Comparative analysis of SNaPshot and massively parallel sequencing for body fluid-specific DNA methylation markers.

The identification of tissue-specific differentially methylated regions has significantly contributed to the field of forensic genetics, particularly in body fluid identification crucial for linking evidence to crimes. Among the various approaches to analyzing DNA methylation, the SNaPshot assay has been popularly studied in numerous researches. However, there is a growing interest in exploring alternative methods such as the use of massively parallel sequencing (MPS), which can process a large number of samples simultaneously. This study compares SNaPshot and MPS multiplex assays using nine cytosine-phosphate-guanine markers for body fluid identification. As a result of analyzing 112 samples, including blood, saliva, vaginal fluid, menstrual blood, and semen, both methods demonstrated high sensitivity and specificity, indicating their reliability in forensic investigations. A total of 92.0% samples were correctly identified by both methods. Although both methods accurately identified all blood, saliva, and semen samples, some vaginal fluid samples showed unexpected methylation signals at nontarget loci in addition to the target loci. In the case of menstrual blood samples, due to their complexity, independent typing criteria were applied, and successful menstrual blood typing was possible, whereas a few samples showed profiles similar to vaginal fluid. The MPS method worked better in vaginal fluid samples, and the SNaPshot method performed better in menstrual blood samples. This study offers valuable insights into body fluid identification based on the characteristics of the SNaPshot and MPS methods, which may help in more efficient forensic applications.

DNA methylation-based organ tissue identification: Marker identification, SNaPshot multiplex assay development, and interlaboratory comparison.

Identifying body fluids and organ tissues is highly significant as they can offer crucial evidence in criminal investigations and aid the court in making informed decisions, primarily through evaluating the biological source and possibly at the activity level up to death or fatal damage. In this study, organ tissue-specific CpG markers were identified from Illumina's methylation EPIC array data of nine organ tissues, including epidermis, dermis, heart, skeletal muscle, blood, kidney, brain, lung, and liver, from autopsies of 10 Koreans. Through the validation test using 43 samples, 18 hypomethylation markers, with two markers for each organ tissue type, were selected to construct a SNaPshot assay. Two multiplex assays involving forward and reverse SBE primers were designed to help investigators accurately determine the organ origin of the analyzed tissue samples through repeated analysis of the same PCR products for markers. The developed multiplex demonstrated high accuracy, achieving 100.0 % correct detection of the presence of nine organ tissue types in 88 samples from autopsies of 10 Asians. However, two lung samples showed additional positive indications of the presence of blood. An interlaboratory comparison using 80 autopsy samples (heart, skeletal muscle, blood, kidney cortex, kidney medulla, brain, lung, and liver) from 10 individuals in Germany revealed overall comparable results with correct detection of the presence of eight organ tissue types in 92.5 % samples (74 of 80 samples). In the case of six samples, it was impossible to determine the correct tissue successfully due to drop-outs of unmethylation signals at target tissue marker loci. One of these lung samples revealed only non-intended off-target signals for blood. The observed differences might be due to differences in sample collection during routine autopsy, technical differences due to the PCR cycler, and the threshold used for signal calling. Indicating the presence of additional tissue type and off-target unmethylation signals seems alleviated by applying more stringent hypomethylation thresholds. Therefore, the developed SNaPshot multiplex assays will be valuable for forensic investigators dealing with organ tissue identification, as well as for prosecutors and defense aiming to establish the circumstances that occurred at the crime scene.

Strategies to deal with genetic analyzer-specific DNA methylation measurements.

