mRNA from mouse brains. Analysis of the 5xFAD mouse, a widely used amyloid pathology model, and three mouse models based on LOAD genetics carrying APOE4 and TREM2*R47H alleles demonstrated overlaps with distinct human AD modules that, in turn, were functionally enriched in key disease-associated pathways. Comprehensive comparison with full transcriptome data from same-sample RNA-Seq showed strong correlation between gene expression changes independent of experimental platform.ConclusionsTaken together, we show that the nCounter Mouse AD panel offers a rapid, cost-effective and highly reproducible approach to assess disease relevance of potential LOAD mouse models.

Sequential targeted exome sequencing of 1001 patients affected by unexplained limb-girdle weakness

PurposeSeveral hundred genetic muscle diseases have been described, all of which are rare. Their clinical and genetic heterogeneity means that a genetic diagnosis is challenging. We established an international consortium, MYO-SEQ, to aid the work-ups of muscle disease patients and to better understand disease etiology. MethodsExome sequencing was applied to 1001 undiagnosed patients recruited from more than 40 neuromuscular disease referral centers; standardized phenotypic information was collected for each patient. Exomes were examined for variants in 429 genes associated with muscle conditions. ResultsWe identified suspected pathogenic variants in 52% of patients across 87 genes. We detected 401 novel variants, 116 of which were recurrent. Variants in CAPN3, DYSF, ANO5, DMD, RYR1, TTN, COL6A2, and SGCA collectively accounted for over half of the solved cases; while variants in newer disease genes, such as BVES and POGLUT1, were also found. The remaining well-characterized unsolved patients (48%) need further investigation. ConclusionUsing our unique infrastructure, we developed a pathway to expedite muscle disease diagnoses. Our data suggest that exome sequencing should be used for pathogenic variant detection in patients with suspected genetic muscle diseases, focusing first on the most common disease genes described here, and subsequently in rarer and newly characterized disease genes.

Integrative Biological Network Analysis to Identify Shared Genes in Metabolic Disorders.

Identification of common molecular mechanisms in interrelated diseases is essential for better prognoses and targeted therapies. However, complexity of metabolic pathways makes it difficult to discover common disease genes underlying metabolic disorders; and it requires more sophisticated bioinformatics models that combine different types of biological data and computational methods. Accordingly, we built an integrative network analysis model to identify shared disease genes in metabolic syndrome (MS), type 2 diabetes (T2D), and coronary artery disease (CAD). We constructed weighted gene co-expression networks by combining gene expression, protein-protein interaction, and gene ontology data from multiple sources. For 90 different configurations of disease networks, we detected the significant modules by using MCL, SPICi, and Linkcomm graph clustering algorithms. We also performed a comparative evaluation on disease modules to determine the best method providing the highest biological validity. By overlapping the disease modules, we identified 22 shared genes for MS-CAD and T2D-CAD. Moreover, 19 out of these genes were directly or indirectly associated with relevant diseases in the previous medical studies. This study does not only demonstrate the performance of different biological data sources and computational methods in disease-gene discovery, but also offers potential insights into common genetic mechanisms of the metabolic disorders.

Aberrant Expressions of Co-stimulatory and Co-inhibitory Molecules in Autoimmune Diseases.

Co-signaling molecules include co-stimulatory and co-inhibitory molecules and play important roles in modulating immune responses. The roles of co-signaling molecules in autoimmune diseases have not been clearly defined. We assessed the expressions of co-stimulatory and co-inhibitory molecules in autoimmune diseases through a bioinformatics-based study. By using datasets of whole-genome transcriptome, the expressions of 54 co-stimulatory or co-inhibitory genes in common autoimmune diseases were analyzed using Robust rank aggregation (RRA) method. Nineteen array datasets and 6 RNA-seq datasets were included in the RRA discovery study and RRA validation study, respectively. Significant genes were further validated in several autoimmune diseases including Graves' disease (GD). RRA discovery study suggested that CD160 was the most significant gene aberrantly expressed in autoimmune diseases (Adjusted P = 5.9E-12), followed by CD58 (Adjusted P = 5.7E-06) and CD244 (Adjusted P = 9.5E-05). RRA validation study also identified CD160 as the most significant gene aberrantly expressed in autoimmune diseases (Adjusted P = 5.9E-09). We further found that the aberrant expression of CD160 was statistically significant in multiple autoimmune diseases including GD (P < 0.05), and CD160 had a moderate role in diagnosing those autoimmune diseases. Flow cytometry confirmed that CD160 was differentially expressed on the surface of CD8+ T cells between GD patients and healthy controls (P = 0.002), which proved the aberrant expression of CD160 in GD at the protein level. This study suggests that CD160 is the most significant co-signaling gene aberrantly expressed in autoimmune diseases. Treatment strategy targeting CD160-related pathway may be promising for the therapy of autoimmune diseases.

