Редактирование: SCGN (раздел)

==Publications==

{{medline-entry
|title=Detection and evaluation of DNA methylation markers found at [[SCGN]] and [[KLF14]] loci to estimate human age.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/28854399
|abstract=Recent developments in the analysis of epigenetic DNA methylation patterns have demonstrated that certain genetic loci show a linear correlation with chronological age. It is the goal of this study to identify a new set of epigenetic methylation markers for the forensic estimation of human age. A total number of 27 CpG sites at three genetic loci, [[SCGN]], [[DLX5]] and [[KLF14]], were examined to evaluate the correlation of their methylation status with age. These sites were evaluated using 72 blood samples and 91 saliva samples collected from volunteers with ages ranging from 5 to 73 years. DNA was bisulfite modified followed by PCR amplification and pyrosequencing to determine the level of DNA methylation at each CpG site. In this study, certain CpG sites in [[SCGN]] and [[KLF14]] loci showed methylation levels that were correlated with chronological age, however, the tested CpG sites in [[DLX5]] did not show a correlation with age. Using a 52-saliva sample training set, two age-predictor models were developed by means of a multivariate linear regression analysis for age prediction. The two models performed similarly with a single-locus model explaining 85% of the age variance at a mean absolute deviation of 5.8 years and a dual-locus model explaining 84% of the age variance with a mean absolute deviation of 6.2 years. In the validation set, the mean absolute deviation was measured to be 8.0 years and 7.1 years for the single- and dual-locus model, respectively. Another age predictor model was also developed using a 40-blood sample training set that accounted for 71% of the age variance. This model gave a mean absolute deviation of 6.6 years for the training set and 10.3years for the validation set. The results indicate that specific CpGs in [[SCGN]] and [[KLF14]] can be used as potential epigenetic markers to estimate age using saliva and blood specimens. These epigenetic markers could provide important information in cases where the determination of a suspect's age is critical in developing investigative leads.
|mesh-terms=* Adolescent
* Adult
* Aged
* Aging
* Child
* CpG Islands
* DNA
* DNA Methylation
* Epigenesis, Genetic
* Genetic Markers
* Humans
* Kruppel-Like Transcription Factors
* Middle Aged
* Multivariate Analysis
* Polymerase Chain Reaction
* Saliva
* Secretagogins
* Sp Transcription Factors
* Young Adult
|keywords=* Age prediction
* DNA methylation
* Epigenetic
* Forensic
* Pyrosequencing
|full-text-url=https://sci-hub.do/10.1016/j.fsigen.2017.07.011
}}
{{medline-entry
|title=DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/28254385
|abstract=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 (R =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 (R =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 (R =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.
|mesh-terms=* Adult
* Aged
* Aging
* CpG Islands
* DNA
* DNA Methylation
* Epigenomics
* Forensic Genetics
* High-Throughput Nucleotide Sequencing
* Humans
* Machine Learning
* Middle Aged
* Neural Networks, Computer
* Saliva
* Twins, Monozygotic
|keywords=* Artificial neural networks
* Chronological age prediction
* DNA methylation
* Forensic epigenetics
* Next generation sequencing
|full-text-url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5392537
}}