Targeted bisulfite sequencing using single-base extension (SBE) can be used to measure DNA methylation via capillary electrophoresis on genetic analyzers in forensic labs. Several accurate age prediction models have been reported using this method. However, using different genetic analyzers with different software settings can generate different methylation values, leading to significant errors in age prediction. To address this issue, the study proposes and compares four methods as follows: (1) adjusting methylation values using numerous actual body fluid DNA samples, (2) adjusting methylation values using control DNAs with varying methylation ratios, (3) constructing new age prediction models for each genetic analyzer type, and (4) constructing new age prediction models that could be applied to all types of genetic analyzers. To test the methods for adjusting values using actual body fluid DNA samples, previously reported adjusting equations were used for blood/saliva DNA age prediction markers (ELOVL2, FHL2, KLF14, MIR29B2CHG/C1orf132, and TRIM59). New equations were generated for semen DNA age prediction markers (TTC7B, LOC401324/cg12837463, and LOC729960/NOX4) by drawing polynomial regression lines between the results of the three types of genetic analyzers (3130, 3500, and SeqStudio). The same method was applied to obtain adjustment equations using 11 control DNA samples. To develop new age prediction models for each genetic analyzer type, linear regression analysis was conducted using DNA methylation data from 150 blood, 150 saliva, and 62 semen samples. For the genetic analyzer-independent models, control DNAs were used to formulate equations for calibrating the bias of the data from each genetic analyzer, and linear regression analysis was performed using calibrated body fluid DNA data. In the comparison results, the genetic analyzer-specific models showed the highest accuracy. However, genetic analyzer-independent models through bias adjustment also provided accurate age prediction results, suggesting its use as an alternative in situations with multiple constraints.

Epigenetic age prediction using costal cartilage for the investigation of disaster victims and missing persons.

The DNA intelligence tool, DNA methylation-based age prediction, can help identify disaster victims and suspects in criminal investigations. In this study, we developed a costal cartilage-based age prediction tool that uses massive parallel sequencing (MPS) of age-associated DNA methylation markers. Costal cartilage samples were obtained from 85 deceased Koreans, aged between 26 and 89 years. An MPS library was prepared using two rounds of multiplex polymerase chain reaction of nine genes (TMEM51, MIR29B2CHG, EDARADD, FHL2, TRIM59, ELOVL2, KLF14, ASPA, and PDE4C). The DNA methylation status of 45 CpG sites was determined and used to train an age prediction model via stepwise regression analysis. Nine CpGs in MIR29B2CHG, FHL2, TRIM59, ELOVL2, KLF14, and ASPA were selected for regression model construction. A leave-one-out cross-validation analysis revealed the high performance of the age prediction model, with a mean absolute error (MAE) and root mean square error of 4.97 and 6.43 years, respectively. Additionally, our model showed good performance with a MAE of 6.06 years in the analysis of data of 181 costal cartilage samples collected from Europeans. Our model effectively estimates the age of deceased individuals using costal cartilage samples; therefore, it can be a valuable forensic tool for disaster victim and missing person investigation.

Age prediction using DNA methylation of Y-chromosomal CpGs in semen samples.

In cases of sexual assault, the evidence often exists as a mixture of female and male body fluids, and in many cases, contains a higher proportion of female body fluids than males. In these cases, Y-STR, rather than autosomal STRs, can provide useful information. It becomes very difficult to identify the true suspect if there is no match among known suspects or if a match exists for two or more suspects, e.g. two suspects from the same paternal lineage. However, age prediction using the DNA methylation of Y-chromosomal CpGs can help narrow the search for unknown suspects and discriminate between older and younger suspects. Therefore, the DNA methylation profiles of semen samples from 56 healthy Korean males were generated using Illumina's Infinium MethylationEPIC BeadChip Array. Among the ten identified age-associated CpG markers located in the Y-chromosome, nine were used to construct age prediction models. The identified markers were further investigated in the MPS analysis of 147 semen samples, and the multiplex assay was validated with the reliability, reproducibility and sensitivity tests. Several age prediction models were constructed using the MPS data with the multiple linear regression, stepwise linear regression, ridge linear regression, lasso regression, elastic net linear regression and support vector machine analyses, and all showed MAEs of 5 to 7 years in the test set samples. Six single-source female samples were also subjected to MPS analysis but showed very low coverage that could not affect the analysis of the mixed samples. Therefore, the age prediction models of the present study are expected to provide useful investigative leads, especially in mixed male and female samples from sexual assault cases.

