Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

Abstract Regression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been stu...

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Autores principales:	Zhonghui Thong, Jolena Ying Ying Tan, Eileen Shuzhen Loo, Yu Wei Phua, Xavier Liang Shun Chan, Christopher Kiu-Choong Syn
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e03e7ab9479e4ccea08feeeb5d8137a6
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spelling	oai:doaj.org-article:e03e7ab9479e4ccea08feeeb5d8137a62021-12-02T15:23:39ZArtificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples10.1038/s41598-021-81556-22045-2322https://doaj.org/article/e03e7ab9479e4ccea08feeeb5d8137a62021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81556-2https://doaj.org/toc/2045-2322Abstract Regression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.Zhonghui ThongJolena Ying Ying TanEileen Shuzhen LooYu Wei PhuaXavier Liang Shun ChanChristopher Kiu-Choong SynNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhonghui Thong Jolena Ying Ying Tan Eileen Shuzhen Loo Yu Wei Phua Xavier Liang Shun Chan Christopher Kiu-Choong Syn Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples
description	Abstract Regression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.
format	article
author	Zhonghui Thong Jolena Ying Ying Tan Eileen Shuzhen Loo Yu Wei Phua Xavier Liang Shun Chan Christopher Kiu-Choong Syn
author_facet	Zhonghui Thong Jolena Ying Ying Tan Eileen Shuzhen Loo Yu Wei Phua Xavier Liang Shun Chan Christopher Kiu-Choong Syn
author_sort	Zhonghui Thong
title	Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples
title_short	Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples
title_full	Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples
title_fullStr	Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples
title_full_unstemmed	Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples
title_sort	artificial neural network, predictor variables and sensitivity threshold for dna methylation-based age prediction using blood samples
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e03e7ab9479e4ccea08feeeb5d8137a6
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Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

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