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

Detalhes bibliográficos
Autor(a) principal: Roudbar, Mahmoud Amiri
Data de Publicação: 2021
Outros Autores: Mousavi, Seyedeh Fatemeh, Ardestani, Siavash Salek, Lopes, Fernando Brito [UNESP], Momen, Mehdi, Gianola, Daniel, Khatib, Hasan
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1093/g3journal/jkab112
http://hdl.handle.net/11449/229232
Resumo: 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.
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spelling Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human agingAgingBayesian ridge regressionReproducing kernel Hilbert spacesWhole-methylome predictionThe 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.Department of Animal Science Safiabad-Dezful Agricultural and Natural Resources Research and Education Center Agricultural Research Education and Extension Organization (AREEO)Department of Animal Science Faculty of Agriculture Engineering University of KurdistanDepartment of Animal Science and Aquaculture Dalhousie UniversityDepartment of Animal Sciences Sao Paulo State University Julio de Mesquita Filho (UNESP), Prof. Paulo Donato, JaboticabalDepartment of Surgical Sciences School of Veterinary Medicine University of Wisconsin-MadisonDepartment of Animal and Dairy Sciences University of Wisconsin-MadisonDepartment of Animal Sciences Sao Paulo State University Julio de Mesquita Filho (UNESP), Prof. Paulo Donato, JaboticabalEducation and Extension Organization (AREEO)University of KurdistanDalhousie UniversityUniversidade Estadual Paulista (UNESP)University of Wisconsin-MadisonRoudbar, Mahmoud AmiriMousavi, Seyedeh FatemehArdestani, Siavash SalekLopes, Fernando Brito [UNESP]Momen, MehdiGianola, DanielKhatib, Hasan2022-04-29T08:31:21Z2022-04-29T08:31:21Z2021-07-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1093/g3journal/jkab112G3: Genes, Genomes, Genetics, v. 11, n. 7, 2021.2160-1836http://hdl.handle.net/11449/22923210.1093/g3journal/jkab1122-s2.0-85111573938Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengG3: Genes, Genomes, Geneticsinfo:eu-repo/semantics/openAccess2024-06-07T18:39:17Zoai:repositorio.unesp.br:11449/229232Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-06-07T18:39:17Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging
title Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging
spellingShingle Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging
Roudbar, Mahmoud Amiri
Aging
Bayesian ridge regression
Reproducing kernel Hilbert spaces
Whole-methylome prediction
title_short Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging
title_full Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging
title_fullStr Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging
title_full_unstemmed Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging
title_sort Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging
author Roudbar, Mahmoud Amiri
author_facet Roudbar, Mahmoud Amiri
Mousavi, Seyedeh Fatemeh
Ardestani, Siavash Salek
Lopes, Fernando Brito [UNESP]
Momen, Mehdi
Gianola, Daniel
Khatib, Hasan
author_role author
author2 Mousavi, Seyedeh Fatemeh
Ardestani, Siavash Salek
Lopes, Fernando Brito [UNESP]
Momen, Mehdi
Gianola, Daniel
Khatib, Hasan
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Education and Extension Organization (AREEO)
University of Kurdistan
Dalhousie University
Universidade Estadual Paulista (UNESP)
University of Wisconsin-Madison
dc.contributor.author.fl_str_mv Roudbar, Mahmoud Amiri
Mousavi, Seyedeh Fatemeh
Ardestani, Siavash Salek
Lopes, Fernando Brito [UNESP]
Momen, Mehdi
Gianola, Daniel
Khatib, Hasan
dc.subject.por.fl_str_mv Aging
Bayesian ridge regression
Reproducing kernel Hilbert spaces
Whole-methylome prediction
topic Aging
Bayesian ridge regression
Reproducing kernel Hilbert spaces
Whole-methylome prediction
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021-07-01
2022-04-29T08:31:21Z
2022-04-29T08:31:21Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1093/g3journal/jkab112
G3: Genes, Genomes, Genetics, v. 11, n. 7, 2021.
2160-1836
http://hdl.handle.net/11449/229232
10.1093/g3journal/jkab112
2-s2.0-85111573938
url http://dx.doi.org/10.1093/g3journal/jkab112
http://hdl.handle.net/11449/229232
identifier_str_mv G3: Genes, Genomes, Genetics, v. 11, n. 7, 2021.
2160-1836
10.1093/g3journal/jkab112
2-s2.0-85111573938
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv G3: Genes, Genomes, Genetics
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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