Auxetic Photonic Patterns with Ultrasensitive Mechanochromism.

Photonic crystals with mechanochromic properties are currently under intensive study to provide intuitive colorimetric detection of strains for various applications. However, the sensitivity of color change to strain is intrinsically limited, as the degree of deformation determines the wavelength shift. To overcome this limitation, auxetic photonic patterns that exhibit ultra-sensitive mechanochromism are designed. These patterns have a regular arrangement of cuts that expand to accommodate the strain, while the skeletal framework undergoes torsional deformation. Elastic photonic crystals composed of a non-close-packed array of colloidal particles are embedded in the cut area of the auxetic patterns. As the cut area amplifies the strains, the elastic photonic crystals show significant color change even for small total strains. The degree of local-strain amplification, or sensitivity of color change, is controllable by adjusting the width of cuts in the auxetic framework. In this work, a maximum sensitivity of up to 60 nm/% is achieved, which is 20 times higher than bulk films. It is believed that the auxetic photonic patterns with ultra-sensitive mechanochromism will provide new opportunities for the pragmatic use of mechanochromic materials in various fields, including structural health monitoring.

Advanced Additive Manufacturing of Structurally-Colored Architectures.

Direct ink writing (DIW) stands out as a facile additive manufacturing method, minimizing material waste. Nonetheless, developing homogeneous Bingham inks with high yield stress and swift liquid-to-solid transitions for versatile 3D printing remains a challenge. In this study, high-performance Bingham inks are formulated by destabilizing silica particle suspensions in acrylate-based resin. A colloidal network forms in the shear-free state through interparticle attraction, achieved by disrupting the solvation layer of large resin molecules using polar molecules. The network is highly dense, with evenly distributed linkage strength as monodisperse particles undergo gelation at an ultra-high fraction. Crucially, the strength is calibrated to ensure a sufficiently large yield stress, while still allowing the network to reversibly melt under shear flow. The inks immediately undergo a liquid-to-solid transition upon discharge, while maintaining fluidity without nozzle clogging. The dense colloidal networks develop structural colors due to the short-range order. This enables the rapid and sophisticated drawing of structurally-colored 3D structures, relying solely on rheological properties. Moreover, the printed composite structures exhibit high mechanical stability due to the presence of the colloidal network, which expands the range of potential applications.

Application of array-based age prediction models to post-mortem tissue samples.

Since DNA methylation at specific CpG sites exhibits a strong age association, researchers have developed numerous age prediction models based on the methylation BeadChip array. These models harness epigenetic clocks that hold the potential to narrow down the search range for unknown suspects and unidentified victims. This study collected 180 post-mortem tissue samples comprising nine tissue types (blood, brain, heart, lung, liver, kidney, muscle, epidermis, and dermis) from autopsies of 20 Koreans aged 18-78. Subsequently, DNA methylation profiling was conducted using the Infinium MethylationEPIC array. We tested several array-based age prediction models using the data obtained from various tissues. The pan-tissue clock exhibited a moderately accurate prediction across all nine tissue types (MAE = 8.7 years, r = 0.88). Notably, the DNAm ages of the Hannum clock, the skin & blood clock, and the Zhang clock strongly correlated with the actual age in blood samples (MAE < approximately 5 years, r > 0.9). PhenoAge yielded an MAE of 10.1 years and an r-value of 0.92. The muscle-specific epigenetic clock, the MEAT package, demonstrated high prediction accuracy in muscle samples (MAE = 4.7 years, r = 0.93). Those previously reported array-based age prediction models were mainly constructed in Europeans but performed well in Koreans. In addition, tests involving various quantities of DNA and fragmented DNA have shown that DNA quantity and quality affected methylation measurements and age prediction results. However, robust age prediction models exist under low amounts of DNA and fragmented DNA conditions.

