Research paperDevelopment of a methylation marker set for forensic age estimation using analysis of public methylation data and the Agena Bioscience EpiTYPER system
Graphical abstract
Introduction
The age of a DNA donor is a key individual characteristic that can potentially be used as part of forensic DNA phenotyping tests (FDP) by analyzing the methylation level of epigenetic markers detected in DNA recovered from the crime scene. In recent years, FDP has received much attention as an effective way to enhance DNA-based intelligence from the prediction of a person’s physical appearance [1], [2]. Extensive research in this field has led to the development of DNA tests applicable to investigations that may lack any other leads [3], [4], [5]. Besides human pigmentation, other visible traits including facial morphology [6], stature [7] and hair shape [8] have all been proposed to provide useful data about an unidentified person. Prediction of externally visible characteristics such as hair colour [9], [10] or early onset male pattern baldness [11] are potentially highly informative but are hindered by a lack of accuracy related to changes in phenotypic expression as a person gets older. Therefore, inference of a person’s age can provide an important contribution to the following forensic analyses: i. to guide police investigations in the absence of eyewitness testimony or a national DNA database entry; ii. to help in the analysis of unidentified human remains; iii. to contribute to individual age estimation in legal disputes; and iv. to improve age-associated phenotype prediction.
Estimation of individual age based on progressive changes in biomolecules has been explored for some time [12]. However, previous studies of mitochondrial deletions, telomere shortening or aspartic acid racemization gave low age-predictive accuracy or technical difficulties. An improved prediction system was developed more recently by estimating the abundance of episomal signal joint T-cell receptor excision circles (sjTRECs) [13]. However, the most significant advances in forensic age-predictive tests have come from analysis of the methylation status of epigenetic CpG positions [14]. CpGs are dinucleotides that can be methylated by the addition of a methyl group derived from S-adenosyl-methionine (SAM) onto the C5 position of the cytosine residue. Genome-wide DNA hypomethylation of intronic and intergenic regions as well as localized DNA hypermethylation of CpG islands has been observed during the maturation and aging processes of most organisms [15]. The CpG methylation patterns in humans have been most extensively studied by applying dedicated genome-wide DNA arrays; principally the Illumina Infinium HumanMethylation450 BeadChip [16]. Consequently, a large body of human methylation pattern data has been generated, which has been compiled and can be accessed directly from public databases such as NCBI GEO [17]. Epigenetic studies using HumanMethylation450 data have been the basis for development of preliminary age-predictive tests using a high number of CpG sites (or CpG positions) [18], [19], [20], [21] and capable of predicting age with a deviation interval of ±5 years from the actual age. Recently, prediction models based on small-scale CpG assays have been explored in blood [22], [23], [24], [25], bloodstains [26], saliva [27], semen [28] and teeth [29]. However, some of these studies have small sample sizes covering a restricted range of ages between young and old, while the prediction frameworks tend to use linear regression models which cannot adjust the prediction deviation interval in those age ranges prone to show higher differences between predicted and actual age, notably amongst the most elderly.
In the studies described here, we selected a total of 177 CpGs in twenty-two candidate genomic regions from assessments of public data of 3702 control blood samples ranging from 19 to 101 years old, analyzed with the HumanMethylation450 chip. The CpG sites selected from each candidate region (here ‘sites’ denotes single CpGs or small clusters of CpGs) were analyzed with EpiTYPER technology in 725 European individuals. From these methylation analyses, a novel age prediction model using multivariate quantile regression analysis was built based on the seven most age-correlated DNA methylation markers and then tested on an independent set of 104 monozygotic twin samples.
Section snippets
DNA samples and quantification
A total of 3702 control blood samples from 19 to 101 years old were compiled from GSE40279, GSE55763 and GSE42861 NCBI GEO datasets (Illumina HumanMethylation450) for in silico discovery of age-correlated DNA methylation loci. For our own analyses, peripheral blood DNA samples from 725 unrelated European donors (354 males and 371 females from 18 to 104 years old) were obtained from the Spanish National DNA Bank Carlos III, University of Salamanca, selected to create a balanced distribution of
In silico selection of candidate CpGs
Table 1 summarizes the CpG sites selected from bibliographic searches. A total of eighteen CpG sites located in twelve genes (EDARADD, NPTX2, Tom1L1, ELN, NHLRC1, CCDC105, GPR25, RAB36, ASPA, ITGA2AB, PDE4C and ELOVL2) have been described as highly age-correlated markers and were therefore selected for further analysis. Significant age-correlated CpGs proposed by Bocklandt et al. [18] highlighted the additional use of cg05822532 from ELN and subsequently this CpG site was also included in the
Discussion
Forensic age estimation has received considerable attention in the last two years, since promising DNA methylation markers were first reported as highly informative age predictors. Knowledge of the age of the donor of contact traces can guide investigations either as a biological parameter by itself or to clarify predictions of physical traits that undergo changes with increasing age. We identified twenty-two genomic regions with 177 CpG sites from which the highest seven age-correlated were
Concluding remarks: further challenges
Although the age prediction model developed in our study provides improvements to previous outcomes, there are additional issues that require further exploration. First, as DNA methylation changes do not occur at a constant rate during a lifetime but accumulate rapidly up to adulthood, inference of age in the very young should be further studied in detail based on suitable age-correlated candidate CpG sites that have been recently reported [41]. Both EDARADD and ITGA2B were covered by our
Acknowledgements
AFA was supported by post-doctorate funding awarded by the Xunta de Galicia Spain (as part of the Plan Galego de Investigación, Innovación e Crecemento 2011–2015, Axudas de apoio á etapa de formación postdoutoral, Plan I2C). MVL was supported by the Ministry of Economy and Competitiveness, Spain (BIO2013-42188-R). AM received financial support from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 285487 (EUROFORGEN-NoE). The National DNA Bank Carlos III
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