PGS: penalized generalized estimating equations with grid search for epigenome-wide multiple measurement study
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE55950 | GEO | 2017/12/21
SECONDARY ACCESSION(S): PRJNA241416
REPOSITORIES: GEO
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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: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
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 | GSE71666 | GEO
Project description:Environmental factors during perinatal development influence developmental plasticity and disease susceptibility via alterations to the epigenome. Developmental exposure to the endocrine active compound, bisphenol A (BPA), has previously been associated with altered methylation at candidate gene loci. Here, we undertake the first genome-wide characterization of DNA methylation profiles in the liver of murine offspring perinatally exposed to multiple doses of BPA through the maternal diet. Using a tiered focusing approach, our strategy proceeds from unbiased broad DNA methylation analysis using methylation-based next generation sequencing technology to in-depth quantitative site-specific CpG methylation determination using the Sequenom EpiTYPER MassARRAY platform to profile liver DNA methylation patterns in offspring maternally exposed to BPA during gestation and lactation to doses ranging from 0 BPA/kg (Ctr), 50 M-BM-5g BPA/kg (UG), or 50 mg BPA/kg (MG) diet (N=4 per group). Genome-wide analyses indicate non-monotonic effects of DNA methylation patterns following perinatal exposure to BPA, corroborating previous studies using multiple doses of BPA with non-monotonic outcomes. We observed enrichment of regions of altered methylation (RAMs) within CpG island (CGI) shores, but little evidence of RAM enrichment in CGIs. An analysis of promoter regions identified several hundred novel BPA-associated methylation events, and methylation alterations in the Myh7b and Slc22a12 gene promoters were validated. Using the Comparative Toxicogenomics Database, a number of candidate genes that have previously been associated with BPA-related gene expression changes were identified, and gene set enrichment testing identified epigenetically dysregulated pathways involved in metabolism and stimulus response. In this study, non-monotonic dose dependent alterations in DNA methylation among BPA-exposed mouse liver samples and their relevant pathways were identified and validated. The comprehensive methylome map presented here provides candidate loci underlying the role of early BPA exposure and later in life health and disease status. For this study, liver DNA from a subset of a/a wild-type animals was analyzed for full methylome characteristics: 1) standard diet (Ctr, n = 4 offspring; 2 male and 2 female); 2) 50 M-BM-5g BPA/kg diet (UG, n = 4 offspring; 2 male and 2 female); 3) 50 mg BPA/kg diet (MG, n = 4 offspring; 1 male and 3 female).
2013-11-17 | E-GEOD-46183 | biostudies-arrayexpress