Improved age determination of blood and teeth samples using a selected set of DNA methylation markers

Measuring Animal Age with DNA Methylation: From Humans to Wild Animals

DNA methylation (DNAm) is a key mechanism for regulating gene expression in animals and levels are known to change with age. Recent studies have used DNAm changes as a biomarker to estimate chronological age in humans and these techniques are now also being applied to domestic and wild animals. Animal age is widely used to track ongoing changes in ecosystems, however chronological age information is often unavailable for wild animals. An ability to estimate age would lead to improved monitoring of (i) population trends and status and (ii) demographic properties such as age structure and reproductive performance. Recent studies have revealed new examples of DNAm age association in several new species increasing the potential for developing DNAm age biomarkers for a broad range of wild animals. Emerging technologies for measuring DNAm will also enhance our ability to study age-related DNAm changes and to develop new molecular age biomarkers.

Chronological age prediction based on DNA methylation: Massive parallel sequencing and random forest regression

The use of DNA methylation (DNAm) to obtain additional information in forensic investigations showed to be a promising and increasing field of interest. Prediction of the chronological age based on age-dependent changes in the DNAm of specific CpG sites within the genome is one such potential application. Here we present an age-prediction tool for whole blood based on massive parallel sequencing (MPS) and a random forest machine learning algorithm. MPS allows accurate DNAm determination of pre-selected markers and neighboring CpG-sites to identify the best age-predictive markers for the age-prediction tool. 15 age-dependent markers of different loci were initially chosen based on publicly available 450K microarray data, and 13 finally selected for the age tool based on MPS (DDO, ELOVL2, F5, GRM2, HOXC4, KLF14, LDB2, MEIS1-AS3, NKIRAS2, RPA2, SAMD10, TRIM59, ZYG11A). Whole blood samples of 208 individuals were used for training of the algorithm and a further 104 individuals were used for model evaluation (age 18-69). In the case of KLF14, LDB2, SAMD10, and GRM2, neighboring CpG sites and not the initial 450K sites were chosen for the final model. Cross-validation of the training set leads to a mean absolute deviation (MAD) of 3.21 years and a root-mean square error (RMSE) of 3.97 years. Evaluation of model performance using the test set showed a comparable result (MAD 3.16 years, RMSE 3.93 years). A reduced model based on only the top 4 markers (ELOVL2, F5, KLF14, and TRIM59) resulted in a RMSE of 4.19 years and MAD of 3.24 years for the test set (cross validation training set: RMSE 4.63 years, MAD 3.64 years). The amplified region was additionally investigated for occurrence of SNPs in case of an aberrant DNAm result, which in some cases can be an indication for a deviation in DNAm. Our approach uncovered well-known DNAm age-dependent markers, as well as additional new age-dependent sites for improvement of the model, and allowed the creation of a reliable and accurate epigenetic tool for age-prediction without restriction to a linear change in DNAm with age.

DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups

Improving accuracy of the available predictive DNA methods is important for their wider use in routine forensic work. Information on age in the process of identification of an unknown individual may provide important hints that can speed up the process of investigation. DNA methylation markers have been demonstrated to provide accurate age estimation in forensics, but there is growing evidence that DNA methylation can be modified by various factors including diseases. We analyzed DNA methylation profile in five markers from five different genes (ELOVL2, C1orf132, KLF14, FHL2, and TRIM59) used for forensic age prediction in three groups of individuals with diagnosed medical conditions. The obtained results showed that the selected age-related CpG sites have unchanged age prediction capacity in the group of late onset Alzheimer’s disease patients. Aberrant hypermethylation and decreased prediction accuracy were found for TRIM59 and KLF14 markers in the group of early onset Alzheimer’s disease suggesting accelerated aging of patients. In the Graves’ disease patients, altered DNA methylation profile and modified age prediction accuracy were noted for TRIM59 and FHL2 with aberrant hypermethylation observed for the former and aberrant hypomethylation for the latter. Our work emphasizes high utility of the ELOVL2 and C1orf132 markers for prediction of chronological age in forensics by showing unchanged prediction accuracy in individuals affected by three diseases. The study also demonstrates that artificial neural networks could be a convenient alternative for the forensic predictive DNA analyses.

DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing

The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person's lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2-90 years) analysed with Illumina's genome-wide methylation platforms (27K/450K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R2=0.92, mean absolute error (MAE)=4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R2=0.96) with a MAE=3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes NHLRC1, SCGN and CSNK1D. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE=7.1 years) and a cohort of 1011 disease state individuals (MAE=7.2 years). Furthermore, we highlighted the age markers' potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R2=0.96) with a MAE=3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next generation sequencing (NGS)-based method able to quantify the methylation status of the selected 16 CpG sites was developed using the Illumina MiSeq® platform. The method was validated using DNA standards of known methylation levels and the age prediction accuracy has been initially assessed in a set of 46 whole blood samples. Although the resulted prediction accuracy using the NGS data was lower compared to the original model (MAE=7.5years), it is expected that future optimization of our strategy to account for technical variation as well as increasing the sample size will improve both the prediction accuracy and reproducibility.

Analysis of DNA methylation of potential age-related methylation sites in canine peripheral blood leukocytes

Reliable methodology for predicting the age of mature dogs is currently unavailable. In this study, amplicon sequencing of 50 blood samples obtained from diseased dogs was used to measure methylation in seven DNA regions. Significant correlations between methylation level and age were identified in four of the seven regions. These four regions were then tested in samples from 31 healthy toy poodles, and correlations were detected in two regions. The age of another 11 dogs was predicted using data from the diseased dogs and the healthy poodles. The mean difference between the actual and calculated ages was 34.3 and 23.1 months, respectively. Further research is needed to identify additional sites of age-related methylation and allow accurate age prediction in dogs.

Forensic DNA methylation profiling from evidence material for investigative leads

DNA methylation is emerging as an attractive marker providing investigative leads to solve crimes in forensic genetics. The identification of body fluids that utilizes tissue-specific DNA methylation can contribute to solving crimes by predicting activity related to the evidence material. The age estimation based on DNA methylation is expected to reduce the number of potential suspects, when the DNA profile from the evidence does not match with any known person, including those stored in the forensic database. Moreover, the variation in DNA implicates environmental exposure, such as cigarette smoking and alcohol consumption, thereby suggesting the possibility to be used as a marker for predicting the lifestyle of potential suspect. In this review, we describe recent advances in our understanding of DNA methylation variations and the utility of DNA methylation as a forensic marker for advanced investigative leads from evidence materials. [BMB Reports 2016; 49(7): 359-369]

DNA methylation-based variation between human populations

Several studies have proved that DNA methylation affects regulation of gene expression and development. Epigenome-wide studies have reported variation in methylation patterns between populations, including Caucasians, non-Caucasians (Blacks), Hispanics, Arabs, and numerous populations of the African continent. Not only has DNA methylation differences shown to impact externally visible characteristics, but is also a potential biomarker for underlying racial health disparities between human populations. Ethnicity-related methylation differences set their mark during early embryonic development. Genetic variations, such as single-nucleotide polymorphisms and environmental factors, such as age, dietary folate, socioeconomic status, and smoking, impacts DNA methylation levels, which reciprocally impacts expression of phenotypes. Studies show that it is necessary to address these external influences when attempting to differentiate between populations since the relative impacts of these factors on the human methylome remain uncertain. The present review summarises several reported attempts to establish the contribution of differential DNA methylation to natural human variation, and shows that DNA methylation could represent new opportunities for risk stratification and prevention of several diseases amongst populations world-wide. Variation of methylation patterns between human populations is an exciting prospect which inspires further valuable research to apply the concept in routine medical and forensic casework. However, trans-generational inheritance needs to be quantified to decipher the proportion of variation contributed by DNA methylation. The future holds thorough evaluation of the epigenome to understand quantification, heritability, and the effect of DNA methylation on phenotypes. In addition, methylation profiling of the same ethnic groups across geographical locations will shed light on conserved methylation differences in populations.

