scholarly journals Genetic Interactions Affect Lung Function in Patients with Systemic Sclerosis

2019 ◽  
Vol 10 (1) ◽  
pp. 151-163 ◽  
Author(s):  
Anna Tyler ◽  
J. Matthew Mahoney ◽  
Gregory W. Carter
Keyword(s):  
Systemic Sclerosis ◽  
Lung Function ◽  
Lung Disease ◽  
Association Studies ◽  
Treatment Strategies ◽  
Specific Treatment ◽  
Genetic Interactions ◽  
Genome Wide Association Studies ◽  
Lung Pathology ◽  
Large Numbers

Scleroderma, or systemic sclerosis (SSc), is an autoimmune disease characterized by progressive fibrosis of the skin and internal organs. The most common cause of death in people with SSc is lung disease, but the pathogenesis of lung disease in SSc is insufficiently understood to devise specific treatment strategies. Developing targeted treatments requires not only the identification of molecular processes involved in SSc-associated lung disease, but also understanding of how these processes interact to drive pathology. One potentially powerful approach is to identify alleles that interact genetically to influence lung outcomes in patients with SSc. Analysis of interactions, rather than individual allele effects, has the potential to delineate molecular interactions that are important in SSc-related lung pathology. However, detecting genetic interactions, or epistasis, in human cohorts is challenging. Large numbers of variants with low minor allele frequencies, paired with heterogeneous disease presentation, reduce power to detect epistasis. Here we present an analysis that increases power to detect epistasis in human genome-wide association studies (GWAS). We tested for genetic interactions influencing lung function and autoantibody status in a cohort of 416 SSc patients. Using Matrix Epistasis to filter SNPs followed by the Combined Analysis of Pleiotropy and Epistasis (CAPE), we identified a network of interacting alleles influencing lung function in patients with SSc. In particular, we identified a three-gene network comprising WNT5A, RBMS3, and MSI2, which in combination influenced multiple pulmonary pathology measures. The associations of these genes with lung outcomes in SSc are novel and high-confidence. Furthermore, gene coexpression analysis suggested that the interactions we identified are tissue-specific, thus differentiating SSc-related pathogenic processes in lung from those in skin.

scholarly journals Genetic interactions affect lung function in patients with systemic sclerosis

2019 ◽  
Author(s):  
Anna L. Tyler ◽  
J. Matthew Mahoney ◽  
Gregory W. Carter
Keyword(s):  
Systemic Sclerosis ◽  
Autoimmune Disease ◽  
Lung Function ◽  
Lung Disease ◽  
Cause Of Death ◽  
Genetic Variants ◽  
Gene Network ◽  
Genetic Interactions ◽  
Internal Organs ◽  
Autoantibody Status

AbstractScleroderma, or systemic sclerosis (SSc), is an autoimmune disease characterized by progressive fibrosis of the skin and internal organs. The most common cause of death in people with SSc is lung disease, but the pathogenesis of lung disease in SSc is insufficiently understood to devise specific treatment strategies. Developing targeted treatments requires not only the identification of molecular processes involved in SSc-associated lung disease, but also understanding of how these processes interact to drive pathology. One potentially powerful approach is to identify alleles that interact genetically to influence lung outcomes in patients with SSc. Analysis of interactions, rather than individual allele effects, has the potential to delineate molecular interactions that are important in SSc-related lung pathology. However, detecting genetic interactions, or epistasis, in human cohorts is challenging. Large numbers of variants with low minor allele frequencies, paired with heterogeneous disease presentation, reduce power to detect epistasis. Here we present an analysis that increases power to detect epistasis in human genome-wide association studies (GWAS). We tested for genetic interactions influencing lung function and autoantibody status in a cohort of 416 SSc patients. Using Matrix Epistasis to filter SNPs followed by the Combined Analysis of Pleiotropy and Epistasis (CAPE), we identified a network of interacting alleles influencing lung function in patients with SSc. In particular, we identified a three-gene network comprisingWNT5A, RBMS3, andMSI2, which in combination influenced multiple pulmonary pathology measures. The associations of these genes with lung outcomes in SSc are novel and high-confidence. Furthermore, gene coexpression analysis suggested that the interactions we identified are tissue-specific, thus differentiating SSc-related pathogenic processes in lung from those in skin.Author summarySystemic sclerosis (SSc), or scleroderma, is a devastating autoimmune disease. Patients experience progressive fibrosis of their skin and internal organs, reduced quality of life, and increased risk of death. Lung disease associated with SSc is particularly dangerous and is currently the leading cause of death in SSc patients. There are no specific treatments for SSc or SSc-related lung disease, but promising work in the genetics of this disease has identified more than 200 genetic variants that influence SSc [1]. Piecing together how genetic variants interact with each other to influence disease may provide clues for targeted therapies. Here we present a novel analytical approach for identifying genetic interactions in a human disease cohort. In this approach we first filtered SNPs to those that are most likely to interact to influence the disease traits. We then applied the Combined Analysis of Pleiotropy and Epistasis (CAPE), which combines information across multiple traits to increase power to detect genetic interactions. Using this approach, we identified a three-gene network amongMSI2, WNT5A, andRBMS3that influenced autoantibody status and lung function in a cohort of 416 SSc patients. Gene expression data suggest that this interaction network is tissue- and disease-specific, and may thus provide a specific target for SSc therapy.

