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

2019 ◽  
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.

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

2021 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Evgeniia Golovina ◽  
Tayaza Fadason ◽  
Justin M. 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

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.

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

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

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.

scholarly journals Transethnic meta-analysis identifies GSDMA and PRDM1 as susceptibility genes to systemic sclerosis

2017 ◽  
Vol 76 (6) ◽  
pp. 1150-1158 ◽  
Author(s):  
Chikashi Terao ◽  
Takahisa Kawaguchi ◽  
Philippe Dieude ◽  
John Varga ◽  
Masataka Kuwana ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
T Cell ◽  
Autoimmune Diseases ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Susceptibility Genes ◽  
Genome Wide Association Studies ◽  
Hla Association ◽  
Genome Wide

ObjectivesSystemic sclerosis (SSc) is an autoimmune disease characterised by skin and systemic fibrosis culminating in organ damage. Previous genetic studies including genome-wide association studies (GWAS) have identified 12 susceptibility loci satisfying genome-wide significance. Transethnic meta-analyses have successfully expanded the list of susceptibility genes and deepened biological insights for other autoimmune diseases.MethodsWe performed transethnic meta-analysis of GWAS in the Japanese and European populations, followed by a two-staged replication study comprising a total of 4436 cases and 14 751 controls. Associations between significant single nuclear polymorphisms (SNPs) and neighbouring genes were evaluated. Enrichment analysis of H3K4Me3, a representative histone mark for active promoter was conducted with an expanded list of SSc susceptibility genes.ResultsWe identified two significant SNP in two loci, GSDMA and PRDM1, both of which are related to immune functions and associated with other autoimmune diseases (p=1.4×10−10 and 6.6×10−10, respectively). GSDMA also showed a significant association with limited cutaneous SSc. We also replicated the associations of previously reported loci including a non-GWAS locus, TNFAIP3. PRDM1 encodes BLIMP1, a transcription factor regulating T-cell proliferation and plasma cell differentiation. The top SNP in GSDMA was a missense variant and correlated with gene expression of neighbouring genes, and this could explain the association in this locus. We found different human leukocyte antigen (HLA) association patterns between the two populations. Enrichment analysis suggested the importance of CD4-naïve primary T cell.ConclusionsGSDMA and PRDM1 are associated with SSc. These findings provide enhanced insight into the genetic and biological basis of SSc.

scholarly journals Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel L. McCartney ◽  
Josine L. Min ◽  
Rebecca C. Richmond ◽  
Ake T. Lu ◽  
Maria K. Sobczyk ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genome Wide Association ◽  
Biological Aging ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Biomarkers Of Aging ◽  
Genome Wide ◽  
Shared Genetic

Abstract Background Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. Results Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. Conclusion This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

scholarly journals Analysing electrocardiographic traits and predicting cardiac risk in UK biobank

2021 ◽  
Vol 10 ◽  
pp. 204800402110236
Author(s):  
Julia Ramírez ◽  
Stefan van Duijvenboden ◽  
William J Young ◽  
Michele Orini ◽  
Aled R Jones ◽  
...  
Keyword(s):  
Genetic Information ◽  
Association Studies ◽  
Cardiac Risk ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Biological Mechanisms ◽  
Clinical Tool ◽  
Genome Wide ◽  
Response To Exercise ◽  
Electrocardiogram Ecg

The electrocardiogram (ECG) is a commonly used clinical tool that reflects cardiac excitability and disease. Many parameters are can be measured and with the improvement of methodology can now be quantified in an automated fashion, with accuracy and at scale. Furthermore, these measurements can be heritable and thus genome wide association studies inform the underpinning biological mechanisms. In this review we describe how we have used the resources in UK Biobank to undertake such work. In particular, we focus on a substudy uniquely describing the response to exercise performed at scale with accompanying genetic information.

scholarly journals Association analysis of juvenile idiopathic arthritis genetic susceptibility factors in Estonian patients

