PGS: penalized generalized estimating equations with grid search for epigenome-wide multiple measurement study
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE55950 | GEO | 2017/12/21
SECONDARY ACCESSION(S): PRJNA241416
REPOSITORIES: GEO
Similar Datasets
Project description:PGS: penalized generalized estimating equations with grid search for epigenome-wide multiple measurement study
| PRJNA241416 | ENA
Project description:Omics data integration is becoming necessary to investigate the still unknown genomic mechanisms of complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological mechanisms. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we applied penalized regression methods (LASSO and ENET) to explore relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples and have proposed a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whom expression levels were associated with both SNPs and GPGs. Importantly, we replicated results for 36 (75%) of them in an independent data set (TCGA). We checked the performance of the proposed method with a simulation study and further supported our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexibly and easy to implement when analyzing several omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complexity of disease genetic mechanisms. Bisulphite modification of 46 tumor DNA samples using EZ-96 DNA METHYLATIONGOLD KIT (Zymo Research, Irvin, CA, USA), CpG methylation data was generated using the Infinum Human Methylation 27 BeadChip Kit that detected the CpG sites with two probes, one designed against the unmethylated site (signal U) and the other against the methylated site (signal M).
2015-08-04 | E-GEOD-71666 | biostudies-arrayexpress
Project description:Background: Maternal smoking during pregnancy is a major risk factor for adverse health outcomes. The main objective of the study was to assess the impact of in utero tobacco exposure on DNA methylation in children born at term with appropriate weight at birth. Methods: Twenty mother-newborn dyads, after uncomplicated pregnancies, in the absence of perinatal illness were included. All mothers were healthy with no cardiovascular risk factors, except for the associated risks among those mothers who smoked. Umbilical cord blood (for methylation arrays) and maternal peripheral venous blood (for cotine level measurement) were collected and an epigenome-wide association study was performed using a 450K epigenome-wide scan (Illumina Infinium HumanMethylation 450BeadChip) with adjustment to normalize the DNA methylation for data cell variability in whole blood. Results: The maternal plasmatic cotinine levels ranged from 10.70-115.40 ng/ml in the exposed group to 0-0.59 ng/ml in the non-exposed group. After adjusting for multiple comparisons in 427102 probes, statistically significant differences for 31 CpG sites, associated to 25 genes were observed. There was a greater than expected proportion of statistically-significant loci located in CpG islands (FisherM-bM-^@M-^Ys exact test, p=0.029) and of those CpG islands, 90.3% exhibit higher methylation levels in the exposed group. The most striking and significant CpG site, cg05727225, is located in the chromosome 11p15.4, within the adrenomedullin gene. Conclusions: In utero tobacco exposure, even in the absence of fetal growth restriction, may alter the epigenome, contributing to global DNA hypomethylation. Therefore, DNA status can be used as a biomarker of prenatal insults. Considering the possibility to reverse epigenetic modifications, a window of opportunity exists to change the programmed chronic disease. Twenty mother-newborn dyads, after uncomplicated pregnancies, in the absence of perinatal illness were included. All mothers were healthy with no cardiovascular risk factors, except for the associated risks among those mothers who smoked. Umbilical cord blood were collected and an epigenome-wide association study was performed using a 450K epigenome-wide scan (Illumina Infinium HumanMethylation 450BeadChip) with adjustment to normalize the DNA methylation for data cell variability in whole blood.
2015-02-16 | E-GEOD-64316 | biostudies-arrayexpress
Project description:Granulomas are organized immune cell aggregates formed in response to chronic infection or antigen persistence. The bacterial pathogen Yersinia pseudotuberculosis (Yp) blocks innate inflammatory signalling and immune defence, inducing neutrophil-rich pyogranulomas (PGs) within lymphoid tissues. Here we uncover that Yp also triggers PG formation within the murine intestinal mucosa. Mice lacking circulating monocytes fail to form defined PGs, have defects in neutrophil activation and succumb to Yp infection. Yersinia lacking virulence factors that target actin polymerization to block phagocytosis and reactive oxygen burst do not induce PGs, indicating that intestinal PGs form in response to Yp disruption of cytoskeletal dynamics. Notably, mutation of the virulence factor YopH restores PG formation and control of Yp in mice lacking circulating monocytes, demonstrating that monocytes override YopH-dependent blockade of innate immune defence. This work reveals an unappreciated site of Yersinia intestinal invasion and defines host and pathogen drivers of intestinal granuloma formation.
2022-02-07 | GSE194334 | GEO
Project description:Background Cisplatin-induced ototoxicity (CIO), characterized by irreversible and progressive bilateral hearing loss, is a prevalent adverse effect of cisplatin chemotherapy. Alongside clinical risk factors, genetic variants contribute to CIO and genome-wide association studies (GWAS) have highlighted the polygenicity of this adverse drug reaction. Polygenic scores (PGS), which integrate information from multiple genetic variants across the genome, offer a promising tool for the identification of individuals who are at higher risk for CIO. Integrating large-scale hearing loss GWAS data with single cell omics data holds potential to overcome limitations related to small sample sizes associated with CIO studies, enabling the creation of PGSs to predict CIO risk. Results We utilized a large-scale hearing loss GWAS and murine inner ear single nuclei RNA-sequencing (snRNA-seq) data to develop two polygenic scores: a hearing loss PGS (PGSHL) and a biologically informed PGS for CIO (PGSCIO). The PGSCIO included only variants which mapped to genes that were differentially expressed within cochlear cells that showed differential abundance in the murine snRNA-seq data post-cisplatin treatment. Evaluation of the association of these PGSs with CIO in our target CIO cohort revealed that PGSCIO demonstrated superior performance (P = 5.54 × 10− 5) relative to PGSHL (P = 2.93 × 10− 3). PGSCIO was also associated with CIO in our test cohort (P = 0.04), while the PGSHL did not show a significant association with CIO (P = 0.52). Conclusion This study developed the first PGS for CIO using a large-scale hearing loss dataset and a biologically informed filter generated from cisplatin-treated murine inner ear snRNA-seq data. This innovative approach offers new avenues for developing PGSs for pharmacogenomic traits, which could contribute to the implementation of tailored therapeutic interventions. Further, our approach facilitated the identification of specific cochlear cells that may play critical roles in CIO. These novel insights will guide future research aimed at developing targeted therapeutic strategies to prevent CIO.