Genetic Interactions Effects of Cardiovascular Disorder Using Computational Models: A Review

2020 ◽  
Vol 9 (3) ◽  
pp. 177-191
Author(s):  
Sridharan Priya ◽  
Radha K. Manavalan
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiovascular Diseases ◽  
Computational Models ◽  
Genetic Interaction ◽  
Association Studies ◽  
Genetic Interactions ◽  
Computational Techniques ◽  
Genome Wide Association Studies ◽  
Artery Disease

Background: The diseases in the heart and blood vessels such as heart attack, Coronary Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/ LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome- Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures of CVD. Objective: Genetic interactions or Epistasis infer the interactions between two or more genes where one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the Epistasis relationship through biological or laboratory methods needs an enormous workforce and more cost. Hence, this paper presents the review of various statistical and Machine learning approaches so far proposed to detect genetic interaction effects for the identification of various Cardiovascular diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid phenotypes data, and Body Mass Index dataset. Conclusion: This study reveals that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits, limitations, and issues of the various computational techniques for the evolution of epistasis responsible for cardiovascular diseases are exhibited.

scholarly journals Impact of Lung Function Decline on Time to Hospitalisation Events in Systemic Sclerosis-associated Interstitial Lung Disease (SSc-ILD): a Joint Model Analysis

2021 ◽  
Author(s):  
Michael Kreuter ◽  
Francesco Del Galdo ◽  
Corinna Miede ◽  
Dinesh Khanna ◽  
Wim A. Wuyts ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Lung Function ◽  
Lung Disease ◽  
Fold Increase ◽  
Phase Iii ◽  
Lung Function Decline ◽  
Related Hospitalisation ◽  
The Relationship ◽  
Increase In Risk

Abstract Background: Interstitial lung disease (ILD) is a common organ manifestation in systemic sclerosis (SSc) and is the leading cause of death in patients with SSc. A decline in forced vital capacity (FVC) is an indicator of ILD progression and is associated with mortality in patients with SSc-associated ILD (SSc-ILD). However, the relationship between FVC decline and hospitalisation events in patients with SSc-ILD is largely unknown. The objective of this post-hoc analysis was to investigate the relationship between FVC decline and clinically important hospitalisation endpoints.Methods: We used data from SENSCIS®, a Phase III trial investigating the efficacy and safety of nintedanib in patients with SSc-ILD. Joint models for longitudinal and time-to-event data were used to assess the association between rate of decline in FVC% predicted and hospitalisation-related endpoints (including time to first all-cause hospitalisation or death; time to first SSc-related hospitalisation or death; and time to first admission to an emergency room [ER] or admission to hospital followed by admission to intensive care unit [ICU] or death) during the treatment period, over 52 weeks in patients with SSc-ILD.Results: There was a statistically significant association between FVC decline and the risk of all-cause (n=78) and SSc-related (n=42) hospitalisations or death (both P<0.0001). A decrease of 3% in FVC corresponded to a 1.43-fold increase in risk of all-cause hospitalisation or death (95% confidence interval [CI] 1.24, 1.65) and a 1.48-fold increase in risk of SSc-related hospitalisation or death (95% CI 1.23, 1.77). No statistically significant association was observed between FVC decline and admission to ER or to hospital followed by admission to ICU or death (n=75; P=0.15). The estimated slope difference for nintedanib versus placebo in the longitudinal sub-model was consistent with the primary analysis in SENSCIS®.Conclusions: The association of lung function decline with an increased risk of hospitalisation suggests that slowing FVC decline in patients with SSc-ILD may prevent hospitalisations. Our findings also provide evidence that FVC decline may serve as a surrogate endpoint for clinically relevant hospitalisation-associated endpoints.Trial registration: Clinialtrials.gov, NCT02597933. Registered 8 October 2015, https://clinicaltrials.gov/ct2/show/study/NCT02597933.

