scholarly journals Age Prediction of Human Based on DNA Methylation by Blood Tissues

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 870
Author(s):  
Jiansheng Zhang ◽  
Hongli Fu ◽  
Yan Xu
Keyword(s):  
Dna Methylation ◽  
Pearson Correlation ◽  
Close Correlation ◽  
Gradient Boosting ◽  
Tissue Samples ◽  
Regression Methods ◽  
Cpg Sites ◽  
Testing Dataset ◽  
Age Related ◽  
Better Than

In recent years, scientists have found a close correlation between DNA methylation and aging in epigenetics. With the in-depth research in the field of DNA methylation, researchers have established a quantitative statistical relationship to predict the individual ages. This work used human blood tissue samples to study the association between age and DNA methylation. We built two predictors based on healthy and disease data, respectively. For the health data, we retrieved a total of 1191 samples from four previous reports. By calculating the Pearson correlation coefficient between age and DNA methylation values, 111 age-related CpG sites were selected. Gradient boosting regression was utilized to build the predictive model and obtained the R2 value of 0.86 and MAD of 3.90 years on testing dataset, which were better than other four regression methods as well as Horvath’s results. For the disease data, 354 rheumatoid arthritis samples were retrieved from a previous study. Then, 45 CpG sites were selected to build the predictor and the corresponded MAD and R2 were 3.11 years and 0.89 on the testing dataset respectively, which showed the robustness of our predictor. Our results were better than the ones from other four regression methods. Finally, we also analyzed the twenty-four common CpG sites in both healthy and disease datasets which illustrated the functional relevance of the selected CpG sites.

scholarly journals Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 424 ◽  
Author(s):  
Xingyan Li ◽  
Weidong Li ◽  
Yan Xu
Keyword(s):  
Dna Methylation ◽  
Pearson Correlation ◽  
Training Data ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Independent Dataset ◽  
Blood Samples ◽  
Cpg Sites ◽  
Age Related ◽  
Age Prediction

All tissues of organisms will become old as time goes on. In recent years, epigenetic investigations have found that there is a close correlation between DNA methylation and aging. With the development of DNA methylation research, a quantitative statistical relationship between DNA methylation and different ages was established based on the change rule of methylation with age, it is then possible to predict the age of individuals. All the data in this work were retrieved from the Illumina HumanMethylation BeadChip platform (27K or 450K). We analyzed 16 sets of healthy samples and 9 sets of diseased samples. The healthy samples included a total of 1899 publicly available blood samples (0–103 years old) and the diseased samples included 2395 blood samples. Six age-related CpG sites were selected through calculating Pearson correlation coefficients between age and DNA methylation values. We built a gradient boosting regressor model for these age-related CpG sites. 70% of the data was randomly selected as training data and the other 30% as independent data in each dataset for 25 runs in total. In the training dataset, the healthy samples showed that the correlation between predicted age and DNA methylation was 0.97, and the mean absolute deviation (MAD) was 2.72 years. In the independent dataset, the MAD was 4.06 years. The proposed model was further tested using the diseased samples. The MAD was 5.44 years for the training dataset and 7.08 years for the independent dataset. Furthermore, our model worked well when it was applied to saliva samples. These results illustrated that the age prediction based on six DNA methylation markers is very effective using the gradient boosting regressor.

scholarly journals Age-related differences in monocyte DNA methylation and immune function in healthy Kenyan adults and children

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Katherine R. Dobbs ◽  
Paula Embury ◽  
Emmily Koech ◽  
Sidney Ogolla ◽  
Stephen Munga ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Young Adults ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Innate Immune ◽  
Inflammatory Gene Expression ◽  
Cpg Sites ◽  
Age Related ◽  
Adults And Children