Forensic genetic analysis of single-nucleotide polymorphisms and microhaplotypes in Koreans through next-generation sequencing using precision ID identity panel.

BACKGROUND: Forensic DNA analysis has seen remarkable advancements with the advent of Next Generation Sequencing (NGS). In particular, NGS analysis of single nucleotide polymorphisms (SNPs) offers significant advantages in the analysis of challenging samples compared to conventional STR analysis. OBJECTIVE: This study aimed to investigate the SNPs of the Precision ID Identity Panel, a commercially available NGS panel for personal identification, by generating genetic profiles of 298 Koreans and comparing them with other global populations. METHODS: A total of 124 SNPs, including 90 autosomal and 34 Y-SNPs, were analyzed using the Precision ID Identity Panel, and forensic parameters, microhaplotypes, and population differences were investigated. RESULTS: The NGS data were successfully obtained from 298 Koreans. The analysis of forensic parameters exhibited a low combined match probability of 1.532 × 10- 34, which is comparable to that obtained from commonly used STR analysis. Additionally, the microhaplotype analysis revealed that the use of 16 microhaplotypes provided higher discriminatory power compared to single target SNPs. Furthermore, the adoption of microhaplotype data resulted in an increase of over 20% in expected heterozygosity at five loci. Inter-population analysis showed a close genetic relationship between Koreans and individuals from China and Myanmar in East and Southeast Asia, which are geographically adjacent to Korea. CONCLUSIONS: The results of this study show that the Precision ID Identity panel can be a useful alternative where traditional STR typing is not feasible. Also, the data from our study will be useful as a reference for Koreans in forensic investigations and the prosecution of criminal justice.

DNA methylome profiling of blood to identify individuals in a pair of monozygotic twins.

BACKGROUND: Short tandem repeat (STR) markers cannot be used to distinguish between genetically identical monozygotic (MZ) twins, causing problems in a case with an MZ twin as a suspect. Many studies have shown that in older MZ twins, there are significant differences in overall content and genomic distribution of methylation. OBJECTIVE: In this study, we analyzed the DNA methylome profile of blood to identify recurrent differentially methylated CpG sites (DMCs) to discriminate between MZ twins. METHODS: Blood samples were collected from 47 paired MZ twins. We performed the DNA methylation profiling using the HumanMethylation EPIC BeadChip platform and identified recurrent DMCs between MZ twins. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and motif enrichment analyses were performed to reveal the biological functions of recurrent DMCs. We collected DNA methylome data from the Gene Expression Omnibus (GEO) public database to verify the recurrent DMCs between MZ twins. RESULTS: We identified recurrent DMCs between MZ twin samples and observed that they were enriched in immune-related genes. In addition, we verified our DMCs in a public dataset. CONCLUSION: Our results suggest that the methylation level at recurrent DMCs between MZ twins may serve as a valuable biomarker for identification of individuals in a pair of MZ twins.

A collaborative exercise on DNA methylation-based age prediction and body fluid typing.

