DNA methylation profiling of blood from healthy Australian donors
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE235717 | GEO | 2023/09/15
REPOSITORIES: GEO
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Project description:We evaluated biological aging using five epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge and DunedinPoAm) calculated from DNA methylation measured in peripheral blood cells in a trans-diagnostic psychiatric sample including healthy controls. We found that burden of psychiatric disease, represented by a weighted score, was significantly associated with biological age acceleration as measured by GrimAge and DunedinPoAm. The faster pace of aging was even further accelerated in individuals exposed to physical abuse in childhood
2023-01-14 | GSE222468 | GEO
Project description:In this study, we investigated whether stroke patients who later developed cancer exhibited an accelerated epigenetic age compared to those who did not. A total of 648 stroke patients were followed for 15 years, during which 83 individuals were diagnosed with cancer. Biological age (B-age) was estimated from DNA methylation data derived from whole blood samples collected within 24 hours of stroke onset. These samples were analyzed using the 450K or EPIC Illumina Beadchip arrays. We employed several epigenetic clocks to calculate B-age, including the Hannum, Horvath, PhenoAge, ZhangBLUP, ZhangEN, and the mitotic epiTOC clocks. After adjusting for multiple testing, competing risks, and key confounders, we found that patients who developed cancer were biologically older, as measured by the Hannum clock. This repository provides the raw idat files and processed DNA methylation values.
2024-11-12 | GSE280206 | GEO
Project description:DNA methylation microarray analysis was performed on human donor whole blood samples from patients with and without AMD. A total of 30 patient samples including 16 Normal, 3 AREDS grade 2 (early AMD) and 11 AREDS grade 3 (intermediate AMD) (AMD total, n = 14) were selected. Samples were obtained from individuals phenotyped according to the Age-Related Eye Disease Study (AREDS) classification. DNAm levels were measured using the EPIC-array (Illumina Inc., San Diego, CA, USA). Samples run on the EPIC-array were randomized and balanced for disease status and smoking status to minimise chip and row specific effects. The EPIC-array incorporated technical controls into the experimental design. In total, 500 ng (50 ng/μL) total peripheral whole blood-derived gDNA was bisulfite converted using the EZ-96 DNA methylation kit (Zymo Research, Irvine, CA, USA) and hybridised to the EPIC-array according to the manufacturer’s instructions. Quality control analysis was performed using GenomeStudio (v2011.1). Raw IDAT files were then read into R (version 3.31) using the read.metharray.exp function within the minfi package. DNA methylation microarray data was collected in order to assess the estimated DNA methylation age using the Horvath multi-tissue, Hannum and Skin & Blood epigenetic clocks and to identify loci of differential methylation between the experimental groups.
2021-12-31 | E-MTAB-11279 | biostudies-arrayexpress
Project description:Background: Increased epigenetic age acceleration (EAA) in survivors of childhood cancer is associated with specific treatment exposures, unfavorable health behaviors, and presence of certain chronic health conditions. To better understand inter-individual variability, we investigated the genetic basis underlying EAA. Methods: Genome-wide association studies of EAA based on multiple epigenetic clocks (Hannum, Horvath, PhenoAge, and GrimAge) were performed. MethylationEPIC BeadChip array and whole-genome sequencing data were generated with blood-derived DNA from participants in the St. Jude Lifetime Cohort Study (discovery: 2,138 pre-existing and 502 newly generated data, all survivors; exploratory: 282 community controls). Linear regression models were fit for each epigenetic age against allelic dose of each genetic variant, adjusting for age at sampling, sex, and cancer treatment exposures. Fixed-effects meta-analysis was used to combine summary statistics from two discovery data sets. LD (Linkage disequilibrium) score regression was used to estimate single-nucleotide polymorphism (SNP)-based heritability. Results: For EAA-Horvath, a genome-wide significant association was mapped to SELP gene with the strongest SNP rs732314 (meta-GWAS: β=0.57, P=3.30×10-11). Moreover, the stratified analysis of the association between rs732314 and EAA-Horvath showed substantial heterogeneity between children and adults (meta-GWAS: β=0.97 vs. 0.51, I2=73.1%) as well as between survivors with and without chest/abdominal/pelvic-RT exposure (β=0.64 vs. 0.31, I2=66.3%). For EAA-Hannum, an association was mapped to HLA locus with the strongest SNP rs28366133 (meta-GWAS: β=0.78, P=3.78×10-11). There was no genome-wide significant hit for EAA-PhenoAge or EAA-GrimAge. Interestingly, among community controls, rs732314 was associated with EAA-Horvath (β=1.09, P=5.43×10-5), whereas rs28366133 was not associated with EAA-Hannum (β=0.21, P=0.49). The estimated heritability was 0.33 (SE=0.20) for EAA-Horvath and 0.17 (SE=0.23) for EAA-Hannum, but close to zero for EAA-PhenoAge and EAA-GrimAge. Conclusions: We identified novel genetic variants in SELP gene and HLA region associated with EAA-Horvath and EAA-Hannum, respectively, among survivors of childhood cancer. The new genetic variants in combination with other replicated known variants can facilitate identification of survivors at higher risk in developing accelerated aging, and potentially inform drug targets for future intervention strategies among vulnerable survivors.