Epigenome-wide differential DNA methylation between HIV-infected and uninfected individuals

Epigenetic control of human immunodeficiency virus-1 (HIV-1) genes is critical for viral integration and latency. However, epigenetic changes in the HIV-1-infected host genome have not been well characterized. Here, we report the first large-scale epigenome-wide association study of DNA methylation for HIV-1 infection. We recruited HIV-infected (n = 261) and uninfected (n = 117) patients from the Veteran Aging Cohort Study (VACS) and all samples were profiled for 485,521 CpG sites in DNA extracted from the blood. After adjusting for cell type and clinical confounders, we identified 20 epigenome-wide significant CpGs for HIV-1 infection. Importantly, 2 CpGs in the promoter of the NLR family, CARD domain containing gene 5 (NLRC5), a key regulator of major histocompatibility complex class I gene expression, showed significantly lower methylation in HIV-infected subjects than in uninfected subjects (cg07839457: t = −6.03, P_nominal = 4.96 × 10⁻⁹; cg16411857: t = −7.63, P_nominal = 3.07 × 10⁻¹³). Hypomethylation of these 2 CpGs was replicated in an independent sample (GSE67705: cg07839457: t = −4.44, P_nominal = 1.61 × 10⁻⁵; cg16411857: t = −5.90; P = 1.99 × 10⁻⁸). Methylation of these 2 CpGs in NLRC5 was negatively correlated with viral load in the 2 HIV-infected samples (cg07839457: P = 1.8 × 10⁻⁴; cg16411857: P = 0.03 in the VACS; and cg07839457: P = 0.04; cg164111857: P = 0.01 in GSE53840). Our findings demonstrate that differential DNA methylation is associated with HIV infection and suggest the involvement of a novel host gene, NLRC5, in HIV pathogenesis.

Pubertal development in healthy children is mirrored by DNA methylation patterns in peripheral blood

Puberty marks numerous physiological processes which are initiated by central activation of the hypothalamic–pituitary–gonadal axis, followed by development of secondary sexual characteristics. To a large extent, pubertal timing is heritable, but current knowledge of genetic polymorphisms only explains few months in the large inter-individual variation in the timing of puberty. We have analysed longitudinal genome-wide changes in DNA methylation in peripheral blood samples (n = 102) obtained from 51 healthy children before and after pubertal onset. We show that changes in single methylation sites are tightly associated with physiological pubertal transition and altered reproductive hormone levels. These methylation sites cluster in and around genes enriched for biological functions related to pubertal development. Importantly, we identified that methylation of the genomic region containing the promoter of TRIP6 was co-ordinately regulated as a function of pubertal development. In accordance, immunohistochemistry identified TRIP6 in adult, but not pre-pubertal, testicular Leydig cells and circulating TRIP6 levels doubled during puberty. Using elastic net prediction models, methylation patterns predicted pubertal development more accurately than chronological age. We demonstrate for the first time that pubertal attainment of secondary sexual characteristics is mirrored by changes in DNA methylation patterns in peripheral blood. Thus, modulations of the epigenome seem involved in regulation of the individual pubertal timing.

Development of a methylation marker set for forensic age estimation using analysis of public methylation data and the Agena Bioscience EpiTYPER system

Individual age estimation has the potential to provide key information that could enhance and extend DNA intelligence tools. Following predictive tests for externally visible characteristics developed in recent years, prediction of age could guide police investigations and improve the assessment of age-related phenotype expression patterns such as hair colour changes and early onset of male pattern baldness. DNA methylation at CpG positions has emerged as the most promising DNA tests to ascertain the individual age of the donor of a biological contact trace. Although different methodologies are available to detect DNA methylation, EpiTYPER technology (Agena Bioscience, formerly Sequenom) provides useful characteristics that can be applied as a discovery tool in localized regions of the genome. In our study, a total of twenty-two candidate genomic regions, selected from the assessment of publically available data from the Illumina HumanMethylation 450 BeadChip, had a total of 177 CpG sites with informative methylation patterns that were subsequently investigated in detail. From the methylation analyses made, a novel age prediction model based on a multivariate quantile regression analysis was built using the seven highest age-correlated loci of ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, C1orf132 and chr16:85395429. The detected methylation levels in these loci provide a median absolute age prediction error of ±3.07years and a percentage of prediction error relative to the age of 6.3%. We report the predictive performance of the developed model using cross validation of a carefully age-graded training set of 725 European individuals and a test set of 52 monozygotic twin pairs. The multivariate quantile regression age predictor, using the CpG sites selected in this study, has been placed in the open-access Snipper forensic classification website.