title: A novel strategy for forensic age prediction by DNA methylation and support vector regression model.
abstract: High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R(2) > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20-80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications.
authors.affiliation: Institute of Forensic Science, Key Laboratory of Forensic Genetics, Ministry of Public Security, Beijing, China.|People's Public Security University of China, Beijing, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Xu
authors.affiliation: Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
authors.forename: Hongzhu
authors.initials: H
authors.lastname: Qu
authors.affiliation: Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
authors.forename: Guangyu
authors.initials: G
authors.lastname: Wang
authors.affiliation: Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
authors.forename: Bingbing
authors.initials: B
authors.lastname: Xie
authors.affiliation: Institute of Forensic Science, Key Laboratory of Forensic Genetics, Ministry of Public Security, Beijing, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Shi
authors.affiliation: Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
authors.forename: Yaran
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Institute of Forensic Science, Key Laboratory of Forensic Genetics, Ministry of Public Security, Beijing, China.
authors.forename: Zhao
authors.initials: Z
authors.lastname: Zhao
authors.affiliation: Institute of Forensic Science, Key Laboratory of Forensic Genetics, Ministry of Public Security, Beijing, China.
authors.forename: Lan
authors.initials: L
authors.lastname: Hu
authors.affiliation: Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
authors.forename: Xiangdong
authors.initials: X
authors.lastname: Fang
authors.affiliation: Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
authors.forename: Jiangwei
authors.initials: J
authors.lastname: Yan
authors.affiliation: Institute of Forensic Science, Key Laboratory of Forensic Genetics, Ministry of Public Security, Beijing, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Feng
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002681:China; D018899:CpG Islands / Q000235:genetics; D019175:DNA Methylation / Q000235:genetics*; D005260:Female; D053803:Forensic Genetics; D005819:Genetic Markers; D006801:Humans; D008297:Male; D008875:Middle Aged; D016133:Polymerase Chain Reaction / Q000379:methods*; D012044:Regression Analysis; D060388:Support Vector Machine
chemical_list: D005819:Genetic Markers