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Humanomni2.5 8 array

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The HumanOmni2.5–8 array is a high-density genotyping array designed for genome-wide association studies (GWAS). The array includes over 2.5 million markers, providing comprehensive coverage of common and rare variants across the human genome. It is intended for use in large-scale genetic research projects.

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Lab products found in correlation

Infinium methylationepic array, by Illumina (3 mentions) Methylationepic array, by Illumina (2 mentions) 450k beadchip, by Illumina (1 mentions)

10 protocols using humanomni2.5 8 array

1

DNA Methylation Age Estimation in Brain

Cited 6 times
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Genotypes were obtained from motor cortex DNA samples using the Illumina HumanOmni2.5–8 array. DNAm from each brain region was assayed on the Illumina Infinium MethylationEPIC array. DNAm age estimates were calculated using the Horvath algorithm (Horvath, 2013 (link)) for all three regions. We also estimated DNAm age using two recently developed tissue-specific algorithms that were developed for human brain (Choi et al., 2019 (link)) and human cortical tissue (Shireby et al., 2020 (link)). Details of DNA extraction, QC procedures, genotype and DNAm data cleaning and imputation, ancestry assignment and ancestral principal component (PC) estimation, DNAm cell type estimates, and DNAm age (Horvath DNAm age, Choi brain-specific DNAm age, and Shireby cortical DNAm age) calculation are reviewed in the Supplementary Materials and were previously described (Logue et al., 2021 (link); Morrison, Miller, et al., 2019 (link); Wolf et al., 2020 (link), 2021 (link)).
Zhao X., Logue M.W., Hawn S.E., Neale Z.E., Zhou Z., Huber B.R., Miller M.W, & Wolf E.J. (2022). PTSD, Major Depression, and Advanced Transcriptomic Age in Brain Tissue. Depression and anxiety, 39(12), 824-834.
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2

Quality Confirmation for Sequencing Data

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All sequencing reads were evaluated for quality using the Bioconductor ShortRead package80 (link). To confirm that all samples were identified correctly, all exome and RNA-seq data variants that overlapped with Illumina HumanOmni2.5-8 array data were compared and checked for consistency. An all-against-all sample comparison was carried out on germline variants to confirm matched tumor-normal pairing before additional data analysis.
Durinck S., Stawiski E.W., Pavía-Jiménez A., Modrusan Z., Kapur P., Jaiswal B.S., Zhang N., Toffessi-Tcheuyap V., Nguyen T.T., Pahuja K.B., Chen Y.J., Saleem S., Chaudhuri S., Heldens S., Jackson M., Peña-Llopis S., Guillory J., Toy K., Ha C., Harris C.J., Holloman E., Hill H.M., Stinson J., Rivers C.S., Janakiraman V., Wang W., Kinch L.N., Grishin N.V., Haverty P.M., Chow B., Gehring J.S., Reeder J., Pau G., Wu T.D., Margulis V., Lotan Y., Sagalowsky A., Pedrosa I., de Sauvage F.J., Brugarolas J, & Seshagiri S. (2014). Spectrum of diverse genomic alterations define non–clear cell renal carcinoma subtypes. Nature genetics, 47(1), 13-21.
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3

Epigenetic Aging and Mortality Risk

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DNA was isolated from peripheral blood samples. In both cohorts, genotypes were assayed on the Illumina HumanOmni2.5–8 array and DNAm on the Illumina Infinium MethylationEPIC array. Genotyping and DNAm quality control methods are described in the Supplementary Materials, as are the calculation procedures for GrimAge, other DNAm age estimates, and ancestry principal components (PCs). An index of age-adjusted GrimAge was created by regressing GrimAge on chronological age and saving the residuals (“GrimAge residuals”). Conceptually, positive residual values index mortality risk that is elevated compared to chronological age, while negative residual values suggest reduced mortality risk relative to age. Blood sample white blood cell proportions (CD8-T and CD4-T cells, natural killer cells, b-cells, monocytes) were calculated from the methylation data [35 (link), 36 (link)] and included as covariates (technical confounders) in analyses with GrimAge residuals as the outcome. As cell type composition may be confounded with DNAm, even when DNAm-based variables are the exogenous variable, we created a second GrimAge residual variable for use in follow-up analyses of models in which GrimAge was the independent variable in which variance associated with chronological age and cell type estimates was regressed out from raw GrimAge estimates.
Hawn S.E., Zhao X., Sullivan D.R., Logue M., Fein-Schaffer D., Milberg W., McGlinchey R., Miller M.W, & Wolf E.J. (2022). For whom the bell tolls: psychopathological and neurobiological correlates of a DNA methylation index of time-to-death. Translational Psychiatry, 12, 406.
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4

