Genomic DNA (gDNA) was extracted from flash-frozen liver tissue with PureLink Genomic DNA Mini Kit (Thermo) and quantified using Nanodrop (Thermo). In total 1 μg of isolated gDNA was bisulfite converted, denatured, fragmented and hybridized to Infinium Methylation Bead Chip, following the manufacturer protocol (Infinium MethylationEPIC kit, Illumina). BeadChips were imaged using an Illumina Scan System and intensity was determined by iScan Control Software (Illumina). Sample intensities were normalized using functional normalization from the minfi package (v1.24.0)60 (link). Probes failing a detection p-value threshold (0.01) in at least 50% of samples were removed, as were probes identified as containing a SNP with a MAF >0.05. Differentially methylated probes were identified by applying limma (v3.34.3)54 (link) contrasts to M values (absolute change in beta value >0.1, FDR-corrected P-value < 0.05). Differentially methylated regions were identified using DMRcate (v1.14.0)61 (link) setting a threshold of absolute change in beta value in >0.1 and of Stouffer’s value in <0.05.
Infinium methylation bead chip
Manufactured by Illumina
The Infinium Methylation Bead Chip is a laboratory equipment product from Illumina. It is designed for the analysis of DNA methylation patterns across the human genome. The product utilizes Illumina's proprietary bead-based technology to interrogate the methylation status of targeted CpG sites.
Automatically generated - may contain errors
2 protocols using infinium methylation bead chip
1
Genome-Wide DNA Methylation Analysis in Liver Tissue
2
Illumina Methylation Profiling and Statistical Analysis
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The R statistical computing environment 36 (v.3.6.3) was used to perform Illumina's Infinium Methylation BeadChip quality control and preprocessing, as well as all statistical analyses and figures. Univariate analyses were calculated using chi-squared and the Mann-Whitney U test; for the multivariate analysis, we used generalized linear models. Multivariate linear models were used to adjust for covariates in each individual cohort, based on the differences found among the groups compared (Table 1). Discovery cohort, model 1: accounts for smoking status and dyslipidemia; replication cohort, model 2: accounts for smoking status and hypertension. Pearson correlation test was used to measure the linear correlation between chronological and biological age. To analyze the differences between stroke subgroups, we used the ANOVA statistical test. Finally, Spearman correlation test was used to assess the relationship between the EAA measures and SOMAScan protein levels.
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