Unravelling the shared genetic mechanisms underlying 18 autoimmune diseases using a systems approach

Autoimmune diseases (AiDs) are complex heterogeneous diseases characterized by hyperactive immune responses against self. Genome-wide association studies have identified thousands of single nucleotide polymorphisms (SNPs) associated with several AiDs. While these studies have identified a handful of pleiotropic loci that confer risk to multiple AiDs, they lack the power to detect shared genetic factors residing outside of these loci. Here, we integrated chromatin contact, expression quantitative trait loci and protein-protein interaction (PPI) data to identify genes that are regulated by both pleiotropic and non-pleiotropic SNPs. The PPI analysis revealed complex interactions between the shared and disease-specific genes. Furthermore, pathway enrichment analysis demonstrated that the shared genes co-occur with disease-specific genes within the same biological pathways. In conclusion, our results are consistent with the hypothesis that genetic risk loci associated with multiple AiDs converge on a core set of biological processes that potentially contribute to the emergence of polyautoimmunity.

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Unravelling the Shared Genetic Mechanisms Underlying 18 Autoimmune Diseases Using a Systems Approach

Frontiers in Immunology ◽

10.3389/fimmu.2021.693142 ◽

2021 ◽

Vol 12 ◽

Author(s):

Sreemol Gokuladhas ◽

William Schierding ◽

Evgeniia Golovina ◽

Tayaza Fadason ◽

Justin O’Sullivan

Keyword(s):

Autoimmune Diseases ◽

Systems Approach ◽

Association Studies ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Protein Protein Interaction ◽

Disease Specific ◽

Shared Genetic

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Identification of 67 pleiotropic genes for seven autoimmune diseases using multivariate statistical analysis

10.1101/563973 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xiaocan Jia ◽

Nian Shi ◽

Zhenhua Xia ◽

Yu Feng ◽

Yifan Li ◽

...

Keyword(s):

Multivariate Analysis ◽

Autoimmune Diseases ◽

Association Studies ◽

Enrichment Analysis ◽

Genome Wide Association Studies ◽

Biological Mechanisms ◽

Genetic Components ◽

Term Enrichment Analysis ◽

Term Enrichment ◽

Shared Genetic

AbstractAlthough genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitation in detecting complex genotype-phenotype correlations. It is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune diseases with a multivariate analysis. In this study, the GWAS summary statistics including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions was analyzed to identify shared variants of seven autoimmune diseases using metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological function of the identified genes. After metaCCA analysis, 4,962 SNPs (P<1.21×10−6) and 1,044 pleotropic genes (P<4.34×10−6) were identified. By screening the results of gene-based p-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes which achieved significance threshold in metaCCA analysis and were also associated with at least one autoimmune disease in the VEGAS2 analysis. The metaCCA method could identify novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for some common therapeutic approaches of autoimmune diseases based on the pleiotropic genes and common mechanisms identified.Author summaryAlthough previous researches have clearly indicated varying degrees of overlapping genetic sensitivities in autoimmune diseases, it has proven GWAS only explain small percent of heritability. Here, we take advantage of recent technical and methodological advances to identify pleiotropic genes that act on common biological mechanisms and the overlapping pathophysiological pathways of autoimmune diseases. After selection using multivariate analysis and verification using gene-based analyses, we successfully identified a total of 67 pleiotropic genes and performed the functional term enrichment analysis. In particularly, 27 genes were identified to be pleiotropic in previous different types of studies, which were validated by our present study. Forty significant genes (16 genes were associated with one disease earlier, and 24 were novel) might be the novel pleiotropic candidate genes for seven autoimmune diseases. The improved detection not only yielded the shared genetic components but also provided better understanding for exploring the potential common biological pathogenesis of these major autoimmune diseases.

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Multivariate Analysis of Potential Pleiotropic Genes For Breast, Ovarian And Cervical Cancers Using Gene-Based Association Analysis

10.21203/rs.3.rs-695526/v1 ◽

2021 ◽

Author(s):

XiaoCan Jia ◽

Nian Shi ◽

Yu Feng ◽

Huili Zhu ◽

Yuping Wang ◽

...

Keyword(s):

Network Analysis ◽

Association Studies ◽

Statistical Significance ◽

Enrichment Analysis ◽

Biological Pathways ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Ppi Network ◽

Cervical Cancers ◽

Protein Protein Interaction

Abstract Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery in gynecological malignancies, the single nucleotide polymorphisms (SNPs) identified by this prevailing univariate approach only explain a small percentage of heredity. The extensive previous studies have repeatedly shown breast, ovarian and cervical cancers have common genetic mechanisms and the overlapping pathophysiological pathways. Novel multivariate analytical methods are necessary to identify shared pleiotropic genes. In this study, a total of 40,859 SNPs mapped in 11,597 gene regions were performed to identify potential common variants by using metaCCA and VEGAS2 analysis. Gene enrichment and protein-protein interaction (PPI) network analysis were used to explore potential biological pathways and connectivity. After metaCCA analysis, 4,203 SNPs (P<1.22×10−6) and 1,886 pleotropic gene (P<4.31×10−6) were identified. By screening the results of gene-based P-values, the existence of 3 confirmed pleiotropic genes and 16 novel genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one cancer in the VEGAS2 analysis were identified. The enrichment analysis showed the biological pathways of these genes were mainly enriched in 4 signaling pathways and 11 differentially expressed genes were found to encode interacting proteins in PPI network analysis. Altogether, we identified novel genetic variants of breast, ovarian and cervical cancers and provided evidence of biological functions which developed new insights for the diagnosis and treatment of these cancers.