2024-11-14 | GSE281324 | GEO
Project description:Proteoglycans (PGs) are proteins with glycosaminoglycan (GAG) chains, such as chondroitin sulfate (CS) or heparan sulfate (HS), attached to serine residues. We have earlier shown that prohormones can carry CS, thus, constituting a novel class of PGs. The mapping of GAG modifications of proteins in endocrine cells may thus assist us in delineating possible roles of PGs in endocrine cellular physiology. With this aim, we applied a glycoproteomic approach to identify PGs, their GAG chains and their attachment sites in insulin-secreting cells. Glycopeptides carrying GAG chains were enriched from human pancreatic islets, rat (INS-1 832/13) and mouse (MIN6, NIT-1) insulinoma cell lines by ion exchange chromatography, depolymerized with GAG lyases, and analyzed by nanoflow liquid chromatography-tandem mass spectrometry (nLC-MS/MS). We identified CS modifications of chromogranin-A, islet amyloid polypeptide, secretogranin 1 and secretogranin 2, immunoglobulin superfamily member 10, and protein AMBP. Additionally, we identified two HS-modified prohormones (chromogranin-A and secretogranin-1), which was surprising, as prohormones are not typically regarded as HSPGs. For chromogranin-A, the glycosylation site carried either CS or HS, making it a so-called hybrid site. Additional HS sites were found on syndecan-1, syndecan-4, nerurexin-2, protein NDNF, and testican-1. These results demonstrate that several prohormones, and other constituents of the insulin-secreting cells are PGs. Cell-targeted mapping of the GAG glycoproteome forms an important basis for better understanding of endocrine cellular physiology, and the novel CS and HS sites presented here provide important knowledge for future studies.
2022-02-16 | PXD024230 | Pride
Project description:Epigenome-Wide Association Study in multiple system atrophy (MSA)
| PRJNA599119 | ENA
Project description:Background: Maternal smoking during pregnancy is a major risk factor for adverse health outcomes. The main objective of the study was to assess the impact of in utero tobacco exposure on DNA methylation in children born at term with appropriate weight at birth. Methods: Twenty mother-newborn dyads, after uncomplicated pregnancies, in the absence of perinatal illness were included. All mothers were healthy with no cardiovascular risk factors, except for the associated risks among those mothers who smoked. Umbilical cord blood (for methylation arrays) and maternal peripheral venous blood (for cotine level measurement) were collected and an epigenome-wide association study was performed using a 450K epigenome-wide scan (Illumina Infinium HumanMethylation 450BeadChip) with adjustment to normalize the DNA methylation for data cell variability in whole blood. Results: The maternal plasmatic cotinine levels ranged from 10.70-115.40 ng/ml in the exposed group to 0-0.59 ng/ml in the non-exposed group. After adjusting for multiple comparisons in 427102 probes, statistically significant differences for 31 CpG sites, associated to 25 genes were observed. There was a greater than expected proportion of statistically-significant loci located in CpG islands (Fisher’s exact test, p=0.029) and of those CpG islands, 90.3% exhibit higher methylation levels in the exposed group. The most striking and significant CpG site, cg05727225, is located in the chromosome 11p15.4, within the adrenomedullin gene. Conclusions: In utero tobacco exposure, even in the absence of fetal growth restriction, may alter the epigenome, contributing to global DNA hypomethylation. Therefore, DNA status can be used as a biomarker of prenatal insults. Considering the possibility to reverse epigenetic modifications, a window of opportunity exists to change the programmed chronic disease.
2015-02-16 | GSE64316 | GEO
Project description:Omics data integration is becoming necessary to investigate the still unknown genomic mechanisms of complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological mechanisms. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we applied penalized regression methods (LASSO and ENET) to explore relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples and have proposed a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whom expression levels were associated with both SNPs and GPGs. Importantly, we replicated results for 36 (75%) of them in an independent data set (TCGA). We checked the performance of the proposed method with a simulation study and further supported our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexibly and easy to implement when analyzing several omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complexity of disease genetic mechanisms. Gene expression data were obtained from 44 tumor samples using the Affymetrix DNA Microarray Human Gene 1.0 ST Array with 32,321 probes.
2015-08-04 | E-GEOD-71576 | biostudies-arrayexpress
Project description:Omics data integration is becoming necessary to investigate the still unknown genomic mechanisms of complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological mechanisms. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we applied penalized regression methods (LASSO and ENET) to explore relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples and have proposed a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whom expression levels were associated with both SNPs and GPGs. Importantly, we replicated results for 36 (75%) of them in an independent data set (TCGA). We checked the performance of the proposed method with a simulation study and further supported our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexibly and easy to implement when analyzing several omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complexity of disease genetic mechanisms.
2015-08-04 | GSE71576 | GEO