Identifying communities from multiplex biological networks by randomized optimization of modularity

The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification DREAM challengeestablished a framework to evaluate clustering approaches in a biomedical context, by testing the association of communities with GWAS-derived common trait and disease genes. We implemented here several extensions of the MolTi software that detects communities by optimizing multiplex (and monoplex) network modularity. In particular, MolTi now runs a randomized version of the Louvain algorithm, can consider edge and layer weights, and performs recursive clustering. On simulated networks, the randomization procedure clearly improves the detection of communities. On the DREAM challengebenchmark, the results strongly depend on the selected GWAS dataset and enrichment p -value threshold. However, the randomization procedure, as well as the consideration of weighted edges and layers generally increases the number of trait and disease community detected. The new version of MolTi and the scripts used for the DMI DREAM challenge are available at: https://github.com/gilles-didier/MolTi-DREAM.

Identifying communities from multiplex biological networks by randomized optimization of modularity.

The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification DREAM challengeestablished a framework to evaluate clustering approaches in a biomedical context, by testing the association of communities with GWAS-derived common trait and disease genes. We implemented here several extensions of the MolTi software that detects communities by optimizing multiplex (and monoplex) network modularity. In particular, MolTi now runs a randomized version of the Louvain algorithm, can consider edge and layer weights, and performs recursive clustering. On simulated networks, the randomization procedure clearly improves the detection of communities. On the DREAM challengebenchmark, the results strongly depend on the selected GWAS dataset and enrichment p -value threshold. However, the randomization procedure, as well as the consideration of weighted edges and layers generally increases the number of trait and disease community detected. The new version of MolTi and the scripts used for the DMI DREAM challenge are available at: https://github.com/gilles-didier/MolTi-DREAM.

A Comprehensive Atlas of E3 Ubiquitin Ligase Mutations in Neurological Disorders.

Protein ubiquitination is a posttranslational modification that plays an integral part in mediating diverse cellular functions. The process of protein ubiquitination requires an enzymatic cascade that consists of a ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2) and an E3 ubiquitin ligase (E3). There are an estimated 600–700 E3 ligase genes representing ~5% of the human genome. Not surprisingly, mutations in E3 ligase genes have been observed in multiple neurological conditions. We constructed a comprehensive atlas of disrupted E3 ligase genes in common (CND) and rare neurological diseases (RND). Of the predicted and known human E3 ligase genes, we found ~13% were mutated in a neurological disorder with 83 total genes representing 70 different types of neurological diseases. Of the E3 ligase genes identified, 51 were associated with an RND. Here, we provide an updated list of neurological disorders associated with E3 ligase gene disruption. We further highlight research in these neurological disorders and discuss the advanced technologies used to support these findings.

Molecular genetic diagnostics for ventricular arrhythmias and sudden cardiac death syndromes.

Inherited forms of ventricular arrhythmias are rare diseases, but amajor cause for severe cardiac events, sudden unexplained death syndromes, and death in young adults, infants, and children. Each disorder is genetically heterogeneous (5-20 genes per disease) and molecular testing may include both core genes and less common disease genes as well. Owing to the rapid development and feasibility of sequencing technologies enabling aparallel analysis of several hundred genes up to awhole exome, disease mutations can be identified very efficiently, but have to be seen in the complexity and natural variance of the human genome. Precise phenotypic knowledge and advanced gene variant interpretation are important to ensure adequate patient diagnostics and management. This article focuses on the genetic causes of inherited arrhythmia forms predisposing patients to sudden cardiac death and discusses practical issues and skills for molecular testing.

Expanding the mutation spectrum in ICF syndrome: Evidence for a gender bias in ICF2.

Immunodeficiency, centromeric instability, and facial anomalies (ICF) syndrome is a rare, genetically heterogeneous, autosomal recessive disorder. Patients suffer from recurrent infections caused by reduced levels or absence of serum immunoglobulins. Genetically, 4 subtypes of ICF syndrome have been identified to date: ICF1 (DNMT3B mutations), ICF2 (ZBTB24 mutations), ICF3 (CDCA7 mutations), and ICF4 (HELLS mutations). To study the mutation spectrum in ICF syndrome. Genetic studies were performed in peripheral blood lymphocyte DNA from suspected ICF patients and family members. We describe 7 ICF1 patients and 6 novel missense mutations in DNMT3B, affecting highly conserved residues in the catalytic domain. We also describe 5 new ICF2 patients, one of them carrying a homozygous deletion of the complete ZBTB24 locus. In a meta-analysis of all published ICF cases, we observed a gender bias in ICF2 with 79% male patients. The biallelic deletion of ZBTB24 provides strong support for the hypothesis that most ICF2 patients suffer from a ZBTB24 loss of function mechanism and confirms that complete absence of ZBTB24 is compatible with human life. This is in contrast to the observed early embryonic lethality in mice lacking functional Zbtb24. The observed gender bias seems to be restricted to ICF2 as it is not observed in the ICF1 cohort. Our study expands the mutation spectrum in ICF syndrome and supports that DNMT3B and ZBTB24 are the most common disease genes.