DNA methylation has become one of the most useful biomarkers for age prediction and body fluid identification in the forensic field. Therefore, several assays have been developed to detect age-associated and body fluid-specific DNA methylation changes. Among the many methods developed, SNaPshot-based assays should be particularly useful in forensic laboratories, as they permit multiplex analysis and use the same capillary electrophoresis instrumentation as STR analysis. However, technical validation of any developed assays is crucial for their proper integration into routine forensic workflow. In the present collaborative exercise, two SNaPshot multiplex assays for age prediction and a SNaPshot multiplex for body fluid identification were tested in twelve laboratories. The experimental set-up of the exercise was designed to reflect the entire workflow of SNaPshot-based methylation analysis and involved four increasingly complex tasks designed to detect potential factors influencing methylation measurements. The results of body fluid identification from each laboratory provided sufficient information to determine appropriate age prediction methods in subsequent analysis. In age prediction, systematic measurement differences resulting from the type of genetic analyzer used were identified as the biggest cause of DNA methylation variation between laboratories. Also, the use of a buffer that ensures a high ratio of specific to non-specific primer binding resulted in changes in DNA methylation measurement, especially when using degenerate primers in the PCR reaction. In addition, high input volumes of bisulfite-converted DNA often caused PCR failure, presumably due to carry-over of PCR inhibitors from the bisulfite conversion reaction. The proficiency of the analysts and experimental conditions for efficient SNaPshot reactions were also important for consistent DNA methylation measurement. Several bisulfite conversion kits were used for this study, but differences resulting from the use of any specific kit were not clearly discerned. Even when different experimental settings were used in each laboratory, a positive outcome of the study was a mean absolute age prediction error amongst participant's data of only 2.7 years for semen, 5.0 years for blood and 3.8 years for saliva.

Genetic analyzer-dependent DNA methylation detection and its application to existing age prediction models.

DNA methylation is the most promising biomarker for estimating human age. There are various methods used for analyzing DNA methylation. Among those, the SNaPshot assay-based method provides a semi-quantitative measurement of DNA methylation using capillary electrophoresis on genetic analyzers. However, DNA methylation measures produced using different types of genetic analyzers have never been compared, although differences in methylation values can directly affect age estimates. To evaluate the differences between the results generated by different genetic analyzers, we analyzed the same blood, saliva, and control methylated DNA using three genetic analyzers-the Applied Biosystems 3130, 3500, and SeqStudio-and compared the methylation values at five CpG sites: ELOVL2, FHL2, KLF14, MIR29B2C, and TRIM59. The methylation value at each of the five CpG sites decreased in the order 3130, 3500, and SeqStudio. The differences in the results produced by the different genetic analyzers resulted in significant errors when applying the 3500 and SeqStudio data to a previous age estimation model constructed using the 3130 Genetic Analyzer data. Therefore, DNA methylation measurements from 3500 and SeqStudio were corrected using the regression functions obtained by plotting the DNA methylation data of one instrument versus the other to facilitate the application of DNA methylation data from one instrument to the age prediction model based on other instruments. The age prediction accuracy obtained by applying corrected 3500 and SeqStudio data to the existing age estimation model was as high as observed in the 3130 data.

Validation of BMI genetic risk score and DNA methylation in a Korean population.

When DNA profiles obtained from biological evidence at a crime scene fail to match suspects or anyone in the database, forensic DNA phenotyping, which is the prediction of externally visible characteristics, can facilitate a traced search for an unknown suspect by limiting the search range. Therefore, age, trait, or lifestyle predictors, as well as the predictor for colorations, have been researched in the forensic field. In the present study, for the development of a prediction model for BMI or obesity, we investigated several previously reported BMI- or obesity-associated genetic and epigenetic markers that included four CpGs (cg06500161, cg00574958, cg12593793, and cg10505902 of the ABCG1, CPT1A, LMNA, and PDE4DIP genes, respectively), and eight SNPs (rs12463617, rs1558902, rs591166, rs11030104, rs11671664, rs6545814, rs16858082, and rs574367 near the TMEM18, FTO, MC4R, BDNF, GIPR/QPCTL, ADCY3/RBJ, GNPDA2, and SEC16B genes, respectively) in 700 Koreans within the BMI ranging from 16.1 to 40.6 (27.6 ± 4.5) kg/m2. Linear regression analysis showed that DNA methylation of the four CpG sites explained 10.9% total variance in BMI, and the model constructed using age information, genetic score from eight SNPs, and DNA methylation at four CpG sites could account for 17.4% of BMI variance. Using data mining techniques, i.e., decision tree (Entropy and Gini), random forest, and bagging, a total of eight models with BMI 31 or 32 as a cutoff value were also constructed based on the data obtained from 490 training samples with age and sex as a covariate. Among them, a random forest model with a cutoff value of 31 showed the best performance with 63.3% accuracy and the AUC value of 0.682 in 210 test set samples. In the present study, we could replicate the previous finding that DNA methylation contributes more to BMI than do genetic factors. In addition, although the accuracy for the prediction of BMI was not high, our study is meaningful in respect of the ability to use a small number of markers to achieve similar prediction accuracy to that obtained from a model composed of more than a thousand markers, which adds support to continued research to identify a small set of predictive markers for practical application in the forensic field.