scholarly journals RECENT RESEARCH ON THE GROWTH PLATE: Recent insights into the regulation of the growth plate

2014 ◽  
Vol 53 (1) ◽  
pp. T1-T9 ◽  
Author(s):  
Julian C Lui ◽  
Ola Nilsson ◽  
Jeffrey Baron
Keyword(s):  
Growth Plate ◽  
Bone Morphogenetic Proteins ◽  
Bone Growth ◽  
Association Studies ◽  
Expression Patterns ◽  
Human Growth ◽  
Cytokine Signaling ◽  
Genome Wide Association Studies ◽  
Paracrine Factors ◽  
Large Numbers

For most bones, elongation is driven primarily by chondrogenesis at the growth plates. This process results from chondrocyte proliferation, hypertrophy, and extracellular matrix secretion, and it is carefully orchestrated by complex networks of local paracrine factors and modulated by endocrine factors. We review here recent advances in the understanding of growth plate physiology. These advances include new approaches to study expression patterns of large numbers of genes in the growth plate, using microdissection followed by microarray. This approach has been combined with genome-wide association studies to provide insights into the regulation of the human growth plate. We also review recent studies elucidating the roles of bone morphogenetic proteins, fibroblast growth factors, C-type natriuretic peptide, and suppressor of cytokine signaling in the local regulation of growth plate chondrogenesis and longitudinal bone growth.

Molecular genetics of meningiomas: a systematic review of the current literature and potential basis for future treatment paradigms

2011 ◽  
Vol 30 (5) ◽  
pp. E7 ◽  
Author(s):  
Martin H. Pham ◽  
Gabriel Zada ◽  
Gina M. Mosich ◽  
Thomas C. Chen ◽  
Steven L. Giannotta ◽  
...  
Keyword(s):  
Current Literature ◽  
Tumor Suppressor Genes ◽  
Association Studies ◽  
Treatment Strategies ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genetic Abnormalities ◽  
Molecular Therapies ◽  
Potential Basis ◽  
Skull Base Meningiomas

Although a majority of meningiomas are benign neoplasms, those occurring at the cranial base may be challenging tumors to treat because of extensive tissue invasion, an inability to achieve gross-total microscopic resection, and local tumor recurrence and/or progression. A more comprehensive understanding of the genetic abnormalities associated with meningioma tumorigenesis, growth, and invasion may provide novel targets for grading assessments and individualizing molecular therapies for skull base meningiomas. The authors performed a review of the current literature to identify genes that have been associated with the formation and/or progression of meningiomas. Mutations in the NF2 gene have been most commonly implicated in the formation of the majority of meningiomas. Inactivation of other tumor suppressor genes, including DAL-1 and various tissue inhibitors of matrix metalloproteinases, upregulation of several oncogenes including c-sis and STAT3, and signaling dysregulation of pathways such as the Wnt pathway, have each been found to play important, and perhaps, complementary roles in meningioma development, progression, and recurrence. Identification of these genetic factors using genome-wide association studies and high-throughput genomics may provide data for future individualized treatment strategies.

Role of Genetic Interactions in Lung Diseases Detection using Computational Approaches: A Review

2021 ◽  
Vol 01 ◽  
Author(s):  
S Priya ◽  
R Manavalan
Keyword(s):  
Lung Cancer ◽  
Lung Diseases ◽  
Computational Models ◽  
Association Studies ◽  
Genetic Interactions ◽  
Genome Wide Association Studies ◽  
Computational Approaches ◽  
Genome Wide ◽  
Human Disorders ◽  
Brca1 Brca2

: Genome-wide Association Studies (GWAS) give special insight into genetic differences and environmental influences that are part of different human disorders and provide prognostic help to increase the survival of patients. Lung diseases such as lung cancer, asthma, and tuberculosis are detected by analyzing Single Nucleotide Polymorphism (SNP) genetic variations. The key causes of lung-related diseases are genetic factors, environmental and social behaviors. The epistasis effects act as a blueprint for the researchers to observe the genetic variation associated with lung diseases. The manual examination of the enormous genetic interactions is complicated to detect the lungs syndromes for diagnosis of acute respiratory. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2006 and 2020. In this critical review, various computational approaches are extensively discussed in detecting respondent Epistasis effects for various lung diseases such as Asthma, Tuberculosis, lung cancer, and Nicotine drug dependence. The analysis shows that different computational models identified candidate genes such as CHRNA4, CHRNB2, BDNF, TAS2R16, TAS2R38, BRCA1, BRCA2, RAD21, IL4Ra, IL-13 and IL-1β, have important causes for genetic variants linked to pulmonary disease. These computational approaches' strengths and limitations are described. The issues behind the computational methods while identifying the lung diseases through epistasis effects and the parameters used by various researchers for their evaluation are presented.