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Accumulation of Minor Alleles of Common SNPs in Schizophrenia

10.1101/106815 ◽

2017 ◽

Author(s):

Pei He ◽

Xiaoyun Lei ◽

Dejian Yuan ◽

Zuobin Zhu ◽

Shi Huang

Keyword(s):

Large Scale ◽

Association Studies ◽

Enrichment Analysis ◽

Individual Risk ◽

Pathway Enrichment Analysis ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Risk Alleles ◽

Minor Alleles ◽

Public Datasets

Schizophrenia is a common neuropsychiatric disorder with a lifetime risk of 1%. A number of large scale genome wide association studies have identified numerous individual risk single nucleotide polymorphisms (SNPs) whose precise roles in schizophrenia remain unknown. Accumulation of many of these risk alleles has been found to be a more important risk factor. Consistently, recent studies showed a role for enrichment of minor alleles (MAs) in complex diseases. Here we studied the role of MAs in general in schizophrenia using public datasets. Relative to matched controls, schizophrenia cases showed higher minor allele content (MAC), especially for the sporadic cases. By linkage analysis, we identified 82 419 SNPs that could be used to predict 2.2% schizophrenia cases with 100% certainty. Pathway enrichment analysis of these SNPs identified 17 pathways, 15 of which are known to be linked with Schizophrenia with the remaining 2 associated with other mental disorders. These results suggest a role for a collective effect of MAs in schizophrenia and provide a method to genetically screen for schizophrenia.

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Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets

BMC Pulmonary Medicine ◽

10.1186/s12890-020-01303-7 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Zhouzhou Dong ◽

Yunlong Ma ◽

Hua Zhou ◽

Linhui Shi ◽

Gongjie Ye ◽

...

Keyword(s):

Bayesian Analysis ◽

Severe Asthma ◽

Antigen Processing ◽

Association Studies ◽

Expression Patterns ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Type I ◽

Genome Wide Association Studies ◽

Bioinformatics Analyses

Abstract Background Severe asthma is a chronic disease contributing to disproportionate disease morbidity and mortality. From the year of 2007, many genome-wide association studies (GWAS) have documented a large number of asthma-associated genetic variants and related genes. Nevertheless, the molecular mechanism of these identified variants involved in asthma or severe asthma risk remains largely unknown. Methods In the current study, we systematically integrated 3 independent expression quantitative trait loci (eQTL) data (N = 1977) and a large-scale GWAS summary data of moderate-to-severe asthma (N = 30,810) by using the Sherlock Bayesian analysis to identify whether expression-related variants contribute risk to severe asthma. Furthermore, we performed various bioinformatics analyses, including pathway enrichment analysis, PPI network enrichment analysis, in silico permutation analysis, DEG analysis and co-expression analysis, to prioritize important genes associated with severe asthma. Results In the discovery stage, we identified 1129 significant genes associated with moderate-to-severe asthma by using the Sherlock Bayesian analysis. Two hundred twenty-eight genes were prominently replicated by using MAGMA gene-based analysis. These 228 replicated genes were enriched in 17 biological pathways including antigen processing and presentation (Corrected P = 4.30 × 10− 6), type I diabetes mellitus (Corrected P = 7.09 × 10− 5), and asthma (Corrected P = 1.72 × 10− 3). With the use of a series of bioinformatics analyses, we highlighted 11 important genes such as GNGT2, TLR6, and TTC19 as authentic risk genes associated with moderate-to-severe/severe asthma. With respect to GNGT2, there were 3 eSNPs of rs17637472 (PeQTL = 2.98 × 10− 8 and PGWAS = 3.40 × 10− 8), rs11265180 (PeQTL = 6.0 × 10− 6 and PGWAS = 1.99 × 10− 3), and rs1867087 (PeQTL = 1.0 × 10− 4 and PGWAS = 1.84 × 10− 5) identified. In addition, GNGT2 is significantly expressed in severe asthma compared with mild-moderate asthma (P = 0.045), and Gngt2 shows significantly distinct expression patterns between vehicle and various glucocorticoids (Anova P = 1.55 × 10− 6). Conclusions Our current study provides multiple lines of evidence to support that these 11 identified genes as important candidates implicated in the pathogenesis of severe asthma.

