Predicting Chronological Age from DNA Methylation Data: A Machine Learning Approach for Small Datasets and Limited Predictors

TY - CHAP

T1 - Predicting Chronological Age from DNA Methylation Data

T2 - A Machine Learning Approach for Small Datasets and Limited Predictors

AU - Aliferi, Anastasia

AU - Ballard, David

N1 - Publisher Copyright: © 2022, Springer Science+Business Media, LLC, part of Springer Nature.

PY - 2022

Y1 - 2022

N2 - Recent research studies using epigenetic data have been exploring whether it is possible to estimate how old someone is using only their DNA. This application stems from the strong correlation that has been observed in humans between the methylation status of certain DNA loci and chronological age. While genome-wide methylation sequencing has been the most prominent approach in epigenetics research, recent studies have shown that targeted sequencing of a limited number of loci can be successfully used for the estimation of chronological age from DNA samples, even when using small datasets. Following this shift, the need to investigate further into the appropriate statistics behind the predictive models used for DNA methylation-based prediction has been identified in multiple studies. This chapter will look into an example of basic data manipulation and modeling that can be applied to small DNA methylation datasets (100–400 samples) produced through targeted methylation sequencing for a small number of predictors (10–25 methylation sites). Data manipulation will focus on converting the obtained methylation values for the different predictors to a statistically meaningful dataset, followed by a basic introduction into importing such datasets in R, as well as randomizing and splitting into appropriate training and test sets for modeling. Finally, a basic introduction to R modeling will be outlined, starting with feature selection algorithms and continuing with a simple modeling example (linear model) as well as a more complex algorithm (Support Vector Machine).

AB - Recent research studies using epigenetic data have been exploring whether it is possible to estimate how old someone is using only their DNA. This application stems from the strong correlation that has been observed in humans between the methylation status of certain DNA loci and chronological age. While genome-wide methylation sequencing has been the most prominent approach in epigenetics research, recent studies have shown that targeted sequencing of a limited number of loci can be successfully used for the estimation of chronological age from DNA samples, even when using small datasets. Following this shift, the need to investigate further into the appropriate statistics behind the predictive models used for DNA methylation-based prediction has been identified in multiple studies. This chapter will look into an example of basic data manipulation and modeling that can be applied to small DNA methylation datasets (100–400 samples) produced through targeted methylation sequencing for a small number of predictors (10–25 methylation sites). Data manipulation will focus on converting the obtained methylation values for the different predictors to a statistically meaningful dataset, followed by a basic introduction into importing such datasets in R, as well as randomizing and splitting into appropriate training and test sets for modeling. Finally, a basic introduction to R modeling will be outlined, starting with feature selection algorithms and continuing with a simple modeling example (linear model) as well as a more complex algorithm (Support Vector Machine).

KW - Age prediction

KW - DNA methylation

KW - Machine learning

KW - Small datasets

KW - Targeted sequencing

UR - http://www.scopus.com/inward/record.url?scp=85129625170&partnerID=8YFLogxK

U2 - 10.1007/978-1-0716-1994-0_14

DO - 10.1007/978-1-0716-1994-0_14

M3 - Chapter

C2 - 35505216

AN - SCOPUS:85129625170

T3 - Methods in Molecular Biology

SP - 187

EP - 200

BT - Methods in Molecular Biology

PB - Humana Press Inc

ER -