Genetics of connective tissue diseases

2013 ◽  
Author(s):  
Myles Lewis ◽  
Tim Vyse
Keyword(s):  
Systemic Sclerosis ◽  
Connective Tissue ◽  
Large Scale ◽  
Apoptotic Cell ◽  
Association Studies ◽  
Group Analysis ◽  
Connective Tissue Diseases ◽  
Susceptibility Genes ◽  
Type I ◽  
Genome Wide Association Studies

The advent of genome-wide association studies (GWAS) has been an exciting breakthrough in our understanding of the genetic aetiology of autoimmune diseases. Substantial overlap has been found in susceptibility genes across multiple diseases, from connective tissue diseases and rheumatoid arthritis (RA) to inflammatory bowel disease, coeliac disease, and psoriasis. Major technological advances now permit genotyping of millions of single nucleotide polymorphisms (SNPs). Group analysis of SNPs by haplotypes, aided by completion of the Hapmap project, has improved our ability to pinpoint causal genetic variants. International collaboration to pool large-scale cohorts of patients has enabled GWAS in systemic lupus erythematosus (SLE), systemic sclerosis and Behçet's disease, with studies in progress for ANCA-associated vasculitis. These 'hypothesis-free' studies have revealed many novel disease-associated genes. In both SLE and systemic sclerosis, identified genes map to known pathways including antigen presentation (MHC, TNFSF4), autoreactivity of B and T lymphocytes (BLK, BANK1), type I interferon production (STAT4, IRF5) and the NFκ‎B pathway (TNIP1). In SLE alone, additional genes appear to be involved in dysregulated apoptotic cell clearance (ITGAM, TREX1, C1q, C4) and recognition of immune complexes (FCGR2A, FCGR3B). Future developments include whole-genome sequencing to identify rare variants, and efforts to understand functional consequences of susceptibility genes. Putative environmental triggers for connective tissue diseases include infectious agents, especially Epstein-Barr virus; cigarette smoking; occupational exposure to toxins including silica; and low vitamin D, due to its immunomodulatory effects. Despite numerous studies looking at toxin exposure and connective tissue diseases, conclusive evidence is lacking, due to either rarity of exposure or rarity of disease.

scholarly journals Lung Function and Survival in Systemic Sclerosis Interstitial Lung Disease

2014 ◽  
Vol 41 (11) ◽  
pp. 2326-2328 ◽  
Author(s):  
SAMAR SHADLY AHMED ◽  
SINDHU R. JOHNSON ◽  
CHRISTOPHER MEANEY ◽  
CATHY CHAU ◽  
THEODORE K. MARRAS
Keyword(s):  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Lung Function ◽  
Lung Disease

Using Zebrafish to Test the Genetic Basis of Human Craniofacial Diseases

2017 ◽  
Vol 96 (11) ◽  
pp. 1192-1199 ◽  
Author(s):  
R. Grecco Machado ◽  
B. Frank Eames
Keyword(s):  
Candidate Genes ◽  
Genetic Manipulation ◽  
Association Studies ◽  
Underlying Disease ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Genetic Loci ◽  
Large Numbers ◽  
Genetic Features ◽  
New Treatments

Genome-wide association studies (GWASs) opened an innovative and productive avenue to investigate the molecular basis of human craniofacial disease. However, GWASs identify candidate genes only; they do not prove that any particular one is the functional villain underlying disease or just an unlucky genomic bystander. Genetic manipulation of animal models is the best approach to reveal which genetic loci identified from human GWASs are functionally related to specific diseases. The purpose of this review is to discuss the potential of zebrafish to resolve which candidate genetic loci are mechanistic drivers of craniofacial diseases. Many anatomic, embryonic, and genetic features of craniofacial development are conserved among zebrafish and mammals, making zebrafish a good model of craniofacial diseases. Also, the ability to manipulate gene function in zebrafish was greatly expanded over the past 20 y, enabling systems such as Gateway Tol2 and CRISPR-Cas9 to test gain- and loss-of-function alleles identified from human GWASs in coding and noncoding regions of DNA. With the optimization of genetic editing methods, large numbers of candidate genes can be efficiently interrogated. Finding the functional villains that underlie diseases will permit new treatments and prevention strategies and will increase understanding of how gene pathways operate during normal development.

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