A case of 46,XX/46,XX chimerism in a phenotypically normal woman.

Chimerism is the presence of two genetically different cell lines within a single organism, which is rarely observed in humans. Usually, chimerism in the human body is revealed by the finding of an abnormal phenotype during a medical examination or is unexpectedly detected in routine genetic analysis. However, the incidence or underlying mechanism of chimerism remains unclear due to the lack of information on this infrequent biological event. A phenotypically normal woman with a 46,XX karyotype and atypical short tandem repeat (STR) allelic patterns observed in DNA analysis was investigated with various genetic testing methods, including STR typing based on capillary electrophoresis and massively parallel sequencing, genome-wide SNP array, and a differentially methylated parental allele assay (DMPA). The proband's parents were not available for testing to discriminate the parental allelic contribution, but the parents' alleles were recovered from testing the proband's siblings. Based on the results consistently found in multiple analyses using STR and single nucleotide polymorphism (SNP) polymorphism markers, dispermic fertilization was suggested as the underlying mechanism. The application of various molecular genetic testing methods was used to elucidate the chimerism observed in the proband in this study. In the future, the development of novel genetic markers or techniques, such as DMPA, may have potential use in the investigation of chimerism.

Large-scale identification of human bone remains via SNP microarray analysis with reference SNP database.

Single nucleotide polymorphisms (SNPs) are valuable markers complementary to conventional forensic short tandem repeat (STR) markers in genetic typing, with potential advantages in challenging forensic casework. With the advent of high-throughput technologies, such as microarrays and massively parallel sequencing, the use of SNP typing has now expanded to large-scale forensic applications. Herein, a forensic case is presented to demonstrate the usefulness of SNP typing in identifying large-scale human bone remains with reference database construction. A total of 402 bone remains were recovered from an island in the Jeju Province of Korea where a massive disaster occurred in 1948. The first phase of the identification process was accomplished via conventional DNA typing methods including autosomal and Y-chromosomal STR typing, and mitochondrial DNA sequencing, which resulted in the identification of 74 of 402 remains. The second phase of the identification involved the remaining 327 unidentified remains using SNP typing as a supplementary tool based on Affymetrix resequencing array. The SNP markers of 782 family members were also analyzed and a reference database was constructed for comparison. An additional 51 bone remains were identified in the second phase. SNP data obtained from the supplementary genotyping yielded additional genetic information as well as contributed to kinship testing to determine the second degrees of relationship. In addition SNPs are useful in discriminating ambiguous relationship when only STR data are available. A software program developed for SNP typing system enabled efficient kinship analysis for large-scale forensic identification. The results and the casework are described and discussed.

Epigenetic age signatures in bones.

