Evaluation of three statistical prediction models for forensic age prediction based on DNA methylation

Forensic Science International. Genetics
Inge SmeersBram Bekaert

Abstract

DNA methylation is a promising biomarker for forensic age prediction. A challenge that has emerged in recent studies is the fact that prediction errors become larger with increasing age due to interindividual differences in epigenetic ageing rates. This phenomenon of non-constant variance or heteroscedasticity violates an assumption of the often used method of ordinary least squares (OLS) regression. The aim of this study was to evaluate alternative statistical methods that do take heteroscedasticity into account in order to provide more accurate, age-dependent prediction intervals. A weighted least squares (WLS) regression is proposed as well as a quantile regression model. Their performances were compared against an OLS regression model based on the same dataset. Both models provided age-dependent prediction intervals which account for the increasing variance with age, but WLS regression performed better in terms of success rate in the current dataset. However, quantile regression might be a preferred method when dealing with a variance that is not only non-constant, but also not normally distributed. Ultimately the choice of which model to use should depend on the observed characteristics of the data.

Citations

Jan 1, 2020·American Journal of Physical Anthropology·Fei FanZhenhua Deng
Nov 27, 2019·Genome Biology·Christopher G BellVardhman K Rakyan
Nov 30, 2019·Genes·Zahra Momeni, Mohammad Saniee Abadeh
Jan 4, 2020·International Journal of Legal Medicine·Ana Belén Márquez-RuizAurora Valenzuela
Mar 4, 2021·Forensic Science International. Genetics·Cordula HaasJack Ballantyne
Jun 23, 2018·Forensic Science International. Genetics·Ana Freire-AradasMaria Victoria Lareu
Apr 28, 2020·Computers in Biology and Medicine·Najmeh Sadat Jaddi, Mohammad Saniee Abadeh
Mar 22, 2021·Aging·Athina VidakiManfred Kayser
Nov 30, 2018·Analytical Chemistry·Bruce R McCordGeorge Duncan

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