Abstract Background Age-related changes in adaptive and innate immune cells have been associated with a decline in effective immunity and chronic, low-grade inflammation. Epigenetic, transcriptional, and functional changes in monocytes occur with aging, though most studies to date have focused on differences between young adults and the elderly in populations with European ancestry; few data exist regarding changes that occur in circulating monocytes during the first few decades of life or in African populations. We analyzed DNA methylation profiles, cytokine production, and inflammatory gene expression profiles in monocytes from young adults and children from western Kenya. Results We identified several hypo- and hyper-methylated CpG sites in monocytes from Kenyan young adults vs. children that replicated findings in the current literature of differential DNA methylation in monocytes from elderly persons vs. young adults across diverse populations. Differentially methylated CpG sites were also noted in gene regions important to inflammation and innate immune responses. Monocytes from Kenyan young adults vs. children displayed increased production of IL-8, IL-10, and IL-12p70 in response to TLR4 and TLR2/1 stimulation as well as distinct inflammatory gene expression profiles. Conclusions These findings complement previous reports of age-related methylation changes in isolated monocytes and provide novel insights into the role of age-associated changes in innate immune functions.

scholarly journals MapReduce-Based Parallel Genetic Algorithm for CpG-Site Selection in Age Prediction

Genes ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 969
Author(s):  
Zahra Momeni ◽  
Mohammad Saniee Abadeh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Gradient Boosting ◽  
Range Data ◽  
Parallel Genetic Algorithm ◽  
Data Parallel ◽  
Cpg Sites ◽  
Age Related ◽  
Age Range ◽  
Age Prediction

Genomic biomarkers such as DNA methylation (DNAm) are employed for age prediction. In recent years, several studies have suggested the association between changes in DNAm and its effect on human age. The high dimensional nature of this type of data significantly increases the execution time of modeling algorithms. To mitigate this problem, we propose a two-stage parallel algorithm for selection of age related CpG-sites. The algorithm first attempts to cluster the data into similar age ranges. In the next stage, a parallel genetic algorithm (PGA), based on the MapReduce paradigm (MR-based PGA), is used for selecting age-related features of each individual age range. In the proposed method, the execution of the algorithm for each age range (data parallel), the evaluation of chromosomes (task parallel) and the calculation of the fitness function (data parallel) are performed using a novel parallel framework. In this paper, we consider 16 different healthy DNAm datasets that are related to the human blood tissue and that contain the relevant age information. These datasets are combined into a single unioned set, which is in turn randomly divided into two sets of train and test data with a ratio of 7:3, respectively. We build a Gradient Boosting Regressor (GBR) model on the selected CpG-sites from the train set. To evaluate the model accuracy, we compared our results with state-of-the-art approaches that used these datasets, and observed that our method performs better on the unseen test dataset with a Mean Absolute Deviation (MAD) of 3.62 years, and a correlation (R2) of 95.96% between age and DNAm. In the train data, the MAD and R2 are 1.27 years and 99.27%, respectively. Finally, we evaluate our method in terms of the effect of parallelization in computation time. The algorithm without parallelization requires 4123 min to complete, whereas the parallelized execution on 3 computing machines having 32 processing cores each, only takes a total of 58 min. This shows that our proposed algorithm is both efficient and scalable.

scholarly journals Blood-based epigenetic estimators of chronological age in human adults using DNA methylation data from the Illumina MethylationEPIC array

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yunsung Lee ◽  
Kristine L. Haftorn ◽  
William R. P. Denault ◽  
Haakon E. Nustad ◽  
Christian M. Page ◽  
...  
Keyword(s):  
Dna Methylation ◽  
High Precision ◽  
Pearson Correlation ◽  
Gene Expression Omnibus ◽  
Chronological Age ◽  
Large Sample Size ◽  
Training Set ◽  
Age Related ◽  
Illumina Humanmethylation450 Beadchip ◽  
Human Adults

