Identification of Potential Pleiotropic Genes for Immune and Skeletal Diseases Using Multivariate MetaCCA Analysis.
Ontology highlight
ABSTRACT: Background
Immune and skeletal systems physiologically and pathologically interact with each other. 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. The 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 diseases, 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
SUBMITTER: He P
PROVIDER: S-EPMC8922324 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Genome-wide association studies (GWASs) have identified more than 20 genetic loci as risk predictors associated with stroke. However, these studies were generally performed for single-trait and failed to consider the pleiotropic effects of these risk genes among the multiple risk factors for stroke. In this study, we applied a novel metaCCA method followed by gene-based VEGAS2 analysis to identify the risk genes for stroke that may overlap between seven correlated risk factors (including atrial fibrillation, hypertension, coronary artery disease, heart failure, diabetes, body mass index, and total cholesterol level) by integrating seven corresponding GWAS data. We detected 20 potential pleiotropic genes that may be associated with multiple risk factors of stroke. Furthermore, using gene-to-trait pathway analysis, we suggested six potential risk genes (FUT8, GMIP, PLA2G6, PDE3A, SMARCA4, SKAPT) that may affect ischemic or hemorrhage stroke through multiple intermediate factors such as MAPK family. These findings provide novel insight into the genetic determinants contributing to the concurrent development of biological conditions that may influence stroke susceptibility, and also indicate some potential therapeutic targets that can be further studied for the prevention of cerebrovascular disease.
| S-EPMC7394724 | biostudies-literature
Project description:BackgroundGenome-wide association studies have been extensively applied in identifying SNP associated with major psychiatric disorders. However, the SNPs identified by the prevailing univariate approach only explain a small percentage of the genetic variance of traits, and the extensive data have shown the major psychiatric disorders have common biological mechanisms and the overlapping pathophysiological pathways.MethodsWe applied the genetic pleiotropy-informed metaCCA method on summary statistics data from the Psychiatric Genomics Consortium Cross-Disorder Group to examine the overlapping genetic relations between the five major psychiatric disorders. Furthermore, to refine all genes, we performed gene-based association analyses for the five disorders respectively using VEGAS2. Gene enrichment analysis was applied to explore the potential functional significance of the identified genes.ResultsAfter metaCCA analysis, 1147 SNPs reached the Bonferroni corrected threshold (p < 1.06 × 10-6) in the univariate SNP-multivariate phenotype analysis, and 246 genes with a significance threshold (p < 3.85 × 10-6) were identified as potentially pleiotropic genes in the multivariate SNP-multivariate phenotype analysis. By screening the results of gene-based p-values, we identified 37 putative pleiotropic genes which achieved significance threshold in metaCCA analyses and were also associated with at least one disorder in the VEGAS2 analyses.LimitationsAlternative approaches and experimental studies may be applied to check whether novel genes could still be identified/substantiated with these methods.ConclusionsThe metaCCA method identified novel variants associated with psychiatric disorders by effectively incorporating information from different GWAS datasets. Our analyses may provide insights for some common therapeutic approaches of these five major psychiatric disorders based on the pleiotropic genes and common mechanisms identified.
| S-EPMC6343670 | biostudies-literature
Project description:Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitations in detecting complex genotype-phenotype correlations. Multivariate analysis is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune/autoinflammatory diseases. In this study, GWAS summary statistics, including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions, were analyzed to identify shared variants of seven autoimmune/autoinflammatory diseases using the 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 functions of the identified genes. A total of 4,962 SNPs (P < 1.21 × 10-6) and 1,044 pleotropic genes (P < 4.34 × 10-6) were identified by metaCCA analysis. By screening the results of gene-based P-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one autoimmune/autoinflammatory in the VEGAS2 analysis. Using the metaCCA method, we identified novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for the development of common therapeutic approaches for autoimmune/autoinflammatory diseases based on the pleiotropic genes and common mechanisms identified.
