Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging

Mahmoud Amiri Roudbar; Seyedeh Fatemeh Mousavi; Siavash Salek Ardestani; Fernando Brito Lopes; Mehdi Momen; Daniel Gianola; Hasan Khatib

doi:10.1093/g3journal/jkab112

Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging

G3 (Bethesda). 2021 Jul 14;11(7):jkab112. doi: 10.1093/g3journal/jkab112.

Authors

Mahmoud Amiri Roudbar¹, Seyedeh Fatemeh Mousavi², Siavash Salek Ardestani³, Fernando Brito Lopes⁴, Mehdi Momen⁵, Daniel Gianola⁶, Hasan Khatib⁶

Affiliations

¹ Department of Animal Science, Safiabad-Dezful Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education & Extension Organization (AREEO), Dezful, 333, Iran.
² Department of Animal Science, Faculty of Agriculture Engineering, University of Kurdistan, Sanandaj, 66177-15175, Iran.
³ Department of Animal Science and Aquaculture, Dalhousie University, Truro NS B2N 5E3, Canada.
⁴ Department of Animal Sciences, Sao Paulo State University, Julio de Mesquita Filho (UNESP), Prof. Paulo Donato Castelane, Jaboticabal, SP 14884-900, Brazil.
⁵ Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI 53706, USA.
⁶ Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.

Abstract

The use of DNA methylation signatures to predict chronological age and aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers are significantly associated with age, most age-prediction methods use a few markers selected based on either previously published studies or datasets containing methylation information. Here, we implemented reproducing kernel Hilbert spaces (RKHS) regression and a ridge regression model in a Bayesian framework that utilized phenotypic and methylation profiles simultaneously to predict chronological age. We used over 450,000 CpG sites from the whole blood of a large cohort of 4409 human individuals with a range of 10-101 years of age. Models were fitted using adjusted and un-adjusted methylation measurements for cell heterogeneity. Un-adjusted methylation scores delivered a significantly higher prediction accuracy than adjusted methylation data, with a correlation between age and predicted age of 0.98 and a root mean square error (RMSE) of 3.54 years in un-adjusted data, and 0.90 (correlation) and 7.16 (RMSE) years in adjusted data. Reducing the number of predictors (CpG sites) through subset selection improved predictive power with a correlation of 0.98 and an RMSE of 2.98 years in the RKHS model. We found distinct global methylation patterns, with a significant increase in the proportion of methylated cytosines in CpG islands and a decreased proportion in other CpG types, including CpG shore, shelf, and open sea (P < 5e-06). Epigenetic drift seemed to be a widespread phenomenon as more than 97% of the age-associated methylation sites had heteroscedasticity. Apparent methylomic aging rate (AMAR) had a sex-specific pattern, with an increase in AMAR in females with age related to males.

Keywords: Bayesian ridge regression; aging; reproducing kernel Hilbert spaces; whole-methylome prediction.

MeSH terms

Aging* / genetics
Bayes Theorem
Child, Preschool
CpG Islands
DNA Methylation* / genetics
Epigenesis, Genetic
Female
Humans
Male