Abstract Background Epigenetic clocks have been recognized for their precise prediction of chronological age, age-related diseases, and all-cause mortality. Existing epigenetic clocks are based on CpGs from the Illumina HumanMethylation450 BeadChip (450 K) which has now been replaced by the latest platform, Illumina MethylationEPIC BeadChip (EPIC). Thus, it remains unclear to what extent EPIC contributes to increased precision and accuracy in the prediction of chronological age. Results We developed three blood-based epigenetic clocks for human adults using EPIC-based DNA methylation (DNAm) data from the Norwegian Mother, Father and Child Cohort Study (MoBa) and the Gene Expression Omnibus (GEO) public repository: 1) an Adult Blood-based EPIC Clock (ABEC) trained on DNAm data from MoBa (n = 1592, age-span: 19 to 59 years), 2) an extended ABEC (eABEC) trained on DNAm data from MoBa and GEO (n = 2227, age-span: 18 to 88 years), and 3) a common ABEC (cABEC) trained on the same training set as eABEC but restricted to CpGs common to 450 K and EPIC. Our clocks showed high precision (Pearson correlation between chronological and epigenetic age (r) > 0.94) in independent cohorts, including GSE111165 (n = 15), GSE115278 (n = 108), GSE132203 (n = 795), and the Epigenetics in Pregnancy (EPIPREG) study of the STORK Groruddalen Cohort (n = 470). This high precision is unlikely due to the use of EPIC, but rather due to the large sample size of the training set. Conclusions Our ABECs predicted adults’ chronological age precisely in independent cohorts. As EPIC is now the dominant platform for measuring DNAm, these clocks will be useful in further predictions of chronological age, age-related diseases, and mortality.

scholarly journals Age-related Differences in Monocyte DNA Methylation and Immune Function in Healthy Kenyan Adults and Children

2020 ◽  
Author(s):  
Katherine Rose Dobbs ◽  
Paula Embury ◽  
Emmily Koech ◽  
Sidney Ogolla ◽  
Stephen Munga ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Young Adults ◽  
Innate Immune ◽  
European Ancestry ◽  
Inflammatory Gene Expression ◽  
Inflammatory Gene ◽  
Cpg Sites ◽  
Age Related ◽  
Adults And Children

Abstract Background: Age-related changes in adaptive and innate immune cells have been associated with a decline in effective immunity and chronic, low-grade inflammation. Epigenetic, transcriptional, and functional changes in monocytes occur with aging, though most studies to date have focused on differences between young adults and the elderly in populations with European ancestry; few data exist regarding changes that occur in circulating monocytes during the first few decades of life or in African populations. We analyzed DNA methylation profiles, cytokine production, and inflammatory gene expression profi 24 les in monocytes from young adults and children from western Kenya.Results: We identified several hypo- and hyper-methylated CpG sites in monocytes from Kenyan young adults vs. children that replicated findings in the current literature of differential DNA methylation in monocytes from elderly persons vs. young adults across diverse populations. Differentially methylated CpG sites were also noted in gene regions important to inflammation and innate immune responses. Monocytes from Kenyan young adults vs. children displayed increased production of IL-8, IL-10, and IL-12p70 in response to TLR4 and TLR2/1 stimulation as well as distinct inflammatory gene expression profiles.Conclusions: These findings complement previous reports of age-related methylation changes in isolated monocytes and provide novel insights into the role of age-associated changes in innate immune functions.

scholarly journals High-resolution analyses of human sperm dynamic methylome reveal thousands of novel age-related epigenetic alterations

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Mingju Cao ◽  
Xiaojian Shao ◽  
Peter Chan ◽  
Warren Cheung ◽  
Tony Kwan ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Human Sperm ◽  
Distal Region ◽  
Epigenetic Alterations ◽  
Transcription Start Sites ◽  
Neurodevelopmental Disease ◽  
Cpg Sites ◽  
Age Related ◽  
Sperm Dna ◽  
Sperm Dna Methylation

