Homology alignments of human and murine genomes for the 105 CpGs were performed with the Ensembl genome browser with 121 nucleotide windows. A MATLAB script was used to find CpGs in the Illumina HumanMethylation450 BeadChip (450k) that were located either inside these homolog regions (categorized as “homolog”) or we determined the distance in base pairs (bp) to the center of the closest homolog region (categorized as “distance”). To further investigate age-associated changes at these CpGs the GSE40279 dataset was employed (Hannum et al., 2013 (link)). Data was analyzed with the R package GEOquery. Beta-values were used to determine the Pearson correlation with chronological age for all CpGs. The p-value of the correlations (α < 0.01) was estimated with a t-test for linear correlation with Bonferroni-correction for the total amount of CpGs on the 450k array.
Humanmethylation450 beadchip 450k
Manufactured by Illumina
Sourced in United States
The HumanMethylation450 BeadChip (450K) is a high-throughput microarray platform designed for genome-wide DNA methylation analysis. It provides comprehensive coverage of DNA methylation sites across the human genome, enabling researchers to investigate epigenetic changes associated with various biological processes and diseases.
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10 protocols using humanmethylation450 beadchip 450k
1
Homologous Aging-Associated CpG Analysis
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Integrative Analysis of Glioma Methylome and Transcriptome
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The Illumina HumanMethylation450 BeadChip (450K) and gene expression profiles for low-grade glioma (LGG) and glioblastoma multiforme (GBM) were downloaded from TCGA Pan-Cancer (PANCAN) cohort through UCSC Xena (https://xenabrowser.net/ ), which has performed batch effect correction. The patients with both methylome and transcriptome profiles (n = 551) were retained for further analysis. The clinical and molecular features include age, gender, grade, histology, isocitrate dehydrogenase (IDH) mutation, telomerase reverse transcriptase (TERT) mutation, alpha-thalassemia/mental retardation, X-linked (ATRX) mutation, 1p/19q codeletion, O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, tumor mutational burden (TMB), and chromosome aneuploidy of patients were collected from a previous study 15 (link).
An external dataset of 59 glioma patients with paired 450K array and RNA-sequencing data was downloaded from the Gene Expression Omnibus (GEO) with accession number GSE121723. The expression levels of genes were measured as transcripts per million (TPM) and log2 transformed.
The segment of each brain sample for the 18-state Roadmap Epigenome ChromHMM model was downloaded from the Roadmap project 16 . The CpG probes were mapped into different chromatin state regions through BEDTools 17 (link) and averaged their beta values to quantify the methylation level.
An external dataset of 59 glioma patients with paired 450K array and RNA-sequencing data was downloaded from the Gene Expression Omnibus (GEO) with accession number GSE121723. The expression levels of genes were measured as transcripts per million (TPM) and log2 transformed.
The segment of each brain sample for the 18-state Roadmap Epigenome ChromHMM model was downloaded from the Roadmap project 16 . The CpG probes were mapped into different chromatin state regions through BEDTools 17 (link) and averaged their beta values to quantify the methylation level.
3
Epigenomic Profiling of Blood Cells
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Epigenome‐wide CpG methylation was profiled using either the Illumina HumanMethylation450 BeadChip (450K) in the MESA (monocyte), YSCCS (blood), and VACS (blood; 57.2% of the sample) cohorts or Illumina HumanMethylation EPIC BeadChip (EPIC) in 42.8% of the VACS samples and the WIHS (peripheral blood mononuclear cells) samples. All samples in the three study cohorts (YSCCS, VACS, and WIHS) were processed at the Yale Center for Genomic Analysis (Zhang et al., 2017 ). We applied the method described by Houseman et al. (2012 (link); Jaffe & Irizarry, 2014 (link)) to estimate the proportions of CD4+ T cells, CD8+ T cells, NK T cells, B cells, monocytes, and granulocytes in each cohort. Quality control of methylation data for each cohort is presented in Supplementary Information .
4
DNA Methylation Analysis of Depression
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DNA methylation data for depression was extracted from the Gene Expression Omnibus (GEO) with the accession number GSE128235 . The data consisted of 324 depressed and 209 healthy participants of European ethnicity recruited from the Max Planck Institute of Psychiatry. Depressed individuals were diagnosed using the Diagnostic and Statistical Manual of Mental Disorder (DSM) IV criteria. The demographic information of this cohort is shown in Table 1 . Methylation profiles were obtained using the Illumina HumanMethylation450 BeadChip (450K), of which the details have been previously described (Zannas et al., 2019 (link)).