Epigenetic Aging in Brain Regions

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DNA extraction was conducted from all three regions (see Supplementary Materials for details), with genotypes determined from motor cortex samples. Genotypes were assayed on the Illumina HumanOmni2.5-8 array and DNAm from each region was assessed with the Illumina Infinium MethylationEPIC array. We calculated the Horvath (2013) (link) DNAm age estimates, a multi-tissue age predictor that included brain tissue in the development of the algorithm, using the 335 probes on the EPIC array that overlap those in the algorithm which was originally developed for the Illumina 450 K BeadChip. Horvath DNAm age estimates correlated with age at death in each region at r = 0.91 to 0.93. As reported in Wolf et al. (2021) (link), DNAm age estimates across brain regions were highly correlated (r = 0.92 to 0.93, ps < 0.001). Chronological age was regressed out from each DNAm age estimate in the sample of n = 116 with DNAm data and the unstandardized residuals were saved (“DNAm age residuals”) to index slowed to advanced epigenetic age relative to chronological age. As per Wolf et al. (2021) (link), DNAm age residuals were moderately correlated with each other across brain regions (r = 0.50 to 0.51, ps < 0.001). Ancestral variation was estimated via principal components (PC) analysis of 100,000 common polymorphisms, with the first three PCs retained as covariates in analyses.
Wolf E.J., Zhao X., Hawn S.E., Morrison F.G., Zhou Z., Fein-Schaffer D., Huber B., Miller M.W, & Logue M.W. (2021). Gene expression correlates of advanced epigenetic age and psychopathology in postmortem cortical tissue. Neurobiology of Stress, 15, 100371.
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5

Genetic and Epigenetic Profiling of PTSD

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DNA was isolated from peripheral blood samples. Genotypes were assayed on the Illumina HumanOmni2.5-8 array and DNAm on the Illumina Infinium MethylationEPIC array. Chips were counter balanced for sex and PTSD diagnostic status. Additional information regarding genotyping and DNAm data generation and quality control methods are available in the Supporting Information. Global ancestry principal components (PCs) were calculated from common variants (see Supporting Information) and were included as covariates in all DNAm analyses. Proportional white blood cell (WBC) estimates (CD8-T and CD4-T cells, natural killer cells, b-cells, monocytes) were calculated from the methylation data (Aryee et al., 2014 (link); Fortin, et al., 2017 (link)) and included as covariates in all DNAm analyses.
Hawn S.E., Neale Z., Wolf E.J., Zhao X., Pierce M., Fein-Schaffer D., Milberg W., McGlinchey R., Logue M, & Miller M.W. (2022). Methylation of the AIM2 gene: An epigenetic mediator of PTSD-related inflammation and neuropathology plasma biomarkers. Depression and anxiety, 39(4), 323-333.
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6

Genetic Analysis of Derealization Symptoms

Cited 2 times
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Blood samples were obtained and DNA extracted from buffy coat. Genotyping was derived from the Illumina HumanOmni2.5–8 array. We examined the 10 SNPs from our prior GWAS of derealization/depersonalization symptom severity (Wolf et al., 2014 (link)) that achieved p < 10−5 in that study. These SNPs all passed quality control filters and had a minor allele frequencies (MAFs) of at least 5%. Given differences in linkage disequilibrium (LD) as a function of ancestry, these analyses were conducted in the genetically confirmed white, non-Hispanic cohort (n = 193) to match the population structure examined in Wolf et al., 2014 (link). Population substructure within this cohort was modeled through principal components (PC) analysis of 100,000 randomly selected SNPs with at least 5% MAF, and the top three PCs were included as covariates in genotype analyses (see Wolf et al., 2019 (link) for details).
Lin Y.H., Huang C.J., Chao J.R., Chen S.T., Lee S.F., Yen J.J, & Yang-Yen H.F. (2001). Coupling of Osteopontin and Its Cell Surface Receptor CD44 to the Cell Survival Response Elicited by Interleukin-3 or Granulocyte-Macrophage Colony-Stimulating Factor. Molecular and Cellular Biology, 21(6), 2248.
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7

