A Ge.F.I. Collaborative Study: Evaluating Reproducibility and Accuracy of a DNA-Methylation-Based Age-Predictive Assay for Routine Implementation in Forensic Casework.

The increasing interest in DNA methylation (DNAm) analysis within the forensic scientific community prompted a collaborative project by Ge.F.I. (Genetisti Forensi Italiani). The study evaluated a standardized bisulfite conversion-based Single Base Extension (SBE) protocol for the analysis of the methylation levels at five age-predictive loci (ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59). The study encompassed three phases: (1) setting up and validating the protocol to ensure consistency and reproducibility; (2) comparing fresh peripheral blood with blood spots; and (3) evaluating sources of intra- and inter-laboratory variability. Samples from 22 Italian volunteers were analyzed by 6 laboratories in replicates for a total of 528 records. From phase I emerged that the choice of genetic sequencer significantly contributed to inter-laboratory data variation, resulting in separate regression analyses performed for each laboratory. In phase II, blood spots were found to be a reliable source for DNAm analysis, despite exhibiting increased experimental variation compared to fresh peripheral blood. In phase III, a strong correlation between the individual's predicted and true ages was observed across different laboratories. Analysis of variance (ANOVA) of the residuals indicated that one-third of the total variance could be attributed to laboratory-specific factors, whereas two-thirds could be attributed to inter-individual biological differences. The leave-one-out cross-validation (LOO-CV) method yielded an overall mean absolute deviation (MAD) value of 4.41 years, with an average 95% confidence interval of 5.24 years. Stepwise regression analysis proved that a restricted model (ELOVL2, C1orf132/MIR29B2C, and TRIM59) produced results virtually indistinguishable from the five-loci model. Additionally, the analysis of samples in replicates greatly improved the fit of the regression model, balancing the slight effects of intra-laboratory variability. In conclusion, the bisulfite conversion-based SBE protocol, combined with replicate analysis and in-lab calibration of a regression-prediction model, proves to be a reliable and easily implementable method for age prediction in forensic laboratories.

Onofri M ，Alessandrini F ，Aneli S ，Buscemi L ，Chierto E ，Fabbri M ，Fattorini P ，Garofano P ，Gentile F ，Presciuttini S ，Previderè C ，Robino C ，Severini S ，Tommolini F ，Tozzo P ，Verzeletti A ，Carnevali E ... -  《-》

Age estimation of burnt human remains through DNA methylation analysis.

Grignani P ，Bertoglio B ，Monti MC ，Cuoghi Costantini R ，Ricci U ，Onofri M ，Fattorini P ，Previderè C ... -  《-》

Molecular age prediction using skull bone samples from individuals with and without signs of decomposition: a multivariate approach combining analysis of posttranslational protein modifications and DNA methylation.

The prediction of the chronological age of a deceased individual at time of death can provide important information in case of unidentified bodies. The methodological possibilities in these cases depend on the availability of tissues, whereby bones are preserved for a long time due to their mineralization under normal environmental conditions. Age-dependent changes in DNA methylation (DNAm) as well as the accumulation of pentosidine (Pen) and D-aspartic acid (D-Asp) could be useful molecular markers for age prediction. A combination of such molecular clocks into one age prediction model seems favorable to minimize inter- and intra-individual variation. We therefore developed (I) age prediction models based on the three molecular clocks, (II) examined the improvement of age prediction by combination, and (III) investigated if samples with signs of decomposition can also be examined using these three molecular clocks. Skull bone from deceased individuals was collected to obtain a training dataset (n = 86), and two independent test sets (without signs of decomposition: n = 44, with signs of decomposition: n = 48). DNAm of 6 CpG sites in ELOVL2, KLF14, PDE4C, RPA2, TRIM59 and ZYG11A was analyzed using massive parallel sequencing (MPS). The D-Asp and Pen contents were analyzed by high performance liquid chromatography (HPLC). Age prediction models based on ridge regression were developed resulting in mean absolute errors (MAEs)/root mean square errors (RMSE) of 5.5years /6.6 years (DNAm), 7.7 years /9.3 years (Pen) and 11.7 years /14.6 years (D-Asp) in the test set. Unsurprisingly, a general lower accuracy for the DNAm, D-Asp, and Pen models was observed in samples from decomposed bodies (MAE: 7.4-11.8 years, RMSE: 10.4-15.4 years). This reduced accuracy could be caused by multiple factors with different impact on each molecular clock. To acknowledge general changes due to decomposition, a pilot model for a possible age prediction based on the decomposed samples as training set improved the accuracy evaluated by leave-one-out-cross validation (MAE: 6.6-12 years, RMSE: 8.1-15.9 years). The combination of all three molecular age clocks did reveal comparable MAE and RMSE results to the pure analysis of the DNA methylation for the test set without signs of decomposition. However, an improvement by the combination of all three clocks was possible for the decomposed samples, reducing especially the deviation in case of outliers in samples with very high decomposition and low DNA content. The results demonstrate the general potential in a combined analysis of different molecular clocks in specific cases.

Becker J ，Bühren V ，Schmelzer L ，Reckert A ，Eickhoff SB ，Ritz S ，Naue J ... -  《-》

Erratum: Eyestalk Ablation to Increase Ovarian Maturation in Mud Crabs.

《Jove-Journal of Visualized Experiments》

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.

Vidaki A ，Ballard D ，Aliferi A ，Miller TH ，Barron LP ，Syndercombe Court D ... -  《-》