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SNP-based pathway enrichment analysis for genome-wide association studies

BMC Bioinformatics ◽

10.1186/1471-2105-12-99 ◽

2011 ◽

Vol 12 (1) ◽

pp. 99 ◽

Cited By ~ 78

Author(s):

Lingjie Weng ◽

Fabio Macciardi ◽

Aravind Subramanian ◽

Guia Guffanti ◽

Steven G Potkin ◽

...

Keyword(s):

Association Studies ◽

Enrichment Analysis ◽

Genome Wide Association ◽

Pathway Enrichment Analysis ◽

Genome Wide Association Studies ◽

Pathway Enrichment ◽

Genome Wide

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Unravelling the Roles Of susceptibility Loci for Autoimmune Diseases in the Post-GWAS Era

10.20944/preprints201805.0160.v1 ◽

2018 ◽

Author(s):

Jody Ye ◽

Kathleen Gillespie ◽

Santiago Rodriguez

Keyword(s):

Autoimmune Diseases ◽

Disease Risk ◽

Association Studies ◽

Copy Number Variations ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Susceptibility Loci ◽

Single Nucleotide ◽

Environmental Interactions ◽

Genome Wide

Although genome-wide association studies (GWAS) have identified several hundred loci associated with autoimmune diseases, their mechanistic insights are still poorly understood. The human genome is more complex than common single nucleotide polymorphisms (SNPs) that are interrogated by GWAS arrays. Some structural variants such as insertions-deletions, copy number variations, and minisatellites that are not very well tagged by SNPs cannot be fully explored by GWAS. Therefore, it is possible that some of these loci may have large effects on autoimmune disease risk. In addition, other layers of regulations such as gene-gene interactions, epigenetic-determinants, gene and environmental interactions also contribute to the heritability of autoimmune diseases. This review focuses on discussing why studying these elements may allow us to gain a more comprehensive understanding of the aetiology of complex autoimmune traits.

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Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge

Handbook of Research on Computational and Systems Biology ◽

10.4018/978-1-60960-491-2.ch006 ◽

2011 ◽

pp. 128-147

Author(s):

Kristine A. Pattin ◽

Jason H. Moore

Keyword(s):

Expert Knowledge ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Protein Protein Interaction ◽

Genome Wide ◽

Technological Developments ◽

Or Gene ◽

High Dimensional Datasets

Recent technological developments in the field of genetics have given rise to an abundance of research tools, such as genome-wide genotyping, that allow researchers to conduct genome-wide association studies (GWAS) for detecting genetic variants that confer increased or decreased susceptibility to disease. However, discovering epistatic, or gene-gene, interactions in high dimensional datasets is a problem due to the computational complexity that results from the analysis of all possible combinations of single-nucleotide polymorphisms (SNPs). A recently explored approach to this problem employs biological expert knowledge, such as pathway or protein-protein interaction information, to guide an analysis by the selection or weighting of SNPs based on this knowledge. Narrowing the evaluation to gene combinations that have been shown to interact experimentally provides a biologically concise reason why those two genes may be detected together statistically. This chapter discusses the challenges of discovering epistatic interactions in GWAS and how biological expert knowledge can be used to facilitate genome-wide genetic studies.

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From Single Nucleotide Polymorphisms to Constant Immunosuppression: Mesenchymal Stem Cell Therapy for Autoimmune Diseases

BioMed Research International ◽

10.1155/2013/929842 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 8

Author(s):

Raghavan Chinnadurai ◽

Edmund K. Waller ◽

Jacques Galipeau ◽

Ajay K. Nooka

Keyword(s):

Single Nucleotide Polymorphisms ◽

Autoimmune Diseases ◽

Association Studies ◽

Autoimmune Disorders ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Mesenchymal Stem Cell Therapy ◽

Immune Mediated ◽

Wide Range

The regenerative abilities and the immunosuppressive properties of mesenchymal stromal cells (MSCs) make them potentially the ideal cellular product of choice for treatment of autoimmune and other immune mediated disorders. Although the usefulness of MSCs for therapeutic applications is in early phases, their potential clinical use remains of great interest. Current clinical evidence of use of MSCs from both autologous and allogeneic sources to treat autoimmune disorders confers conflicting clinical benefit outcomes. These varied results may possibly be due to MSC use across wide range of autoimmune disorders with clinical heterogeneity or due to variability of the cellular product. In the light of recent genome wide association studies (GWAS), linking predisposition of autoimmune diseases to single nucleotide polymorphisms (SNPs) in the susceptible genetic loci, the clinical relevance of MSCs possessing SNPs in the critical effector molecules of immunosuppression is largely undiscussed. It is of further interest in the allogeneic setting, where SNPs in the target pathway of MSC's intervention may also modulate clinical outcome. In the present review, we have discussed the known critical SNPs predisposing to disease susceptibility in various autoimmune diseases and their significance in the immunomodulatory properties of MSCs.

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