| S-EPMC7008725 | biostudies-literature
Project description:MotivationA dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests.ResultsWe introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies.Availability and implementationCode is available at https://github.com/aalto-ics-kepacoContactsanna.cichonska@helsinki.fi or matti.pirinen@helsinki.fiSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4920109 | biostudies-literature
Project description:Previous studies have demonstrated the genetic correlations between type 2 diabetes, obesity and dyslipidemia, and indicated that many genes have pleiotropic effects on them. However, these pleiotropic genes have not been well-defined. It is essential to identify pleiotropic genes using systematic approaches because systematically analyzing correlated traits is an effective way to enhance their statistical power. To identify potential pleiotropic genes for these three disorders, we performed a systematic analysis by incorporating GWAS (genome-wide associated study) datasets of six correlated traits related to type 2 diabetes, obesity and dyslipidemia using Meta-CCA (meta-analysis using canonical correlation analysis). Meta-CCA is an emerging method to systematically identify potential pleiotropic genes using GWAS summary statistics of multiple correlated traits. 2,720 genes were identified as significant genes after multiple testing (Bonferroni corrected p value < 0.05). Further, to refine the identified genes, we tested their relationship to the six correlated traits using VEGAS-2 (versatile gene-based association study-2). Only the genes significantly associated (Bonferroni corrected p value < 0.05) with more than one trait were kept. Finally, 25 genes (including two confirmed pleiotropic genes and eleven novel pleiotropic genes) were identified as potential pleiotropic genes. They were enriched in 5 pathways including the statin pathway and the PPAR (peroxisome proliferator-activated receptor) Alpha pathway. In summary, our study identified potential pleiotropic genes and pathways of type 2 diabetes, obesity and dyslipidemia, which may shed light on the common biological etiology and pathogenesis of these three disorders and provide promising insights for new therapies.
| S-EPMC6093635 | biostudies-literature
Project description:BackgroundPrevious studies have shown that heart failure (HF) and chronic kidney disease (CKD) have common genetic mechanisms, overlapping pathophysiological pathways, and therapeutic drug-sodium-glucose cotransporter 2 (SGLT2) inhibitors.MethodsThe genetic pleiotropy metaCCA method was applied on summary statistics data from two independent meta-analyses of GWAS comprising more than 1 million people to identify shared variants and pleiotropic effects between HF and CKD. Targets of SGLT2 inhibitors were predicted by SwissTargetPrediction and DrugBank databases. To refine all genes, we performed using versatile gene-based association study 2 (VEGAS2) and transcriptome-wide association studies (TWAS) for HF and CKD, respectively. Gene enrichment and KEGG pathway analyses were used to explore the potential functional significance of the identified genes and targets.ResultsAfter metaCCA analysis, 4,624 SNPs and 1,745 genes were identified to be potentially pleiotropic in the univariate and multivariate SNP-multivariate phenotype analyses, respectively. 21 common genes were detected in both metaCCA and SGLT2 inhibitors' target prediction. In addition, 169 putative pleiotropic genes were identified, which met the significance threshold both in metaCCA analysis and in the VEGAS2 or TWAS analysis for at least one disease.ConclusionWe identified novel variants associated with HF and CKD using effectively incorporating information from different GWAS datasets. Our analysis may provide new insights into HF and CKD therapeutic approaches based on the pleiotropic genes, common targets, and mechanisms by integrating the metaCCA method, TWAS and VEGAS2 analyses, and target prediction of SGLT2 inhibitors.
| S-EPMC8443964 | biostudies-literature
Project description:BackgroundCurrent Genome-Wide Association Studies (GWAS) are performed in a single trait framework without considering genetic correlations between important disease traits. Hence, the GWAS have limitations in discovering genetic risk factors affecting pleiotropic effects.ResultsThis work reports a novel data mining approach to discover patterns of multiple phenotypic associations over 52 anthropometric and biochemical traits in KARE and a new analytical scheme for GWAS of multivariate phenotypes defined by the discovered patterns. This methodology applied to the GWAS for multivariate phenotype highLDLhighTG derived from the predicted patterns of the phenotypic associations. The patterns of the phenotypic associations were informative to draw relations between plasma lipid levels with bone mineral density and a cluster of common traits (Obesity, hypertension, insulin resistance) related to Metabolic Syndrome (MS). A total of 15 SNPs in six genes (PAK7, C20orf103, NRIP1, BCL2, TRPM3, and NAV1) were identified for significant associations with highLDLhighTG. Noteworthy findings were that the significant associations included a mis-sense mutation (PAK7:R335P), a frame shift mutation (C20orf103) and SNPs in splicing sites (TRPM3).ConclusionsThe six genes corresponded to rat and mouse quantitative trait loci (QTLs) that had shown associations with the common traits such as the well characterized MS and even tumor susceptibility. Our findings suggest that the six genes may play important roles in the pleiotropic effects on lipid metabolism and the MS, which increase the risk of Type 2 Diabetes and cardiovascular disease. The use of the multivariate phenotypes can be advantageous in identifying genetic risk factors, accounting for the pleiotropic effects when the multivariate phenotypes have a common etiological pathway.
