Similar Datasets
Project description:We report a high-throughput and low-cost targeted bisulfite sequencing method called TIME-Seq for discovery and assay of DNA methylation clocks. Data is related to the initial publication of TIME-Seq clocks and shallow sequencing-based predictions using scAge. Demultiplexing information as well as more detailed metadata for each sample in the pools will be posted at: https://github.com/patricktgriffin/TIME-Seq
2023-10-12 | GSE232346 | GEO
Project description:In this study, we have optimized and directly compared epigenetic age predictors based on pyrosequencing, ddPCR and BBA-seq of specific age-associated regions. Bisulfite barcoded amplicon sequencing (BBA-seq) was performed on 9 genomic region of 77 human blood DNA and 11 genomic regions of 95 buccal swab DNA to measure age-associated regions for epigenetic age prediction. Furthermore, our data indicate that the correlation of age-associated DNAm with chronological age peaks close to CTCF binding sites. Age-associated DNAm is not coherently modified on individual DNA strands and this enabled alternative single-read age-predictors that reflect heterogeneity in epigenetic aging within a specimen.
2020-06-03 | GSE151641 | GEO
Project description:BackgroundAge-associated DNA methylation changes provide a promising biomarker for the aging process. While genome-wide DNA methylation profiles enable robust age-predictors by integration of many age-associated CG dinucleotides (CpGs), there are various alternative approaches for targeted measurements at specific CpGs that better support standardized and cost-effective high-throughput analysis.ResultsIn this study, we utilized 4647 Illumina BeadChip profiles of blood to select CpG sites that facilitate reliable age-predictions based on pyrosequencing. We demonstrate that the precision of DNA methylation measurements can be further increased with droplet digital PCR (ddPCR). In comparison, bisulfite barcoded amplicon sequencing (BBA-seq) gave slightly lower correlation between chronological age and DNA methylation at individual CpGs, while the age-predictions were overall relatively accurate. Furthermore, BBA-seq data revealed that the correlation of methylation levels with age at neighboring CpG sites follows a bell-shaped curve, often associated with a CTCF binding site. We demonstrate that within individual BBA-seq reads the DNA methylation at neighboring CpGs is not coherently modified, but reveals a stochastic pattern. Based on this, we have developed a new approach for epigenetic age predictions based on the binary sequel of methylated and non-methylated sites in individual reads, which reflects heterogeneity in epigenetic aging within a sample.ConclusionTargeted DNA methylation analysis at few age-associated CpGs by pyrosequencing, BBA-seq, and particularly ddPCR enables high precision of epigenetic age-predictions. Furthermore, we demonstrate that the stochastic evolution of age-associated DNA methylation patterns in BBA-seq data enables epigenetic clocks for individual DNA strands.
| S-EPMC7315536 | biostudies-literature
Project description:Age-associated DNA methylation reflects aspect of biological aging-therefore epigenetic clocks for mice can elucidate how the aging process in this model organism is affected by specific treatments or genetic background. Initially, age-predictors for mice were trained for genome-wide DNA methylation profiles and we have recently described a targeted assay based on pyrosequencing of DNA methylation at only three age-associated genomic regions. Here, we established alternative approaches using droplet digital PCR (ddPCR) and barcoded bisulfite amplicon sequencing (BBA-seq). At individual CG dinucleotides (CpGs) the correlation of DNA methylation with chronological age was slightly higher for pyrosequencing and ddPCR as compared to BBA-seq. On the other hand, BBA-seq revealed that neighboring CpGs tend to be stochastically modified at murine age-associated regions. Furthermore, the binary sequel of methylated and non-methylated CpGs in individual reads can be used for single-read predictions, which may reflect heterogeneity in epigenetic aging. In comparison to C57BL/6 mice the single-read age-predictions using BBA-seq were also accelerated in the shorter-lived DBA/2 mice, and in C57BL/6 mice with a lifespan quantitative trait locus of DBA/2 mice. Taken together, we describe alternative targeted methods for epigenetic age predictions that provide new perspectives for aging-intervention studies in mice.
| S-EPMC7775437 | biostudies-literature
Project description:Age-associated DNA methylation reflects aspect of biological aging - therefore epigenetic clocks for mice can help to elucidate the impact of treatments or genetic background on the aging process in this model organism. Initially, age-predictors for mice were trained for genome-wide DNA methylation profiles and we have recently described a targeted assay based on pyrosequencing of DNA methylation at only three CG dinucleotides (CpGs). Here, we have re-evaluated pyrosequencing approaches in comparison to droplet digital PCR (ddPCR) and barcoded bisulfite amplicon sequencing (BBA-seq). At individual CpGs the correlation of DNA methylation with chronological age was slightly higher for pyrosequencing and ddPCR as compared to BBA-seq. On the other hand, BBA-seq revealed that neighboring CpGs tend to be stochastically modified at murine age-associated regions. Furthermore, the binary sequel of methylated and non-methylated CpGs in individual reads can be used for single-read predictions, which may reflect heterogeneity in epigenetic aging. In comparison to C57BL/6 mice the epigenetic age-predictions using BBA-seq were also accelerated in the shorter-lived DBA/2 mice, and in C57BL/6 mice with a lifespan quantitative trait locus of DBA/2 mice. Taken together, we describe further optimized and alternative targeted methods to determine epigenetic clocks in mice.
2020-08-14 | GSE156193 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2024-04-19 | GSE247198 | GEO
Project description:Evaluation of biological age, as opposed to chronological age, is of high relevance for interventions to increase healthy ageing. Highly reproducible age-associated DNA methylation (DNAm) changes can be integrated into algorithms for epigenetic age predictions. These predictors have mostly been trained to correlate with chronological age, but they are also indicative for biological ageing. For example, accelerated epigenetic age of blood is associated with higher risk of all-cause mortality in later life. The perceived age of facial images (face-age) is also associated with all-cause mortality and other ageing-associated traits. In this study, we therefore tested the hypothesis that an epigenetic predictor for biological age might be trained on face-age as a surrogate for biological age rather than on chronological age. Our data demonstrate that facial ageing does not correlate with either the epigenetic clock or blood-based DNAm measures.
| S-EPMC6225560 | biostudies-literature
Project description:New Targeted Approaches for Epigenetic Age Predictions
| PRJNA636753 | ENA
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2016-03-28 | E-GEOD-77634 | biostudies-arrayexpress
Project description:IntroductionBrain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age-matched healthy controls.MethodsWe used densely sampled T1-weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pretrained deep learning model.ResultsWe found small within-subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data and inconclusive effects of scan quality.ConclusionThe absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.
| S-EPMC10570486 | biostudies-literature