Identification of 67 Pleiotropic Genes Associated With Seven Autoimmune/Autoinflammatory Diseases Using Multivariate Statistical Analysis

Jia, Xiaocan; Shi, Ning; Feng, Yu; Li, Yifan; Tan, Jiebing; Xu, Fei; Wang, Wei; Sun, Changqing; Deng, Hong‐Wen; Yang, Yongli; Shi, Xiaoguang

doi:10.3389/fimmu.2020.00030

Cited by 8 publications

(5 citation statements)

References 54 publications

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“…It is interesting that this result is relevant with some previous studies. For example, the gene TERT was found to be a pleiotropic cancer gene associated with 12 different cancer types [ 81 ], while PTPN2 was confirmed as a pleiotropic gene associated with several autoimmune diseases [ 82 ]. This result indicates the promising usefulness of

to predict the unknown pleiotropic role of functionally important genes.…”

Section: Resultsmentioning

confidence: 99%

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

Mazaya

Kwon

2022

Biomolecules

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Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene–gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene–gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene–phenotype relations.

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to predict the unknown pleiotropic role of functionally important genes.…”

Section: Resultsmentioning

confidence: 99%

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

Mazaya

Kwon

2022

Biomolecules

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“…DGKQ, a critical member of DGKs, consists of three cysteine-rich domains (CRDs), a proline/glycine-rich region, and a pleckstrin homology (PH) domain that overlaps with a Ras-binding domain [47]. Previous studies have showed that DGKQ might be linked to several autoimmune diseases, including systemic lupus erythematosus (SLE), type 1 diabetes (T1D), RA, idiopathic inflammatory myopathies (IIM), primary biliary cirrhosis (PBC), Sjögren's syndrome(SS)and so on [48][49][50]. The high-risk SNP alleles were found to be linked with increased expression of DGKQ in fibroblasts, lymphocytes and lung.…”

Section: Discussionmentioning

confidence: 99%

Key common genes and pathways in ulcerative colitis and ankylosing spondylitis based on bioinformatics analysis

Xie

et al. 2023

Preprint

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Associations between ulcerative colitis (UC) and ankylosing spondylitis (AS) have been observed in multiple studies, but the common etiology of UC and AS remain unknown. Thus, the current research was conducted to investigate the shared genes and relevant mechanisms in UC and AS. GSE87466 and GSE25101 datasets were used to identify DEGs involved in UC and AS, respectively. The clusterProfiler R package was utilized to detect the biological processes of DEGs in UC and AS. The performance of common DEGs in distinguishing UC or AS samples from control ones were evaluated by ROC curves. The miRWalk, Cistrome and TransmiR database were utilized to construct the network of TF-miRNA-diagnostic biomarker. GSEA method and CTD database were used to investigate the common KEGG pathways shared by UC and AS. In addition, CTD database was also used to detect the interaction score between diagnostic biomarkers and diseases associated with UC or AS. Moreover, prospective diagnostic biomarker-targeting drugs were identified using the DGIdb database. A total of 20 common DEGs were obtained by analyzing data in GSE97466 and GSE25101 datasets. ROC curves revealed that GMFG, GNG11, CLEC4D, CMTM2, VAMP5, S100A8, S100A12 and DGKQ may serve as diagnostic biomarkers for the individuals with AS and UC. A network of TF-miRNA-diagnostic biomarker, composed of 212 nodes and 721 edges, was constructed and visualized by Cytoscape software. Toll-like receptor signaling pathway, antigen processing and presentation, allograft rejection, viral myocarditis, pathways in cancer, graft versus host disease and natural killer cell mediated cytotoxicity were identified as common pathways in UC and AS. For the first time, our study identified 8 common key genes and 7 common pathways in UC and AS. These findings may help to clarify the relationship between UC and AS, and provide guidance in the diagnosis and treatment of UC and AS patients.

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“…The SNPs in the six summary statistics were pruned before multivariate analysis using the genotype data from the 1000 Genomes project as a reference [ 18 ]. Briefly, six datasets were combined to select overlapping SNP; then, an LD-based algorithm was adopted to prune SNPs with high pairwise correlations.…”

Section: Methodsmentioning

confidence: 99%

Identification of Potential Pleiotropic Genes for Immune and Skeletal Diseases Using Multivariate MetaCCA Analysis

Cao

Dèng

et al. 2021

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Background: Immune and skeletal systems physiologically and pathologically interact with each other. The immune and skeletal diseases may share potential pleiotropic genetics factors, but the shared specific genes are largely unknown Objective: This study aimed to investigate the overlapping genetic factors between multiple diseases (including rheumatoid arthritis (RA), psoriasis, osteoporosis, osteoarthritis, sarcopenia and fracture) Methods: The canonical correlation analysis (metaCCA) approach was used to identify the shared genes for six diseases by integrating genome-wide association study (GWAS)-derived summary statistics. Versatile Gene-based Association Study (VEGAS2) method was further applied to refine and validate the putative pleiotropic genes identified by metaCCA. Results: About 157 (p<8.19E-6), 319 (p<3.90E-6) and 77 (p<9.72E-6) potential pleiotropic genes were identified shared by two immune disease, four skeletal diseases, and all of the six diseases, respectively. The top three significant putative pleiotropic genes shared by both immune and skeletal diseases, including HLA-B, TSBP1 and TSBP1-AS1 (p<E-300) were located in the major histocompatibility complex (MHC) region. Nineteen of 77 putative pleiotropic genes identified by metaCCA analysis were associated with at least one disease in the VEGAS2 analysis. Specifically, majority (18) of these 19 putative validated pleiotropic genes were associated with RA. Conclusion: The metaCCA method identified some pleiotropic genes shared by the immune and skeletal diseases. These findings help to improve our understanding of the shared genetic mechanisms and signaling pathways underlying immune and skeletal diseases.

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Identification of 67 Pleiotropic Genes Associated With Seven Autoimmune/Autoinflammatory Diseases Using Multivariate Statistical Analysis

Cited by 8 publications

References 54 publications

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

Key common genes and pathways in ulcerative colitis and ankylosing spondylitis based on bioinformatics analysis

Identification of Potential Pleiotropic Genes for Immune and Skeletal Diseases Using Multivariate MetaCCA Analysis

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