Understanding the Pathogenesis of Kawasaki Disease by Network and Pathway Analysis

Kawasaki disease (KD) is a complex disease, leading to the damage of multisystems. The pathogen that triggers this sophisticated disease is still unknown since it was first reported in 1967. To increase our knowledge on the effects of genes in KD, we extracted statistically significant genes so far associated with this mysterious illness from candidate gene studies and genome-wide association studies. These genes contributed to susceptibility to KD, coronary artery lesions, resistance to initial IVIG treatment, incomplete KD, and so on. Gene ontology category and pathways were analyzed for relationships among these statistically significant genes. These genes were represented in a variety of functional categories, including immune response, inflammatory response, and cellular calcium ion homeostasis. They were mainly enriched in the pathway of immune response. We further highlighted the compelling immune pathway of NF-AT signal and leukocyte interactions combined with another transcription factor NF-κB in the pathogenesis of KD. STRING analysis, a network analysis focusing on protein interactions, validated close contact between these genes and implied the importance of this pathway. This data will contribute to understanding pathogenesis of KD.

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Twelve years of GWAS discoveries for osteoporosis and related traits: advances, challenges and applications

Bone Research ◽

10.1038/s41413-021-00143-3 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Xiaowei Zhu ◽

Weiyang Bai ◽

Houfeng Zheng

Keyword(s):

Drug Targets ◽

Complex Disease ◽

Association Studies ◽

Osteoporotic Fractures ◽

Disease Prediction ◽

Genome Wide Association Studies ◽

Mineral Density ◽

Small Effect Size ◽

Endocrine Status ◽

Meta Analyses

AbstractOsteoporosis is a common skeletal disease, affecting ~200 million people around the world. As a complex disease, osteoporosis is influenced by many factors, including diet (e.g. calcium and protein intake), physical activity, endocrine status, coexisting diseases and genetic factors. In this review, we first summarize the discovery from genome-wide association studies (GWASs) in the bone field in the last 12 years. To date, GWASs and meta-analyses have discovered hundreds of loci that are associated with bone mineral density (BMD), osteoporosis, and osteoporotic fractures. However, the GWAS approach has sometimes been criticized because of the small effect size of the discovered variants and the mystery of missing heritability, these two questions could be partially explained by the newly raised conceptual models, such as omnigenic model and natural selection. Finally, we introduce the clinical use of GWAS findings in the bone field, such as the identification of causal clinical risk factors, the development of drug targets and disease prediction. Despite the fruitful GWAS discoveries in the bone field, most of these GWAS participants were of European descent, and more genetic studies should be carried out in other ethnic populations to benefit disease prediction in the corresponding population.

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Host Genetics and Gut Microbiome: Perspectives for Multiple Sclerosis

Genes ◽

10.3390/genes12081181 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1181

Author(s):

Alessandro Maglione ◽

Miriam Zuccalà ◽

Martina Tosi ◽

Marinella Clerico ◽

Simona Rolla

Keyword(s):

Multiple Sclerosis ◽

Environmental Factors ◽

Lupus Erythematosus ◽

Gut Microbiome ◽

Complex Disease ◽

Current Knowledge ◽

Association Studies ◽

Future Research ◽

Genome Wide Association Studies ◽

Host Genetics

As a complex disease, Multiple Sclerosis (MS)’s etiology is determined by both genetic and environmental factors. In the last decade, the gut microbiome has emerged as an important environmental factor, but its interaction with host genetics is still unknown. In this review, we focus on these dual aspects of MS pathogenesis: we describe the current knowledge on genetic factors related to MS, based on genome-wide association studies, and then illustrate the interactions between the immune system, gut microbiome and central nervous system in MS, summarizing the evidence available from Experimental Autoimmune Encephalomyelitis mouse models and studies in patients. Finally, as the understanding of influence of host genetics on the gut microbiome composition in MS is in its infancy, we explore this issue based on the evidence currently available from other autoimmune diseases that share with MS the interplay of genetic with environmental factors (Inflammatory Bowel Disease, Rheumatoid Arthritis and Systemic Lupus Erythematosus), and discuss avenues for future research.

