Forensic DNA Phenotyping refers to the prediction of appearance traits of unknown sample donors, or unknown deceased (missing) persons, directly from biological materials found at the scene. "Biological witness" outcomes of Forensic DNA Phenotyping can provide investigative leads to trace unknown persons, who are unidentifiable with current comparative DNA profiling. This intelligence application of DNA marks a substantially different forensic use of genetic material rather than that of current DNA profiling presented in the courtroom. Currently, group-specific pigmentation traits are already predictable from DNA with reasonably high accuracies, while several other externally visible characteristics are under genetic investigation. Until individual-specific appearance becomes accurately predictable from DNA, conventional DNA profiling needs to be performed subsequent to appearance DNA prediction. Notably, and where Forensic DNA Phenotyping shows great promise, this is on a (much) smaller group of potential suspects, who match the appearance characteristics DNA-predicted from the crime scene stain or from the deceased person's remains. Provided sufficient funding being made available, future research to better understand the genetic basis of human appearance will expectedly lead to a substantially more detailed description of an unknown person's appearance from DNA, delivering increased value for police investigations in criminal and missing person cases involving unknowns.

Forensic DNA Phenotyping: Predicting human appearance from crime scene material for investigative purposes.

Forensic DNA phenotyping needs to be supplemented with age prediction to become a relevant source of information on human appearance Recent progress in analysis of the human methylome has enabled selection of multiple candidate loci showing linear correlation with chronological age Practical application in forensic science depends on successful validation of these potential age predictors In this study, eight DNA methylation candidate loci were analysed using convenient and reliable pyrosequencing technology A total number of 41 CpG sites was investigated in 420 samples collected from men and women aged from 2 to 75 years The study confirmed correlation of all the investigated markers with human age The five most significantly correlated CpG sites in ELOVL2 on 6p242, C1orf132 on 1q322, TRIM59 on 3q2533, KLF14 on 7q323 and FHL2 on 2q122 were chosen to build a prediction model This restriction allowed the technical analysis to be simplified without lowering the prediction accuracy significantly Model parameters for a discovery set of 300 samples were R 2 =094 and the standard error of the estimate=45 years An independent set of 120 samples was used to test the model performance Mean absolute deviation for this testing set was 39 years The number of correct predictions ±5 years achieved a very high level of 867% in the age category 2–19 and gradually decreased to 50% in the age category 60–75 The prediction model was deterministic for individuals belonging to these two extreme age categories The developed method was implemented in a freely available online age prediction calculator

Development of a forensically useful age prediction method based on DNA methylation analysis.

DNA methylation-based biomarkers of aging are highly correlated with actual age. Departures of methylation-estimated age from actual age can be used to define epigenetic measures of child development or age acceleration (AA) in adults. Very little is known about genetic or environmental determinants of these epigenetic measures of aging. We obtained DNA methylation profiles using Infinium HumanMethylation450 BeadChips across five time-points in 1018 mother-child pairs from the Avon Longitudinal Study of Parents and Children. Using the Horvath age estimation method, we calculated epigenetic age for these samples. AA was defined as the residuals from regressing epigenetic age on actual age. AA was tested for associations with cross-sectional clinical variables in children. We identified associations between AA and sex, birth weight, birth by caesarean section and several maternal characteristics in pregnancy, namely smoking, weight, BMI, selenium and cholesterol level. Offspring of non-drinkers had higher AA on average but this difference appeared to resolve during childhood. The associations between sex, birth weight and AA found in ARIES were replicated in an independent cohort (GOYA). In children, epigenetic AA measures are associated with several clinically relevant variables, and early life exposures appear to be associated with changes in AA during adolescence. Further research into epigenetic aging, including the use of causal inference methods, is required to better our understanding of aging.