5
Epigenome-wide CpG Methylation Profiling
6
Epigenetic Age Analysis of IDU-HCV Cohort
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Epigenome-wide CpG methylation was profiled using either Illumina HumanMethylation450 BeadChip (450K) (San Diego, CA, USA) (57.2% of the sample) or Illumina HumanMethylation EPIC BeadChip (EPIC) (San Diego, CA, USA) (42.8% of the sample) in VACS. All samples were processed at the Yale Center for Genomic Analysis [27 (link)]. The same QC criteria were used as in our previous studies [27 (link),30 (link),31 (link)]. We retrieved methylation raw data using the minfi R package (version 1.18.1) and all probes were normalized. Methylation-inferred sex agreed with self-reported sex (100% of the sample was male).
Epigenetic age was estimated using four epigenetic clocks, the Horvath [10 (link)], Hannum [11 (link)], Pheno [12 (link)], and Grim [13 (link)] clocks. EAA was defined as the residuals of regressing epigenetic age on chronological age, which was applied to test the mediation effect on the relationship between IDUHCV and mortality risk [10 (link),14 (link)]. We also calculated the mean difference in EAA between IDU+HCV+ and IDU-HCV- and denoted it as the median difference of EAA (MDEAA).
Epigenetic age was estimated using four epigenetic clocks, the Horvath [10 (link)], Hannum [11 (link)], Pheno [12 (link)], and Grim [13 (link)] clocks. EAA was defined as the residuals of regressing epigenetic age on chronological age, which was applied to test the mediation effect on the relationship between IDUHCV and mortality risk [10 (link),14 (link)]. We also calculated the mean difference in EAA between IDU+HCV+ and IDU-HCV- and denoted it as the median difference of EAA (MDEAA).
7
Genome-wide Promoter Methylation Analysis
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Genome-wide promoter methylation status was determined using the HumanMethylation450 Beadchip (450 K) (Illumina). Bisulfite converted DNA was used for hybridization onto the beadchip. Raw fluorescence intensity values were normalized with Illumina Genome Studio software, and used to calculate DNA methylation levels (β-values).
8
DNA Methylation Profiling of Autism Spectrum Disorder
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The Illumina HumanMethylation450 BeadChip (450K), covering 485,000 CpG sites (Illumina, Inc., California, USA) was used to perform DNA methylation profiling. The samples were randomized and processed together to minimize the batch effect. The processed array chips were fluorescently stained and imaged on Illumina iScan. CpG loci within 10bp of SNPs were excluded because the latter can alter the methylation status of proximate cytosine loci. Statistical and bioinformatic analyses were performed followed by data processing and quality control. A β-value based on the ratio of methylated and unmethylated signal intensities were calculated for each CpG loci using GenomeStudio software as detailed in our prior publications [12 (link), 22 ]. FDR (Benjamini-Hochberg) p-value < 0.05 was considered significant. R-packages dplyr, reshape2, and ROCR were used to calculate Area Under the Receiver Operating Characteristic (AUC-ROC) curves with 95% CI for ASD prediction. An unsupervised Principal Component Analysis (PCA) and heatmap were generated using an online tool “MetaboAnalyst 4.0” which is based on the R program [23 (link)] (Fig 1 ).
9
DNA Methylation Profiling from Biosamples
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In both cohorts, ve hundred nanograms of genomic DNA was sodium bisul te-treated for unmethylated cytosine (C) to thymine (T) conversion using the EZ DNA Methylation-Gold kit (Zymo Research). Brie y, converted DNA was ampli ed, fragmented, and hybridized. The converted DNA was then scanned using the HumanMethylation EPIC/850 BeadChip (Dutch cohort) and 450 BeadChip (USA cohort) following the manufacturer's guidelines. Illumina recently replaced the HumanMethylation450 BeadChip (450K) with the EPIC BeadChip, which nearly doubles the measured CpG sites to >850,000. However data obtained from two platforms is comparable within cohorts (Solomon et al., 2018) .
10
Age-associated CpG Methylation Analysis
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Homology alignments of human and murine genomes for the 105 CpGs were performed with the Ensembl genome browser with 121 nucleotide windows. A MATLAB script was used to find CpGs in the Illumina HumanMethylation450 BeadChip (450k) that were located either inside these homolog regions (categorized as "homolog") or we determined the distance in bp to the center of the closest homolog region (categorized as "distance"). To further investigate age-associated changes at these CpGs the GSE40279 dataset was employed (Hannum et al., 2013) . Data was analyzed with the R package GEOquery. Beta-values were used to determine the Pearson correlation with chronological age for all CpGs. The p-value of the correlations (α < 0.01) was estimated with a t-test for linear correlation with Bonferroni-correction for the total amount of CpGs on the 450k array.
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