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Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach

Journal of Clinical Medicine ◽

10.3390/jcm10081666 ◽

2021 ◽

Vol 10 (8) ◽

pp. 1666

Author(s):

Micaela F. Beckman ◽

Farah Bahrani Mougeot ◽

Jean-Luc C. Mougeot

Keyword(s):

Protein Interactions ◽

Association Studies ◽

Meta Analysis ◽

Holistic Approach ◽

Gene Interaction ◽

Snp Analysis ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Protein Protein Interactions ◽

Data Mining Approach

The COVID-19 pandemic has led to over 2.26 million deaths for almost 104 million confirmed cases worldwide, as of 4 February 2021 (WHO). Risk factors include pre-existing conditions such as cancer, cardiovascular disease, diabetes, and obesity. Although several vaccines have been deployed, there are few alternative anti-viral treatments available in the case of reduced or non-existent vaccine protection. Adopting a long-term holistic approach to cope with the COVID-19 pandemic appears critical with the emergence of novel and more infectious SARS-CoV-2 variants. Our objective was to identify comorbidity-associated single nucleotide polymorphisms (SNPs), potentially conferring increased susceptibility to SARS-CoV-2 infection using a computational meta-analysis approach. SNP datasets were downloaded from a publicly available genome-wide association studies (GWAS) catalog for 141 of 258 candidate COVID-19 comorbidities. Gene-level SNP analysis was performed to identify significant pathways by using the program MAGMA. An SNP annotation program was used to analyze MAGMA-identified genes. Differential gene expression was determined for significant genes across 30 general tissue types using the Functional and Annotation Mapping of GWAS online tool GENE2FUNC. COVID-19 comorbidities (n = 22) from six disease categories were found to have significant associated pathways, validated by Q–Q plots (p < 0.05). Protein–protein interactions of significant (p < 0.05) differentially expressed genes were visualized with the STRING program. Gene interaction networks were found to be relevant to SARS and influenza pathogenesis. In conclusion, we were able to identify the pathways potentially affected by or affecting SARS-CoV-2 infection in underlying medical conditions likely to confer susceptibility and/or the severity of COVID-19. Our findings have implications in future COVID-19 experimental research and treatment development.

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Genetic and Epigenetic Modifiers of Alcoholic Liver Disease

International Journal of Molecular Sciences ◽

10.3390/ijms19123857 ◽

2018 ◽

Vol 19 (12) ◽

pp. 3857 ◽

Cited By ~ 20

Author(s):

Marica Meroni ◽

Miriam Longo ◽

Raffaela Rametta ◽

Paola Dongiovanni

Keyword(s):

Liver Disease ◽

Alcohol Consumption ◽

Alcoholic Liver Disease ◽

Complex Disease ◽

Wide Spectrum ◽

Association Studies ◽

Phenotypic Variability ◽

Genome Wide Association Studies ◽

Epigenetic Factors ◽

Excessive Alcohol Consumption

Alcoholic liver disease (ALD), a disorder caused by excessive alcohol consumption is a global health issue. More than two billion people consume alcohol in the world and about 75 million are classified as having alcohol disorders. ALD embraces a wide spectrum of hepatic lesions including steatosis, alcoholic steatohepatitis (ASH), fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). ALD is a complex disease where environmental, genetic, and epigenetic factors contribute to its pathogenesis and progression. The severity of alcohol-induced liver disease depends on the amount, method of usage and duration of alcohol consumption as well as on age, gender, presence of obesity, and genetic susceptibility. Genome-wide association studies and candidate gene studies have identified genetic modifiers of ALD that can be exploited as non-invasive biomarkers, but which do not completely explain the phenotypic variability. Indeed, ALD development and progression is also modulated by epigenetic factors. The premise of this review is to discuss the role of genetic variants and epigenetic modifications, with particular attention being paid to microRNAs, as pathogenic markers, risk predictors, and therapeutic targets in ALD.

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Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis

10.1101/827923 ◽

2019 ◽

Author(s):

Jing Yang ◽

Amanda McGovern ◽

Paul Martin ◽

Kate Duffus ◽

Xiangyu Ge ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

T Cells ◽

Complex Disease ◽

Target Genes ◽

Disease Risk ◽

Association Studies ◽

Dna Interaction ◽

Genome Wide Association Studies ◽

Causal Genes

AbstractGenome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T-cells over 24 hours, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T-cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

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Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

10.1101/011569 ◽

2014 ◽

Cited By ~ 1

Author(s):

Daniel S Himmelstein ◽

Sergio E Baranzini

Keyword(s):

Data Integration ◽

Protein Interactions ◽

Heterogeneous Network ◽

Association Studies ◽

Genome Wide Association Studies ◽

Genetic Associations ◽

Protein Coding ◽

Multiple Node ◽

Pathogenic Variants ◽

Disease Associated Genes

The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants, and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks—graphs with multiple node and edge types—for accomplishing both tasks. First we constructed a network with 18 node types—genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database)collections—and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as fundamental mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data integration across multiple domains.