Abstract Background Children of aged fathers are at a higher risk of developing mental disorders. Alterations in sperm DNA methylation have been implicated as a potential cause. However, age-dependent modifications of the germ cells’ epigenome remain poorly understood. Our objective was to assess the DNA methylation profile of human spermatozoa during aging. Results We used a high throughput, customized methylC-capture sequencing (MCC-seq) approach to characterize the dynamic DNA methylation in spermatozoa from 94 fertile and infertile men, who were categorized as young, 48 men between 18–38 years or old 46 men between 46–71 years. We identified more than 150,000 age-related CpG sites that are significantly differentially methylated among 2.65 million CpG sites covered. We conducted machine learning using our dataset to predict the methylation age of subjects; the age prediction accuracy based on our assay provided a more accurate prediction than that using the 450 K chip approach. In addition, we found that there are more hypermethylated (62%) than hypomethylated (38%) CpG sites in sperm of aged men, corresponding to 798 of total differential methylated regions (DMRs), of which 483 are hypermethylated regions (HyperDMR), and 315 hypomethylated regions (HypoDMR). Moreover, the distribution of age-related hyper- and hypomethylated CpGs in sperm is not random; the CpG sites that were hypermethylated with advanced age were frequently located in the distal region to genes, whereas hypomethylated sites were near to gene transcription start sites (TSS). We identified a high density of age-associated CpG changes in chromosomes 4 and 16, particularly HyperDMRs with localized clusters, the chr4 DMR cluster overlaps PGC1α locus, a protein involved in metabolic aging and the chr16 DMR cluster overlaps RBFOX1 locus, a gene implicated in neurodevelopmental disease. Gene ontology analysis revealed that the most affected genes by age were associated with development, neuron projection, differentiation and recognition, and behaviour, suggesting a potential link to the higher risk of neurodevelopmental disorders in children of aged fathers. Conclusion We identified thousands of age-related and sperm-specific epigenetic alterations. These findings provide novel insight in understanding human sperm DNA methylation dynamics during paternal aging, and the subsequently affected genes potentially related to diseases in offspring.

Biological age is better than chronological as predictor of 3-month outcome in ischemic stroke

Neurology ◽  
2017 ◽  
Vol 89 (8) ◽  
pp. 830-836 ◽  
Author(s):  
Carolina Soriano-Tárraga ◽  
Marina Mola-Caminal ◽  
Eva Giralt-Steinhauer ◽  
Angel Ois ◽  
Ana Rodríguez-Campello ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Ischemic Stroke ◽  
Biological Age ◽  
Bivariate Analysis ◽  
Stroke Severity ◽  
Stroke Outcome ◽  
Explanatory Variables ◽  
Age Related ◽  
Better Than ◽  
Independent Cohort

Objective:To analyze the effect of age-related DNA methylation changes in multiple cytosine-phosphate-guanine (CpG) sites (biological age [b-age]) on patient outcomes at 3 months after an ischemic stroke.Methods:We included 511 patients with first-ever acute ischemic stroke assessed at Hospital del Mar (Barcelona, Spain) as the discovery cohort. Demographic and clinical data, including chronological age (c-age), vascular risk factors, initial stroke severity, recanalization treatment, and previous and 3-month modified Rankin Scale (p-mRS and 3-mRS, respectively) were registered. B-age was estimated with an algorithm, based on DNA methylation in 71 CpGs. Bivariate analysis determined variables associated with 3-mRS for inclusion in ordinal multivariate analysis.Results:After ordinal regressions for 3-month ischemic stroke outcome (3-mRS), b-age was associated with outcome (odds ratio 1.04 [95% confidence interval 1.01–1.07]), nullifying c-age. Stepwise regression kept b-age, basal NIH Stroke Scale, sex, p-mRS, and recanalization treatment as better explanatory variables, instead of c-age. These results were successfully replicated in an independent cohort.Conclusions:B-age, estimated by DNA methylation, is an independent predictor of ischemic stroke outcome regardless of chronological years.

scholarly journals AltumAge: A Pan-Tissue DNA-Methylation Epigenetic Clock Based on Deep Learning