2022-03-09 | GSE197675 | GEO
Project description:Background: Increased epigenetic age acceleration (EAA) in survivors of childhood cancer is associated with specific treatment exposures, unfavorable health behaviors, and presence of certain chronic health conditions. To better understand inter-individual variability, we investigated the genetic basis underlying EAA. Methods: Genome-wide association studies of EAA based on multiple epigenetic clocks (Hannum, Horvath, PhenoAge, and GrimAge) were performed. MethylationEPIC BeadChip array and whole-genome sequencing data were generated with blood-derived DNA from participants in the St. Jude Lifetime Cohort Study (discovery: 2,138 pre-existing and 502 newly generated data, all survivors; exploratory: 282 community controls). Linear regression models were fit for each epigenetic age against allelic dose of each genetic variant, adjusting for age at sampling, sex, and cancer treatment exposures. Fixed-effects meta-analysis was used to combine summary statistics from two discovery data sets. LD (Linkage disequilibrium) score regression was used to estimate single-nucleotide polymorphism (SNP)-based heritability. Results: For EAA-Horvath, a genome-wide significant association was mapped to SELP gene with the strongest SNP rs732314 (meta-GWAS: β=0.57, P=3.30×10-11). Moreover, the stratified analysis of the association between rs732314 and EAA-Horvath showed substantial heterogeneity between children and adults (meta-GWAS: β=0.97 vs. 0.51, I2=73.1%) as well as between survivors with and without chest/abdominal/pelvic-RT exposure (β=0.64 vs. 0.31, I2=66.3%). For EAA-Hannum, an association was mapped to HLA locus with the strongest SNP rs28366133 (meta-GWAS: β=0.78, P=3.78×10-11). There was no genome-wide significant hit for EAA-PhenoAge or EAA-GrimAge. Interestingly, among community controls, rs732314 was associated with EAA-Horvath (β=1.09, P=5.43×10-5), whereas rs28366133 was not associated with EAA-Hannum (β=0.21, P=0.49). The estimated heritability was 0.33 (SE=0.20) for EAA-Horvath and 0.17 (SE=0.23) for EAA-Hannum, but close to zero for EAA-PhenoAge and EAA-GrimAge. Conclusions: We identified novel genetic variants in SELP gene and HLA region associated with EAA-Horvath and EAA-Hannum, respectively, among survivors of childhood cancer. The new genetic variants in combination with other replicated known variants can facilitate identification of survivors at higher risk in developing accelerated aging, and potentially inform drug targets for future intervention strategies among vulnerable survivors.