Identification of ITPA on chromosome 20 as a susceptibility gene for young-onset tuberculosis.

Tuberculosis (TB) is a complex disease, and both genetic and environmental factors contribute to disease progression. A previous genome-wide linkage study in Thailand determined that chromosome 20p13-12.3 may contain risk factors for young-onset disease. The present study aimed to identify novel susceptibility genes for young-onset TB within a 1-Mbp target region adjacent to the top-ranking risk marker in Chr.20p13-12.3. We performed next-generation sequencing (NGS) of the region in 13 young patients from multi-case families in Thailand. We then selected the functionally interesting single-nucleotide polymorphisms as candidates for subsequent analyses. The detected candidates rs13830 and rs1127354 in ITPA showed an association with young (<45 years old) TB patients. However, there was no association in old (⩾45 years old) patients. These findings confirm that stratifying patients based on age of TB onset can be important for identifying genetic risk factors for TB susceptibility. In addition, in silico expression quantitative trait loci analyses indicated that ITPA expression was associated with rs13830 genotype. This is the first study to use NGS resequencing to gain insight into host genetic factors associated with TB and to report a significant association for ITPA with host susceptibility in young-onset TB. The study also demonstrated the effectiveness of NGS in identifying susceptibility genes in common diseases.

Meta-Analysis of Genetic Association Studies

The object of this review is to help readers to understand meta-analysis of genetic association study. Genetic association studies are a powerful approach to identify susceptibility genes for common diseases. However, the results of these studies are not consistently reproducible. In order to overcome the limitations of individual studies, larger sample sizes or meta-analysis is required. Meta-analysis is a statistical tool for combining results of different studies on the same topic, thus increasing statistical strength and precision. Meta-analysis of genetic association studies combines the results from independent studies, explores the sources of heterogeneity, and identifies subgroups associated with the factor of interest. Meta-analysis of genetic association studies is an effective tool for garnering a greater understanding of complex diseases and potentially provides new insights into gene-disease associations.

Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment

BackgroundTranslational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases.MethodsWe integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases.ResultsThe analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4+ T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification.ConclusionsIn summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles.

Network Topology and Functional Analysis of Genes in Ocular Disorders

Most common genetic disorders have a complex inheritance and they are results of mutations in many genes, each gene contribute effects to the disease. The diseases belonging to the same disease class do not always share common disease genes; share a protein-protein interaction and gene ontology biological process with different diseases. This study focused on analyzing the genetic complexity of ocular disorders.

Advances on gene-based association analysis

Genome-wide association studies (GWAS) have been widely used for hunting the susceptibility genes for common diseases in the past years; however, the abundant information for the disease mechanism based on the GWAS data has not been fully mined. Recently, some researchers focused on the biological network and pathway analysis for the GWAS data to explore the potential disease mechanism. Since genes are the basic units for the biological network and pathway, the genetic effects from all or part of single nucleotide polymorphisms (SNPs) in the genes should be integrated into genetic scores, which are so-called "gene-based association analysis". Gene-based association analysis takes into account some important factors such as genetic effects of the SNPs, the number of the SNPs in the genes and the linkage disequilibrium structure of the SNPs. In this review, we will focus on the progress, principle and application of gene-based association analysis.

A novel mutation of the LMNA gene in a family with dilated cardiomyopathy, conduction system disease, and sudden cardiac death of young females

The LMNA gene, which encodes the nuclear envelope protein lamin A/C, is considered to be the most common autosomal disease gene associated with familial dilated cardiomyopathy. To date, each mutation of the LMNA gene has been associated with a specific disease phenotype. Clinical data, family histories, and blood samples were collected from 27 biological members of a family with dilated cardiomyopathy, prominently occurring as heart failure and conduction system disease with a high incidence of sudden cardiac death in young females. Twelve exons of the LMNA gene were screened for nucleotide alterations. A novel insertion mutation (nucleotide 1526insA, amino acid T510Y) in exon nine of the LMNA gene was identified in seven subjects (7/27, 25.9%). This reveals that the LMNA gene insertion mutation (T510Y frameshift mutation) can cause dilated cardiomyopathy, conduction system disease, and sudden cardiac death without skeletal myopathy, clinically manifested with early onset, severe symptoms, and poor prognosis.