| S-EPMC3287479 | biostudies-literature
Project description:Genetic pleiotropy refers to the simultaneous association of a gene with multiple phenotypes. It is widely distributed in the whole genome and can help to understand the common genetic mechanism of diseases or traits. In this study, a multivariate response best-subset selection (MRBSS) model based pleiotropic association analysis method is proposed. Different from the traditional genetic association model, the high-dimensional genotypic data are viewed as response variables while the multiple phenotypic data as predictor variables. Moreover, the response best-subset selection procedure is converted into an 0-1 integer optimization problem by introducing a separation parameter and a tuning parameter. Furthermore, the model parameters are estimated by using the curve search under the modified Bayesian information criterion. Simulation experiments show that the proposed method MRBSS remarkably reduces the computational time, obtains higher statistical power under most of the considered scenarios, and controls the type I error rate at a low level. The application studies in the datasets of maize yield traits and pig lipid traits further verifies the effectiveness.
| S-EPMC10712198 | biostudies-literature
Project description:We propose a multivariate genome-wide association test for mixed continuous, binary, and ordinal phenotypes. A latent response model is used to estimate the correlation between phenotypes with different measurement scales so that the empirical distribution of the Fisher's combination statistic under the null hypothesis is estimated efficiently. The simulation study shows that our proposed correlation estimation methods have high levels of accuracy. More importantly, our approach conservatively estimates the variance of the test statistic so that the type I error rate is controlled. The simulation also shows that the proposed test maintains the power at the level very close to that of the ideal analysis based on known latent phenotypes while controlling the type I error. In contrast, conventional approaches-dichotomizing all observed phenotypes or treating them as continuous variables-could either reduce the power or employ a linear regression model unfit for the data. Furthermore, the statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that conducting a multivariate test on multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests. The proposed method also offers a new approach to analyzing the Fagerström Test for Nicotine Dependence as multivariate phenotypes in genome-wide association studies.
| S-EPMC5231271 | biostudies-literature
Project description:Background Metabolic syndrome (MetS) refers to a cluster of metabolic disorders that are mainly caused by obesity. Skeletal muscle is a central component of systemic metabolism. However, the mechanism of skeletal muscle metabolic impairment in obesity remains unclear. This study aimed to identify key early biomarkers in skeletal muscle for the prevention and treatment of MetS in obesity. Methods The GSE85439 dataset was downloaded from the Gene Expression Omnibus database. Gene set enrichment and immune cell infiltration analyses were performed for genome-wide genes. Differentially expressed genes (DEGs) between obese and control mice were screened and subjected to functional enrichment analysis, and a protein–protein interaction network was constructed. The results of the bioinformatics analysis were confirmed by immunofluorescence and real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Results Enrichment analysis indicated that the genes expressed in obese mice were mainly associated with acute inflammatory response. Immune cell infiltration analysis of 190 DEGs with consistent trends showed that the numbers of mast cells (MCs) and active dendritic cells were significantly higher in obese mice than in control mice. Immunofluorescence analysis confirmed that the number of MCs present in the skeletal muscle was higher in obese mice than in control mice, although no difference was observed in the active dendritic cell count. Functional enrichment analysis showed that the DEGs were mainly associated with transcriptional regulation. In the clusters of the protein-protein interaction network, four acute-phase-response genes (SAA1, SAA2, ORM1, and HP) were significantly correlated with transcription-regulating genes (SHH, IGF2, and CELA1); these seven genes were identified as hub genes. The qRT-PCR results showed that the expression levels of SAA1, SAA2, IGF2, and CELA1 were significantly higher in obese mice than in control mice; however, those of HP, ORM1, and SHH did not differ significantly between the two groups. Conclusions The skeletal muscle of obese mice exhibits elevated MC infiltration and increased SAA1, SAA2, CELA1, and IGF2 expression. The identification of these biomarkers has increased our understanding of the potential functional mechanisms of skeletal muscle in obesity. These potential biomarkers may serve as targets for the prevention and treatment of MetS.
| S-EPMC9622484 | biostudies-literature