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An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome

10.1101/467910 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tom G. Richardson ◽

Sean Harrison ◽

Gibran Hemani ◽

George Davey Smith

Keyword(s):

Web Application ◽

Large Scale ◽

Complex Disease ◽

Association Studies ◽

Risk Scores ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Genetic Liability ◽

Polygenic Risk ◽

The Uk

AbstractThe age of large-scale genome-wide association studies (GWAS) has provided us with an unprecedented opportunity to evaluate the genetic liability of complex disease using polygenic risk scores (PRS). In this study, we have analysed 162 PRS (P<5×l0 05) derived from GWAS and 551 heritable traits from the UK Biobank study (N=334,398). Findings can be investigated using a web application (http://mrcieu.mrsoftware.org/PRS_atlas/), which we envisage will help uncover both known and novel mechanisms which contribute towards disease susceptibility.To demonstrate this, we have investigated the results from a phenome-wide evaluation of schizophrenia genetic liability. Amongst findings were inverse associations with measures of cognitive function which extensive follow-up analyses using Mendelian randomization (MR) provided evidence of a causal relationship. We have also investigated the effect of multiple risk factors on disease using mediation and multivariable MR frameworks. Our atlas provides a resource for future endeavours seeking to unravel the causal determinants of complex disease.

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Prioritization of genes associated with the pathogenesis of leukosis in cattle

Vavilov Journal of Genetics and Breeding ◽

10.18699/vj18.451 ◽

2019 ◽

Vol 22 (8) ◽

pp. 1063-1069 ◽

Cited By ~ 1

Author(s):

N. S. Yudin ◽

N. L. Podkolodnyy ◽

T. A. Agarkova ◽

E. V. Ignatieva

Keyword(s):

Protein Interactions ◽

Genome Wide Association Study ◽

Association Studies ◽

Mammalian Species ◽

Genome Wide Association ◽

Farm Animals ◽

Genome Wide Association Studies ◽

Protein Protein Interactions ◽

Genome Wide ◽

A Genome

Selection by means of genetic markers is a promising approach to the eradication of infectious diseases in farm animals, especially in the absence of eﬀective methods of treatment and prevention. Bovine leukemia virus (BLV) is spread throughout the world and represents one of the biggest problems for the livestock production and food security in Russia. However, recent genome-wide association studies have shown that sensitivity/resistance to BLV is polygenic. The aim of this study was to create a catalog of cattle genes and genes of other mammalian species involved in the pathogenesis of BLV-induced infection and to perform gene prioritization using bioinformatics methods. Based on manually collected information from a range of open sources, a total of 446 genes were included in the catalog of cattle genes and genes of other mammals involved in the pathogenesis of BLV-induced infection. The following criteria were used to prioritize 446 genes from the catalog: (1) the gene is associated with leukemia according to a genome-wide association study; (2) the gene is associated with leukemia according to a case-control study; (3) the role of the gene in leukemia development has been studied using knockout mice; (4) protein-protein interactions exist between the gene-encoded protein and either viral particles or individual viral proteins; (5) the gene is annotated with Gene Ontology terms that are overrepresented for a given list of genes; (6) the gene participates in biological pathways from the KEGG or REACTOME databases, which are over-represented for a given list of genes; (7) the protein encoded by the gene has a high number of protein-protein interactions with proteins encoded by other genes from the catalog. Based on each criterion, a rank was assigned to each gene. Then the ranks were summarized and an overall rank was determined. Prioritization of 446 candidate genes allowed us to identify 5 genes of interest (TNF,LTB,BOLA-DQA1,BOLA-DRB3,ATF2), which can aﬀect the sensitivity/resistance of cattle to leukemia.

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Genetics of Atrial Fibrillation in 2020

Circulation Research ◽

10.1161/circresaha.120.316575 ◽

2020 ◽

Vol 127 (1) ◽

pp. 21-33 ◽

Cited By ~ 4

Author(s):

Carolina Roselli ◽

Michiel Rienstra ◽

Patrick T. Ellinor

Keyword(s):

Atrial Fibrillation ◽

Complex Disease ◽

Association Studies ◽

Heart Rhythm ◽

Genome Wide Association Studies ◽

Future Directions ◽

Genetics Research ◽

Genome Wide ◽

Increased Risk ◽

Rhythm Disorder

Atrial fibrillation is a common heart rhythm disorder that leads to an increased risk for stroke and heart failure. Atrial fibrillation is a complex disease with both environmental and genetic risk factors that contribute to the arrhythmia. Over the last decade, rapid progress has been made in identifying the genetic basis for this common condition. In this review, we provide an overview of the primary types of genetic analyses performed for atrial fibrillation, including linkage studies, genome-wide association studies, and studies of rare coding variation. With these results in mind, we aim to highlighting the existing knowledge gaps and future directions for atrial fibrillation genetics research.

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