2022-03-09 | GSE197676 | GEO
Project description:Background: Increased epigenetic age acceleration (EAA) in survivors of childhood cancer is associated with specific treatment exposures, unfavorable health behaviors, and presence of certain chronic health conditions. To better understand inter-individual variability, we investigated the genetic basis underlying EAA. Methods: Genome-wide association studies of EAA based on multiple epigenetic clocks (Hannum, Horvath, PhenoAge, and GrimAge) were performed. MethylationEPIC BeadChip array and whole-genome sequencing data were generated with blood-derived DNA from participants in the St. Jude Lifetime Cohort Study (discovery: 2,138 pre-existing and 502 newly generated data, all survivors; exploratory: 282 community controls). Linear regression models were fit for each epigenetic age against allelic dose of each genetic variant, adjusting for age at sampling, sex, and cancer treatment exposures. Fixed-effects meta-analysis was used to combine summary statistics from two discovery data sets. LD (Linkage disequilibrium) score regression was used to estimate single-nucleotide polymorphism (SNP)-based heritability. Results: For EAA-Horvath, a genome-wide significant association was mapped to SELP gene with the strongest SNP rs732314 (meta-GWAS: β=0.57, P=3.30×10-11). Moreover, the stratified analysis of the association between rs732314 and EAA-Horvath showed substantial heterogeneity between children and adults (meta-GWAS: β=0.97 vs. 0.51, I2=73.1%) as well as between survivors with and without chest/abdominal/pelvic-RT exposure (β=0.64 vs. 0.31, I2=66.3%). For EAA-Hannum, an association was mapped to HLA locus with the strongest SNP rs28366133 (meta-GWAS: β=0.78, P=3.78×10-11). There was no genome-wide significant hit for EAA-PhenoAge or EAA-GrimAge. Interestingly, among community controls, rs732314 was associated with EAA-Horvath (β=1.09, P=5.43×10-5), whereas rs28366133 was not associated with EAA-Hannum (β=0.21, P=0.49). The estimated heritability was 0.33 (SE=0.20) for EAA-Horvath and 0.17 (SE=0.23) for EAA-Hannum, but close to zero for EAA-PhenoAge and EAA-GrimAge. Conclusions: We identified novel genetic variants in SELP gene and HLA region associated with EAA-Horvath and EAA-Hannum, respectively, among survivors of childhood cancer. The new genetic variants in combination with other replicated known variants can facilitate identification of survivors at higher risk in developing accelerated aging, and potentially inform drug targets for future intervention strategies among vulnerable survivors.
2022-03-09 | GSE197674 | GEO
Project description:DNA methylation profiling of blood from healthy Australian donors
| PRJNA986944 | ENA
Project description:Human DNA methylation Beadchip v1.2 was used to profile n=335 whole blood samples. The main goal of the study was to measure the epigenetic age (also known as DNA methylation age) of Hispanics and Caucasians from the USA. To measure DNA methylation age, we used the epigenetic clock software described in Horvath S (n=2013) DNA methylation age of human tissues and cell types. Genome Biology.2013, 14:R115. DOI: 10.1186/10.1186/gb-2013-14-10-r115 PMID: 24138928.
2016-07-13 | GSE72775 | GEO
Project description:Human DNA methylation Beadchip v1.2 was used to obtain n=113 Illumina DNA methylation array from whole blood samples. The main goal of the study was to measure the epigenetic age (also known as DNA methylation age) of subjects with a severe developmental disorder (known as syndrome X) and controls. To measure DNA methylation age, we used the epigenetic clock software described in Horvath S (n=2013) DNA methylation age of human tissues and cell types. Genome Biology.2013, 14:R115. DOI: 10.1186/10.1186/gb-2013-14-10-r115 PMID: 24138928.
2015-05-28 | GSE64495 | GEO
Project description:Human DNA methylation Beadchip v1.2 was used to profile 508 whole blood samples. The main goal of the study was to relate Parkinson's disease status to measures of epigenetic age acceleration based on DNA methylation data. Here we focus on Caucasian subjects. To measure DNA methylation age, we used the epigenetic clock software described in Horvath S (n=2013) DNA methylation age of human tissues and cell types. Genome Biology.2013, 14:R115. DOI: 10.1186/10.1186/gb-2013-14-10-r115 PMID: 24138928.
2018-09-04 | GSE72774 | GEO