2021 ◽  
Author(s):  
Lucas Paulo de Lima ◽  
Louis R Lapierre ◽  
Ritambhara Singh
Keyword(s):  
Dna Methylation ◽  
Deep Learning ◽  
Placental Tissue ◽  
Training Data ◽  
Data Sets ◽  
Epigenetic Clock ◽  
Neural Network Approach ◽  
Cpg Sites ◽  
Age Related ◽  
Model Predictions

Several age predictors based on DNA methylation, dubbed epigenetic clocks, have been created in recent years. Their accuracy and potential for generalization vary widely based on the training data. Here, we gathered 143 publicly available data sets from several human tissues to develop AltumAge, a highly accurate and precise age predictor based on deep learning. Compared to Horvath's 2013 model, AltumAge performs better across both normal and malignant tissues and is more generalizable to new data sets. Interestingly, it can predict gestational week from placental tissue with low error. Lastly, we used deep learning interpretation methods to learn which methylation sites contributed to the final model predictions. We observed that while most important CpG sites are linearly related to age, some highly-interacting CpG sites can influence the relevance of such relationships. We studied the associated genes of these CpG sites and found literary evidence of their involvement in age-related gene regulation. Using chromatin annotations, we observed that the CpG sites with the highest contribution to the model predictions were related to heterochromatin and gene regulatory regions in the genome. We also found age-related KEGG pathways for genes containing these CpG sites. In general, neural networks are better predictors due to their ability to capture complex feature interactions compared to the typically used regularized linear regression. Altogether, our neural network approach provides significant improvement and flexibility to current epigenetic clocks without sacrificing model interpretability.

scholarly journals Correlation of Infinium HumanMethylation450K and MethylationEPIC BeadChip arrays in cartilage

2019 ◽  
Author(s):  
Kathleen Cheung ◽  
Marjolein J. Burgers ◽  
David A. Young ◽  
Simon Cockell ◽  
Louise N. Reynard
Keyword(s):  
Dna Methylation ◽  
Correlation Coefficient ◽  
Whole Blood ◽  
Pearson Correlation ◽  
Cell Types ◽  
Poor Correlation ◽  
450K Array ◽  
Human Cartilage ◽  
Cpg Sites ◽  
Cpg Site

AbstractBackgroundDNA methylation of CpG sites is commonly measured using Illumina Infinium BeadChip platforms. The Infinium MethylationEPIC array has replaced the Infinium Methylation450K array. The two arrays use the same technology, with the EPIC array assaying 865859 CpG sites, almost double the number of sites present on the 450K array. In this study, we compare DNA methylation values of shared CpGs of the same human cartilage samples assayed using both platforms.MethodsDNA methylation was measured in 21 human cartilage samples using the Illumina Infinium Methylation450K BeadChip and the Infinium methylationEPIC array. Additional matched 450K and EPIC data in whole tumour and whole blood were downloaded from GEO GSE92580 and GSE86833 respectively. Data were processed using the Bioconductor package Minfi. Additionally, DNA methylation of six CpG sites was validated for the same 21 cartilage samples by use of pyrosequencing.ResultsIn cartilage samples, overall sample correlations between methylation values generated by the two arrays were high (Pearson correlation coefficient r > 0.96). However, 50.5% of CpG sites showed poor correlation (r < 0.2) between arrays. Sites with limited variance and with either very high or very low methylation levels in cartilage exhibited lower correlation values, corroborating prior studies in whole blood. Bisulfite pyrosequencing did not highlight one array as generating more accurate methylation values that the other. For a specific CpG site, the array methylation correlation coefficient differed between cartilage, tumour and whole blood, reflecting the difference in methylation variance between cell types. These patterns can be observed across different tissues with different CpG site variances. When performing differential methylation analysis, the mean probe correlation co-efficient increased with increasing Δβ threshold used.ConclusionCpG sites with low variability within a tissue showed poor reproducibility between arrays. However, variance and thus reproducibility differs across different tissue types. Therefore, researchers should be cautious when analysing methylation of CpG sites that show low methylation variance within the cell type of interest, regardless of platform or method used to assay methylation.

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