Stringent GWAS Quality Control

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Individuals (n = 5000) were genotyped on the Illumina HumanOmni2.5-8 array, and 4872 were retained following a pre-quality control stage. GWAS genotype data were subjected to stringent quality control filtering. Of a total of 2 314 174 autosomal variants genotyped, 39 368 were excluded because they did not pass SNP quality thresholds for call rate (<97%, n = 25 037 SNPs) and deviation from Hardy–Weinberg equilibrium (HWE) (P < 10−8, n = 14 331 SNPs) as reported in (8 (link)). We excluded 91 individuals who failed to meet the quality control for call rate (>97%) or had gender mismatch compared with X-chromosome. We carried out further quality control for the GWAS analysis, for which three samples were excluded as heterozygosity outliers (heterozygosity ≥3 SD from mean). Additional six samples were excluded due to potential contamination.
Fatumo S., Chikowore T., Kalyesubula R., Nsubuga R.N., Asiki G., Nashiru O., Seeley J., Crampin A.C., Nitsch D., Smeeth L., Kaleebu P., Burgess S., Nyirenda M., Franceschini N., Morris A.P., Tomlinson L, & Newton R. (2021). Discovery and fine-mapping of kidney function loci in first genome-wide association study in Africans. Human Molecular Genetics, 30(16), 1559-1568.
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8

Epigenetic Age and Telomere Analysis

Cited 2 times
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DNA was extracted from blood samples. Genotype calls were based on the Illumina HumanOmni2.5–8 array and DNAm on the Illumina Infinium MethylationEPIC array (Supplementary Materials). We examined all SNPs on the chip that passed quality controls with at least 5% minor allele frequency (MAF), < 5% missing calls, and within 5,000 base pair of the KL gene (per the GRCh37/hg19 build; Supplementary Materials). Ancestry-based principal components (PCs) were generated from common SNPs (Supplementary Materials). Proportional white blood cell (WBC) types, including CD8-T and CD4-T cells, natural killer cells, b-cells, and monocytes, were calculated from the methylation data (Aryee et al., 2014 (link); Fortin et al., 2017 (link)) and were simultaneously included as covariates in analyses examining epigenetic age and telomeres (Supplementary Materials). DNAm age (Horvath, 2013 (link)) was estimated using the R script version of the Horvath algorithm based on 335 DNAm probes (Supplementary Materials).
Wolf E.J., Morrison F.G., Sullivan D.R., Logue M.W., Guetta R.E., Stone A., Schichman S.A., McGlinchey R.E., Milberg W.P, & Miller M.W. (2019). The Goddess Who Spins the Thread of Life: Klotho, Psychiatric Stress, and Accelerated Aging. Brain, behavior, and immunity, 80, 193-203.
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9

SNP Array Analysis of Kidney Cancer

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Illumina HumanOmni2.5-8 arrays were used to assay 112 samples (including 105 tumor-normal pairs) for geno-type, DNA copy number and loss of heterozygosity (LOH) at ~2.5 million SNP positions. The data from these arrays were processed as described recently90 (link). The PICNIC algorithm95 (link) was used to estimate normal contamination, ploidy and chromosomal segments with LOH. After adjusting the raw data for normal contamination, the cghFLasso algorithm96 (link) was used to obtain the final estimation and segmentation of total copy number. A subset of 2,228,703 high-quality SNPs was selected for all analyses.
Genomic regions with recurrent DNA copy gain and loss were identified by computing the frequency of a log2 copy number ratio of >0.45 or <−0.45 for gains and losses, respectively, for each tumor subtype. Given that our algorithm for estimating copy number ratios assumes segmental changes, we applied manual recentering to the chRCC data where we observed many whole- chromosome losses.
Durinck S., Stawiski E.W., Pavía-Jiménez A., Modrusan Z., Kapur P., Jaiswal B.S., Zhang N., Toffessi-Tcheuyap V., Nguyen T.T., Pahuja K.B., Chen Y.J., Saleem S., Chaudhuri S., Heldens S., Jackson M., Peña-Llopis S., Guillory J., Toy K., Ha C., Harris C.J., Holloman E., Hill H.M., Stinson J., Rivers C.S., Janakiraman V., Wang W., Kinch L.N., Grishin N.V., Haverty P.M., Chow B., Gehring J.S., Reeder J., Pau G., Wu T.D., Margulis V., Lotan Y., Sagalowsky A., Pedrosa I., de Sauvage F.J., Brugarolas J, & Seshagiri S. (2014). Spectrum of diverse genomic alterations define non–clear cell renal carcinoma subtypes. Nature genetics, 47(1), 13-21.
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10

Genotyping and Intensity Analysis of 1000 Genomes

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Normalized SNP intensity data from Illumina HumanOmni2.5-8 arrays generated for the entire set of 1000 Genomes Project samples were analyzed (Supplementary Table 1)(Supplementary Table 1). Data were downloaded from the FTP site of the 1000 Genomes Project: ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20120131_omni_genotypes_and_intensities/.
Flores Espinosa J.R., Ayub Q., Chen Y., Xue Y, & Tyler-Smith C. (2015). Structural variation on the human Y chromosome from population-scale resequencing. Croatian Medical Journal, 56(3), 194-207.
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