Age prediction can help identify skeletal remains by limiting the search range for a missing person. Although age prediction methods based on odontology and anthropology are frequently used in the forensic field, DNA methylation is particularly promising age-predictive biomarker. In this study, we generated genome-wide DNA methylation profiles of bone samples from 32 identified skeletal remains with an age at death ranging from 31 to 96 years. Only 12 provided more than 800 K quality-filtered CpG methylation values using Illumina's Infinium MethylationEPIC BeadChip array. Methylation ages of the bone samples calculated using a recently developed skin & blood clock composed of 391 CpG sites were found to be very similar to their actual ages (MAD = 6.4 years). However, the low success rate in methylation profiling of bone DNA samples may prevent researchers from applying the array to this type of samples. Therefore, we selected a set of CpG sites that would enable age prediction based on only a few CpG sites in bone DNA samples. Nineteen age-associated CpG marker candidates were selected from 720 K quality-filtered CpG values of 21 male skeletal remain samples. Because age signatures for blood, such as markers on the ELOVL2, FHL2, KLF14 and TRIM59 genes, had showed strong age associations in 12 bone samples, we further tested the age association of the 5 well-known markers in a blood-based model and the 13 out of 19 CpG markers from the array of 21 bone samples with an independent set of 30 skeletal remain samples using SNaPshot multiplex based on single nucleotide primer extension. Four CpG sites on TMEM51, TRIM59, ELOVL2, and EPHA6 genes showed moderate or weak correlations between methylation and age, which suggests further investigation of these markers to predict the age of bones.

DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples.

Many studies have reported age-associated DNA methylation changes and age-predictive models in various tissues and body fluids. Although age-associated DNA methylation changes can be tissue-specific, a multi-tissue age predictor that is applicable to various tissues and body fluids with considerable prediction accuracy might be valuable. In this study, DNA methylation at 5 CpG sites from the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes were investigated in 448 samples from blood, saliva, and buccal swabs. A multiplex methylation SNaPshot assay was developed to measure DNA methylation simultaneously at the 5 CpG sites. Among the 5 CpG sites, 3 CpG sites in the ELOVL2, KLF14 and TRIM59 genes demonstrated strong correlation between DNA methylation and age in all 3 sample types. Age prediction models built separately for each sample type using the DNA methylation values at the 5 CpG sites showed high prediction accuracy with a Mean Absolute Deviation from the chronological age (MAD) of 3.478 years in blood, 3.552 years in saliva and 4.293 years in buccal swab samples. A tissue-combined model constructed with 300 training samples including 100 samples from each blood, saliva and buccal swab samples demonstrated a very strong correlation between predicted and chronological ages (r = 0.937) and a high prediction accuracy with a MAD of 3.844 years in the 148 independent test set samples of 50 blood, 50 saliva and 48 buccal swab samples. Although more validation might be needed, the tissue-combined model's prediction accuracies in each sample type were very much similar to those obtained from each tissue-specific model. The multiplex methylation SNaPshot assay and the age prediction models in our study would be useful in forensic analysis, which frequently involves DNA from blood, saliva, and buccal swab samples.

Platform-independent models for age prediction using DNA methylation data.

Age prediction has been in the spotlight recently because it can provide an important information about the contributors of biological evidence left at crime scenes. Specifically, many researchers have actively suggested age-prediction models using DNA methylation at several CpG sites and tested the candidates using platforms such as the HumanMethylation 450 array and pyrosequencing. With DNA methylation data obtained from each platform, age prediction models were constructed using diverse statistical methods typically with multivariate linear regression. However, because each developed model is based on single-platform data, the prediction accuracy is reduced when applying DNA methylation data obtained from other platforms. In this study, bisulfite sequencing data for 95 saliva samples were generated using massively parallel sequencing (MPS) and compared with methylation SNaPshot data from the same 95 individuals. The predicted age obtained by applying MPS data to an age-prediction model built for methylation SNaPshot data differed greatly from the chronological age due to platform differences. Therefore, novel variables were introduced to indicate the platform type, and construct platform-independent age predictive models using a neural network and multivariate linear regression. The final neural network model had a mean absolute deviation (MAD) of 3.19 years between the predicted and chronological age, and the mean absolute percentage error (MAPE) was 8.89% in the test set. Similarly, the linear regression model showed 3.69 years of MAD and 10.44% of MAPE in the same test set. The platform-independent age-prediction model was made extensible to an increasing number of platforms by introducing platform variables, and the idea of platform variables can be applied to age prediction models for other body fluids.