2021 ◽  
Author(s):  
Tiit Nikopensius ◽  
Priit Niibo ◽  
Toomas Haller ◽  
Triin Jagomägi ◽  
Ülle Voog-Oras ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Juvenile Idiopathic Arthritis ◽  
Autoimmune Diseases ◽  
Genetic Risk ◽  
Association Studies ◽  
Control Sample ◽  
Case Control ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Background Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic condition of childhood. Genetic association studies have revealed several JIA susceptibility loci with the strongest effect size observed in the human leukocyte antigen (HLA) region. Genome-wide association studies have augmented the number of JIA-associated loci, particularly for non-HLA genes. The aim of this study was to identify new associations at non-HLA loci predisposing to the risk of JIA development in Estonian patients. Methods We performed genome-wide association analyses in an entire JIA case–control sample (All-JIA) and in a case–control sample for oligoarticular JIA, the most prevalent JIA subtype. The entire cohort was genotyped using the Illumina HumanOmniExpress BeadChip arrays. After imputation, 16,583,468 variants were analyzed in 263 cases and 6956 controls. Results We demonstrated nominal evidence of association for 12 novel non-HLA loci not previously implicated in JIA predisposition. We replicated known JIA associations in CLEC16A and VCTN1 regions in the oligoarticular JIA sample. The strongest associations in the All-JIA analysis were identified at PRKG1 (P = 2,54 × 10−6), LTBP1 (P = 9,45 × 10−6), and ELMO1 (P = 1,05 × 10−5). In the oligoarticular JIA analysis, the strongest associations were identified at NFIA (P = 5,05 × 10−6), LTBP1 (P = 9,95 × 10−6), MX1 (P = 1,65 × 10−5), and CD200R1 (P = 2,59 × 10−5). Conclusion This study increases the number of known JIA risk loci and provides additional evidence for the existence of overlapping genetic risk loci between JIA and other autoimmune diseases, particularly rheumatoid arthritis. The reported loci are involved in molecular pathways of immunological relevance and likely represent genomic regions that confer susceptibility to JIA in Estonian patients. Key Points• Juvenile idiopathic arthritis (JIA) is the most common childhood rheumatic disease with heterogeneous presentation and genetic predisposition.• Present genome-wide association study for Estonian JIA patients is first of its kind in Northern and Northeastern Europe.• The results of the present study increase the knowledge about JIA risk loci replicating some previously described associations, so adding weight to their relevance and describing novel loci.• The study provides additional evidence for the existence of overlapping genetic risk loci between JIA and other autoimmune diseases, particularly rheumatoid arthritis.

scholarly journals Functional genomics of autoimmune diseases

2021 ◽  
pp. annrheumdis-2019-216794
Author(s):  
Akari Suzuki ◽  
Matteo Maurizio Guerrini ◽  
Kazuhiko Yamamoto
Keyword(s):  
Autoimmune Diseases ◽  
Association Studies ◽  
Linkage Disequilibrium Block ◽  
Causal Variant ◽  
Genome Wide Association Studies ◽  
Coding Region ◽  
Risk Variants ◽  
Non Coding Rna ◽  
Genome Wide ◽  
Long Non Coding Rna

For more than a decade, genome-wide association studies have been applied to autoimmune diseases and have expanded our understanding on the pathogeneses. Genetic risk factors associated with diseases and traits are essentially causative. However, elucidation of the biological mechanism of disease from genetic factors is challenging. In fact, it is difficult to identify the causal variant among multiple variants located on the same haplotype or linkage disequilibrium block and thus the responsible biological genes remain elusive. Recently, multiple studies have revealed that the majority of risk variants locate in the non-coding region of the genome and they are the most likely to regulate gene expression such as quantitative trait loci. Enhancer, promoter and long non-coding RNA appear to be the main target mechanisms of the risk variants. In this review, we discuss functional genetics to challenge these puzzles.

scholarly journals Genome-wide genetic links between amyotrophic lateral sclerosis and autoimmune diseases

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chun Yu Li ◽  
Tian Mi Yang ◽  
Ru Wei Ou ◽  
Qian Qian Wei ◽  
Hui Fang Shang
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Autoimmune Diseases ◽  
Genetic Correlation ◽  
Functional Enrichment ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
Genome Wide ◽  
Shared Risk ◽  
Lateral Sclerosis ◽  
Shared Genetic

Abstract Background Epidemiological and clinical studies have suggested comorbidity between amyotrophic lateral sclerosis (ALS) and autoimmune disorders. However, little is known about their shared genetic architecture. Methods To examine the relation between ALS and 10 autoimmune diseases, including asthma, celiac disease (CeD), Crohn’s disease (CD), inflammatory bowel disease (IBD), multiple sclerosis (MS), psoriasis, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), and ulcerative colitis (UC), and identify shared risk loci, we first estimated the genetic correlation using summary statistics from genome-wide association studies, and then analyzed the genetic enrichment leveraging the conditional false discovery rate statistical method. Results We identified a significant positive genetic correlation between ALS and CeD, MS, RA, and SLE, as well as a significant negative genetic correlation between ALS and IBD, UC, and CD. Robust genetic enrichment was observed between ALS and CeD and MS, and moderate enrichment was found between ALS and UC and T1D. Thirteen shared genetic loci were identified, among which five were suggestively significant in another ALS GWAS, namely rs3828599 (GPX3), rs3849943 (C9orf72), rs7154847 (G2E3), rs6571361 (SCFD1), and rs9903355 (GGNBP2). By integrating cis-expression quantitative trait loci analyses in Braineac and GTEx, we further identified GGNBP2, ATXN3, and SLC9A8 as novel ALS risk genes. Functional enrichment analysis indicated that the shared risk genes were involved in four pathways including membrane trafficking, vesicle-mediated transport, ER to Golgi anterograde transport, and transport to the Golgi and subsequent modification. Conclusions Our findings demonstrate a specific genetic correlation between ALS and autoimmune diseases and identify shared risk loci, including three novel ALS risk genes. These results provide a better understanding for the pleiotropy of ALS and have implications for future therapeutic trials.

Sign in / Sign up

Export Citation Format

Share Document