DNA methylation age estimation in blood samples of living and deceased individuals using a multiplex SNaPshot assay

Dias, Helena Correia; Cordeiro, Cristina Robalo; Pereira, Janet; Pinto, Carla; Real, Francisco Corte; Cunha, Eugénia; Manco, Licínio

doi:10.1016/j.forsciint.2020.110267

Cited by 43 publications

(55 citation statements)

References 16 publications

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“…Among the multiple machine learning algorithms adopted for the present study, RFR outperformed the others with a RMSE value of 5.072 years and a MAE value of 4.065 years (R 2 = 0.902), followed by SVR with a MAE of 4.166 years and a RMSE of 5.936 years (R 2 = 0.865). This estimation errors were close to previous predictive models using other biomarkers[8, 15]. RFR model explained 94% of the variation in real and predicted age (±10 years) with the accuracy of a MAE of 3.479 years in the ≤30 years age group and 6.657 years in the older group (≥ 61 years).…”

Section: Discussionsupporting

confidence: 83%

“…Although years (R 2 = 0.865). This estimation errors were close to previous predictive models using other biomarkers [8,15]. RFR model explained 94% of the variation in real and predicted age (±10 years) with the accuracy of a MAE of 3.479 years in the ≤30 years age group and 6.657 years in the older group (≥ 61 years).…”

Section: Discussionsupporting

confidence: 81%

“…To predict an individual’s age from DNA, considerable attempts have been made to find biomarkers as indicators of chronological age. Several molecular methods have been proposed such as telomere shortening[3, 4], mitochondrial DNA deletion[5, 6], signal-joint T-cell receptor excision circle (sjTRECs)[7], and DNA methylation[8–11]. Among these biomarkers, DNA methylation pattern was considered as the most promising age-predictive biomarkers for forensic and clinic use due to its high prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Circular RNA as a potential biomarker for forensic age prediction using multiple machine learning models: A preliminary study

Wang

et al. 2020

Preprint

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In forensic science, accurate estimation of the age of a victim or suspect can facilitate the investigators to narrow a search and aid in solving a crime. Aging is a complex process associated with various molecular regulation on DNA or RNA levels. Recent studies have shown that circular RNAs (circRNAs) upregulate globally during aging in multiple organisms such as mice and elegans because of their ability to resist degradation by exoribonucleases. In the current study, we attempted to investigate circRNAs’ potential capability of age prediction. Here, we identified more than 40,000 circRNAs in the blood of thirteen Chinese unrelated healthy individuals with ages of 20-62 years according to their circRNA-seq profiles. Three methods were applied to select age-related circRNAs candidates including false discovery rate, lasso regression, and support vector machine. The analysis uncovered a strong bias for circRNA upregulation during aging in human blood. A total of 28 circRNAs were chosen for further validation in 50 healthy unrelated subjects aged between 19 and 72 years by RT-qPCR and finally, 7 age-related circRNAs were chosen for final age prediction models. Several different algorithms including multivariate linear regression (MLR), regression tree, bagging regression, random forest regression (RFR), and support vector regression (SVR) were compared based on root mean square error (RMSE) and mean average error (MAE) values. Among five modeling methods, random forest regression (RFR) performed better than the others with an RMSE value of 5.072 years and an MAE value of 4.065 years (R2 = 0.902). In this preliminary study, we firstly used circRNAs as additional novel age-related biomarkers for developing forensic age estimation models. We propose that the use of circRNAs to obtain additional clues for forensic investigations and serve as aging indicators for age prediction would become a promising field of interest.Author summaryIn forensic investigations, estimation of the age of biological evidence recovered from crime scenes can provide additional information such as chronological age or the appearance of a culprit, which could give valuable investigative leads especially when there is no eyewitness available. Hence, generating an accurate model for age prediction using body fluids such as blood commonly seen at a crime scene can be of vital importance. Various molecular changes on DNA or RNA levels were discovered that they upregulated or downregulated during a person’s lifetime. Although some biomarkers have been proved to be associated with aging and used to predict age, several disadvantages such as low sensitivity, prediction accuracy, instability and susceptibility of diseases or immune states, thus limiting their applicability in the field of age estimation. Here, we utilized a novel biomarker namely circular RNA (circRNA) to generate highly accurate age prediction models. We propose that circRNA is more suitable for forensic degradation samples because of its unique molecular structure. This preliminary research offers a new thought for exploring potential biomarker for age prediction.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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Circular RNA as a potential biomarker for forensic age prediction using multiple machine learning models: A preliminary study

Wang

et al. 2020

Preprint

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“…In a previous study by Dias et al. [13], the DNA methylation status of the CpG sites of the ELOVL2, FHL2, KLF14, MIR29B2C , and TRIM59 genes were obtained from the SeqStudio Genetic Analyzer using SNaPshot products obtained as described in Jung et al. [2].…”

Section: Resultsmentioning

confidence: 99%

“…Dias et al. [13], recently reported that applying SNaPshot data obtained from DNA from Portuguese blood to an age prediction model built using SNaPshot data from Korean blood DNA samples [2] resulted in a mean absolute deviation from chronological age (MAD) of 15.26 years, which differs significantly from previous results in Koreans (MAD = 3.48 years). It was suggested that differences in the DNA methylation patterns between the two populations was the cause of the difference in MAD values.…”

Section: Introductionmentioning

confidence: 99%

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

Lee

2021

Electrophoresis

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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.

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Pyrosequencing: Current forensic methodology and future applications—a review

et al. 2022

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The recent development of small, single-amplicon-based benchtop systems for pyrosequencing has opened up a host of novel procedures for applications in forensic science. Pyrosequencing is a sequencing by synthesis technique, based on chemiluminescent inorganic pyrophosphate detection. This review explains the pyrosequencing workflow and illustrates the step-by-step chemistry, followed by a description of the assay design and factors to keep in mind for an exemplary assay. Existing and potential forensic applications are highlighted using this technology. Current applications include identifying species, identifying bodily fluids, and determining smoking status. We also review progress in potential applications for the future, including research on distinguishing monozygotic twins, detecting alcohol and drug abuse, and other phenotypic characteristics such as diet and body mass index. Overall, the versatility of the pyrosequencing technologies renders it a useful tool in forensic genomics.

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DNA methylation age estimation in blood samples of living and deceased individuals using a multiplex SNaPshot assay

Cited by 43 publications

References 16 publications

Circular RNA as a potential biomarker for forensic age prediction using multiple machine learning models: A preliminary study

Circular RNA as a potential biomarker for forensic age prediction using multiple machine learning models: A preliminary study

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

Pyrosequencing: Current forensic methodology and future applications—a review

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