/pdf/prenatal-and-early-life-influences-on-epigenetic-age-in-4z7uz0h1rq.pdf

Prenatal and early life influences on epigenetic age in children: a study of mother–offspring pairs from two cohort studies

We explored the association between gestational age and cord blood DNA methylation at birth and whether DNA methylation could be effective in predicting gestational age due to limitations with the presently used methods. We used data from the Norwegian Mother and Child Birth Cohort study (MoBa) with Illumina HumanMethylation450 data measured for 1753 newborns in two batches: MoBa 1, n = 1068; and MoBa 2, n = 685. Gestational age was computed using both ultrasound and the last menstrual period. We evaluated associations between DNA methylation and gestational age and developed a statistical model for predicting gestational age using MoBa 1 for training and MoBa 2 for predictions. The prediction model was additionally used to compare ultrasound and last menstrual period-based gestational age predictions. Furthermore, both CpGs and associated genes detected in the training models were compared to those detected in a published prediction model for chronological age. There were 5474 CpGs associated with ultrasound gestational age after adjustment for a set of covariates, including estimated cell type proportions, and Bonferroni-correction for multiple testing. Our model predicted ultrasound gestational age more accurately than it predicted last menstrual period gestational age. DNA methylation at birth appears to be a good predictor of gestational age. Ultrasound gestational age is more strongly associated with methylation than last menstrual period gestational age. The CpGs linked with our gestational age prediction model, and their associated genes, differed substantially from the corresponding CpGs and genes associated with a chronological age prediction model.

/pdf/prediction-of-gestational-age-based-on-genome-wide-2nzhgthrxz.pdf

Prediction of gestational age based on genome-wide differentially methylated regions

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.

/pdf/dna-methylation-based-forensic-age-prediction-using-2ayd0wu0fh.pdf

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

Age estimation in forensic investigations may complement the prediction of externally visible characteristics and the inference of biogeographical ancestry, thus allowing a better description of an unknown individual. Multiple CpG sites that show linear correlation between age and degree of DNA methylation have been identified in the human genome, providing a selection of candidates for age prediction. In this study, we optimized an assay based on bisulfite conversion and pyrosequencing of 7 CpG sites located in the ELOVL2 gene. Examination of 303 blood samples collected from individuals aged 2-75 years allowed selection of the most informative site, explaining 83% of variation in age. The final linear regression model included two CpG sites in ELOVL2 and enabled age prediction with R(2)=0.859, prediction error=6.85 and mean absolute deviation MAD=5.03. Examination of a testing set of 124 blood samples (MAD=5.75) showed that 68.5% of samples were correctly predicted, assuming that chronological and predicted ages matched ± 7 years. It was found that the ELOVL2 methylation status in bloodstains had not changed significantly after 4 weeks of storage in room temperature conditions. Analysis of 45 bloodstains deposited on tissue paper after 5, 10 and 15 years of storage in room conditions indicated that although a gradual decrease of positive PCR results was observed, the general age prediction success rate remained similar and equaled 60-78%. The obtained results show that the ELOVL2 locus provides a very good source of information about human chronological age based on analysis of blood, including bloodstains, and it may constitute a powerful and reliable predictor in future forensic age estimation models.

Examination of DNA methylation status of the ELOVL2 marker may be useful for human age prediction in forensic science.

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Wewnętrzna walidacja metody kwantyfikacji DNA z wykorzystaniem zestawu Quantifiler® Human DNA Quantification Kit i aparatu 7500 Real-Time PCR System wraz z oprogramowaniem Hid Real-Time PCR Analysis Software V 1.1 w Zakładzie Biologii Centralnego Laboratorium Kryminalistycznego Policji

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Examination of DNA methylation status of the ELOVL2 marker may be useful for human age prediction in forensic science.

Wewnętrzna walidacja metody kwantyfikacji DNA z wykorzystaniem zestawu Quantifiler® Human DNA Quantification Kit i aparatu 7500 Real-Time PCR System wraz z oprogramowaniem Hid Real-Time PCR Analysis Software V 1.1 w Zakładzie Biologii Centralnego Laboratorium Kryminalistycznego Policji